Patent Application
20240012395
The present teachings relate generally to computer assisted chemical production of an article comprising thermoplastic polyurethane (“TPU”) and/or expanded thermoplastic polyurethane (“ETPU”).
The conventional manufacture of articles such as shoe soles, for example those for sport shoes, generally involves processing various plastic components. It has become known to produce such articles or parts thereof, such as midsoles for shoes, from particles or beads of expanded thermoplastic polyurethane (“ETPU”). For manufacturing such articles, thermoplastic polyurethanes (“TPUs”) and/or ETPUs may be bonded to a desired shape, for example using outsoles made of rubber or any other material using adhesives. It is also known to avoid adhesives, and rather use another interconnecting medium for the particles. Sometimes, an additive may be added, for example to increase the viscosity. Additional steps may also involve annealing.
In such industrial plants, TPUs and/or ETPUs material is processed to manufacture one or more such articles. Properties of the manufactured article thus have a dependence upon manufacturing parameters in various processing steps. It is desirable to correlate manufacturing parameters to at least some properties of the article for ensuring product quality or production stability.
Production environment in an industrial plant can be complex, accordingly the properties of the article may vary according to variations in the production parameters that influence said properties. Usually the dependency of the properties on production parameters can be complex and intertwined with a further dependence on one or more combinations of specific parameters. In some cases, the production process may be divided into multiple stages, which can further aggravate the problem. It may thus be challenging to produce an article with consistent and/or predictable quality.
Usually, an industrial plant producing such articles is a downstream industrial plant that receives at least one or all TPUs and/or ETPUs as precursor material from one or more upstream industrial plants. It is usual that the upstream industrial plant is a separate plant and isolated from the downstream industrial plant. The upstream industrial plant and the downstream industrial plant may be operated by different entities and often the only meaningful link between the two plants may be the supply chain, i.e., ordering and delivery of the precursor material. The precursor material may have a specification range within which one or more of the properties of the precursor material may lie. Hence, the properties of the TPUs and/or ETPUs material used for the production of the article may vary between orders, or possibly even within batches. These properties may vary due to variations in the production of the TPU and/or ETPU material at the upstream plant. Furthermore, there may be variations in production at the downstream plant as well. Accordingly, the article produced at the downstream plant may also have a range within which the properties of such articles may lie. Dependent upon the various variations and their combinations, certain articles may not be having the same quality or performance as others. This can result in variations within the articles and in some cases increased costs of production of articles with consistent quality.
Further, in contrast to discrete processing, manufacturing processes such as continuous, campaign or batch processes, may provide vast amounts of time series data. However, machine learning via traditional time series approaches has proven to be less practical, since it can be difficult to integrate data according to the needs of horizontal integration across the value chain. In particular, easy and meaningful data exchange or standardization can pose major problems.
There is hence a need for approaches that can improve control and production stability of articles made with TPUs and/or ETPUs across the value chain ideally from barrel to end product.
At least some of the problems inherent to the prior art will be shown solved by the subject matter of the accompanying independent claims. At least some of the further advantageous alternatives will be outlined in the dependent claims.
When viewed from a first perspective, there can be provided a method for controlling a downstream production process for manufacturing an article at a downstream industrial plant, the downstream industrial plant comprising at least one downstream equipment, and the article being manufactured by processing, via the downstream equipment, at least one thermoplastic polyurethane (“TPU”) and/or expanded thermoplastic polyurethane (“ETPU”) material using the downstream production process, the method at least partially being performed via a downstream computing unit, and the method comprising:
The applicant has realized that by doing so, the at least one desired downstream performance parameter related to the article can be used to control the way a specific TPU and/or ETPU material with its associated properties is being processed at the downstream equipment. In some cases, the downstream industrial plant may comprise a plurality of equipment zones such that the downstream control settings may be zone-specific.
The article can thus be produced according to the desired properties or performance parameters, not only with respect to the downstream variations, but also accounting for any variations in the TPU and/or ETPU material. The precursor data encapsulated in the downstream object identifier can be used to find the downstream control settings with a goal of achieving the at least one desired downstream performance parameter. Quality if the article can thus be improved and/or made more consistent, for example by accounting not only for random variations in the TPU and/or ETPU properties, but also by selecting the control settings such that the downstream historical data are leveraged for the same purpose, i.e., for achieving the at least one desired downstream performance. The downstream historical data may be from the equipment where the past one or more articles were produced, but they can even at least partially be from another equipment.
According to an aspect, the downstream historical data comprise data from one or more historical downstream object identifiers related to previously processed TPU and/or ETPU material, for example, in the downstream equipment zone.
According to an aspect, at least one of the historical downstream object identifiers has been appended with at least a part of the downstream process data which is indicative of the downstream process parameters and/or equipment operating conditions that the previously processed TPU and/or ETPU material was processed under, for example, in the downstream equipment zone.
Such historical downstream object identifier encapsulates their corresponding part of the downstream process data under which the respective previous input material was processed to produce or process the respective article in the past. The downstream historical data as disclosed herein is thus a highly relevant yet concise data set, which can be used for determining the control settings for the downstream equipment. In cases where the downstream equipment are arranged such that there are multiple downstream equipment zones for manufacturing the article, then the downstream control settings may be provided for each of the downstream equipment zones as a goal for achieving the desired performance for the article as specified via the at least one desired performance parameter.
According to an aspect, each or some of the historical downstream object identifiers include at least one downstream performance parameter related to the one or more properties of associated article produced, for example at the downstream industrial plant in the past. Thus, each or some of the historical downstream object identifiers have been appended with their corresponding at least one downstream performance parameter.
By doing so, the historical downstream data can be made further targeted by associating with each part of the downstream process data within any downstream object identifiers, their respective the at least one downstream performance parameter. From the entire process data, which can be extensive, thus a concise yet effective snapshot of process parameters and/or operational settings is thus digitally coupled to a particular article produced along with its performance. The determination of the control settings can thus be synergistically be improved. This can be achieved by appending at least one downstream performance parameter to the downstream object identifier, details related to which will be discussed in this disclosure. Thus, when the downstream object identifier is used as a historical object identifier for a future downstream production, the associated data within the identifier can be better leveraged to improve the future production.
It will be appreciated that the object identifiers as proposed in context of the present teachings not only can improve the traceability of the article, both downwards and upwards, through the value chain, but also can be used to ensure that the production process is controlled in such a way as to obtain a more consistent quality of the article. Rather than relying upon universal control settings, which can result in a wider variation in a plurality of articles manufactured at different times, at least parts of the production chain, for example, the downstream equipment or equipment zones can be controlled in a more adaptable manner with a goal to achieve a desired performance of the article. Any variability of the TPU and/or ETPU material and/or process parameters and/or equipment operating conditions can thus be at least partially accounted for while providing the downstream control settings or downstream zone-specific control settings for producing the article.
So, the method also comprises:
The downstream production process may be executed by at least some of the downstream control settings being provided to, or entered at, a downstream plant control system operatively coupled to the downstream equipment or downstream equipment zones. Additionally, or alternatively, the downstream production process may be executed by at least some of the downstream control settings the being automatically provided to the downstream plant control system. The downstream control settings may be transmitted directly to the downstream plant control system by the downstream computing unit, or they may be provided at a downstream memory location operatively coupled to the downstream computing unit from which memory location the downstream plant control system may read or fetch the downstream control settings.
In some cases, the downstream computing unit may at least partially be a part of the downstream plant control system such that the downstream computing unit may directly use the downstream control settings to at least partially control the downstream production process. As discussed, the downstream control settings may allow control of the downstream production process at each of the downstream equipment zones. A finer granularity and flexibility of control can thus be achieving the performance of the article according to the at least one desired downstream performance parameter.
According to an aspect, the method further comprises:
The downstream computing unit may thus be communicatively and/or operatively coupled to the downstream equipment or equipment zones.
According to a further aspect, the method comprises:
The downstream computing unit is thus able to select the downstream process data which is relevant for the downstream object identifier. Said relevant data, or the subset of downstream real-time process data, may be selected based on where the TPU and/or ETPU material is located within the production chain, or by using the zone presence signal.
According to another aspect, the method comprises:
As it will be appreciated, the downstream process data may have a variability associated with one or more of the components of the data. For example, two different batches of the TPU and/or ETPU material that have been mixed with the same mixer at different times may have been mixed in non-identical manners. Similar variability may also exist with other parameters and/or operating conditions. The variability between individual components may be random and independent or partly independent of those of the other components. Furthermore, a combination of such variabilities may result in a variability in the performance or quality of the article. Thus, as specified above, dependent upon the subset of the downstream real-time process data, the downstream computing unit may be configured to compute the at least one downstream performance parameter. So, the at least one downstream performance parameter is indicative of the quality of the article may be determined essentially while the TPU and/or ETPU material is being processed in the downstream equipment zone. The at least one downstream performance may be displayed for an operator, for example, via a human machine interface (“HMI”). The operator may then adjust the downstream production process such that each or some of the at least one downstream performance parameter can be the same value as, or it becomes closer to, its associated value of the desired downstream performance parameter.
Alternatively, or in addition, the method comprises:
The downstream performance parameter may be appended for example as metadata to the downstream object identifier. So, the downstream object identifier also encapsulates the at least one downstream performance parameter computed during the downstream production process. Thus, it not only can improve the traceability of the chemical product, but also simplify the quality control for the article.
Alternatively, or in addition, the method comprises:
The computed performance value can thus track the desired performance parameter value. Hence, granularity of control of the production process can be further improved at a finer scale. Such a control can allow at least partially accounting for variability in the various process parameters and/or operating conditions. Potentially, each of the downstream equipment zones may be controlled automatically such that the resulting articles can have a more consistent performance or quality.
Alternatively, or in addition, the method comprises:
Thus, the relevant downstream process data may also be captured and packaged or encapsulated with the precursor data or precursor material data, also in the downstream object identifier such that any relationship of the article with the properties of the TPU and/or ETPU material are also be captured. This can provide a more complete relationship between the various dependencies those may influence any one or more properties or performance of the article. Another advantage can be that the combination between the various interdependencies that may exist between the TPU and/or ETPU material properties and/or the downstream process parameters are also captured within the downstream object identifier. The downstream object identifier is thus enriched with information that can be used not only for tracking the article and/or its specific components such as the TPU and/or ETPU material, but also the specific downstream real-time process data that were responsible for resulting in the article. As a result, the object identifiers such as each of the historical downstream object identifiers can be more easily integrated for any machine learning (“ML”) and such purposes. Thus, the downstream object identifier can also be used as a historical object identifier for future downstream production.
It will be appreciated that the desired downstream performance parameter may either be related directly to one or more properties of the article and/or it may be related to one or more properties of a downstream derivative material that is produced during the downstream production process. For example, in cases where the TPU and/or ETPU material is transformed to a downstream derivative material during the course of the downstream production process, it may sometimes be required to track and/or control the quality or performance of such derivative material as well. It will be understood that in such cases the downstream derivative material is an intermediate material resulting from the TPU and/or ETPU material, which derivative material is then used to produce the article. Since the article is also dependent upon the downstream derivative material, it may sometimes be required to track and control the downstream derivative material as well.
Thus, according to an aspect, at least one of the desired downstream performance parameters is related to one or more properties of the downstream derivative material.
According to an aspect, the downstream zone presence signal may be generated via the downstream computing unit by performing a zone-time transformation, which maps at least one property related to the TPU and/or ETPU material to the specific equipment zone. For example, the property related to the TPU and/or ETPU material may be the weight of the TPU and/or ETPU material, such that by a knowledge of the production process, e.g., via the downstream real-time process data, the presence of the TPU and/or ETPU material or its derivative material produced during the downstream production process can be determined. As an example, if an TPU and/or ETPU material with a certain weight in a first downstream equipment zone traverses during the downstream production process to a second downstream equipment zone, a weight measurement at the second downstream zone, e.g., at or within a pre-determined time, can be used to generate a zone presence signal for the second downstream zone. Similarly, a flow value, e.g., mass flow or volume flow, by which the TPU and/or ETPU material or its derivative material traverses through the production can be a property is used for generating the downstream zone presence signal. Further as an example, the speed or velocity by which the TPU and/or ETPU material traverses along the equipment zones can be used to determine the space or location where the TPU and/or ETPU material or its corresponding derivative material is at a given time. Alternatively, or in addition, other non-limiting example of the property related to the input material are, volume, fill value, level, color, etc.
The applicant has found it advantageous to generate the zone presence signal by mapping the downstream real-time process data, which in a production environment is time-dependent data, e.g., time-series data, to spatial data thus mapping the real-life production flow using digital flow element representing the TPU and/or ETPU material. For example, digital flow of the TPU and/or ETPU material can be tracked via the downstream object identifier and occurrences in the time-dependent downstream real-time process data can be used for locating the material along the downstream production process. The material is thus tracked or located via the time and downstream real-time process data which is already measured, i.e., by using the time dimension of the downstream process data, which correlates to the time dimension of the flow of the TPU and/or ETPU material along the downstream production chain.
The zone presence signal may be intermittent, for example generated via computing at regular or irregular times, or it may be continuously generated. This can have an advantage that the material associated with the respective object identifier can be continuously or essentially continuously located within the production chain and thus enabling the appending of data which are highly relevant for the material and its transformation to the article. The computation at regular or irregular times may for example be done to check the presence of the material at certain checkpoints within the production chain. This may be supplemented by occurrences in the downstream real-time process data, for example, by one or more sensors as outlined below.
Since in chemical production operation parameters relating to the time dimension, like dwell times and flow velocities are known, the zone-time transformation can be a simple mapping in time scales. Alternatively, a more complex model based on process simulations may be used for matching the time scales of material flow and the real-time process data. In any case the time scale of process-data may be finer than the flow of the material so as to attribute the process data parameters more finely to the flow of the material.
Thus, the subset of the downstream real-time process data or even the components thereof, such as each or some of the downstream process parameters and/or equipment operating conditions can be further optimized or made more concise according to the time that the material spends at specific sub-parts of the equipment or within a zone. For example, if an equipment zone such as the first downstream equipment zone comprises a mixer, followed by a heater, then the subset of the downstream real-time data may comprise the downstream process parameters and/or equipment operating conditions related to the mixer only for the time that the TPU and/or ETPU material was at the mixer. Similarly, the downstream process parameters and/or equipment operating conditions related to the heater may only be included from the time that the material was exposed to the heater, for example upon exiting from the mixer. That way the relevance of the dataset can be further minutely managed and optimized according to the relevance for the specific material. An alternative, as would be understood, may be that the subset of downstream process data comprises all of the downstream process parameters and/or equipment operating conditions related to the downstream equipment zone from the time the TPU and/or ETPU material enters the downstream equipment zone and until the time the TPU and/or ETPU leaves the downstream equipment zone, this alternative already has an advantage of providing high relevance data for the downstream object identifier, however by further specifying the individual components of the downstream process data as explained, within the zone itself, the subset of downstream real-time process data can be further optimized and the relevance of data encapsulated within the respective downstream object identifier can be further improved.
Additionally, or alternatively, the downstream zone presence signal may be provided at least partially via a sensor related to a specific zone. For example, a weight sensor and/or image sensor may be used to detect the presence of the TPU and/or ETPU material or the derivative material at a space or in a specific equipment zone.
“Equipment” may refer to any one or more assets within the respective industrial plant such as the downstream industrial plant. As non-limiting examples, the equipment may refer to any one or more, or any of their combination of, computing units or controllers such as programmable logic controller (“PLC”) or distributed control system (“DOS”), sensors, actuators, end effector units, transport elements such as conveyor systems, heat exchangers such as heaters, furnaces, cooling units, distilling units, extractors, reactors, mixers, millers, choppers, compressors, slicers, extruders, dryers, sprayers, pressure or vacuum chambers, tubes, bins, silos and any other kind of apparatus which is used directly or indirectly for or during production in the industrial plant. Preferably, the equipment refers specifically to those assets, apparatuses or components which are involved directly or indirectly in the production process. More preferably, those assets, apparatuses or components which can influence the performance of the article. An equipment may be buffered, or they may be unbuffered. Moreover, the equipment may involve mixing or no mixing, separation or no separation. Some non-limiting examples of unbuffered equipment without mixing are, conveyor system or belt, extruder, pelletizer, and heat exchanger. Some non-limiting examples of buffered equipment without mixing are, buffer silo, bins, etc. Some non-limiting examples of buffered equipment with mixing are, silo with mixer, mixing vessel, cutting mill, double cone blender, curing tube, etc. Some non-limiting examples of unbuffered equipment with mixing are, static or dynamic mixer, etc. Some non-limiting examples of buffered equipment with separation are, column, separator, extraction, thin film vaporizer, filter, sieve, etc. The equipment may even be, or it may include a storage or packaging element such as, octabin filling, drum, bag, tank truck. Sometimes, a combination of two or more pieces of equipment may also be considered as an equipment.
“Equipment zones” in context of the downstream industrial plant refer to physically separated zones that are either a part of the same piece of equipment, or the zones may be different pieces of equipment that is used for manufacturing the article. The zones are thus physically located at non-identical locations. The locations may be non-identical geographical locations laterally and/or vertically. The TPU and/or ETPU material thus starts from an upstream equipment zone and traverses downstream, towards one or more equipment zones that are downstream of the upstream equipment zone. Various steps of the downstream production process may thus be distributed between the zones.
In present disclosure, the terms “equipment” and “equipment zones” may be used inter changeably.
“Equipment operating conditions” refers to any characteristics or values that represent the state of the equipment, for example of a specific zone, for example, any one or more of, setpoint, controller output, production sequence, calibration status, any equipment related warning, vibration measurement, speed, temperature, fouling value such as filter differential pressure, maintenance date, etc.
The term “downstream” shall be understood as being referring to be in the direction of the flow of production. For example, the very last equipment zone where the production process terminates is a downstream equipment zone. The term is, however, used in a relative sense within its meaning in the present disclosure. For example, an intermediate equipment zone that lies between a first equipment zone and the last equipment zone may also be called a downstream zone for the first equipment zone, and an “upstream” equipment zone for the last equipment zone. The last equipment zone is thus a downstream zone for the first equipment zone and for the intermediate equipment zone. Similarly, both the first equipment zone and the intermediate equipment zone are upstream of the last equipment zone.
“Industrial plant” or “plant” may refer, without limitation, to any technical infrastructure that is used for an industrial purpose of manufacturing, producing or processing of one or more industrial products, i.e., a manufacturing or production process or a processing performed by the industrial plant. More specifically, the downstream industrial plant refers to an industrial plant where the article is at least partially manufactured or produced.
The infrastructure may comprise equipment or process units such as any one or more of a heat exchanger, a column such as a fractionating column, a furnace, a reaction chamber, a cracking unit, a storage tank, an extruder, a pelletizer, a precipitator, a blender, a mixer, a cutter, a curing tube, a vaporizer, a filter, a sieve, a pipeline, a stack, a filter, a valve, an actuator, a mill, a transformer, a conveying system, a circuit breaker, a machinery e.g., a heavy duty rotating equipment such as a turbine, a generator, a pulverizer, a compressor, an industrial fan, a pump, a transport element such as a conveyor system, a motor, etc. Sometimes a combination of two or more of these may also be considered an equipment.
Further, an industrial plant typically comprises a plurality of sensors and at least one control system for controlling at least one parameter related to the process, or process parameter, in the plant. Such control functions are usually performed by the control system or controller in response to at least one measurement signal from at least one of the sensors. The controller or control system of the plant may be implemented as a distributed control system (“DCS”) and/or a programmable logic controller (“PLC”).
Thus, at least some of the equipment or process units of the industrial plant, i.e., the upstream industrial plant or the downstream industrial plant, may be monitored and/or controlled for producing one or more of the industrial products. The monitoring and/or controlling may even be done for optimizing the production of the one or more products or articles. The equipment or process units may be monitored and/or controlled via a controller, such as DCS, in response to one or more signals from one or more sensors. In addition, the plant may even comprise at least one programmable logic controller (“PLC”) for controlling some of the processes. The industrial plant may typically comprise a plurality of sensors which may be distributed in the industrial plant for monitoring and/or controlling purposes. Such sensors may generate a large amount of data. The sensors may or may not be considered a part of the equipment. As such, production, such as chemical and/or service production, can be a data heavy environment. Accordingly, each industrial plant may produce a large amount of process related data.
Those skilled in the art will appreciate that the industrial plant usually may comprise instrumentation that can include different types of sensors. Sensors may be used for measuring one or more process parameters and/or for measuring equipment operating conditions or parameters related to the equipment or the process units. For example, sensors may be used for measuring a process parameter such as a flowrate within a pipeline, a level inside a tank, a temperature of a furnace, a chemical composition of a gas, etc., and some sensors can be used for measuring vibration of a pulverizer, a speed of a fan, an opening of a valve, a corrosion of a pipeline, a voltage across a transformer, etc. The difference between these sensors cannot only be based on the parameter that they sense, but it may even be the sensing principle that the respective sensor uses. Some examples of sensors based on the parameter that they sense may comprise: temperature sensors, pressure sensors, radiation sensors such as light sensors, flow sensors, vibration sensors, displacement sensors and chemical sensors, such as those for detecting a specific matter such as a gas. Examples of sensors that differ in terms of the sensing principle that they employ may for example be: piezoelectric sensors, piezoresistive sensors, thermocouples, impedance sensors such as capacitive sensors and resistive sensors, and so forth.
The industrial plant may even be part of a plurality of industrial plants. The term “plurality of industrial plants” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a compound of at least two industrial plants having at least one common industrial purpose. Specifically, the plurality of industrial plants may comprise at least two, at least five, at least ten or even more industrial plants being physically and/or chemically coupled. The plurality of industrial plants may be coupled such that the industrial plants forming the plurality of industrial plants may share one or more of their value chains, educts and/or products.
“Article” refers to any product that is at least partially made from TPU and/or ETPU. The article may even be a molded body or article made at least partially from TPU and/or ETPU. Some non-limiting examples include: footwear such as shoe made entirely or partially of TPU and/or ETPU material for example, shoe intermediate sole, shoe insole and shoe combination sole. The article may even be a saddle, for example bicycle saddle, tire, for example bicycle tire, cushioning element, upholstery, mattress, base, handle, protective foil, a component in the interior or exterior of an automobile, of sports item such as a ball, or a part a sports item such as a grip, or a floor covering, in particular for sports areas, athletics tracks, sports halls, children's playgrounds and sidewalks.
The article hence comprises at least partially TPU and/or ETPU material or a particle foam comprising TPU and/or ETPU for the preparation of a molded body for shoe midsoles, shoe insoles, shoe combination soles or upholstery element for shoes. The shoe may be a street shoe, sports shoe, sandal, boot or safety shoe. For sports shoes, ETPU particle foams have been found to be especially advantageous.
As a non-limiting example, “TPU” may be produced, for example, at the upstream industrial plant using an upstream production process and input material in the form of:
Further additives such as catalysts, stabilizers and/or antioxidants may be added in dependent upon the specifics of the upstream industrial process. Any other suitable process for producing TPU and/or ETPU may be used.
TPU production may involve carrying out in a twin-screw extruder, ZSK58 MC, of the company Coperion with a process length of 48D (12 housings). The discharge of the melt (polymer melt) from the extruder may be carried out by means of a gear pump. After the melt filtration, the polymer melt may be processed into granules by means of underwater granulation, which may be dried continuously in a heating vortex bed, at 40-90° C. The polyol, the chain extender and the diisocyanate as well as a catalyst may be dosed into the first zone. The addition of further additives, as described above, takes place in Zone 8. The housing temperatures range from 150 to 230° C. The melting and underwater-granulation may be carried out with melting temperatures of 210-230° C. The screw speed may be between 180 and 240 rpm. The throughput may range from 180 to 220 kg/h. There may or may not be additional production steps than shown in this example for the TPU production.
As a further non-limiting example, ETPU production, or production of the expanded particles (foamed granules) from the TPU, may involve, a twin-screw extruder with a screw diameter of 44 mm and a ratio of length to diameter of 42 being used with subsequent melting pump, a start-up valve with screen changer, a perforated plate and an underwater granulation. The thermoplastic polyurethane being dried before processing at 80° C. for 3 h in order to obtain a residual moisture of less than 0.02 wt. %. The TPU used may be dosed via a gravimetric dosing device into the feed of the twin-screw extruder. After dosing the materials into the feed of the twin-screw extruder, the materials may be melted and mixed. Subsequently, the propellants CO2 and N2 may be added via one injector each. The remaining extruder length may be used for homogeneous incorporation of the propellant into the polymer melt. After the extruder, the polymer/propellant mixture may be pressed into a perforated plate by means of a gear pump via a start-up valve with screen changer into a perforated plate. Via the perforated plate individual strands may be produced. These strands may be conveyed to the pressurized cutting chamber of the underwater granulation unit, in which the strands may be cut into granules and further transported with the water while the granules are expanded. The separation of the expanded particles or granules from the process water may be done by means of a centrifugal dryer. The total throughput of the extruder, polymers and propellants may be 40 kg/h. After the separation of the expanded granules from the water by means of a centrifugal dryer, the expanded granules may be dried at 60° C. for 3 h to remove the remaining surface water as well as possible moisture in the particle in order to not distort a further analysis of the particles.
In addition to processing in the extruder, expanded particles may also be produced in an autoclave. For this purpose, the pressure vessel may be filled with a filling degree of 80% with the solid/liquid phase, wherein the phase ratio is 0.32. Solid phase here is the TPU and the liquid phase a mixture of water with calcium carbonate and a surface-active substance. With pressure onto this solid/liquid phase, the blowing agent/propellant (butane) may be pressed into the tight pressure vessel, which is previously rinsed with nitrogen. The pressure vessel may be heated by stirring the solid/liquid phase at a temperature of 50° C. and then nitrogen may be pressed into the pressure vessel up to a pressure of 8 bar. Subsequently, further heating may be carried out until the desired impregnation temperature is reached. When the impregnation temperature and the impregnation pressure are reached, the pressure vessel may be relaxed via a valve after a given holding time.
There may or may not be additional production steps than shown in this example for the ETPU production.
The ETPU production may either be done at the upstream industrial plant, or the ETPU material may be provided, for example as a precursor, to the downstream industrial plant for the manufacture of the article. Alternatively, if requited, the downstream industrial plant may produce the ETPU material prior to manufacturing the article. Hence, either one of TPU and ETPU, or both TPU and ETPU may be precursor material used by the downstream industrial plant for the manufacture of the article. Or, ETPU production may or may not be a part of the downstream industrial plant.
The TPU production process and/or the ETPU production processes may or may not be the same as shown in the above representative examples. Those skilled in the art shall appreciate that a specific production process is not limiting to the scope or generality of the present teachings.
A non-limiting example of a production process for manufacturing an article or a part thereof can also be shown. For manufacture of moldings by steam chest molding/water vapor fusing to obtain a mold/particle foam based molded article is shown as a representative example. The expanded granules or ETPU may be fused on a molding machine from Kurtz ersa GmbH (Energy Foamer) to square plates with a side length of 200 mm and a thickness of 10 mm or 20 mm by covering with water vapor. With regards to plate thickness, the fusing parameters may differ only in terms of cooling. The fusing parameters of the different materials may be chosen in such a way that the plate side of the final molded part facing the moving side of the tool had as few collapsed ETPU particles as possible. Usually, steaming times in the range of 3 to 50 seconds may be used for the respective steps. Through the movable side of the tool, a slit steaming may also be carried out if necessary. Irrespective of the experiment, regarding the fixed and the movable side of the tool, at the end a cooling time of 120 s may always be set at a plate thickness of 20 mm and a cooling time of 100 s may always be set with a 10 mm thick plate. The plates may be stored in the oven for 4 hours at 70° C.
Here too, the article production processes may or may not be the same as shown in the above representative example. Those skilled in the art shall appreciate that a specific downstream production process is not limiting to the scope or generality of the present teachings.
A point that may be made using the above examples is that the downstream production process for manufacturing the article can comprise multiple steps, which may further involve various process parameters and/or operating conditions to be tightly controlled to obtain an article that possesses desired properties. In addition, as it can also be seen, the way the ETPU is produced may also have an effect on the article properties. The ETPU properties may depend upon the properties of the TPU which was used in ETPU production. Further additionally, the process parameters and/or operating conditions of the ETPU and even TPU production from input material may create a dependence all the way to the value chain. These variations may also create complex interdependencies, which may align in different ways under different conditions to result in a variable quality between articles produced at different times and/or different materials.
The present teachings can not only allow establishing the relationship between at least some of these relevant data that may reflect such interdependencies, but also allow for monitoring and/or an adaptable control of at least the downstream production process with consistent quality of the article as a goal.
The downstream control settings may at least partially be determined via an upstream computing unit. Additionally, or alternatively, the downstream control settings may at least partially be determined via the downstream computing unit. The upstream computing unit may be a part of the upstream industrial plant or facility where the TPU and/or ETPU material is produced or supplied from to the downstream industrial plant.
It will be appreciated that the downstream industrial plant is that which receives at its input for downstream production process the TPU and/or ETPU material, or precursor material, from the upstream industrial plant. The downstream industrial plant may thus be remote from the upstream industrial plant. The precursor material may be provided at the downstream industrial plant via a suitable transport medium, for example, via trucks, rails, boats their likes or even their combinations, for example, a transport done via a truck and then via a boat. The transport medium may even be an enclosed medium such as a pipeline or their likes. In some cases, the TPU and/or ETPU material may be packaged in packets with fixed quantity during production at the upstream plant and/or prior to shipping, for example, packets containing 10 kg each of the TPU and/or ETPU material. Additionally, or alternatively, the TPU and/or ETPU material may be supplied in any other suitable one or more containing units such as an octabin, cylinder, or a box.
The TPU and/or ETPU material may be stored and/or produced at the upstream industrial plant, and then transported or shipped to the downstream industrial plant for the manufacture of the article. The transport or shipping may be performed in response of an order for the TPU and/or ETPU material, said order issued via the downstream plant for receiving the TPU and/or ETPU material. The TPU and/or ETPU material received at the downstream plant may thus be used for the manufacture of the article.
In some cases, the downstream control settings those are determined via the upstream computing unit, may be provided at the downstream memory location via by the upstream computing unit. The advantage can be that the control settings can be directly provided as a service by the upstream industrial plant producing the TPU and/or ETPU material. A scenario where this may be beneficial can be that the upstream plant already has infrastructure and computing resources to predict and provide the downstream control settings such that these settings may be determined according to the specifics of the TPU and/or ETPU material via its associated object identifiers. The settings can thus be provided to the downstream plant who may be a customer of the upstream plant, such that the settings can be deployed out of the box without any additional computing effort by the downstream plant. Thus, the downstream plant can enjoy an optimized production and improved quality of the article without modifying their production environment or any additional computational resources.
In such cases, the downstream object identifier may be provided via the upstream computing unit. The at least one desired downstream performance parameter may be provided to the upstream industrial plant or the upstream computing unit, for example, as a quality measure that the downstream plant requires for the article. The downstream industrial plant may thus provide the downstream historical data, preferably including one or more downstream performance parameters, to the upstream industrial plant or the upstream computing unit for determining the downstream control settings. At least some of the data to be provided to the upstream computing unit may be sensitive data which the downstream plant would like to protect. These may for example be provided at a shared memory location which is accessible by both the upstream plant and the downstream plant. The shared memory location may be a cloud storage accessible via a plant-specific access policy. The access policy may determine which kind of access rights either of the plants, i.e., the upstream plant or upstream computing unit and the downstream plant or downstream computing, have. The access policy may also define the authentication measures such as encryption and/or multi-factor authentication.
It is also possible that the downstream memory location is for example the shared memory location or registry that is accessible both by the upstream computing unit and the downstream computing unit.
By using an isolated shared registry that is accessible by both plants, isolation and security can be maintained between the two plants. For example, the downstream plant or computing unit may be provided with a read access such that the downstream computing unit can read or fetch the settings without exposing the downstream control system or equipment to external access.
Similarly, in case the downstream historical data and/or the desired performance parameter is required to be provided to the upstream computing unit, read access may be provided to the upstream computing unit. Thus, neither the upstream computing unit nor the downstream computing unit may require an access to the other plant, hence reducing security loopholes for either plants.
In some cases, the downstream control settings may be provided via a tag which is related to a shipment of the TPU and/or ETPU material to the downstream plant. The tag may, for example, be transported together with the TPU and/or ETPU material to the downstream plant or it may be provided separately. The tag may be a hardware tag such as an electronic chip and/or a near field communication (“NFC”) based tag and/or a digitally readable code which can be read out at the downstream industrial plant to retrieve the control settings that are suitable for producing the article, using the TPU and/or ETPU supplied, with a goal to achieve the at least one desired performance parameter. The tag may even be encrypted with restricted access provided for the downstream industrial plant.
In some cases, the downstream control settings which are determined via the downstream computing unit, are dependent upon the precursor data related to the TPU and/or ETPU material. The precursor data may also be provided at the shared memory location as discussed previously.
According to an aspect, an upstream object identifier is provided via the upstream industrial plant. For example, the upstream object identifier is provided in response to an order signal received at the upstream industrial plant for the TPU and/or ETPU material to be supplied at the downstream industrial plant. The upstream object identifier may be automatically provided in response to the order signal, for example via the upstream computing unit. The order signal may be received via an enterprise resource planning (“ERP”) system of the upstream industrial plant in response to which the upstream computing unit may provide the upstream object identifier. The upstream object identifier may be appended with input material data, wherein the input material data is indicative of one or more properties of an input material used for the production of the TPU and/or ETPU material, and the upstream object identifier is provided for the input material at the upstream industrial plant. The upstream object identifier may be appended with a subset of upstream process data of the upstream industrial plant, which subset comprises upstream process parameters and/or equipment operating conditions which the input material is processed under for producing the TPU and/or ETPU material.
The upstream object identifier may be provided via an upstream interface, preferably at an upstream memory storage operatively coupled to the upstream computing unit. Either one or both of the upstream memory storage and the upstream computing unit may be at least partially be a part of a cloud platform or service. Similarly, either one or both of the downstream memory storage and the downstream computing unit may be at least partially be a part of a cloud platform or service.
An advantage of providing the upstream object identifier can be that the relevant part of the upstream process data, or the subset, is appended to the specific input material used for production of the TPU and/or ETPU material. Which means that not only the properties of the input material, but also the conditions under which the specific TPU and/or ETPU material is produced can be captured within the upstream object identifier, thus better defining the one or more properties of the precursor material. The ways in which one or more upstream object identifiers are provided can be similar to the alternatives as discussed for the downstream object identifiers. Aspects may thus not be repeated for both upstream and downstream identifiers. Hence, those skilled in the art will appreciate that the aspects from one may apply to the other without explicitly requiring stating herein. For example, the upstream industrial plant may also comprise upstream equipment zones, and that similar to what was discussed for the downstream process data, upstream process data from the respective zones may also be captured and appended to one or more upstream object identifiers in a similar manner. An overall advantage being that essentially full traceability and quality tracking and/or control can be provided from the input material to the final product, i.e., the article via the object identifiers. Also, the aspects of zone presence may be used in the upstream industrial plant or equipment to determine the respective subset of process data that is appended to the respective object identifier.
According to an aspect, the downstream object identifier is appended with at least a part of the data from the upstream object identifier. Such a downstream object identifier may be termed an appended downstream object identifier. Thus, the appended downstream object identifier, or the downstream object identifier that is appended with at least a part of the data from the upstream object identifier, can provide a more holistic picture of the production chain and can thus result in a more complete dataset at least referenced or encapsulated by the object identifiers encompassing from the input material to the TPU and/or ETPU material, which can allow better determination of the downstream control settings. For example, the subset of upstream real-time process data appended to the upstream object identifier is also at least referenced to the downstream object identifier. Additionally, or alternatively, any one or more upstream performance parameters that were determined using sampling and/or computed via the upstream computing unit may also be provided via the upstream object identifier to the downstream object identifier.
In some cases, at least some of the downstream control settings are determined via the downstream computing unit. In such cases, the subset of upstream real-time process data provided via the upstream object identifier may be used by the downstream computing unit for determining the downstream control settings. For example, the upstream computing unit may provide the upstream object identifier at a shared memory storage, similar to what was discussed previously. It is also possible that the downstream object identifier is provided via the upstream computing unit at the shared memory storage. The downstream computing unit may thus use the downstream object identifier for determining the set of downstream control settings.
An advantage of such an approach can be that the upstream industrial plant does not need to have an access to the downstream historical data. There may be information protection and security concerns due to which the downstream plant may decide to perform local determination of the control settings. Thus, information or data can be protected better by the downstream plant. It will be appreciated that instead of directly providing the downstream object identifier, the upstream computing unit provides the upstream object identifier, which is then used by the downstream computing unit to generate the downstream object identifier. Those skilled in the art will realize that this case may be equivalent to the providing of the downstream object identifier by the upstream computing unit.
In some cases, the upstream computing unit may provide the downstream object identifier, or the upstream object identifier, which encapsulates a prediction and/or control logic that is usable for determining the set of downstream control settings based upon the downstream historical data. To alleviate any information protection concerns of the downstream industrial plant, the prediction and/or control logic may be trained at the downstream industrial plant, e.g., via the downstream computing unit.
The prediction and/or control logic may comprise a prediction model that when trained using the downstream historical data may result in a downstream data driven model. “Data driven model” refers to a model that is at least partially derived from data, in this case from downstream historical data. In contrast to a rigorous model that is purely derived using physio-chemical laws, a data driven model can allow describing relations that cannot be modelled by physio-chemical laws. The use of data driven models can allow to describe relations without solving equations from physio-chemical laws, e.g., related to the processes taking place within the respective production process. This can reduce computational power and/or improve speed. Additionally, the upstream industrial plant may not need to know the specifics of the downstream production to provide such a model usable at the downstream industrial plant.
The data driven model may be a regression model. The data driven model may be a mathematical model. The mathematical model may describe the relation between provided performance properties and determined performance properties as a function.
In some cases, the prediction and/or control logic or the prediction model may include an upstream data driven model, i.e., a model that has been trained using upstream historical data, from the upstream industrial plant. The trained prediction and/or control logic, or the trained prediction model, can provide a more holistic prediction at the downstream plant without requiring to expose the upstream production details to the downstream plant.
Thus, in the present context, the data-driven model, preferably data-driven machine learning (“ML”) model or a merely data-driven model, refers to a trained mathematical model that is parametrized according to the respective training data set, such as upstream historical data or downstream historical data, to reflect reaction kinetics or physio-chemical processes related to the respective production process. An untrained mathematical model refers to a model that does not reflect reaction kinetics or physio-chemical processes, e. g. the untrained mathematical model is not derived from physical law providing a scientific generalization based upon empirical observation. Hence, the kinetic or physio-chemical properties may not be inherent to the untrained mathematical model. The untrained model does not reflect such properties. Feature engineering and training with the respective training data sets enable parametrization of the untrained mathematical model. The result of such training is a merely data-driven model, preferably data-driven ML model, which as a result of the training process, preferably solely as a result of the training process, reflects reaction kinetics or physio-chemical processes related to the respective production processes.
The prediction and/or control logic may even be a hybrid model. A hybrid model may refer to a model that comprises first-principles parts, so called white box, as well as data-driven parts as explained previously, so called black box. The prediction and/or control logic may comprise a combination of a white-box-model and a black-box-model and/or a grey-box-model. The white-box-model may be based on physio-chemical laws. The physio-chemical laws may be derived from first principles. The physio-chemical laws may comprise one or more of chemical kinetics, conservation laws of mass, momentum and energy, particle population in arbitrary dimension. The white-box-model may be selected according to the physio-chemical laws that govern the respective production process or parts thereof. The black-box-model may be based on historical data, such as the downstream historical data and/or the upstream historical data. The black-box-model may be built by using one or more of machine learning, deep learning, neural networks, or other form of artificial intelligence. The black-box-model may be any model that yields a good fit between the training data set and test data. The grey-box-model is a model that combines a partial theoretical structure with data to complete the model.
The trained model may comprise a serial or parallel architecture. In the serial architecture output of the white-box-model may be used as input for the black-box-model or output of the black-box-model may be used as input for the white-box-model. In the parallel architecture a combined output of the white-box-model and the black-box-model may be determined such as by superposition of the outputs. As a non-limiting example, a first sub-model may predict at least one of the performance parameters and/or at least some of the control settings based on a hybrid model with an analytical white-box-model and a data-driven model that serves as a black-box corrector trained on the respective historical data. This first sub-model may have a serial architecture, wherein the output of the white-box-model is input for the black-box-model, or the first sub-model may have parallel architecture. Predicted output of the white-box model may be compared with a test data set comprising a part of historical data. An error between the computed white-box output and test data can be learned by the data-driven model and can then applied for arbitrary predictions. The second sub-model may have a parallel architecture. Other examples can be possible, too.
As used herein, the term “machine learning” or “ML” may refer to a statistical method that enables machines to “learn” tasks from data without explicitly programming. Machine learning techniques may comprise “traditional machine learning”—the workflow in which one manually selects features and then trains the model. Examples of traditional machine learning techniques may include decision trees, support vector machines, and ensemble methods. In some examples, the data driven model may comprises a data driven deep learning model. Deep learning is a subset of machine learning modeled loosely on the neural pathways of the human brain. Deep refers to the multiple layers between the input and output layers. In deep learning, the algorithm automatically learns what features are useful. Examples of deep learning techniques may include convolutional neural networks (“CNNs”), recurrent neural networks such as long short-term memory (“LSTM”), and deep Q networks.
According to an aspect, the prediction and/or control logic is configured to generate modification data which is usable for modifying the prediction and/or control logic such that the computation of the downstream control settings is improved.
According to another aspect, the trained prediction, i.e., the prediction and/or control logic may be trained at the downstream industrial plant, and/or the modification data is provided to the upstream industrial plant. The trained prediction and/or control logic and/or the modification data may be provided for example, via the downstream object identifier or a part thereof, provided to the upstream computing unit. The same shared memory storage or another suitable medium may be used for that purpose. An advantage of this approach can be that the production data of the downstream plant is protected from the upstream plant yet for the purposes of improving the upstream production process, the downstream object identifier appended with trained prediction and/or control logic may be used. Data protection between the two plants is thus improved.
Another advantage can be that the trained prediction and/or control logic can even be provided as a service to other one or more downstream plants for improving their production processes while respecting data security of the downstream plant providing the trained prediction and/or control logic to the upstream industrial plant, e.g., to the upstream computing unit.
The prediction and/or control logic may even be obfuscated, for example, encapsulated in a protected container such that the logic is protected from unauthorized access or readout. The advantage of such a case can be that the upstream plant can provide a service for improving production for the downstream plant whilst reducing the security concerns of providing the logic being exposed to an unauthorized party. Moreover, the downstream plant does not require to develop the solution in house, and does not need to expose the downstream historical data, but still enjoy the improvement in the downstream production provided via the object identifiers and the logic provided by the upstream industrial plant. Data security for both the upstream plant and the downstream plant can be improved, whilst providing improvements in production potentially at both ends.
“Production process” for example, the downstream production process refers to any industrial process which when, used on, or applied to the TPU and/or ETPU material provides the article. The article is thus provided by transforming the TPU and/or ETPU either directly, or via one or more derivative materials, via the downstream production process to result in the article. Similarly, the upstream production process refers to any industrial process which when, used on, or applied to the input material provides the TPU and/or ETPU material.
The production process can thus be any suitable manufacturing or treatment process involving at least partially one or more chemical processes or a combination of a plurality of processes that are used for obtaining the article at least partially from the TPU and/or ETPU material. The production process may even include packaging and/or stacking of the chemical product. The production process may thus be a combination of chemical and physical processes.
The terms “to manufacture”, “to produce” or “to process” will be used interchangeably in the context of the respective production process. The terms may encompass any kind of application of an industrial process including a chemical process to the input material that results in one or more of the TPU and/or ETPU materials, and any kind of application of an industrial process including a chemical process to the TPU and/or ETPU that results in one or more articles.
“Precursor material” or simply “precursor” in this disclosure refers generally to the TPU and/or ETPU material. However, the term may also refer to other substance or material which is combined with the TPU and/or ETPU to result in the article. Such other substance or material may be any one or more of, adhesive, filler, additive, and their likes.
As such material such as precursors used for the manufacture of the article can be difficult to trace or track especially during their production process, not to mention to establish traceability to a particular starting material which they were produced from. It will be appreciated that the input material can be termed the starting material for the TPU and/or ETPU material produced at the upstream plant. Similarly, the TPU and/or ETPU material or precursor can be termed the starting material for the article, produced at the downstream plant. As an example, during production, the input material may be mixed with other material, and/or the input material may be split in different parts down the production chain, for example, for processing in different ways. The input material may be transformed more than once, for example to one or more derivative materials before being transformed to the TPU and/or ETPU material. Similarly, the TPU and/or ETPU may also be mixed and/or split and/or transformed multiple times during the downstream production process. Moreover, different parts of the TPU and/or ETPU material may be shipped to different downstream industrial plants, or customers. For example, the TPU and/or ETPU material may be split and packaged in different packages. Although in some cases it may be possible to label the packaged TPU and/or ETPU material or its portions, it may be hard to attach the specifics of the production process which were responsible for producing that specific TPU and/or ETPU material or its portions. Similar problems may also exist in the downstream production chain. In many cases, the input material and/or the TPU and/or ETPU and/or the article may be in a form where it is difficult to label them physically. The present teachings thus provide ways in which one or more object identifiers can also be used for overcoming such limitations.
The production process, i.e., the upstream production process and/or the downstream production process, may be continuous, in campaigns, for example, when based on catalysts which require recovery, it may be a batch chemical production process. One main difference between these production types is in the frequencies occurring in the data that is generated during production. For example, in a batch process the production data extends from start of the production process to the last batch over different batches that have been produced in that run. In a continues setting, the data is more continuous with potential shifts in operation of the production and/or with maintenance driven down times.
“Process data” refers to data comprising values, for example, numerical or binary signal values, measured during the respective production process, for example, via the one or more sensors. The process data may be time-series data of one or more of the process parameters and/or the equipment operating conditions, for example in the case of the downstream plant, downstream time-series data. Preferably, respective of the process data comprise temporal information of the process parameters and/or the equipment operating conditions of their respective plant, e.g., the data contains time stamps for at least some of the data points related to the process parameters and/or the equipment operating conditions. More preferably, the process data comprises time-space data, i.e., temporal data and the location or data related to one or more equipment zones that are located physically apart, such that time-space relationship can be derived from the data. The time-space relationship can be used, for example for computing the location of the input material at a given time.
“Real-time process data” refers to the process data that are measured or is in a transient state essentially while a specific material, e.g., the TPU and/or ETPU material or precursor, is being processed using the respective production process. For example, the real-time process data, or the upstream real-time process data, for the input material is the upstream process data from or around the same time as the processing of the input material using the upstream production process. Similarly, the real-time process data, or the downstream real-time process data, for the TPU and/or ETPU material is the downstream process data from or around the same time as the processing of the precursor material using the downstream production process.
Here, around the same time means with little or no time delay. The term “real-time” is understood in the technical field of computers and instrumentation. As a specific non-limiting example, a time delay between a production occurrence during the respective production process being performed on the respective material and the process data being measured or read-out is less than 15 s, specifically of no more than 10 s, more specifically of no more than 5 s. For high throughput processing the delay is less than a second, or less than a couple of milliseconds, or even. The real-time data can thus be understood as a stream of time-dependent process data being generated during the processing of the respective material at their respective plants.
“Process parameters” may refer to any of the production process related variables, for example any one or more of, temperature, pressure, time, level, etc.
“Input material” may refer to at least one feedstock or unprocessed material that is used for producing the TPU and/or ETPU material. A few non-limiting examples of the input material can be any one or more of: polyether alcohol, polyether diol, polytetrahydrofuran, polyester diol such as based on adipic acid and butane-1,4-diol, isocyanates, filler material—either organic or inorganic material such as wood powder, starch, flax, hemp, ramie, jute, sisal, cotton, cellulose or aramid fibers, silicates, barite, glass spheres, zeolites, metals or metal oxides, talc, chalk, kaolin, aluminum hydroxide, magnesium hydroxide, aluminum nitrite, aluminum silicate, barium sulfate, calcium carbonate, calcium sulfate, silica, quartz powder, aerosil, clay, mica or wollastonite, iron powder, glass spheres, glass fibers or carbon fibers.
As a further non-limiting example, the input material may be methylene diphenyl diisocyanate (“MDI”) and/or polytetrahydrofuran (“PTHF”) which is subjected to at least a part of the production process to obtain the TPU. It will be appreciated that the input material is thus chemically processed in one or more equipment zones to obtain TPU and/or ETPU. The derivative material in this case means a material which originates from the input material but further processed for obtaining the TPU and/or ETPU material. For example, the TPU may be further processed in one or more further equipment zones to obtain the ETPU.
“Input material data” refer to data related to one or more characteristics or properties of the input material. Accordingly, the input material data may comprise any one or more of the values indicative of properties, such as quantity, of the input material. Alternatively, or in addition, the value indicative of the quantity may be fill degree and/or mass flow of the input material. The values are preferably measured via one or more sensors operatively coupled to or included in the upstream equipment. Alternatively, or in addition, the input material data may comprise sample/test data related to the input material. Alternatively, or in addition, the input material data may comprise values indicative of any physical and/or chemical characteristics of the input material, such as any one or more of, density, concentration, purity, pH, composition, viscosity, temperature, weight, volume, etc.
“Precursor data” or “Precursor material data” refer to data related to one or more characteristics or properties of the TPU and/or ETPU material. Accordingly, the precursor material data may comprise any one or more of the values indicative of properties, such as quantity, of the TPU and/or ETPU material. Alternatively, or in addition, the value indicative of the quantity may be fill degree and/or mass flow of the TPU and/or ETPU material. At least some of the values are may be measured via one or more sensors operatively coupled to or included in the downstream equipment. Some of the values may be provided by the upstream plant or upstream computing unit, for example via the upstream object identifier, or in some cases by providing the downstream object identifier itself. Alternatively, or in addition, the precursor data may comprise sample/test data related to the TPU and/or ETPU material. So, alternatively, or in addition, the precursor material data may comprise values indicative of any physical and/or chemical characteristics of the TPU and/or ETPU material, such as any one or more of, density, concentration, purity, pH, composition, viscosity, temperature, weight, volume, etc. Especially in the case of TPU, the precursor data may comprise, e.g., values indicative of, any one or more of the parameters or results of: gas chromatography, Young's modulus, shore hardness, melt flow value, melt flow rate (“MFR”) and color value. Especially in the case of ETPU, the precursor data may comprise, e.g., values indicative of, any one or more of the parameters or results of: particle weight or bead weight, split tear, dimensional stability test or shrinkage test, tensile test, resilience or rebound resilience, abrasion, bulk density, bead density or particle density or foam density, hardness, compression properties (e.g. stiffness, measured via compression set or compression stress), tensile strength, elongation at break, tear strength, Differential Scanning calorimetry (“DSC”), dynamic mechanic analysis (“DMA”), thermo mechanic analysis (“TMA”), nuclear magnetic resonance spectroscopy (“NMR”), Fourier-Transformation Infrared Spectroscopy (“FT-IR”), gel permeation chromatography (“GPC”), size exclusion chromatography, hydrolysis measurement, sun test, visual appearance (e.g. 3D structure), particle size distribution (“PSD”). The values may either be computed via the upstream computing unit and/or they may be from measurement results performed, for example, from one or more quality control or lab analysis. The examples shown above for the ETPU parameters may also apply for the article or a part thereof, for example, a shoe sole made by processing, e.g., molding, the ETPU material in the form of particles. Hence, any of the ETPU parameters may apply for bulk ETPU particles and/or for processed article made from the particles.
In some cases, the precursor data may comprise a part of data from the upstream object identifier, for example, the precursor data may then comprise a reference or link to the upstream object identifier or even in some cases at least a part of the subset of upstream process data.
It has to be mentioned that the input material being processed by the processing equipment of an underlying chemical production environment is divided into physical or real-world packages, in the following called “package objects” (or “physical packages” or “product packages”, respectively). The package size of such package objects can be fixed, e.g. by material weight or by material amount, or can be determined based on a weight or amount, for which considerably constant process parameters or equipment operation parameters can be provided by the processing equipment. Such package objects can be created from an input liquid and/or solid raw material by means of a dosing unit.
The subsequent processing of such package objects is managed by means of corresponding data objects which include so-called “Object identifiers”, which are assigned to each package object via a computing unit being coupled with the mentioned equipment, or even being a part of the equipment. The data objects, including the corresponding “Object identifiers” of an underlying package object, are stored at a memory storage element of the computing unit.
The data objects can be generated in response to a trigger signal being provided via the equipment, preferably in response to the output of a corresponding sensor being arranged at each of an equipment unit. As mentioned above, an underlying industrial plant may include different types of sensors, e.g. sensors for measuring one or more process parameters and/or for measuring equipment operating conditions or parameters related to the equipment or the process units.
A mentioned “Object identifier”, more particularly, refers to a digital identifier for its respective material. For example, the upstream object identifier is provided for the input material. Similarly, a historical upstream object identifier corresponds to a specific historical input material that was processed earlier. The object identifier is preferably generated via a computing unit. The providing or generation of the object identifier may be triggered by the respective equipment, or in response to a trigger event or signal, for example from the upstream equipment. The object identifier may be stored in a memory storage or memory storage element operatively coupled to the computing unit. For example, the upstream memory storage operatively coupled to the upstream computing unit. Similarly, the downstream memory storage operatively coupled to the downstream computing unit. In some cases, as discussed, a shared memory storage may also be provided which is operatively coupled or accessible to both the upstream computing unit and the downstream computing unit. In some cases, the shared memory storage may either be, or at least partially be a part of the upstream memory storage, and/or the shared memory storage may either be, or at least partially be a part of the downstream memory storage. The memory storage may comprise, or it may be a part of, at least one database. Accordingly, the object identifier may even be a part of the database. It will be appreciated that the object identifier may be provided via any suitable manner, such as it may be transmitted, received or it may be generated.
The respective “computing unit”, i.e., the upstream computing unit or the downstream computing unit, may comprise, or it may be, a processing means or computer processor such as a microprocessor, microcontroller, or their like, having one or more processing cores. In some cases, the computing unit may at least partially be a part of the equipment, for example it may be a process controller such as programmable logic controller (“PLC”) or a distributed control system (“DOS”), and/or it may be at least partially be a remote server. Accordingly, the respective computing unit may receive one or more input signals from one or more sensors operatively connected to the respective equipment. If the respective computing unit is not a part of the respective equipment, it may receive one or more input signals from the respective equipment. Alternatively, or in addition, the respective computing unit may control one or more actuators or switches operatively coupled to the respective equipment. The one or more actuators or switches operatively may even be a part of the equipment.
“Memory storage” or “memory storage element”, e.g., the upstream memory storage and/or the downstream memory storage, may refer to a device or system for storage of information, in the form of data, in a suitable storage medium. Preferably, the memory storage is a digital storage suitable for storing the information in a digital form which is machine-readable, for example digital data that are readable via a computer processor. The memory storage may thus be realized as a digital memory storage device that is readable by a computer processor. The memory storage may at least partially be implemented in a cloud service. Further preferably, the memory storage on the digital memory storage device may also be manipulated via a computer processor. For example, any part of the data recorded on the digital memory storage device may be written and/or erased and/or overwritten, partially or wholly, with new data by the computer processor.
The respective “computing unit”, i.e., the upstream computing unit or the downstream computing unit, may comprise, or it may be, a processing means or computer processor such as a microprocessor, microcontroller, or their like, having one or more processing cores. In some cases, the respective computing unit may at least partially be a part of the respective equipment, for example it may be a process controller such as programmable logic controller (“PLC”) or a distributed control system (“DOS”), and/or it may be at least partially be a remote server and/or cloud service. Accordingly, the respective computing unit may receive one or more input signals from one or more sensors operatively connected to the respective equipment or the plurality of equipment zones. If the computing unit is not a part of the equipment, it may receive one or more input signals from the equipment or equipment zones. Alternatively, or in addition, the computing unit may control one or more actuators or switches operatively coupled to the equipment. The one or more actuators or switches operatively may even be a part of the equipment. The computing unit is operatively coupled to the equipment or the plurality of equipment zones.
Accordingly, the respective computing unit may be able to manipulate one or more parameters related to the respective production process by controlling any one or more of the actuators or switches and/or end effector units, for example via manipulating one or more of the respective equipment operating conditions. The controlling is preferably done in response to the one or more signals retrieved from the equipment.
“End effector unit” or “end effector” in this context refers to a device that is either a part of the respective equipment and/or is operatively connected to the equipment, and hence controllable via the equipment and/or the respective computing unit, with a purpose to interact with the environment around the equipment. As a few non-limiting examples, the end effector may be a cutter, gripper, sprayer, mixing unit, extruder tip, or their likes, or even their respective parts that are designed to interact with the environment, for example, the input material and/or the precursor and/or the article.
“Property” or “properties” when it comes to the respective material, i.e., the input material or the TPU and/or ETPU material or the derivative material may refer to any one or more of quantity of the respective material, batch information, one or more values specifying quality such as purity, concentration, viscosity, or any characteristic of the material. For TPU and/or ETPU, at least some of the properties may be derivable from the precursor data. Those skilled in the art will appreciate that any one or more the properties may be derivable from any one or more of the parameters or results discussed earlier in context of the precursor data.
“Interface” may be a hardware and/or a software component, either at least partially a part of the respective equipment, or a part of another computing unit where the object identifier is provided. For example, the interface may be an application programming interface (“API”). In some cases, the interface may also connect to at least one network, for example, for interfacing two pieces of hardware components and/or protocol layers in the network. For example, the interface may be an interface between the respective equipment and the respective computing unit. In case of the downstream plant, the downstream interface may be an interface between the downstream equipment and the downstream computing unit. Similarly, in case of the upstream plant, the upstream interface may be an interface between the upstream equipment and the upstream computing unit. In some cases, the respective equipment may be communicatively coupled to their respective computing unit via the respective network. Thus, the interface may even be a network interface, or it may comprise the network interface. In some cases, the interface may even be a connectivity interface, or it may comprise the connectivity interface.
“Network interface” refers to a device or a group of one or more hardware and/or software components that allow an operative connection with the network.
“Connectivity interface” refers to a software and/or hardware interface for establishing communication such as transfer or exchange or signals or data. The communication may either be wired, or it may be wireless. Connectivity interface is preferably based on or it supports one or more communication protocols. The communication protocol may a wireless protocol, for example: short distance communication protocol such as Bluetooth®, or WiFi, or long communication protocol such as cellular or mobile network, for example, second-generation cellular network or (“2G”), 3G, 4G, Long-Term Evolution (“LTE”), or 5G. Alternatively, or in addition, the connectivity interface may even be based on a proprietary short distance or long distance protocol. The connectivity interface may support any one or more standards and/or proprietary protocols. The connectivity interface and the network interface may either be the same unit or they may be different units.
“Network” discussed herein may be any suitable kind of data transmission medium, wired, wireless, or their combination. A specific kind of network is not limiting to the scope or generality of the present teachings. The network can hence refer to any suitable arbitrary interconnection between at least one communication endpoint to another communication endpoint. Network may comprise one or more distribution points, routers or other types of communication hardware. The interconnection of the network may be formed by means of physically hard wiring, optical and/or wireless radio-frequency methods. The network specifically may be or may comprise a physical network fully or partially made by hardwiring, such as a fiber-optical network or a network fully or partially made by electrically conductive cables or a combination thereof. The network may at least partially comprise the internet. At least a part of the network at the upstream plant, or upstream network, may be isolated from at least a part of the network at the downstream plant, or downstream network. Moreover, the upstream network and the downstream network may be at least partially non-public networks, i.e., isolated from public network such as the internet. By isolation, it will be understood that said networks may be isolated using security measures such as one or more network firewalls at each plant. Alternatively, or additionally other security and isolation measures for protecting the networks and the production environment at one or both plants may be in place.
As it was discussed that in some cases, the respective subset of process data is appended to the respective object identifier. For example, either the subset of the upstream real-time process data which the input material is processed by the upstream equipment is included in the upstream object identifier in its entirely, or a part thereof is appended or saved. Thus, a snapshot of the upstream real-time process data that was relevant for processing the input material in the upstream equipment or equipment zone(s) is made available or linked with the upstream object identifier. Whether the real-time process data is saved in its entirety, or a part thereof may, for example, be based on a determination via the upstream computing unit regarding which part of subset of process data should be appended to the object identifier. Similarly, either the subset of the downstream real-time process data which the TPU and/or ETPU material is processed by the downstream equipment is included in the downstream object identifier in its entirely, or a part thereof is appended or saved.
Alternatively, or in addition, to what was discussed previously, or in addition, the determination may, for example, be done based upon most dominant of the respective process parameters and/or equipment operating conditions than have influence on the desired properties of the resulting TPU and/or ETPU material or the article. This can be advantageous in certain cases, especially when the relevant real-time process data is large in volume, so rather than appending a large amount of data, to the respective object identifier, the respective computing unit may determine which of the subset of the respective real-time process data is to be appended. Hence, the part of the real-time process data appended to the object identifier may be determined via the respective computing unit. For example, the downstream computing unit may determine which of the subset of the downstream real-time process data is to be appended to the downstream object identifier.
Furthermore, the determination can be based upon one or more ML models. Such models will be discussed in more detail in this disclosure.
According to further an aspect, the upstream object identifier is also appended with upstream process specific data. The upstream process specific data may be any one or more of, upstream enterprise resource planning (“ERP”) data, such as an order number from the downstream plant and/or production code and/or production process recipe and/or batch data, recipient data such as downstream plant data, and a digital model or logic related to the transformation of the input material and/or the precursor material to the chemical product. An example of such a digital model was discussed previously in terms of the prediction and/or control logic.
The ERP data may be received from an ERP system related to the upstream industrial plant.
The digital model may be any one or more of: a computer readable mathematical model representing one or more physical and/or chemical changes that are related to the transformation of the input material and/or precursor to the chemical product. The batch data may be related to the batch under production and/or data related to the previous products manufactured via the same equipment. By doing so, traceability of the TPU and/or ETPU material can be further improved by bundling the associated process specific data. More specifically, the batch data can be used to more optimally sequence the production of various lots or batched of TPU and/or ETPU material that are produced at least partially via the same upstream equipment.
Similarly, the downstream object identifier may be appended with downstream process specific data. The downstream process specific data may be any one or more of, downstream enterprise resource planning (“ERP”) data, such as an order number to the upstream plant and/or production code and/or production process recipe and/or batch data, vendor data such as upstream plant data, and a digital model or logic related to the transformation of the input material and/or the precursor material to the chemical product. An example of such a digital model was discussed previously in terms of the prediction and/or control logic, which may be provided for example by the upstream computing unit.
“Control settings” refer to any respective controllable settings and/or values that can be influenced by respective one or more plant control systems that are functionally or operatively coupled to the respective equipment in such a manner that the settings and/or controllable values influence the manner in which the respective material, and if relevant derivative material, is processed to produce the TPU and/or ETPU material or the article respectively. For example, downstream control settings determine downstream process parameters and/or operating conditions using which the article is produced. Similarly, upstream control settings determine upstream process parameters and/or operating conditions using which the TPU and/or ETPU material is produced. For example, a control setting may be a set-point for one or more controllers in the respective plant's one or more plant control system. The control setting may, for example, relate to a temperature set-point that a controller should use for processing at an equipment zone. Another control setting may be a time period for which one or more materials should be processed, e.g., mixed. Other non-limiting examples of control settings are values such as: time such as processing time, pressure, amount such as weight or volume, ratio, level, rate of change such as flow rate, throughput, speed, rotational speed such as rotations per minute (“rpm”), and mass. Additionally, or alternatively, the respective control settings may even determine the recipe with which the precursor or the chemical product is produced. For example, at least some of the respective control settings may determine material amounts or percentage to be used, for example choosing in what ratio two components should be mixed and/or additive dosing amount at the respective equipment. As a non-limiting example, the recipe may be within which ratio does components of the input material e.g., Isocyanate needs to be to Chain extender and/or Polyol for obtaining the TPU material. Those skilled in the art shall appreciate that the recipe such as one or more of the ratios and/or processing method and/or time may be adapted according to the specifics of the input material such that a consistent quality of the TPU and/or the ETPU obtained from the TPU and/or the article obtained from TPU and/or ETPU can be ensured.
“Zone-specific control settings” thus refer the control settings, i.e., any controllable settings and/or values, which are specific to a particular zone, for example, for the upstream equipment zone. Similarly, the downstream control settings may also be zone-specific.
The respective “performance parameter” may be, or it may be indicative of or related to, any one or more properties of the TPU and/or ETPU material or the article respectively. Accordingly, the downstream performance parameter may be such a parameter that should satisfy one or more predefined criteria indicating suitability, or a degree of suitability, of the article for a particular application or use. As non-limiting examples, the performance parameter may be any one or more of, strength such as tensile strength, hardness such as Shore hardness, density such as bulk density, color, concentration, composition, viscosity, melt flow rate, melt flow value (“MFV”) e.g., that of TPU, stiffness such as Young's modulus value, purity or impurity such as parts per million (“ppm”) value, failure rate such as mean time to failure (“MTTF”), or any one or more values or value ranges, for example determined via tests using the predefined criteria. Any of the downstream performance parameters may even be related to a component of the article. For example, a molded sole made from the ETPU material. The performance parameter may either be any one or more of the parameters or results discussed earlier in context of the precursor data, or it may be derivable from those parameters and/or results. For example, values indicative of, any one or more of the parameters or results of: particle weight or bead weight, split tear, dimensional stability test or shrinkage test, tensile test, resilience or rebound resilience, abrasion, bulk density, bead density or particle density or foam density, hardness, compression properties (e.g. stiffness, measured via compression set or compression stress), tensile strength, elongation at break, tear strength, Differential Scanning calorimetry (“DSC”), dynamic mechanic analysis (“DMA”), thermo mechanic analysis (“TMA”), nuclear magnetic resonance spectroscopy (“NMR”), Fourier-Transformation Infrared Spectroscopy (“FT-IR”), gel permeation chromatography (“GPC”), hydrolysis measurement, sun test, visual appearance (e.g. 3D structure), particle size distribution (“PSD”). Similarly, for TPU material, they may be any one or more of the parameters or results of: gas chromatography, Young's modulus, shore hardness, melt flow rate (“MFR”) and color value.
Additionally, or alternatively, for the article, performance parameters may be, or they may be derivable from any one or more of: tensile strength, elongation at break, rebound resilience, compression properties, particle size distribution (“PSD”) and bulk density.
In general, any of the performance parameters may be computed via the corresponding computing unit related to the respective production process. The object identifiers can enable a more effective and reliable computation of these parameters. Any of these parameters can be a part of the historical data to enable that the respective computing unit can determine the production settings and optionally monitor the manufacturing process and/or control product quality. Also, as it was proposed, the historical data may be updated based on current production, e.g., via the downstream object identifier. The historical data can also be used to train one or more ML models, e.g., for on-the-fly prediction of quality via any of these performance parameters. Like in the case of the prediction and/or control logic, such trained ML models may at least partially be data-driven models.
The applicant has realized that especially for the ETPU material, and optionally also for the article, the parameters: particle size distribution (“PSD”) and/or bulk density are, in particular, suitable for monitoring and/or controlling their respective production processes. The parameters may be observed via inline measurements and/or they may be computed via the respective computing unit during the respective production process.
It will be appreciated that in certain cases, a performance parameter may indicate a lack of suitability, or a degree of unsuitability, for a particular application or use of the respective material or product. Similarly, the upstream performance parameter may be such a parameter that should satisfy one or more predefined criteria indicating suitability, or a degree of suitability, of the TPU and/or ETPU material and/or even of the article or a part thereof for a particular application or use. The downstream performance parameter is thus representative of the performance or quality related to the article. The predefined criteria may, for example, be one or more reference values or ranges with respect to which the performance parameter of the article, and/or the precursor, is compared to, for determining the quality or performance of the article and/or the TPU and/or ETPU material. The predefined criteria may have been determined using one or more tests such as lab tests, reliability or wear tests, thus defining the requirements on the performance parameter for the TPU and/or ETPU material or the article to be suitable for one or more particular uses or applications. In some cases, the performance parameter may be related to or measured from the properties of a derivative material.
The respective “desired performance parameter” may be, or it may be indicative of or related to, any one or more desired properties of the TPU and/or ETPU material or the article or a part thereof. So, the desired performance parameter may correspond to a desired value of the performance parameter. For example, the desired upstream performance parameter may correspond to a desired value of the upstream performance parameter. Similarly, the desired downstream performance parameter may correspond to a desired value of the downstream performance parameter.
It will be appreciated that “zone-specific” in the present context refers to pertaining to a specific equipment zone, for example a particular zone in the upstream equipment or a particular zone in the downstream equipment zone respectively.
Usually, the respective performance parameters are determined from one or more samples of the article and/or the TPU and/or ETPU material collected during and/or after their respective production. The samples may be brought to a laboratory and analyzed for determining the respective performance parameters. The results of the analysis, or the determined performance parameters may be included or appended to the respective object identifier, and hence included in the respective historical data.
However, it will be appreciated that the whole activity of collecting samples, processing or testing them, and then analyzing the test results can take significant time and resources. There can thus be a significant delay between collecting of the samples and implementing any adjustments in the input material and/or process parameters and/or equipment operating conditions. This delay or lag may either result in sub-optimal articles being produced, or in the worst case, the production is halted until the samples have been analyzed and any corrective action by adjusting the input material or the TPU and/or ETPU material and/or process parameters and/or equipment operating conditions has been undertaken.
As a solution to at least reducing the variability in the performance of the article and optionally also for the TPU and/or ETPU material, the present teachings can be used to more tightly control their respective production process via the historical data, and in some cases via the at least one zone-specific performance parameter that may be appended to at least some of the historical object identifiers. The need for manual sampling can thus be reduced.
According to an aspect, the computing of at least one downstream performance parameter is done using a downstream analytical computer model. According to another aspect, the determining of the downstream control settings is done using at least one downstream machine learning (“ML”) model. The downstream ML model may be trained based on the downstream historical data, preferably from the one or more historical downstream object identifiers. Like in the case of the prediction and/or control logic, the trained downstream ML model may at least partially be data-driven model.
Similarly, the computing of at least one upstream performance parameter may be done using an upstream analytical computer model. Also, optionally, the determining of the upstream control settings may be done using at least one upstream machine learning (“ML”) model. The upstream ML model may be trained based on the upstream historical data, preferably from the one or more upstream object identifiers. Like in the case of the prediction and/or control logic, the trained upstream ML model may at least partially be a data-driven model.
Production of the article, and that of the TPU and/or ETPU material, can be a data heavy environment, which can produce lots of data from different equipment. It will also be appreciated that the teachings as proposed also make the realization of a monitoring and/or control method or system suitable and more efficient for edge computing in at least for the downstream industrial plant. Similarly, equivalent features may be applied at the upstream industrial plant for further establishing a more complete traceability and quality control from the input material through the article despite the production being carried out in different plants that are isolated from each another. It will also be realized that monitoring such as safety and/or quality control and/or control at least of the downstream production process can thus be done essentially on the spot, for example within each downstream equipment zone, with reduced computational resources such as processing power and/or memory requirements as the object identifiers provide highly targeted data sets of relevant data for computation of the performance parameter. It may also be possible to reduce latency in the computation, thus making sure that there is enough time for number crunching algorithms without slowing down the respective production process. It can also make the training process for the ML model faster and more efficient. Moreover, data and/or logic from the upstream production process can be further leveraged downstream for a more finer control over the article performance.
Due to similar reasons, it also makes the present teachings suitable for cloud-computing because datasets can be made compact and efficient. Many cloud service providers operate with a pay-per-use model based on the utilization of the computational resources, so costs can be reduced and/or the computational power can be more efficiently utilized.
Thus, according to an aspect, at least one downstream ML model may be trained based on the data from the one or more historical downstream object identifiers, or the downstream historical data. The data used for training the downstream ML model may also include historical and/or current laboratory test data, or data such as downstream performance parameters measured from the past and/or recent samples of the article or a part thereof and/or the TPU and/or ETPU material. For example, quality data from one or more analyses such as image analysis, laboratory equipment or other measurement techniques may be used. By including the analyzed performance parameters in their associated historical object identifiers, a more complete relationship between the performance parameters and their corresponding process data are captured in an efficient manner. The costly and time-consuming lab results can thus be more accurately leveraged to improve the quality of the future articles. Scope of human error can also be reduced because the quality data is integrated with its associated snapshot of process data.
In some cases, a sampling object identifier is automatically provided if the article, a part thereof or its derivative material is to be analyzed. This may be based upon a confidence value or if the computing unit cannot minimize the difference between a computed performance parameter and its corresponding desired value. The result of the analysis done on the sample can thus be included or appended at the sampling object identifier, further accurately encapsulating the data and reducing the scope of human error. The data from the sampling object identifier can also be included in the downstream historical data.
The at least one downstream ML model trained with the data, e.g., from historical downstream object identifiers can thus be used for determining at least some of the downstream control settings, which may even be zone-specific control settings for the downstream equipment.
Thus, for determining the downstream control settings the downstream ML model which is trained using the downstream historical data may receive as an input, the precursor data and the at least one desired downstream performance parameter. The downstream ML model can thus provide the downstream control settings as computed values. As was discussed previously, the computed values may be provided to an operator via an HMI and/or the values may be provided directly to the downstream control system. Also similar to as was discussed, the downstream ML model can be used for automatically adapting the downstream production process according to the specifics of the TPU and/or ETPU material obtained from the precursor data, and the desired performance obtained from the at least one desired downstream performance parameter, and the subset of downstream real-time process data. The downstream computing unit can for example, minimize the difference between each or some of the downstream performance parameters, computed via the downstream ML model, and their respective desired performance parameter value.
According to another aspect, the downstream ML model may also provide at least one confidence value indicative of the downstream control settings. In some cases, the confidence value may also be appended to the downstream object identifier, for example as metadata. Should the confidence level of the prediction or computation of any of the downstream control settings fall below an accuracy threshold value, a warning may be triggered at the downstream control system for production. The warning may be generated as a warning signal, for example to initiate downstream production using a set of default settings or it may be used to determine if the downstream ML model should be retrained.
In some cases, in response to the confidence level of the prediction or computation of any of the downstream control settings falling below the accuracy threshold value, a retraining object identifier is automatically provided via the downstream interface. The downstream processing unit may be configured to append the confidence value, the precursor data and the at least one desired downstream performance parameter to the retraining object identifier. The retraining object identifier may be used to determine which insights are lacking for controlling the downstream production process with the set of variables included in the retraining object identifier. The retraining object identifier can thus be used to further improve the downstream historical data for future determinations via the downstream computing unit. According to an aspect, the article produced associated with the retraining object identifier may be sampled and analyzed. The results of the analysis, for example, measured downstream performance parameters may be appended to the retraining object identifier. The retraining object identifier can thus be included in the downstream historical data. This way, full traceability of the material can be maintained, and correct article or a part thereof can be sampled such that the downstream historical data is efficiently enriched even for the cases that were not fully covered with the previous downstream historical data. So, this can allow the correct one or more samples to be collected from production due to tracking provided by the retraining object identifier, and the samples may be analyzed together with the data from the retraining object identifier to find the cause of fall in the confidence level. Complex relationships between various variables can thus be better understood such that downstream control process can be improved further.
In some cases, the same downstream ML model or an another one, may be used by the downstream computing unit for determining which of part or components of the subset of the downstream real-time process data have most dominant effect on the article production. Accordingly, the downstream computing unit is enabled to exclude those of the downstream process parameters and/or equipment operating conditions which have negligible effect on the at least one downstream performance parameter. Relevance of the downstream real-time process data that is appended for specific articles can hence be improved for their respective object identifier.
The downstream object identifier in some cases is appended with at least a part of the upstream object identifier. So, either the entire upstream object identifier may be encapsulated in the downstream object identifier, or just a part thereof. The part may be, for example a reference to the upstream object identifier, or a link which ties the two object identifiers either directly or via one or more other object identifiers that may have been generated in between.
As it was discussed, the downstream object control settings may be zone-specific, different settings for different zones that the precursor material traverses through during downstream production process. This can allow the downstream production process can be adapted within the downstream zones according to the downstream process data that the material was processed upstream. Granularity of the control can thus be further improved and be made more flexible. For example, any suboptimal processing upstream can be corrected by adapting the downstream zone-specific control settings.
In addition to determining the downstream zone-specific control settings, the downstream object identifier may be appended with at least a part of the real-time process data from the respective downstream equipment zones based upon the presence signal as was discussed. Relevance of the downstream object identifier, especially the data encapsulated and/or referenced therein can thus be further improved in addition to providing a more granular control.
As it was discussed, the downstream object identifier may at least partially encapsulate or be enriched with, the upstream object identifier, or more specifically the data from the upstream object identifier which has been appended with at least a part of the subset of the upstream real-time process data. Alternatively, the downstream object identifier may be linked to the upstream object identifier. In other words, it can be said that the downstream object identifier is appended with the upstream object identifier. Thus, the downstream object identifier is related to the upstream object identifier either by the upstream object identifier being at least partially being a part of the downstream object identifier.
The downstream computing unit even provide further downstream object identifiers, for example when the TPU and/or ETPU material is split or combined with other material during the downstream production. Specific subsets of data as discussed previously may be appended to their respective further downstream object identifiers. By doing this, finer visibility into the quality of various components of the downstream production chain can be improved. For example, the performance parameters of each specific zones can also be used to track and control the quality of the material in that specific zone.
Similar to the above discussion, further ML models can also be applied to any of the further downstream object identifiers. The further ML models may be used for predicting performance parameters and/or controlling the downstream production by adapting the zone-specific downstream control settings based upon the output from the respective models.
Those skilled in the art will appreciate that the terms “appending” or “to append” may mean including or attaching, for example saving different data elements such as metadata in the same database, or in the same memory storage element, either in adjacent or at different locations in the database or memory storage. The term may even mean linking, of one or more data elements, packages or streams at the same or different locations, in such a manner that the data packages or streams can be read and/or fetched and/or combined when needed. At least one of the locations may be a part of a remote server or even at least partially a part of a cloud-based service.
“Remote server” refers to one or more computers or one or more computer servers that are located away from the plant. The remote server may thus be located several kilometers or more from the plant. The remote server may even be located in a different country. The remote server may even be at least partially implemented as a cloud based service or platform, for example as platform as a service (“PaaS”). The term may even refer collectively to more than one computers or servers located on different locations. The remote server may be a data management system.
It will be appreciated that the precursor material, TPU and/or ETPU material, after traversing through an initial downstream equipment zone may be substantially different in nature from the time when the precursor entered the initial downstream equipment zone. Hence, as discussed, at entry of the precursor material at a further downstream equipment zone after traversing from the initial downstream equipment zone, the precursor material may have transformed to a derivative material or an intermediate processed material. However, for the sake of simplicity, and without losing generality of the present teachings, the term TPU and/or ETPU material will be used to refer also to the case when the TPU and/or ETPU material during the downstream production process has converted to such an intermediate processed material or derivative material. For example, a batch of precursor material in the form of a mixture of chemical components may have traversed through the initial downstream equipment zone on a conveyor belt where the batch is heated to induce a chemical reaction. As a result, when the precursor material enters the further downstream equipment zone, directly after exiting the initial downstream equipment zone or after traversing also other zones, the material may have become a derivative material different in properties from the precursor material. For example, the precursor in the form of a TPU at the initial downstream equipment zone may have transformed to an ETPU when entering the further downstream equipment zone. The ETPU in this example may be called a derivative or intermediate processed material. However, as mentioned above, such derivative material can still be termed as the precursor material, at least because the relationship between such an intermediate processed material and the precursor material can be defined and determined via the downstream production process. Moreover, in other cases, the precursor material may still essentially retain similar properties even after traversing the initial downstream equipment zone or also other zones, for example when the initial downstream equipment zone simply dries the precursor material or filters it to remove traces of an unwanted material. Hence, those skilled in the art will understand that the precursor material in any intermediate zones may or may not be transformed to a derivative material.
As discussed, in case samples of the TPU and/or ETPU material, derivative material or the article are collected for analysis, such samples may also be provided with a sample object identifier. The sample object identifier can be similar to the object identifiers discussed in the present disclosure and thus appended relevant corresponding process data as discussed. Thus, the samples can also be attached with an accurate snapshot of the downstream production process that is relevant for the properties of said sample. The analysis and quality control can thus be further improved. Furthermore, the downstream production process can be synergistically improved, e.g., based on improved training of one or more ML models.
According to another aspect, when the downstream production process involves the TPU and/or ETPU material being physically transported or moved in a zone or between zones, for example, using a transport element such as a conveyor system, the downstream real-time process data may also include data indicative of the speed of the transport element and/or speed by which the TPU and/or ETPU material is transported during the downstream production process. The speed may be provided directly via one or more of the sensors and/or it may be calculated via the downstream computing unit, for example, based on the time of entry at the zone and the time of exit from the zone or the time of entry at another zone subsequent to the zone. The downstream object identifier can thus be further enriched with processing time aspects in the zone, especially those which may have impact on one or more downstream performance parameters of the article. Moreover, by using the time stamps of entry and exit or a subsequent zone entry, requirement of a speed measuring sensor or device for the transport element can be obviated.
According to another aspect, each object identifier includes a unique identifier, preferably a globally unique identifier (“GUID”). At least tracing of the article back to the TPU and/or ETPU material may be enhanced by attaching the GUID to each virtual package of the article. Optionally, the article may be traced back to the input material that were used for the production of the TPU and/or ETPU material. Via the GUID, data management of process data such as time-series data can also be decreased and a direct correlation between the virtual/physical package, the production history, and the quality control history can be enabled.
As discussed regarding ML models, according to an aspect, an upstream ML model, may be trained based on the data from the upstream object identifier. The training data may also include past and/or current laboratory test data, or data from the past and/or recent samples of the TPU and/or ETPU material and/or the article. The object identifiers can also make it easier for the upstream plant to link the performance of the article produced at a downstream plant to the specific input material that was used for producing the TPU and/or ETPU, and also the upstream process data that were used to process the material. This can have significant benefits in terms of ensuring consistent quality of the article.
In addition to the previously discussed advantages with the ML models, having the trained models based on zones in the respective production lines can allow more detailed tracking of material and forecasting their respective performance parameters and even the article performance parameters.
In some production scenarios like batch production such models may be used on the fly to flag quality control issues not only for the article as produced, but also for any derivative materials.
Thus, any or each of the equipment zones upstream and/or downstream may be monitored and/or controlled via an individual ML model, the individual ML model being trained based on data from the respective object identifier from that zone.
According to an aspect, providing of the respective object identifier for a zone, e.g., the downstream object identifier, may occur, or be triggered, in response to any one or more of the values indicative of properties of the TPU and/or ETPU material and/or any one or more of the values from the downstream equipment operating conditions and/or any one or more of the values of the downstream process parameters reaching, meeting, or crossing a predefined threshold value. Any such values may be measured via one or more the downstream sensors and/or switches. For example, the predefined threshold can be related to a weight or amount value of the TPU and/or ETPU material introduced at the downstream equipment. Accordingly, when quantity such as the weight of the TPU and/or ETPU material being received at the downstream equipment reaches a predefined quantity threshold such as a weight threshold, a trigger signal may be generated. Ideally, the upstream object identifier is automatically appended to the downstream object identifier, for example via the process specific data and/or tag from the incoming TPU and/or ETPU material. Certain examples of triggering events or occurrences for providing object identifiers were also discussed earlier in the present disclosure. In response to the trigger signal, or directly in response to the quantity or weight reaching the predefined weight threshold, the object identifier may be provided. The trigger signal can either be a separate signal, or it may just be an event, e.g., a particular signal meeting a predefined criteria such as threshold detected via the computing unit and/or the equipment. Thus, it will also be appreciated that, the object identifier may be provided in response to the quantity of the TPU and/or ETPU material reaching a predefined quantity threshold. The quantity may be measured as weight as explained in the example above, and/or it may be any one or more other values such as level, fill or filling degree or volume and/or by summing up or by applying integration on the mass flow of the TPU and/or ETPU material.
Thus, for example, the downstream object identifier may be provided in response to a trigger event or signal, said event or signal preferably being provided via the downstream equipment or the initial downstream equipment zone. This may be done in response to output of any of the one or more downstream sensors and/or switches operatively coupled to the downstream equipment. The trigger event or signal may relate to a quantity value of the TPU and/or ETPU material, for example, to an occurrence of the quantity value reaching or meeting a predetermined quantity threshold value. Said occurrence may be detected via the downstream computing unit and/or the downstream equipment, for example, using one or more weight sensor, level sensor, fill sensor, or any suitable sensor that can measure or detect the quantity of the TPU and/or ETPU material.
An advantage of using quantity as a trigger for providing the downstream object identifier can be that any changes in the quantity of the material during the production process can be used as triggers for providing further one or more downstream object identifiers as explained in the present teachings. The applicant has realized that this can provide an optimal way to segment generation of different object identifiers in an industrial environment for processing or producing one or more articles, any derivative material, and eventually the article can be traced while accounting for quantity or mass flow, essentially throughout the whole production chain. By providing object identifiers just at points where new material is introduced or is inputted, or where the material is split, the number of object identifiers can be minimized while retaining traceability of the material not only at the end points of production, but also within. Within equipment or production zones where no new material is added, or where no material is split, knowledge of the processes within such zones can be used to maintain observability within two adjacent object identifiers.
When viewed from a perspective, there can also be provided a use of the control settings and/or any one or more the performance parameters, generated according to any of the method aspects herein disclosed, for controlling a production process, for example, a downstream plant. More specifically, the downstream control settings and/or at least one downstream performance parameter.
By doing so, any of the downstream plants as discussed can obtain an improved production process for manufacturing one or more articles.
When viewed from another perspective, there can also be provided a system for controlling a downstream production process, the system being configured to perform any of the methods herein disclosed. Or, a system for controlling a downstream production process for manufacturing an article at a downstream industrial plant, the downstream industrial plant comprising at least one downstream equipment, and the article being manufactured by processing, via the downstream equipment, at least at least one thermoplastic polyurethane (“TPU”) and/or expanded thermoplastic polyurethane (“ETPU”) material using the downstream production process, wherein the system is configured to perform any of the methods herein disclosed.
For example, there can be provided a system for controlling a downstream production process for manufacturing an article at a downstream industrial plant, the downstream industrial plant comprising at least one downstream equipment and a downstream computing unit, and the article being manufactured by processing, via the downstream equipment, at least one thermoplastic polyurethane (“TPU”) and/or expanded thermoplastic polyurethane (“ETPU”) material using the downstream production process, wherein the system is configured to:
When viewed from another perspective, there can also be provided a computer program comprising instructions which, when the program is executed by a suitable computing unit, cause the computing unit to carry out any of the methods herein disclosed. There can also be provided a non-transitory computer readable medium storing a program causing a suitable computing unit to execute any method steps herein disclosed.
For example, there can be provided a computer program, or a non-transitory computer readable medium storing the program, comprising instructions which, when the program is executed by a suitable computing unit, operatively coupled to at least one equipment for manufacturing an article at a downstream industrial plant by processing at least one thermoplastic polyurethane (“TPU”) and/or expanded thermoplastic polyurethane (“ETPU”) material using a downstream production process, causes the computing unit to:
It will be appreciated that the set of downstream control settings is suitable for manufacturing the chemical product at the downstream industrial plant.
A computer-readable data medium or carrier includes any suitable data storage device on which is stored one or more sets of instructions (e.g., software) embodying any one or more of the methodologies or functions described herein. The instructions may also reside, completely or at least partially, within the main memory and/or within the processor during execution thereof by the computing unit, main memory, and processing device, which may constitute computer-readable storage media. The instructions may further be transmitted or received over a network via a network interface device.
The computer program for implementing one or more of the embodiments described herein may be stored and/or distributed on a suitable medium, such as an optical storage medium or a solid state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the internet or other wired or wireless telecommunication systems. However, the computer program may also be presented over a network like the World Wide Web and can be downloaded into the working memory of a data processor from such a network.
Furthermore, a data carrier or a data storage medium for making a computer program product available for downloading can be also provided, which computer program product is arranged to perform a method according to any of the aspects herein disclosed.
When viewed from another perspective, there can also be provided a computing unit comprising the computer program code for carrying out the method herein disclosed. Also, there can be provided a computing unit operatively coupled to a memory storage comprising the computer program code for carrying out the method herein disclosed.
That two or more components are “operatively” coupled or connected shall be clear to those skilled in the art. In a non-limiting manner, this means that there may at least be a communicative connection between the coupled or connected components e.g., via the interface or any other suitable interface. The communicative connection may either be fixed, or it may be removable. Moreover, the communicative connection may either be unidirectional, or it may be bidirectional. Furthermore, the communicative connection may be wired and/or wireless. In some cases, the communicative connection may also be used for providing control signals.
“Parameter” in this context refers to any relevant physical or chemical characteristic and/or a measure thereof, such as temperature, direction, position, quantity, density, weight, color, moisture, speed, acceleration, rate of change, pressure, force, distance, pH, concentration and composition. The parameter may also refer to a presence or lack thereof of a certain characteristic.
“Actuator” refers to any component of that is responsible for moving and controlling a mechanism related to an equipment such as a machine, directly or indirectly. The actuator may be a valve, motor, a drive, or their likes. The actuator may be operable electrically, hydraulically, pneumatically, or any of their combination.
“Computer processor” refers to an arbitrary logic circuitry configured for performing basic operations of a computer or system, and/or, generally, to a device which is configured for performing calculations or logic operations. In particular, the processing means or computer processor may be configured for processing basic instructions that drive the computer or system. As an example, the processing means or computer processor may comprise at least one arithmetic logic unit (“ALU”), at least one floating-point unit (“FPU)”, such as a math coprocessor or a numeric coprocessor, a plurality of registers, specifically registers configured for supplying operands to the ALU and storing results of operations, and a memory, such as an L1 and L2 cache memory. In particular, the processing means or computer processor may be a multicore processor. Specifically, the processing means or computer processor may be or may comprise a Central Processing Unit (“CPU”). The processing means or computer processor may be a (“CISC”) Complex Instruction Set Computing microprocessor, Reduced Instruction Set Computing (“RISC”) microprocessor, Very Long Instruction Word (“VLIW”) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing means may also be one or more special-purpose processing devices such as an Application-Specific Integrated Circuit (“ASIC”), a Field Programmable Gate Array (“FPGA”), a Complex Programmable Logic Device (“CPLD”), a Digital Signal Processor (“DSP”), a network processor, or the like. The methods, systems and devices described herein may be implemented as software in a DSP, in a micro-controller, or in any other side-processor or as hardware circuit within an ASIC, CPLD, or FPGA. It is to be understood that the term processing means or processor may also refer to one or more processing devices, such as a distributed system of processing devices located across multiple computer systems (e.g., cloud computing), and is not limited to a single device unless otherwise specified.
“Computer-readable data medium” or carrier includes any suitable data storage device or computer readable memory on which is stored one or more sets of instructions (e.g., software) embodying any one or more of the methodologies or functions described herein. The instructions may also reside, completely or at least partially, within the main memory and/or within the processor during execution thereof by the computing unit, main memory, and processing device, which may constitute computer-readable storage media. The instructions may further be transmitted or received over a network via a network interface device.
Certain aspects of the present teachings will now be discussed with reference to the following drawings that explain the said aspects by the way of examples. Since the generality of the present teachings is not dependent on it, the drawings may not be to scale. Certain features shown in the drawings can be logical features that are shown together with physical features for sake of understanding and without affecting the generality of the present teachings. To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.
Those skilled in the art shall appreciate that the objective of the present example, or even that of the present teachings, is not to show a production process for manufacturing the article 170, rather the objective is to show how the present teachings may be applied to at least the downstream production process. As it was shown in the description, such production processes can be complex, so specific equipment shown in
The downstream industrial plant comprises at least one downstream equipment, which optionally may have a plurality of equipment zones for manufacturing or producing the article 170 using the downstream production process. The article 170 may any product that is at least partially made from TPU and/or ETPU. For example, the article 170 may be footwear such as a shoe made entirely or partially of TPU and/or ETPU material for example, or the article may be a part of a shoe, such as shoe intermediate sole, shoe insole and shoe combination sole. The article may even be a bicycle saddle, bicycle tire, cushioning element, upholstery, mattress, base, handle, protective foil, a component in the interior or exterior of an automobile, sports equipment such as a ball, or a floor covering, in particular for sports areas, athletics tracks, sports halls, children's playgrounds and sidewalks. Accordingly, TPU and/or ETPU material may be a precursor material 114 used in the production of the footwear.
The precursor material 114 may be supplied from an upstream industrial plant, which may be isolated from the downstream industrial plant. The precursor material 114 may be manufactured using at least one input material at the upstream industrial plant. For example, the input material may be methylene diphenyl diisocyanate (“MDI”) and/or polytetrahydrofuran (“PTHF”), which is used for in an upstream production process at the upstream industrial plant for producing the TPU and/or ETPU material, which is then provided or supplied to the downstream industrial plant for producing the article 170.
The precursor material 114 may even be in batches, for example a package of 10 kg each. As it was discussed, due to the nature of such products as the precursor material 114 or even the material that the precursor material 114 is made from, or the material or product that the precursor material 114 is transformed to using the downstream production process, such materials and/or products may be hard to trace in the production chain. For instance, ETPU may be in the form of particles or beads that are molded to form the article 170 or a part thereof, for example using steam chest molding. However, it may be important to ensure that each component, e.g., each unit or package, or even parts inside have consistent and desired properties or quality. The present teachings can enable production that can result in one or more desired downstream performance parameters to be achieved for the article 170.
The downstream equipment may or may not have a plurality of equipment zones. In this example, the downstream equipment in
An object identifier, or in this case, a downstream object identifier 122 is provided for the precursor material 114. The downstream object identifier 122 may be provided at the downstream computing unit 124. The downstream computing unit 124 may provide the downstream object identifier 122 to a downstream memory storage 128, e.g., via a downstream interface. As it was discussed, in some cases the downstream object identifier 122 may be provided by an upstream computing unit to the downstream computing unit 124, for example via a shared memory storage. In some cases, the downstream memory storage 128 may be a shared memory storage accessible via the upstream computing unit. The upstream computing unit may be a computing unit belonging to the upstream industrial plant. The downstream object identifier 122 comprises data related to the precursor material 114, or precursor data. The precursor data is indicative of one or more property of the precursor material 114.
The downstream object identifier 122, or more specifically, data from the downstream object identifier 122 can be used for determining a set of downstream control settings for controlling the production of the article 170. The downstream object identifier 122, at least one desired downstream performance parameter related to the article 170 and downstream historical data may be used for providing the set of downstream control settings. The set of downstream control settings may at least partially be determined by the upstream computing unit and/or at least some of the downstream control settings may be determined by the downstream computing unit 124. The downstream historical data may comprise downstream process parameters and/or operational settings that were used for manufacturing past one or more articles in the past, e.g., via the downstream equipment. The set of downstream control settings is then used for manufacturing the article 170 with a goal of achieving the at least one desired downstream performance parameter. The desired downstream performance parameter relates to desired performance or quality of the article 170.
For example, at least some of the control settings may determine how the first valve 112a and/or the second valve 112b is to be manipulated, for example, how much material should be allowed and in what ratio. They may even decide how the mixing pot 104 should be operated, for example, the time period of mixing and/or the speed of the mixer. Additionally, or alternatively, the control settings may determine and control what value a particular process parameter needs to have and for how long, for example, equipment operating conditions such as set-points. Thus, the control settings are automatically determined based on the specifics of the precursor material 114 and the equipment. It will be appreciated that the set of downstream control settings may comprise control settings for the initial downstream equipment zone, i.e., zone-specific control settings, and similarly zone-specific control settings for any further equipment zones, should they exist. In some cases, the set of downstream control settings may comprise global control settings, i.e., settings that apply for the entire production chain. Additionally, or alternatively, even within an equipment zone, at least some of the zone-specific control settings may be adapted on-the-fly according to the real-time-process data from that zone, for example, in response to the output of one or more analytical models and/or ML models which are trained with the downstream historical data. The control settings may be provided to a plant control system, such as a DCS and/or PLC, for controlling the downstream equipment. The settings are provided preferably automatically to the control system, however in some cases, they may be provided via an operator.
The downstream object identifier 122 may be a unique identifier, preferably a globally unique identifier (“GUID”), distinguishable from other object identifiers. The GUID may be provided dependent upon the specifics of the particular industrial plant and/or the specifics of the article 170 being manufactured and/or specifics of the date and time, and/or specifics of the particular precursor material 114 being used. The downstream object identifier 122 is here shown provided at a downstream memory storage 128 operatively coupled to a downstream computing unit 124. The downstream memory storage 128 may even be a part of the downstream computing unit 124. The downstream memory storage 128 and/or the downstream computing unit 124 may at least partially be a part of a cloud service, for example MS Azure.
The downstream computing unit 124 is operatively coupled to the downstream equipment, for example, via a downstream network 138, which may be any suitable kind of data transmission medium. The downstream computing unit 124 may even be a part of the downstream equipment, for example it may be at least partially be a part of the initial downstream equipment zone. The downstream computing unit 124 may even be at least partially the plant control system of the downstream industrial plant. The downstream computing unit 124 may receive one or more signals from one or more sensors operatively coupled to the downstream equipment, for example, those of the initial downstream equipment zone. For example, the downstream computing unit 124 may receive one or more signals from a fill sensor 144 and/or one or more sensors related to the transport elements 102a-b. Said sensors are also a part of the initial downstream equipment zone. The downstream computing unit 124 may thus even at least partially to control the initial downstream equipment zone, or some parts thereof in according to the control settings as explained previously. For example, the downstream computing unit 124 may control the valves 112a,b, e.g., via their respective actuators, and/or a heater 118 and/or the transport elements 102a-b. The transport elements 102a,b and others in the example of
Without affecting the scope or generality of the present teachings, other kinds of transport elements can also be usable instead or in combination with a conveyor system. In some cases, any kind of equipment that involves a flow of material, e.g., one or more materials in and one or more materials out, may be termed a transport element. Thus, besides a conveyor system or belt, equipment such as extruder, pelletizer, heat exchanger, buffer silo, silo with mixer, mixer, mixing vessel, cutting mill, double cone blender, curing tube, column, separator, extraction, thin film vaporizer, filter, sieve may also be termed transport elements. Thus, it will be appreciated that presence of a transport system as a conveyor system may be optional, at least because in some cases material may move directly from one equipment to another via mass flow, or as normal flow via one equipment to another. For example, a material may move directly from a heat exchanger to a separator or even further such as to a column and so forth. Thus, in some cases, one or more transport elements or system may be inherent to an equipment.
The downstream object identifier 122 in some cases may be provided in response to a trigger signal or event, which may be a signal or an event related to a quantity of the precursor material 114. For example, the fill sensor 144 may be used to detect at least one quantity value such as fill degree and/or weight of the precursor material 114. When the quantity reaches a predetermined threshold, the downstream computing unit 124 may automatically provide the downstream object identifier 122 at the downstream memory storage 128.
The downstream computing unit 124 is then configured to determine a set of process and/or operation parameters based on the downstream object identifier 122 and the at least one desired performance parameter. The downstream computing unit 124 can thus determine zone-specific control settings for each of the equipment zones based on the determined set of process and/or operation parameters and historical data. The historical data may comprise data from one or more historical upstream object identifiers related to previously processed precursor material 114 in the downstream equipment zone. At least one historical upstream object identifier may have appended to it at least a part of the process data which is indicative of the process parameters and/or equipment operating conditions that the previously processed precursor material 114 was processed under in the downstream equipment zone. The zone-specific control settings are then provided for controlling the production process of the article 170. The zone-specific control settings may be provided via an output interface, which may be the same as the interface, or a different component similar to the interface. The zone-specific control settings are thus used by the downstream computing unit 124 and/or the plant control system for manufacturing the article 170.
In some cases, the downstream computing unit 124 may receive downstream real-time process data from all equipment or equipment zones in the downstream industrial plant. The downstream computing unit 124 may determine a subset of the downstream real-time process data based on the downstream object identifier and a downstream zone presence signal. For example, the trigger signal or event may also be used for generating the downstream zone presence signal for the initial downstream equipment zone. Additionally, or alternatively, the downstream zone presence signal is provided by mapping the downstream real-time process data, which in a production environment is time-dependent data, to spatial data. The downstream zone presence signal can hence be used for determining not only the downstream process parameters and/or equipment operating conditions that are relevant for the processing of the precursor material 114 at the initial downstream equipment zone, but also the time aspect of said downstream process parameters and/or equipment operating conditions which are included in the downstream real-time process data.
In some cases, the downstream computing unit 124 may even compute at least one downstream performance parameter that is relevant for the article 170, which is related to the downstream object identifier 122. In some cases, the downstream performance parameter may even be a zone-specific parameter. The computation is based on a subset of downstream real-time process data 126, which in this case is shown optionally appended at the downstream object identifier 122. The computation of the downstream performance parameter is also based on the downstream historical data which may comprise data from one or more historical downstream object identifiers. Each historical downstream object identifier is related to the respective TPU and/or ETPU material which was processed in the downstream equipment zone in the past. At least one, preferably each, of the historical downstream object identifiers is appended with at least a part of the downstream process data which is indicative of the downstream process parameters and/or equipment operating conditions that the previously processed TPU and/or ETPU material was processed under in the downstream equipment, e.g., at the initial downstream equipment zone. In some cases, at least some of the historical downstream object identifiers may also include or be appended with their associated downstream performance parameters.
The at least one downstream performance parameter may be appended to the downstream object identifier 122, for example as metadata. Thus, the downstream object identifier 122 is enriched with the performance parameter that is related to the quality of the article 170. Quality control process can thus be simplified and improved while improving traceability, e.g., by coupling the quality related data with the resulting article 170. Also, at least of the computed downstream performance parameters may be used in further zones downstream for adapting the downstream production process. The downstream production process can thus become more granularly controlled and flexible whilst maintaining performance of the article 170.
The subset of downstream real-time process data 126 from the initial downstream equipment zone may be data within the time window that the precursor material 114 was at the initial downstream equipment zone, or the time window may be even shorter thus just for the time that the precursor material 114 was processed via the mixing pot 104. The downstream real-time process data can be used to determine the time window. Hence, the downstream object identifier 122 can be enriched with high relevance data by using the time-dimension of the downstream real-time process data. Thus, object identifiers not only can be used to track the material in the production process, but also encapsulate high quality data that can make edge computing and/or cloud computing more effective. The object identifier data can be highly suitable for faster training and retraining of machine learning models. Data integration can also be simplified as the data encapsulated in the object identifiers can be more compact than traditional datasets.
The at least part of subset of downstream real-time process data 126 is indicative of the process parameters and/or equipment operating conditions, i.e., the operating conditions of the mixing pot 104 and valves 112a-b, that the TPU and/or ETPU material or precursor material 114 is processed under in the downstream equipment or in the initial downstream equipment zone, for example, any one or more of, incoming mass flow, outgoing mass flow, filling degree, temperature, moisture, time stamps or time of entry, time of exit, etc. The equipment operating conditions in this case may be control signals and/or set-points of the valves 112a,b and/or the mixing pot 104. The downstream control settings can for example be used to control these. The subset of downstream real-time process data 126 may be or it may comprise time-series data, which means that it may include time dependent signals, which may be obtained via one or more sensors, for example, output of the fill sensor 144. The time-series data may comprise signals that are continuous or any of them may be intermittent with regular or irregular time intervals. The subset of downstream real-time process data 126 may even include one or more time-stamps, for example time of entry and/or time of exit, from the mixing pot 104. Thus, a particular precursor material 114 may be associated with the subset of downstream real-time process data 126 relevant for that precursor material 114 via the downstream object identifier 122. The downstream object identifier 122 may be appended to other object identifiers downstream of the production process such that specific process data and/or equipment operating conditions can be correlated to a specific article. Other important benefits were already discussed in other parts of this disclosure, e.g., in the summary section.
For example, the conveyor system comprising the transport elements 102a,b and the associated belt may be considered an intermediate equipment zone which is in downstream direction of the initial downstream equipment zone. The intermediate equipment zone in this example comprises a heater 118 that is used for applying heat to the precursor traversing on the belt. The conveyor system may even comprise one or more sensors, for example any one or more of, speed sensor, weight sensor, temperature sensor, or any other kind of sensor for measuring or detecting the process parameters and/or properties of the precursor material 114 at the intermediate equipment zone. Any or all outputs of the sensors may be provided to the downstream computing unit 124.
As the precursor material 114 progresses along the direction of transverse 120, it is applied heat via the heater 118. The heater 118 may be operatively coupled to the downstream computing unit 124, i.e., the downstream computing unit 124 may receive signals or real-time process data from the heater 118. Furthermore, the heater 118 is controllable via the downstream computing unit 124, for example via one or more control signals and/or set-points that may be the downstream control settings, or may be obtained via the downstream control settings. Thus, the way the precursor material 114 is processed at the downstream equipment is determined via at least some of the downstream control settings. Some of the downstream control settings may be used to control other downstream zones as will be explained further.
Similarly, the conveyor system comprising the transport elements 102a,b and the associated belt may also be operatively coupled to the downstream computing unit 124, i.e., the downstream computing unit 124 may receive signals or a part of downstream process data from the transport elements 102a,b. The coupling may for example be via the downstream network 138. Furthermore, the transport elements 102a,b may even be controllable via the downstream computing unit 124, for example via one or more control signals and/or set-points provided via the downstream computing unit 124, as or in response to the downstream control settings. So, the speed of the transport elements 102a,b may be observable and/or controllable by the downstream computing unit 124.
Optionally, as the quantity of the precursor material 114 is constant or near constant in the intermediate equipment zone, a further object identifier may not be provided for the intermediate equipment zone. Thus, the process data from the intermediate equipment zone, i.e., from the heater 118 and/or the transport elements 102a,b may also be appended to the object identifier of the previous or preceding zone, i.e., the downstream object identifier 122. The appended subset of downstream real-time process data 126 may thus be enriched to be further indicative of the process parameters and/or equipment operating conditions from the intermediate equipment zone, i.e., the operating conditions of the heater 118 and/or transport elements 102a,b, that the precursor material 114 is processed under in the intermediate equipment zone, for example, any one or more of, incoming mass flow, outgoing mass flow, one or more temperature values from the intermediate zone, time of entry, time of exit, speed of the transport elements 102a,b and/or belt, etc. The equipment operating conditions in this case may be control signals and/or set-points of the transport elements 102a,b and/or the heater 118, derivable from the downstream control settings.
It will be clear that the subset of downstream real-time process data 126 predominantly relates to the time-periods within which the precursor material 114 is present in the respective equipment zone. Thus, an accurate snapshot of the relevant process data for the specific precursor material 114 can be provided via the downstream object identifier 122. Further observability of the precursor material 114 may be extracted via the knowledge of the specific portion or part of the downstream production process, e.g., prior-knowledge of a chemical reaction, within the intermediate equipment zone. Alternatively, or in addition, the speed by with the precursor material 114 traverses through the intermediate equipment zone can be used to extract further observability via the downstream computing unit 124. In conjunction with the subset of downstream real-time process data 126 with specific time-stamps, or the time-series data, and/or time of entry and/or time of exit of the precursor material 114 in the intermediate equipment zone, a more granular detail of conditions under which the precursor material 114 is processed in the intermediate equipment zone may be obtained from the downstream object identifier 122.
The data from the downstream object identifier 122 may be used for training one or more downstream ML models for monitoring and/or control of the downstream production process as whole and/or the specific portions thereof, for example, the portion of the downstream production process within the initial downstream equipment zone and/or the intermediate equipment zone. The downstream ML model and/or downstream object identifier 122 may even be used for correlating one or more downstream performance parameters of the article 170 to the specifics of the downstream production process in one or more zones.
It will be appreciated that as the precursor material 114 progresses along the direction of transverse 120, it may change its properties and may transform or convert to a derivative material 116. For example, as the heater 118 heats the precursor material 114, it may result in the derivative material 116. Those skilled in the art will appreciate that for simplicity and ease of understanding, the derivative material 116 may also be sometimes referred to as precursor in the present teachings. For example, in context of the equipment zone or components under discussion, it will thus be clear in which phase the precursor is within the downstream production process as discussed in the description of this example.
Now discussing an example of a zone where a material is divided in multiple parts.
Thus, according to an aspect of the present teachings, an individual object identifier may be provided for each part. In some cases though, an object identifier may only be provided for one of the parts, or for some of the parts, instead of providing an individual object identifier for each part. This may be the case, for example if tracking any of the parts is not of interest. For example, an object identifier may not be provided for a part of the derivative material 116 that is discarded. Now referring back to
The first further downstream object identifier 130a comprises at least a part of the downstream object identifier 122 and similarly the second further downstream object identifier 130b comprises at least a part of the downstream object identifier 122. The downstream computing unit 124 may then determine an another subset of the downstream real-time process data (e.g., a first subset of downstream real-time process data 132a and or second subset of downstream real-time process data 132b) based on the further downstream object identifier and the downstream zone presence signal. The downstream computing unit 124 may then determine further zone-specific control settings for the downstream equipment zone and optionally also for other equipment zones downstream of the downstream equipment zone, based on data from the downstream object identifier 122, the another subset of the real-time process data and downstream historical data from one or more historical downstream object identifiers related to previously processed precursor in the further downstream equipment zone.
The first further downstream object identifier 130a is optionally appended with the first subset of downstream real-time process data 132a and the second further downstream object identifier 130b is optionally appended with the second subset of downstream real-time process data 132b. The first subset of downstream real-time process data 132a may be a copy of the second subset of downstream real-time process data 132b, or they may partly be the same data. For example, in cases when the first divided material 140a and the second divided material 140b undergo the same process, i.e., at essentially the same place and time, then the downstream process data appended to the further downstream object identifier 130a and the second further downstream object identifier 130b may be the same or similar. If, however, within the further downstream equipment zone the further downstream object identifier 130a and the second further downstream object identifier 130b were to be treated differently, the first subset of downstream real-time process data 132a and the second subset of downstream real-time process data 132b may be different from each other.
Those skilled in the art will appreciate that in some cases, however, optionally only one object identifier may be provided at the cutting mill 142 and then multiple object identifiers may be provided subsequent to the cutting mill 142 if the material processed via the cutting mill 142 is split in multiple parts. Thus, dependent upon the specifics of a particular downstream production process, a cutting mill may or may not be a separation device. Similarly, in some cases no new object identifier may be provided for a cutting mill such that process data from the zone is appended to the preceding object identifier. New object identifier may thus be provided at the zones where the material is split and/or it is combined. For example, in some cases, the further downstream object identifier 130a and the second further downstream object identifier 130b may be provided after the cutting mill 142, for example at entry at the different zones subsequent to the cutting mill 142.
In this example, the further downstream equipment zone also comprises an imaging sensor 146, which may be a camera or any other kind of optical sensor. The imaging sensor 146 may also be operatively coupled to the downstream computing unit 124. The imaging sensor 146 may be used for measuring or detecting one or more properties of the derivative material 116 prior to entering the further downstream equipment zone. This may for example be done to reject or divert the material that does not meet a given quality criteria. As quantity or mass flow of the material is altered in the further downstream equipment zone, according to an aspect of the present teachings, another object identifier (not shown in
The providing of the further downstream object identifier 130a and the second further downstream object identifier 130b may be triggered in response to the derivative material 116 passing the quality criteria via the imaging sensor 146. The quality criteria may even determined via one or more downstream performance parameters. By correlating data from the adjacent zones or from the object identifiers, for example, mass flow from the intermediate equipment zone and mass flow to the downstream equipment zone, the downstream computing unit 124 may determine which specific precursor material 114 or derivative material 116 is related to the material entering the subsequent zone. Alternatively, or in addition, two or more of the time stamps may be correlated between the zones, for example time-stamp of exit from the intermediate equipment zone and time-stamp of detection via the imaging sensor 146 and/or entry at the further downstream equipment zone. The speed of the transport elements 102a,b either measured directly via a sensor output or determined from two or more time-stamps can also be used to establish relationship between a specific packet or batch of precursor and its object identifiers. It may thus even be determined where the specific article 170 was within the production process at a given time, thus a time-space relationship may be established. Some or all of these aspects can be usable not only in improving the traceability of the article 170 from the TPU and/or EPU material to finished product, but also in monitoring and improving the production process and making it more adaptable and controllable.
As discussed, the first further downstream object identifier 130a and the second further downstream object identifier 130b are appended with the first subset of downstream real-time process data 132a and the second subset of downstream real-time process data 132b respectively from the further downstream equipment zone. The first subset of downstream real-time process data 132a and the second subset of downstream real-time process data 132b may even be linked to or appended with the downstream object identifier 122. Similar to the previously discussed downstream object identifier 122, the first subset of downstream real-time process data 132a and the second subset of downstream real-time process data 132b are indicative of the downstream process parameters and/or equipment operating conditions, i.e., the output of the imaging sensor 146, the operating conditions of the cutting mill 142 and the second transport elements 106a,b, that the derivative material 116 is processed under in the further downstream equipment zone, for example, any one or more of, incoming mass flow, outgoing mass flow, filling degree, temperature, optical properties, time stamps, etc. The equipment operating conditions in this case may be control signals and/or set-points of the cutting mill 142 and/or the second transport elements 106a,b, that may be derived from the further downstream control settings. The further zone-specific control settings can thus be optimized based upon the data from the downstream object identifier 122, for example the at least one zone-specific performance parameter appended to the downstream object identifier 122.
The first subset of downstream real-time process data 132a and the second subset of downstream real-time process data 132b may comprise time-series data, which means that it may include time dependent signals, which may be obtained via one or more sensors, for example, output of the imaging sensor 146 and/or speed of the second transport elements 106a,b.
As the derivative material 116 proceeds after encountering the imaging sensor 146, it is moved towards the cutting mill 142 in the direction of transverse 154 driven by the second transport elements 106a,b. The second transport elements 106a,b are in this example shown as a part of a second conveyor belt system separate from the conveyor system comprising transport elements 102a,b. It will be appreciated that it the second conveyor belt system may even be a part of the same conveyor system comprising transport elements 102a,b. Accordingly, the further downstream equipment zone may comprise some of the same equipment used in another zone.
As can be seen in
After exiting the initial downstream equipment zone, the first divided material 140a is fed to an extruder 150, while the second divided material 140b is transported for curing at a third equipment zone comprising a curing apparatus 162 and third transport elements 108a,b. The transport elements 108a,b shown are accordingly a non-limiting example, as discussed previously. It will be appreciated that the third equipment zone is downstream of the initial downstream equipment zone and the further downstream equipment zone.
As the second divided material 140b is moved via a belt in the direction of transverse 156, it undergoes the curing process via the curing apparatus 162 to result on a cured second divided material 160. Since no substantial mass change may occur, according to an aspect, no new object identifier may be provided for the third equipment zone. Accordingly, as previously discussed, the process data from the third equipment zone may also be appended to the second further downstream object identifier 130b. Similar to the above, the appended second subset of downstream real-time process data 132b may thus be enriched to be further indicative of the process parameters and/or equipment operating conditions from the third equipment zone, i.e., the operating conditions of the curing apparatus 162 and/or transport elements 108a,b, that the second divided material 140b is processed under in the third equipment zone, for example, any one or more of, incoming mass flow, outgoing mass flow, one or more temperature values from the third zone, time of entry, time of exit, speed of the transport elements 108a,b and/or belt, etc. The equipment operating conditions in this case may be control signals and/or set-points of the transport elements 102a,b and/or the curing apparatus 162 that may also be derived from the further zone-specific control settings. The further zone-specific control settings can thus be optimized based upon the data from the downstream object identifier 122, for example the at least one zone-specific performance parameter appended to the downstream object identifier 122.
Similarly, the first divided material 140a progresses to a fourth equipment zone comprising the extruder 150, a temperature sensor 148 and fourth transport elements 110a,b. Here too, as no substantial mass change may occur, according to an aspect, no new object identifier may be provided for the fourth equipment zone. Accordingly, as previously discussed, the process data from the fourth equipment zone may also be appended to the further downstream object identifier 130a. Similar to the above, the appended first subset of downstream real-time process data 132a may thus be enriched to be further indicative of the process parameters and/or equipment operating conditions from the fourth equipment zone, i.e., the operating conditions of the extruder 150 and/or the temperature sensor 148 and/or transport elements 108a,b, that the first divided material 140a is processed under in the third equipment zone, for example, any one or more of, incoming mass flow, outgoing mass flow, one or more temperature values from the third zone, time of entry, time of exit, speed of the transport elements 110a,b and/or belt, etc. The equipment operating conditions in this case may be control signals and/or set-points of the transport elements 108a,b and/or the extruder 150, which can also be adapted as explained earlier based on computed performance parameters and relevant real-time process data.
Also, properties and dependencies of transformation of the first divided material 140a to an extruded material 152 may also be included in the further downstream object identifier 130a. It will be appreciated that the fourth equipment zone is also downstream of the downstream equipment zone and the further downstream equipment zone.
As can be appreciated, the number of individual object identifiers can be reduced while improving material and product monitoring throughout the production process.
As the extruded material 152 moves further in the direction of traverse 158 generated via the transport elements 108a,b, it may be collected in a collection zone 166. The collection zone 166 may be a storage unit, or it may be a further processing unit for applying further steps of the downstream production process. In the collection zone 166, additional materials may be combined, as shown here that the cured second divided material 160 may be combined with the extruded material 152. Accordingly, a new object identifier may be provided as previously discussed. Such an object identifier is shown as a last downstream object identifier 134. The last downstream object identifier 134 may be appended with a subset of last zone real-time process data 136, which may include whole or a part of the further downstream object identifier 130a and the second further downstream object identifier 130b. The last downstream object identifier 134 is thus provided with the process parameters and/or equipment operating conditions from the collection zone 166, similar to as was discussed in detail in this disclosure. Depending upon the function or further processing if any done in the collection zone 166, data such as, any one or more of, incoming mass flow, outgoing mass flow, one or more temperature values from the collection zone 166, time of entry, time of exit, speed, etc. may be included as last zone real-time process data 136.
In some cases, individual lots from the collection zone 166 may be sent for storage and/or sorting and/or packaging. Such an individual lot is shown as product collection bin 164a. As quantities are being split again, an individual object identifier may be provided for each of the silos such that the article 170 in its silo, i.e., the individual object identifier for the product collection bin 164a can be associated with the process data or conditions that the article 170 is exposed to there.
As will be appreciated, each of the object identifiers may be a GUID. Each may include wholly or partly data from the preceding object identifier, or they may be linked. The relevant quality data can thus be attached as a snapshot or traceable link to a particular article 170.
As was also discussed, one or more downstream ML models may be used for computing or predicting one or more downstream performance parameters and/or the downstream control settings, either or both of which may be zone-specific. It is also possible that each or some of the downstream ML models also are configured to provide a confidence value indicative of the confidence level for the at least one downstream performance parameter and/or the downstream control settings. A warning may be generated as a warning signal, for example to initiate a physical test of a sample for lab analysis should the confidence level in predicting the downstream performance parameter be low than a predetermined limit. It is also possible that in response to the confidence level of the prediction falling below an accuracy threshold value, a sampling object identifier is automatically provided via the interface. The sampling object identifier may be provided in a similar way and the downstream computing unit 124 may append the subset of relevant downstream process data to the sampling object identifier for the material which the sampling object identifier relates to, shown where as a sample article 172. The downstream computing unit 124 may also append the at least one zone-specific performance parameter, which had low confidence level, to the sampling object identifier. The sample article 172 can thus be collected and verified and/or analyzed to further improve the quality control using object identifiers.
Similarly, as the TPU and/or ETPU material progresses to a subsequent zone, it may be determined if another object identifier is to be provided or not. If not, then the downstream process data from the subsequent zone may also be appended to the same object identifier. If it is determined that another object identifier is to be provided, then the process data from the subsequent zone is appended to the another object identifier. Details for each of these options, such as the intermediate equipment zone and the further downstream equipment zone are discussed in detail in the present disclosure, for example, in the summary section as well as with reference to
The block diagram shown in
In the present example a chemical product, as input material, is produced based on a raw material which is provided to the processing line via a liquid raw material reservoir 300, a solid raw material reservoir 302, and a recycling silo 304 which recycles any chemical products or intermediate products that e.g. comprise insufficient material/product properties or an insufficient material/product quality, The respective raw material being input to the processing line 306-318 is processed via the respective processing equipment, namely a dosing unit 306, a subsequent heating unit 308, a subsequent treatment unit including a material buffer 310, and a subsequent sorting unit 312. Downstream of this processing equipment 306-312, there is arranged a transport unit 314 which transports material that needs to be recycled, e.g. due to insufficient quality of the produced material, from the sorting unit to the recycling silo 304. Finally, the material being sorted by the sorting unit 312 is transferred to a first and a second packing unit 316, 318 which pack the according materials into material containers for shipping purposes, e. g. material bags in case of bulk material or bottles in case of liquid material.
The production system 300-318, in the present embodiment, provides a data interface of a computing unit (both not being depicted in this block diagram), via which data objects comprising data about the respective input materials and their changes due to the processing are provided. The entire production process is, at least partially, controlled via the computing unit.
The input material(s) being processed by the processing equipment 306-312 is(are) divided into physical or real-world so-called “package objects” (in the following also called “physical packages” or “product packages”), wherein these package objects are handled or processed by each of the processing units 306-312. The package size of such package objects can be fixed, e.g. by material weight (e.g. 10 kg, 50 kg, etc.) or by material amount (e.g. 1 decimeter, 1/10 cubic meter, etc.), or even can be determined by a weight or amount, for which considerably constant process parameters or equipment operation parameters can be provided by the processing equipment.
The dosing unit 306 first creates such package objects from the input liquid and/or solid raw material and/or the recycled material provided by the recycling silo 304. Having created the package objects, the dosing unit transports these objects to the homogenization unit 308. The homogenization unit 308 homogenizes the materials of the package objects, i.e. homogenizes e.g. a processed liquid material and a solid material, or two liquid or solid materials. After the heating process, the heating unit 308 transports the accordingly heated package objects to the treatment unit 310 which transforms the material of the input package objects into a different physical and/or chemical state, e.g. by heating, drying or humidifying or by a certain chemical reaction. The accordingly transformed package objects are then transported to one or more of the three downstream packing units 316, 318 or the mentioned transport unit 314.
The subsequent processing of the real-world package objects is managed by means of corresponding data objects 330, 332, 334 (or pre-described “object identifiers”, respectively) which are assigned to each package object via the computing unit operatively coupled to the equipment 306-312, or being a part of the equipment, and is stored at a memory storage element of the computing unit. According to the present embodiment, the three data object 330-334 are generated in response to a trigger signal which is provided via the equipment 306-312, namely in response to the output of a corresponding sensor being arranged at each of the equipment units 306-312, or according switches respectively, wherein such sensors are operatively coupled to the equipment units 306-312. As mentioned beforehand, the industrial plant may include different types of sensors, e. g. sensors for measuring one or more process parameters and/or for measuring equipment operating conditions or parameters related to the equipment or the process units. In the present embodiment, sensors for measuring the flowrate and the level of the bulk and/or liquid materials processed inside the equipment units 306-312 are arranged at these units.
The three exemplary data objects 330, 332, 334 depicted in
The first two data objects 330, 332 comprise product package objects which contain process data. The process data comprises processing/treatment information which the related physical package has experienced during its residence/treatment within the several processing units. The process data can be aggregated data such as a calculated average temperature during the residence time of the underlying physical package within the related processing units and/or it can be time series data of the underlying production processes.
The first data object 330 is a first kind of package (in
The Heating unit 308 contains several equipment zones, in the present embodiment, three equipment zones 320, 322, 324 (“Zone 1”, “Zone 2”, “Zone 3”). These different equipment zones are utilized as sorting group for sorting or selecting the related process data. Such a sorting may help to obtain only those data for a package object out of a related equipment zone, which relate to the processing of the underlying physical package within the corresponding point in time during which the related physical package is inside this equipment zone. However, in the present embodiment, the material composition of the physical package is not changed by both processing units 306, 308.
Once the A-package 330 has arrived at the next Treatment unit 310 (in the present embodiment a “treatment unit with buffer”), the material composition of each physical package changes, because this processing unit 310 not only transports physical packages in a plug flow mode. Moreover, corresponding physical packages comprise a buffer volume which is bigger than the original package size, so that such physical packages have a defined back-mixing degree. As a consequence, each physical package which leaves this Treatment unit 310, is another kind of physical package, which is called “B-package” in
The corresponding second data object 332 (“B-package”) also includes a corresponding “Product Package ID”. The data object 332 further includes the data of a defined number of previous data objects, in the present example the data object 330 designated as “A-Package”, in a defined percentage, the so-called “Aggregated data from related A-packages”. An according aggregation scheme or algorithm depends e.g. on the underlying processing unit, on the size of the underlying physical package, on the mixing capabilities of the material of the underlying physical package and on the residence time of the underlying physical package within the underlying processing unit, or a corresponding equipment zone of the processing unit.
Once processed physical (product) packages are packed by one of the two Packing units 316, 318 into discrete physical packages, e. g. by packing processed physical packages into a container, a drum or into an octabin vessel or the like, in the present embodiment, corresponding packed physical packages are handled or tracked via another data object 334 called “Physical package”. This data object 334 includes related previous physical packages (like the “A-Package” and the “B-Package” in the present scenario) which have been packed into it. The designation of corresponding “Product Package IDs” is sufficient e.g. for tracking purposes, instead of using complete data objects, because such Product Package IDs can be easily linked together during a later data processing, e.g. data processing performed by means of an external “cloud computing” platform.
The first data object (or “object identifier”) 330 particularly includes the following information:
The second object identifier 332 additionally includes
The third object identifier 334 is generated by the two packing units 316, 318 with the designation and time stamp “Physical package 1976-02-0619:12:21.123” and includes the following information:
The package general information of the first and second object identifier 330, 332 includes material data of the input raw material, which in the present embodiment, is indicative of a chemical and/or physical property of the input material, or processed material(s) respectively, like the material(s) temperature and/or weight, and in the present embodiment comprises also abovementioned lab sample or test data related to the input material, such as historical test results.
According to the product production process also illustrated by
As described beforehand, the three object identifiers 330-334, in the present embodiment, are used for correlating or mapping the mentioned input material data and/or specific process parameters and/or equipment operating conditions to at least one performance parameter of the chemical product, said performance parameter being, or it being indicative of, any one or more properties of the underlying material(s), e.g. an according chemical product, respectively.
According to the present embodiment shown in
In the embodiment of the product production system shown in
It is noteworthy that any or each of the object identifiers 330-334 may include a unique identifier, preferably a globally unique identifier (“GU ID”), in order to allow for a reliable and safe assignment of an object identifier to a corresponding package during the whole production process.
In the present product processing scenario, the mentioned process data appended to the first object identifier 330 are at least part of the process data gathered from the first equipment zone 320. Accordingly, the second object identifier 332 is appended with at least part of the process data gathered from the second equipment zone 322, wherein the process data gathered from the second equipment zone 322 are indicative of the process parameters and/or equipment operating conditions that the input raw material(s) 300-304 is processed under in the second equipment zone 322.
In the following TABLE 1, another exemplary object identifier is shown, again in a tabular format. This object identifier includes much more information/data than the previously described three object identifiers 330-334.
This exemplary object identifier concerns a so-called “B-Package” with an underlying date and time stamp “1976-02-06 18:31:53.401”, like that shown in
The unique identifier (“Unique ID”), in the present example, comprises a unique URL (“uniqueObjectURL”). The main details of the underlying package (“Package Details”), in the present example, are the date and timestamp of the creation of the package (“Creation Timestamp”) having the two values “02.02.1976 18:31:53.401” and the type of the package (“Package Type”), in the present example having a package type “B”. The current location of the package along the underlying production line (“Package Location”) is defined by a “Package Location Link”, in the present example a transport link to a “Conveyor Belt 1” of the production line.
At the Conveyor Belt 1, there is provided measuring equipment (see “Measuring Points” which include exemplary processing data or values) for measuring the average temperature (“Average Value”) currently revealing a material temperature of 85° C. and an according description (“Description”) of the underlying temperature zone, in the present example “Temperature Zone 1”. In addition, the measuring equipment can also include sensors for detecting the entry date/time of the package at the Conveyor Belt 1 (“Entry Time”), in the present example being “02.02.1976 18:31:54.431” and for detecting the leaving date/time of the package from the Conveyor Belt 1 (“Leaving Time”), in the present example being “02.02.1976 18:31:57.234”. Finally, the measuring equipment includes sensor equipment for detecting time series values (“Time Series Values”) of underlying time series information (“Time Series”) concerning the production process.
In addition, the shown object identifier, in the present example, further includes information about a downstream located “Conveyor Belt 2”, a downstream located “Mixer 1” and a downstream located “Silo 1” for intermediately storing already processed material(s).
An “Upstream process” 400 for processing package objects is connected to a “Sorting Unit” 402 for sorting threw processed package objects. The upstream process 400 and the sorting unit 402 are managed by means of a first data object 404. This data object 404 concerns an already described “B-Package” with an underlying date and time stamp “1976-02-06 18:51:43.431” depicting the date and time of its creation. The data object 404 includes a “Package ID” of a currently processed package object (so-called “object identifier”). The data object 404 further includes n pre-described chemical and/or physical properties about the currently processed package object, in the present example a “Property 1” and a “Property n”.
The input materials, i.e. the corresponding package objects being fed in to the upstream process 400, in the present example, are provided by a “Recycling Silo” 406. The recycling silo 406, on the other hand, gets the underlying recycled materials from a “Transport unit 1” 410 which transports package objects, that have to be recycled and are sorted out by the sorting unit 402 accordingly, to the recycling silo 406. The underlying transport process step 410 is managed by means of a second data object 408 which concerns the above described “B-Package” and includes the mentioned underlying date and time stamp “1976-02-06 18:51:43.431”, the “Package ID” of the currently processed package object and the two chemical and/or physical properties “Property 1” and a “Property n”. However, due to the mentioned requirement to recycle the underlying sorted-out package object, the second data object 408 further includes another chemical and/or physical property of the underlying package object, in the present example a “Property 2”, which particularly includes a respective performance indicator for that package object, in the present example a “low or insufficient material or product performance”.
Package objects being processed by the upstream process 400 and not being sorted out by the sorting unit 402 are provided by the sorting unit 402 to either a first “Packing Unit 1” 412 or a second “Packing Unit 2” 416, depending on performance values for the corresponding package objects. The packing units 412, 416 are used for packing the corresponding package objects to respective containers 414, 418. The packing process being executed by the two packing units 412, 416 is managed by means of a third data object 420 and a fourth data object 422.
The two data objects 420, 422 both concern “Physical Packages” and include the same date “1976-02-06” as the above described “B-Package”, but a later time stamp “19:12:21.123” than the above described “B-Package”. They also include the “Package ID” of the underlying package objects. However, the data objects 420, 422 further include performance indicators for the underlying final products, in the present example a “performance medium range” regarding the products stored in the first container (or filling sack) 414 and a “performance high range” in case of the products stored in the second container (or filling sack) 418. In addition, the two data objects 420, 422 include the “Order no.” and “Lot no.” of the corresponding final products.
The present product processing approach is based on two raw materials, namely a “Raw Material Liquid” 500 and a “Raw Material Solid” 502, in order to produce a polymeric material in a known manner. Like in the previously described production scenarios according to
The technical equipment further includes a “Dosing unit 506” for creating package objects based on the mentioned input raw materials which are processed by a “Reaction unit” 508 which transports package objects along the shown four polymeric reaction zones (“Zones 1-4”) 510, 512, 514, 516 in order to process them and by a “Curing unit” 518 for curing the polymeric material (i.e. the corresponding package objects) being produced in the reaction unit 508. The curing unit 518, in the present embodiment, comprises only a material buffer, but not a back-mixing equipment. The curing unit 518 also transports accordingly processed package objects.
A “Transport unit 1” 520 transports package objects being sorted out for their recycling by means of the recycling silo 504. The finally processed, i.e. not sorted out, units are transported again to a first “Packing Unit 1” 522 and to a second “Packing Unit 2” 524. The two packing units 522, 524 transform and transport the corresponding package objects to respective containers or filling sacks 526, 528.
The production process depicted in
The first data object 530 concerns an “A-Package” with creation date “1976-02-06” and creation time “18:31:53.401”. The data object 530, in the present production scenario, includes again a pre-described “Package ID”, process information about the dosing process (“Dosing properties”) being performed by the dosing unit 506, and further process information (“Reaction unit properties”) about the production of the polymeric material by means of the reaction unit 508. The dosing properties include information about the raw material amounts for each package object, namely the “Percentage raw material 1 (liquid)”, the “Percentage raw material 2 (solid)” and the product temperature. The Reaction unit properties include the temperatures of the four polymeric reaction zones 510-516 (“temperature zone 1”, “temperature zone 2”, “temperature zone 3” and “temperature zone 4”).
Thereupon, the first data object 530 includes the current location of an underlying package object (“Current Package Location”) along the processing line 506-524. The current location of that package object, in the present embodiment, is managed by means of a “Package Location Link” and a corresponding “Zone location”. Finally included is chemical and/or physical information about the underlying polymeric reaction, namely the corresponding “Reaction enthalpy/turnover degree”. Hereby, the processing units 506-524 which transport a given package object, calculate and write/actualize permanently reaction enthalpy values into the first data object 530. This is possible due to existing information about package positions and corresponding residence times and about according process values, e.g. package temperatures. Based on the current values of the reaction enthalpy and/or turnover degree included in the first data object 530, via a communication line 532 between the first data object 530 and the curing unit 518, the curing time parameters are adjusted, based on a calculated value of the reaction enthalpy.
The second data object 534 concerns a “Physical package” being processed by one of the packing units 522, 524 and includes the corresponding creation date/time information “1976-02-06 19:12:21.123”. Included are a “Package ID”, a “Product” description/specification, an “Order no.”, a “Lot no.” and the mentioned value of the calculated enthalpy and/or turnover degree.
The equipment devices, in this embodiment, include material processing units 606, 614 which are connected via a signal and/or data connection with sensors/actors 608, 616 being part of the processing units 606, 614 and which are connected to several input/output (I/O) devices 610, 612 and 618, 620.
In the present embodiment, the first processing unit 606 is further connected with exemplary three product packages (Product Packages 1-3) 622, 624, 626, wherein the second processing unit 614 is further connected with further three product packages (Product Packages 4-n) 628, 630, 632. Only exemplary, “Product Package 3” 626 is connected to a product sample (Sample 1) 634, wherein “Product Package 5” 630 is connected to another product sample (Sample n) 638. “Sample 1” 634 is further connected with an “Inspection lot 1” 636, wherein “Sample n” is further connected with an “Inspection lot n” 640. Finally, both inspection lots 636, 640 are connected with an “Inspecting Instruction 1” unit 642 which serves as a specification on how to create a mentioned inspection lot and on how to realize the analysis/quality control of a respective underlying sample 634, 638.
The topological structure shown in
More particularly, this topological structure provides a high degree of contextual information, based on which the user/operator can easily gather the technical and/or material property of each object. This additionally allows for rather complex queries by the user, e.g. about relevant production-related connections or relations between objects, particularly across several nodes or even topology/hierarchy levels. Thereupon, the objects (nodes) shown in
The equipment devices, in the present embodiment, include material processing units 702 “Unit 1” and “Unit n” 708 which are connected via a signal and/or data connection with sensors/actors “Sensor/Actor 1” 704 and “Sensor/Actor n” 710 which are connected to corresponding input/output (I/O) devices “I/O 1” 706 and “I/O n” 712. These I/O devices comprise a connection to a (not shown) PLC for controlling the operation of the production line 700.
In the present embodiment, the first processing unit (“Unit 1”) 702 is further connected with exemplary three product packages (“Product Portions” 1-3) 714, 716, 718, wherein the second processing unit (“Unit n”) 708 is further connected with further two product packages (“Product Portions” 4 and n) 720, 722. Only exemplarily, product package 3″ 718 is connected to a product sample (“Sample 1”) 724, wherein product package n 722 is connected to another product sample (“Sample n”) 728.
In contrast to the embodiment shown in
Alternatively, such a sample can be a signal that can be generated automatically by a sampling machine. Such an automatically generated signal can e.g. reach the sensor/actor object 704 via the shown I/O object 706, wherein the I/O object 706 receives the mentioned push button information from the (not shown) PLC/DCS. At the moment of taking the sample, the sample object 724 (e.g.) will be created and linked to the product portion located at the sampling station location in that moment.
Based on the accordingly generated samples 724, 728, one or more inspection lots 726, 730 can be generated, even for only one (and the same) sample. However, one or more samples can be generated within one processing line independently, or even at same time. Finally, like in the embodiment shown in
The abstraction layer 800, in the present embodiment, provides a bi-directional communication line 802 with an external Cloud computing platform 804. Further, the abstraction layer 800 communicates also with a number of n production PLC/DCS and/or machine PLCs 806, 808, either bidirectionally 810, as in the case of “PLC/DCS 1” 806, or unidirectionally 812, as in the case of “PLC/DCS n” 808. The Cloud computing platform 804, in the present embodiment, comprises a bidirectional communication line 814 to a Customer integration interface or platform 816, via which customers of the present production plant owner can communicate and/or deliver control signals to pre-described equipment units of the plant.
In the object database 801 further included are other objects concerned herewith, e. g. above described samples, inspection lots, sample instructions, sensors/actors, devices, device-related documentation, users (e.g. machine or plant operators), according user groups and user rights, recipes, orders, setpoint-parameter sets, or inbox objects from cloud/edge devices.
At the Cloud computing platform 804, an Artificial Intelligence (AI) or machine learning (ML) system is implemented, by which to find or create an optimum algorithm which is deployed via a dedicated deployment pipeline 818 to an Internet-of-Things (IoT) Edge device or component 820, in order to use an accordingly created or found algorithm for controlling the Edge device 820. The Edge device 820, in the present embodiment, communicates 822 bidirectionally with the abstraction layer 800.
By means of the abstraction layer 800 and the included object database 801, pre-described physical or product packages are created, as described within this document. The abstraction layer 800 can also connect to certain processing and/or AI (or ML) components within the Cloud computing platform 804. For this connection, the known data streaming protocol “Kafka” can be used. Hereby, at or around the time of creation of an underlying product package, first an empty data packet can be sent out as a message, in particular independent of the underlying timeseries data. After that, another message can be sent out when the final product package has been processed. These messages contain the object identifier of the underlying package as data packet ID, so that the relating packets can be linked again with each other on side of the Cloud platform later. This has the advantage that large-size data packets can be avoided for the transmission to the Cloud, thus minimizing the required transmission bandwidth or capacity.
Within the Cloud computing platform 804, the streamed and received product data is used by mentioned AI methods or ML methods in order to find or create algorithms for getting additional data related to an underlying product, such as predicted product quality control (QC) values. For this procedure being performed within the Cloud computing platform 804, additional data like QC data or measured performance parameters of a related product (or physical) package is needed. This can either be received via the same way from the object database 801 in the form of sample objects and inspection lot objects (see also
Such information can also be received from any other systems than the object database. In this case the other system sends the QC and/or performance data together with a sample/inspection lot ID out of the object database. Within Cloud computing platform 804, this data will be combined and used for finding e.g. ML-based algorithms/models. Hereby the computing power within the Cloud platform 804 can be used effectively.
In the present embodiment, the accordingly found algorithms or models are deployed to the Edge device 820 via the deployment pipeline 818. The Edge device 820 can be a component which is located close to the object database 801 of the abstraction layer 800, and thus also close to the PLC/DCS 1 to PLC/DCS n 806, 808 accordingly, namely in terms of a network security level and location which allows for a low network latency and direct and se-cure communication.
Since, for usage of the ML-model, not such a computing power is needed, the Edge device 820 uses the ML model to generate the mentioned advanced information and provides it to the object database 801. Therefore, the Edge device 820 needs the same information or a subset of the information which is used at the Cloud computing platform 804 to generate the ML-based algorithm or model, the object database 801 can provide this data to the Edge device 820, e.g. via an open network protocol for machine-to-machine communication, like the known “Message Queuing Telemetry Transport” (MQTT) protocol.
This setup enables the realization of an AI/ML-based advanced process control and autonomous manufacturing and according autonomously operating machines.
As illustrated in the embodiment shown in
The AI/ML model can be used for predicting one or more of pre-described performance parameters, said prediction being preferably done via the computing unit. Additionally, or alternatively, the AI/ML model can be used for least partially controlling the production process, preferably via adjusting the equipment operating conditions, and more preferably said controlling being done via the mentioned computing unit. Additionally, or alternatively, the AI/ML model can also be used, e.g., by the computing unit, for determining which of the process parameters and/or equipment operating conditions have a dominant effect on the chemical product, such that those dominant of the process parameters and/or equipment operating conditions are appended to the data object, or the mentioned object identifier, respectively.
Those skilled in the art will appreciate that the method steps, at least those which are performed via the computing unit may be performed in a “real-time” or near real-time manner. The terms are understood in the technical field of computers. As a specific example, a time delay between any two steps performed by the computing unit is no more than 15 s, specifically of no more than 10 s, more specifically of no more than 5 s. Preferably, the delay is less than a second, more preferably, less than a couple of milliseconds. Accordingly, the computing unit may be configured to perform the method steps in a real-time manner. Moreover, the software product may cause the computing unit to perform the method steps in a real-time manner.
The method steps may be performed, for example, in the order as shown listed in the examples or aspects. It shall be noted, however, that, under specific circumstances, a different order may also be possible. Further, it is also possible to perform one or more of the method steps once or repeatedly. The steps may be repeated at regular or irregular time periods. Further, it is possible to perform two or more of the method steps simultaneously or in a timely overlapping fashion, specifically when some or more of the method steps are performed repeatedly. The method may comprise further steps which are not listed.
The word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. A single processing means, processor or controller or other similar unit may fulfil the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.
Further, it shall be noted that in the present disclosure, the terms “at least one”, “one or more” or similar expressions indicating that a feature or element may be present once or more than once typically may have been used only once when introducing the respective feature or element. Thus, in some cases unless specifically stated otherwise, when referring to the respective feature or element, the expressions “at least one” or “one or more” may not have been repeated, non-withstanding the fact that the respective feature or element may be present once or more than once.
Further, the terms “preferably”, “more preferably”, “particularly”, “more particularly”, “specifically”, “more specifically” or similar terms are used in conjunction with optional features, without restricting alternative possibilities. Thus, features introduced by these terms are optional features and are not intended to restrict the scope of the claims in any way. The present teachings may, as the skilled person will recognize, be performed by using alternative features. Similarly, features introduced by “according to an aspect” or similar expressions are intended to be optional features, without any restriction regarding alternatives of the present teachings, without any restrictions regarding the scope of the present teachings and without any restriction regarding the possibility of combining the features introduced in such way with other optional or non-optional features of the present teachings.
Various examples have been disclosed above for a method for controlling a downstream production process; a system for carrying out the method herein disclosed; a system for controlling a downstream production process; a use; a software program; and a computing unit comprising the computer program code for carrying out the method herein disclosed. More specifically, the present teachings relate to a method for controlling a downstream production process for manufacturing an article at a downstream industrial plant by processing at least one thermoplastic polyurethane (“TPU”) and/or expanded thermoplastic polyurethane (“ETPU”) material using the downstream production process, the method comprising: providing, at the downstream computing unit, a set of downstream control settings for controlling the production of the article, wherein the downstream control settings are determined based on: a downstream object identifier; at least one desired downstream performance parameter related to the article; downstream historical data; and wherein the set of downstream control settings is usable for manufacturing the article at the downstream industrial plant. The present teachings also relate to a system for downstream production, and a software product.
Those skilled in the art will understand however that changes and modifications may be made to those examples without departing from the spirit and scope of the accompanying claims and their equivalents. It will further be appreciated that aspects from the method and product embodiments discussed herein may be freely combined.
Summarizing and without excluding further possible embodiments, certain example embodiments of the present teachings are summarized in the following clauses:
Clause 1. A method for controlling a downstream production process for manufacturing an article at a downstream industrial plant, the downstream industrial plant comprising at least one downstream equipment, and the article being manufactured by processing, via the downstream equipment, at least one thermoplastic polyurethane (“TPU”) and/or expanded thermoplastic polyurethane (“ETPU”) material using the downstream production process, the method at least partially being performed via a downstream computing unit, and the method comprising:
Clause 2. The method of clause 1, wherein at least some of the downstream control settings are determined by an upstream computing unit related to an upstream industrial plant, preferably at least one of the TPU and/or ETPU materials is provided by the upstream industrial plant.
Clause 3. The method of clause 2, wherein the at least some of the downstream control settings are provided at a shared memory storage, the shared memory storage being accessible by both the upstream computing unit and the downstream computing unit.
Clause 4. The method of any one or more of clause 1-clause 3, wherein at least some of the downstream control settings are determined by the downstream computing unit.
Clause 5. The method of clause 4, wherein the downstream computing unit determined control settings are determined using an upstream object identifier, which upstream object identifier comprises a subset of upstream process data which comprise upstream process parameters and/or operational settings that were used for manufacturing the TPU and/or ETPU material at the upstream industrial plant, preferably the upstream object identifier also being appended with at least one upstream performance parameter related to the TPU and/or ETPU material and/or related to the article.
Clause 6. The method of clause 4, wherein at least one of the desired downstream performance parameters is particle size distribution (“PSD”) or bulk density.
Clause 7. The method of any one or more of clause 1-clause 6, wherein the upstream object identifier includes a prediction and/or control logic for providing at least some of the downstream control settings based upon the downstream historical data, preferably the prediction and/or control logic being encrypted or obfuscated from unauthorized readout.
Clause 8. The method of clause 7, wherein the prediction and/or control logic comprises a data driven model trainable by the downstream historical data.
Clause 9. The method of clause 8, wherein the trained prediction and/or control logic generates modification data which are usable for modifying the prediction and/or control logic such that the computation of the downstream control settings is improved.
Clause 10. The method of clause 8 or clause 9, wherein the trained prediction and/or control logic and/or the modification data are provided to the upstream computing unit.
Clause 11. The method of any one or more of clause 2-clause 10, wherein the downstream object identifier is provided by the upstream computing unit, preferably the downstream object identifier being appended with data from the upstream object identifier.
Clause 12. The method of clause 11, wherein the downstream object identifier is provided at the shared memory storage and/or the downstream object identifier is provided via a tag such as a chip, an NFC device, or a digitally readable code.
Clause 13. The method of any one or more of clause 1-clause 12, wherein the method also comprises:
Clause 14. The method of any one or more of clause 1-clause 13, wherein the method further comprises:
Clause 15. The method of clause 14, wherein the method comprises:
Clause 16. The method of clause 15, wherein the method also comprises:
Clause 17. The method of clause 15 or clause 16, wherein the method comprises:
Clause 18. The method of any one or more of clause 15-clause 17, wherein the downstream zone presence signal is generated via the downstream computing unit by performing a zone-time transformation, which transformation maps at least one property related to the TPU and/or ETPU material to the specific equipment zone, such as via one or more time-dependent signals from the downstream real-time process data.
Clause 19. The method of any one or more of clause 17-clause 18, wherein the method comprises:
Clause 20. The method of any one or more of clause 17-clause 19, wherein the computing of at least one downstream performance parameter is performed using at least one downstream machine learning (“ML”) model, the downstream ML model being trained preferably using the downstream historical data.
Clause 21. The method of clause 20, wherein the downstream ML model is configured to provide at least one confidence value indicative of the confidence level for the computation of at least one downstream performance parameter.
Clause 22. The method of clause 21, wherein a warning signal is generated, preferably at a control system for the downstream production process, in response to the confidence level of the computation or prediction of the at least one downstream performance parameter falling below an accuracy threshold value.
Clause 23. The method of clause 21 or clause 22, wherein a sampling object identifier is automatically generated in response to the confidence level of the computation or prediction of the at least one downstream performance parameter falling below an accuracy threshold value or in response to the warning signal, the sampling object identifier being related to the material which is at that respective zone at or around the time when the confidence level accuracy crossed the accuracy value.
Clause 24. The method of clause 22 or clause 23, wherein at least one lab analysis is performed in response to the warning signal, preferably the analysis being performed on the material which is at that respective zone related to the warning.
Clause 25. The method of clause 24, wherein the date and/or results of the analysis is appended to the sampling object identifier, preferably data from the sampling object identifier being included in the downstream historical data for future computation by the downstream computing unit.
Clause 26. The method of any one or more of clause 15-clause 25, wherein the at least one downstream equipment comprised a plurality of physically separated equipment zones, the zones comprising an initial downstream equipment zone and a further downstream equipment zone such that during the downstream production process the TPU and/or ETPU material traverses from the initial downstream equipment zone to the further downstream equipment, the downstream object identifier being provided at the initial downstream equipment zone, and wherein the method also comprises:
Clause 27. The method of clause 26, wherein the another downstream historical data comprise data from one or more historical downstream object identifiers related to previously processed TPU and/or ETPU material in the further downstream equipment zone, and wherein at least one of the historical downstream object identifiers is appended with at least a part of the downstream process data which is indicative of the downstream process parameters and/or equipment operating conditions that the previously processed TPU and/or ETPU material was processed under, preferably in the further downstream equipment zone.
Clause 28. The method of clause 26 or clause 27, wherein the method also comprises:
Clause 29. The method of any one or more of clause 26-clause 28, wherein the method also comprises:
Clause 30. The method of any one or more of clause 1-clause 29, wherein any of the object identifiers are provided at a downstream memory storage operatively coupled to the downstream computing unit.
Clause 31. The method of clause 30, wherein the downstream computing unit and/or the downstream memory storage are at least partially implemented via a cloud-based service.
Clause 32. The method of any one or more of clause 1-clause 31, wherein the article is a sports item and/or a footwear.
Clause 33. The method of any one or more of clause 1-clause 32, wherein the downstream equipment operating conditions are any characteristics or values that represent the state of the downstream equipment, for example, any one or more of, setpoint, controller output, production sequence, calibration status, any equipment related warning, vibration measurement, speed such as transport element speed, temperature and fouling value such as filter differential pressure, maintenance date.
Clause 34. The method of any one or more of clause 1-clause 33, wherein the downstream process data comprise at least one numerical value indicative of the downstream process parameters and/or equipment operating conditions measured during the downstream production process.
Clause 35. The method of any one or more of clause 1-clause 34, wherein the downstream process data comprise at least one binary value indicative of the downstream process parameters and/or equipment operating conditions measured or detected during the downstream production process.
Clause 36. The method of any one or more of clause 1-clause 35, wherein the downstream process data comprise time-series data of one or more of the downstream process parameters and/or the equipment operating conditions.
Clause 37. The method of any one or more of clause 1-clause 36, wherein the downstream process data comprise temporal information of the downstream process parameters and/or the equipment operating conditions, or the time-series data.
Clause 38. The method of clause 37, wherein the temporal information is in the form of the data indicating time stamps for at least some of the data points related to the downstream process parameters and/or the equipment operating conditions, or the time-series data.
Clause 39. The method of any one or more of clause 1-clause 38, wherein the precursor data comprise data related to, or indicative of, one or more characteristics or properties of the TPU and/or ETPU material.
Clause 40. The method of any one or more of clause 1-clause 39, wherein the precursor data comprise lab sample or test data related to the TPU and/or ETPU material, such as historical test results.
Clause 41. The method of any one or more of clause 34-clause 40, wherein the at least one numerical value and/or the at least one binary value and/or the time-series data and/or at least some of the values indicative of physical and/or chemical characteristics of the TPU and/or ETPU material are at least partially obtained or measured via signals from one or more sensors and/or switches operatively coupled to the downstream equipment, preferably said sensors and/or switches being a part of the downstream equipment.
Clause 42. The method of any one or more of clause 1-clause 41, wherein the object identifier is provided via a downstream computing unit operatively coupled to the downstream equipment zones, preferably said downstream computing unit being a part of the downstream equipment.
Clause 43. The method of clause 42, wherein the computing unit is, or it is a part of, a controller or a control system, such as a distributed control system (“DCS”) and/or a programmable logic controller (“PLC”).
Clause 44. The method of any one or more of clause 1-clause 43, wherein the object identifier is provided or is generated in response to a trigger event or signal, said event or signal preferably being provided via the equipment, more preferably in response to output of any of the one or more sensors and/or switches operatively coupled to the equipment.
Clause 45. The method of clause 44, wherein the trigger event or signal relates to a quantity value of the TPU and/or ETPU material, more specifically, to an occurrence of the quantity value reaching or meeting a predetermined quantity threshold value, and said occurrence being detected via the downstream computing unit and/or the downstream equipment.
Clause 46. The method of clause 45, wherein the quantity value is a weight value and/or a fill factor and/or a level value and/or a volume value.
Clause 47. The method of any one or more of clause 42-clause 46, wherein the downstream equipment is also operatively coupled to one or more actuators and/or end effector units, preferably said actuators and/or end effector units being a part of the downstream equipment.
Clause 48. The method of any one or more of clause 1-clause 47, wherein any or each of the object identifiers includes a unique identifier, preferably a globally unique identifier (“GUID”).
Clause 49. The method of any one or more of clause 26-clause 48, wherein any or each of the equipment zones are monitored and/or controlled via an individual ML model, the individual ML model being trained based on data preferably from the respective object identifier from that zone.
Clause 50. A system for controlling a downstream production process for manufacturing an article at a downstream industrial plant, the downstream industrial plant comprising at least one downstream equipment, and the article being manufactured by processing, via the downstream equipment, at least at least one thermoplastic polyurethane (“TPU”) and/or expanded thermoplastic polyurethane (“ETPU”) material using the downstream production process, wherein the system is configured to perform any of the method of the above method clauses.
Clause 51. A computer program, or a non-transitory computer readable medium storing the program, comprising instructions which, when the program is executed by a suitable computing unit, cause the computing unit to carry out the method steps of any of the above method clauses.
Clause 52. A system for controlling a downstream production process for manufacturing an article at a downstream industrial plant, the downstream industrial plant comprising at least one downstream equipment and a downstream computing unit, and the article being manufactured by processing, via the downstream equipment, at least one thermoplastic polyurethane (“TPU”) and/or expanded thermoplastic polyurethane (“ETPU”) material using the downstream production process, wherein the system is configured to:
Clause 53. A computer program, or a non-transitory computer readable medium storing the program, comprising instructions which, when the program is executed by a suitable computing unit, operatively coupled to at least one equipment for manufacturing an article at a downstream industrial plant by processing at least one thermoplastic polyurethane (“TPU”) and/or expanded thermoplastic polyurethane (“ETPU”) material using a downstream production process, causes the computing unit to:
Clause 54. Use of the set of downstream control settings, generated according to any of the above method clauses, for controlling a production process at an industrial plant.
| Number | Date | Country | Kind |
|---|---|---|---|
| 20197014.2 | Sep 2020 | EP | regional |
| 20204154.7 | Oct 2020 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2021/075447 | 9/16/2021 | WO |
