Patent Application
20240046190
The disclosure concerns production of renewable fuels and chemicals, in particular monitoring and optimizing the production and environmental footprint thereof.
There is a growing interest in producing chemicals and fuels, such as ammonia, methanol, ethanol, naphtha, jet fuel, diesel, etc., from renewable feedstocks or sources, so-called renewable chemicals, including e-chemicals and fuels, including e-fuels.
The consumers, investors and governments/legislators are focusing on more sustainable solutions for many of the chemical, petrochemical and refining processes worldwide. This puts a pressure on the industry towards lowering their environmental impact. In order to quantify the environmental impact of chemicals and fuels production, a number of different methods have been developed, as e.g. Life Cycle Analysis (LCA), Carbon Intensity (CI), Green House Gas (GHG) emission, or other ways of measuring carbon footprint, e.g. similarly suitable sustainability scores.
One of the potential routes to contribute to the transition is the concept of e-fuels (also mentioned in the literature as Electrofuels or Synthetic Fuels or Power to X (PtX) (Power to Gas (PtG)/Power to Liquid (PtL)). The e-fuels production route combines, in an energy-intensive process, “green” hydrogen produced by the electrolysis of water, using green hydrogen with CO2 captured either from a concentrated source (flue gases from an industrial site, for example) or from the air (Air Capture technologies). Green hydrogen is typically defined as hydrogen produced from renewable energy, i.e., extracted from water using electrolysis and green electricity. The CO2 capture is not from the hydrogen production but from another source. Green hydrogen can also be called e-hydrogen.
E-fuels can be obtained with a very low Green House Gases (GHG) intensity offering one plausible option to effectively contribute to reduce GHG emissions across different transport sectors, and allowing renewable electricity to be ‘stored’ in liquid and gaseous (e-)fuels.
Therefore, it would be advantageous to obtain a method for monitoring and optimizing a sustainability score of a renewable fuel or chemical production, particularly said method being automated and minimizing or eliminating time gaps elapsing from the collection of the data and calculation of optimal plant parameters.
Furthermore, it would be advantageous to monitor and optimize a utilization factor such as the product yield in said plants. An additional advantage would arise from the operation of the unit by letting the system control and adjust the plant, through a closed loop control.
There is also a significant legislative push to include more renewable fuels mixed into fossil fuels, e.g., for transportation, and for monitoring the environmental impact of such fuels. When using the system of the present disclosure to comply with documentation requirements, sustainability scores are continuously calculated and stored and the user can document the GHG emissions to relevant authorities on demand.
To incentivize the shift towards fuels with a higher content of renewable components, legislation on the level of CI score, GHG emission factor, or similar sustainability scores of renewable fuels are being employed. Hence, for the production of renewable fuels continuous or frequent monitoring of the sustainability scores can also be utilized for optimization of profit.
In the following details of embodiments of the disclosure will be illustrated by the attached drawings, in which
Blue hydrogen is typically produced from fossil sources such as natural gas or coal, with carbon capture and storage (CCS) technology. In this case, the emitted CO2 is captured and stored (CCS).
Carbon Intensity or Carbon Intensity Score One of the key performance indicators (KPI) that organizations are focusing on to determine their environmental effect is Carbon Intensity Scores. A Carbon Intensity Score, or CI Score, is a life cycle measurement of all total hydrocarbons, or greenhouse gas emitted, versus e.g. the amount of energy consumed. The CI value is typically used in United States of America and other countries in the Greenhouse Gases, Regulated Emissions and Energy Use in Transportation (GREET) Model and is calculated by compiling all the carbon emitted along the supply chain for that fuel including all the carbon used to (where applicable) explore, mine, collect, produce, transport, distribute, dispense and burn the fuel, however there are other calculation modes for the CI score. Lower CI scores are most favorable because they are the cleanest solutions.
Catalytic reaction or step is a process where chemical reaction rates are altered by the addition of a catalyst, that is not itself changed during the chemical reaction. A method of the present disclosure comprises receiving sensor data for temperature and pressure in the catalytic reaction step.
Closed loop system: System to measure, monitor, and control a process and one way in which to accurately control the process is by monitoring its output and “feeding” some of it back to compare the actual output with the desired output so as to reduce the error and if disturbed, bringing the output of the system back to the original or desired response. There may be one or more feedback loops or paths between its output and its input.
Composition means the identity of the components of a mixture, such as feedstock or other information about a certain composition may be provided as standard, batch related or regulated data but also may be assessed and/or monitored by use of sensors or predicted by a computer model, such as a software application predicting the composition at different stages of production (underlying variables) or retrieved by analyzing collected samples in a laboratory and storing them in an accessible database. Said sensor-based assessment may also be performed at any stage during operation in a plant, as well as with various intervals between measurements.
A computer-implemented method or system involves the use of a computer, computer network or other programmable apparatus, where one or more features are realised wholly or partly by means of a computer program. Illustrative embodiments of computer-implemented methods and systems are shown on
Data cleansing within the context of the present disclosure means outlier detection/removal, low-pass filter, and steady-state detection. Data cleansing may be performed as part of the measurements of the individual sensors, when combining sensor data from one or more sensors, or as the input data is received for calculating the sustainability score. Data cleansing can also comprise reconciliation, where the measured data are corrected to ensure that the overall plant mass and energy balance is fulfilled.
Data reconciliation within the context of the present disclosure means correcting the input data to ensure overall mass energy and component balances are fulfilled.
Deviation in measured data (115, 215, 415, 515) is a result of a variation in an underlying variable or parameter. To identify a deviation, the observed current status (230, 430, 530) and the desired or expected status (240, 440, 540) is compared. When it has been established which change in the underlying variables cause a deviation in the observed measured (e.g., sensor) data and thus in the calculated sustainability score, opposing changes may be made in these underlying parameters so as to realize the expected or desired sustainability score.
E-chemicals may be comprised by “power-to-X” or e-fuels, power-to-liquids or synthetic fuels. An e-chemical is a chemical such as, e.g., ammonia or methanol produced from renewable energy. For example, e-methanol can be produced from waste streams, electrolysis hydrogen, and CO2 capture. E-chemicals may be defined as synthetic chemicals resulting from the combination of green or e-hydrogen produced by electrolysis of water with renewable electricity and CO2 captured either from a concentrated source or from the air.
Environmental footprint means the effect or impact that a company, activity, plant, unit, etc. has on the environment, e.g., the amount of natural resources that they use and the amount of harmful gases (emission) that they produce.
Feedstock is used here to signify a type of renewable biomass or waste that is converted into a renewable fuel or chemical product. Oxygenates are a preferred category of renewable feedstocks which typically comprise one or more oxygenates taken from the group consisting of triglycerides, fatty acids, resin acids, ketones, aldehydes or alcohols where said oxygenates originate from one or more of a biological source and a thermal and/or catalytic degradation process, including a gasification process or a pyrolysis process, such that a wide range of feedstocks, especially of renewable origin may be converted into hydrocarbon. This includes feedstocks originating from plants, algae, animals, fish, vegetable oil refining, other biological sources, domestic waste incl. plastic waste and end of life tires, industrial biological waste like tall oil or black liquor as well as non-biological waste comprising suitable compositions, such as plastic fractions, typically after a thermal and/or catalytic degradation process. Further examples of renewable feedstocks are: Corn starch, soybean oil, switchgrass, rapeseed oil, tall oil, used cooking oil and landfill biogas, among several others. When multiple feedstocks are comingled and converted to renewable fuel together (even though each could be processed independently to make the same type of fuel, for example corn starch and grain sorghum starch processed at the same time to make ethanol), the United States Environmental Protection Agency (EPA) evaluates these feedstocks separately when calculating the lifecycle greenhouse gas emissions for a fuel pathway.
Green Hydrogen is typically defined as hydrogen produced from renewable energy, i.e., extracted from water using electrolysis and green electricity. Green hydrogen can also be called e-hydrogen. Hydrogen in e-fuels is green hydrogen.
Green House Gas (GHG) is a gas that absorbs and emits radiant energy within the thermal infrared range, causing the greenhouse effect, which is the process by which radiation from a planet's atmosphere warms the planet's surface to a temperature above what it would be without this atmosphere. The primary greenhouse gases in Earth's atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
Green House Gas (GHG) emissions are often measured in carbon dioxide (CO2) equivalent. To convert emissions of a gas into CO2 equivalent, its emissions are multiplied by the gas's Global Warming Potential (GWP). The GWP takes into account the fact that many gases are more effective at warming Earth than CO2, per unit mass. The GWP depends on the time range used in the life cycle analysis.
Green House Gas Emission Score is similar to the above defined Carbon Intensity Score and is typically used in European countries and other applicable countries, using e.g. the Renewable Energy Directive Recast (RED II) directive model.
HHV—higher heating value (also known gross calorific value or gross energy) of a fuel is defined as the amount of heat released by a specified quantity (initially at 25° C.) once it is combusted and the products have returned to a temperature of 25° C., which takes into account the latent heat of vaporization of water in the combustion products.
Input Data are in some cases obtained directly from a sensor, i.e., as a directly measured parameter. In other cases, sensor data from one or more sensors are combined, e.g., as a relative measurement, calibration or compensation, to produce input data for the method. Input data comprise “Input variables” and “Plant data”. In particular, input data include online sensor data and offline data such as feed, utilities and effluent properties, obtained by analytical measures or other.
Input variables are a subset of “Input data” and used as manipulated variables in the optimization of strategy. A manipulated variable is an independent variable subject to adjustments of an optimization strategy to optimize its effect on the objective function—a non-negative measure of plant performance to be minimized. Input variables comprise process variables, underlying variables, benchmark targets, among others.
Plant data refer to data coming from means of registering, such as sensors, analytical measurements or other, relevant for operating and optimizing the renewables plant.
Life cycle assessment or LCA (also known as life cycle analysis) is a methodology for assessing environmental impacts associated with all the stages of the life cycle of a commercial product, process, or service. For instance, in the case of a manufactured product, environmental impacts are assessed from raw material extraction and processing (cradle), through the product's manufacture, distribution and use, to the recycling or final disposal of the materials composing it (grave). Hence, it is a technique to assess environmental impacts associated with all the stages of a product's life from raw material extraction through materials processing, manufacture, distribution, use, repair and maintenance, and disposal or recycling. The results are used to help decision-makers select products or processes that result in the least impact to the environment by considering an entire product system and avoiding sub-optimization that could occur if only a single process were used.
LHV—lower heating value (also known as net calorific value) of a fuel is defined as the amount of heat released by combusting a specified quantity (initially at 25° C.) and returning the temperature of the combustion products to 150° C., which assumes the latent heat of vaporization of water in the reaction products is not recovered.
The term material input is used to signify both input of renewable feedstock and other raw materials into the plant. Examples of other raw materials could for instance be fossil materials. Furthermore, it refers to production or consumption of energy in the process, and to a utilization factor, such as production volume or production rate of product, such as hydrocarbons or other.
For monitoring purposes, cleansed data is used to determine the environmental factor. The direct or indirect measurements are subject to data cleansing, involving data imputation, outlier detection, low-pass filtering, and steady-state estimation. Moreover, the workflow for estimating optimal adjustments to the renewables plant operational setpoints, referred to as independent variables and subject to box constraints and dependent plant design constraints, involves executing an optimization routine. The optimization problem formulation provides the best tradeoff performance between utilization factor, such as product yield and environmental footprint. The final step involves transmitting executable information, constituting the feasible solution to the (open-loop or closed-loop) optimal control problem.
Multi-objective optimization problem involves more than one objective function that may be conflicting, meaning that improvement to one objective may come at the expense of another objective. There is not a single, optimal solution to multi-objective problems, but a set of solutions that represent the optimal tradeoffs between competing objectives.
Open loop system: System in which the output quantity has no effect upon the input to the control system, and that open-loop system is just an open ended non-feedback system, the purpose of which comprises monitoring and measuring. In such a system, feedback may be provided to the operators, which can then be used to adjust the input variable based on recommendations from the system.
Power-to-X (also P2X and P2Y) is a number of electricity conversion, energy storage, and reconversion pathways that use electric power. Power-to-X conversion technologies allow for the decoupling of power from the electricity sector for use in other sectors (such as transport or chemicals), possibly using power that has been provided by additional investments in generation. The X in the terminology can refer to at least one of the following: power-to-ammonia, power-to-chemicals, power-to-fuel, power-to-gas, power-to-hydrogen, power-to-liquid, power-to-methane, power-to-food, power-to-power, and power-to-syngas. Electric vehicle charging, space heating and cooling, and water heating can be shifted in time to match generation, forms of demand response that some term power-to-mobility and power-to-heat. Collectively power-to-X schemes which use surplus power fall under the heading of flexibility measures and are particularly useful in energy systems with high shares of renewable generation and/or with strong decarbonization targets. A large number of pathways and technologies are encompassed by the term.
Renewable feedstock typically comprises one or more oxygenates taken from the group consisting of triglycerides, fatty acids, resin acids, ketones, aldehydes or alcohols where said oxygenates originate from one or more of a biological source, a gasification process, a pyrolysis process, Fischer-Tropsch synthesis, methanol based synthesis or a further synthesis process, with the associated benefit of such a process being a process viable for receiving a wide range of feedstocks, especially of renewable origin, such as originating from plants, algae, animals, fish, vegetable oil refining, other biological sources, domestic waste incl. plastic waste and end of life tires, industrial organic waste like tall oil or black liquor. In the particular case of e-fuels, feedstock can be CO2, hydrogen or (electric) power.
In this context, a Renewable Fuel includes liquid and gaseous fuels and electricity derived from renewable feedstocks, including biomass or energy sources, e.g., to qualify for the Renewable Fuel Standard (RFS) program, the fuel must be intended for use as transportation fuel, heating oil or jet fuel. Examples: Ethanol, biodiesel, cellulosic diesel and compressed natural gas and electricity from renewable biomass. EPA's lifecycle greenhouse gas (GHG) analysis include evaluation of all of the process energy and materials used in a production process (i.e., emissions from the storage and handling of the feedstock, as well as the production, storage and handling of the fuel and co-products). EPA evaluates the lifecycle greenhouse gas emissions of finished fuels that do not require further chemical alteration to be used for their final purpose. A fuel type may be considered a finished fuel if it is blended with another fuel but not chemically altered. For example, EPA evaluates undenatured ethanol as a fuel type, even though it is blended with denaturant and gasoline before it is used as a transportation fuel.
Renewables plant within the scope of the present application means a plant for producing chemicals, including e-chemicals or fuels, including e-fuels at least partly from renewable feedstock or source.
Set point means selected plant outputs the controller must keep at or near specified reference values. Optimal set-points can be determined by closed-loop operations or using an open-loop case where manipulated variables are taken as the desired optimum set points, applying a rolling horizon policy.
Sustainability score or environmental sustainability score is or comprises a Carbon Intensity (CI) score, carbon intensity (CI), a Green House Gas (GHG) emission score, GHG emissions, or another carbon footprint calculation result or metric, or a Life Cycle Assessment (LCA) or LCA score. An improved sustainability score means that the carbon footprint is reduced.
Thermal decomposition as used herein, the term “thermal decomposition” shall for convenience be used broadly for any decomposition process, in which a material is partially decomposed at elevated temperature (typically 250° C. to 800° C. or even 1000° C.), in the presence of substoichiometric amount of oxygen (including no oxygen). The product will typically be a combined liquid and gaseous stream, as well as an amount of solid char. The term shall be construed to include processes known as pyrolysis and hydrothermal liquefaction, both in the presence and absence of a catalyst.
Underlying variables/parameters or Process variables/parameters are relevant for optimization, i.e., variables that can be manipulated in order to obtain improved utilization factors, such as product yields or sustainability scores. These are regarded as input variables.
Utilization factor within the context of the present invention means the metric used in the performance assessment of a renewables plant converting a feedstock into single or multiple renewable fuel or chemical products. A renewables plant producing e-methanol from waste streams, electrolysis hydrogen, and CO2 capture is an example of a single-product process with one main output stream. Optimal plant utilization of a single-product process typically implies maximizing the single-product production rate, i.e. the single-product mass- or volumetric flow rate. For a renewables plant producing multiple products, e.g. naphtha is a natural byproduct that comes from converting renewable feedstock into diesel, optimal plant utilization implies maximizing the target product yield, i.e. the quantity of the target product formed in relation to the feedstock consumed and usually expressed as a percentage. Utilization factor may therefore refer to, depending on what is being produced at the renewables plant, metrics such as production rate, utility rate, yield, production volume and other.
The present disclosure provides for the following advantages:
In illustrative embodiments, one or more of these advantages can be obtained by a computer-implemented method of controlling production of a chemical or fuel product by a renewables plant at least partly from a renewable feedstock or source, the plant comprising means for registering input data, wherein the method comprises: (a) at a predetermined measuring interval, or continuously, receiving input data obtained from the means for registering input data and indicative of a measure of at least a material input to the production process, a production energy consumption, and a utilization factor, such as production volume or production rate of the chemical or fuel product; (b) at a predetermined calculating interval, or continuously, calculating a sustainability score from the received input data; (c) determining a deviation in the sustainability score; (d) determining an underlying variable as a cause of the deviation; and (e) changing the underlying variable to obtain a target sustainability score. One or more of these advantages can also be obtained by using a computer-implemented system configured to perform this method.
In a preferred embodiment of the present disclosure, sensors are embedded in the renewables plant so as to at least provide a direct or indirect measurement of material input to the production process, e.g., a direct or indirect measurement of a production energy consumption, and a direct or indirect measurement of a utilization factor such as production volume or production rate of a hydrocarbon rich product. If the production process, e.g catalytic conversion process, requires more than one material input, typically more than one sensor will be used. Likewise, if more than one chemical product is produced, typically more than one sensor will be used to measure a utilization factor such as production volume. Many different sensors may be employed, such as fluid flow sensors, temperature sensors, pressure sensors, electric consumption meters, chemical sensors, etc. From the abovementioned sensor data, a sustainability score 120 is calculated. Different sustainability scores may be used, depending on the legislation at the location of production and/or the location of sale of the product. Examples of sustainability scores are Carbon Intensity (CI), Green House Gas (GHG) emissions, carbon footprint calculation, or a Life Cycle Assessment (LCA).
Alternatively, in some preferred embodiments, such a deviation may be converted to executable information that is subsequently transmitted to the operator, or directly to a control system of the plant. A deviation in the measured data 215 is a result of a deviation in an underlying variable or parameter. For instance, a reaction in a catalytic reactor may be exothermal, in which case an observed rise in temperature of the catalytic bed may in fact be caused by a flow of feedstock being too high or a flow of quench gas or recycle being too low. Thus, the underlying parameter may rather be the flowrate rather than the temperature per se. When it has been established which change in the underlying variables cause a deviation in the observed sensor values and thus in the calculated sustainability score, opposing changes may be made in these underlying parameters so as to realize the expected or desired sustainability score.
Monitoring the production process of this plant may comprise registering any of the following parameters:
One or more software modules 170 may be encoded in the memory 160. The software modules 170 may comprise one or more software programs or applications having computer program code or a set of instructions configured to be executed by the processor 155. Such computer program code or instructions for carrying out operations for aspects of the systems and methods disclosed herein may be written in any combination of one or more programming languages.
The software modules 170 may include a program for performing perform the computer-implemented monitoring and control methods disclosed here in exemplary embodiments and one or more additional applications configured to be executed by the processor 155. During execution of the software modules 170, the processor 155 configures the computing device 120 to perform various operations relating to the computer-implemented monitoring and control according to embodiments of the present disclosure.
Other information and/or data relevant to the operation of the present systems and methods, such as a database 185, may also be stored on the memory 160. The database 185 may contain and/or maintain various data items and elements that are utilized throughout the various operations of computer-implemented monitoring and control. It should be noted that although the database 185 is depicted as being configured locally to the computing device 120, in certain implementations the database 185 and/or various other data elements stored therein may be located remotely. Such elements may be located on a remote device or server—not shown, and connected to the computing device 120 through a network in a manner known to those skilled in the art, in order to be loaded into a processor and executed.
Further, the program code of the software modules 170 and one or more computer readable storage devices (such as the memory 160) form a computer program product that may be manufactured and/or distributed in accordance with the present disclosure, as is known to those of skill in the art.
The communication interface 165 can also be operatively connected to the processor 155 and may be any interface that enables communication between the computing device 120 and external devices, machines and/or elements including, e.g., a server or other computer. The communication interface 165 is configured for transmitting and/or receiving data. For example, the communication interface 165 may include but is not limited to a Bluetooth, Wi-Fi or cellular transceiver, a satellite communication transmitter/receiver, an optical port and/or any other such, interfaces for wirelessly connecting the computing device 120 to the server or other computer.
A user interface 150 can also be operatively connected to the processor 155. The user interface may comprise one or more input device(s) such as switch(es), button(s), key(s), and a touchscreen. The user interface 150 functions to allow the entry of data. The user interface 150 functions to facilitate the capture of commands from the user such as an on-off commands or settings related to operation of the above-described method.
A display 190 can also be operatively connected to the processor 155. The display 190 may include a screen or any other such presentation device that enables the user to view various options, parameters, and results, such as the group identifiers. The display 190 may be a digital display such as an LED display. The user interface 150 and the display 190 may be integrated into a touch screen display. The operation of the computing device 120 and the various elements and components described above will be understood by those skilled in the art with reference to computer-implemented monitoring and control.
It will be understood that what has been described herein is an exemplary system for effecting computer-implemented monitoring and control. While the present disclosure has been described with reference to exemplary arrangements it will be understood that it is not intended to limit the disclosure to such arrangements as modifications can be made without departing from the spirit and scope of the present teaching. The method of the present teaching may be implemented in software, firmware, hardware, or a combination thereof. In one mode, the method is implemented in software, as an executable program, and is executed by one or more special or general purpose digital computer(s). The steps of the method may be implemented by a server or computer in which the software modules reside or partially reside.
Generally, in terms of hardware architecture, such a computer will include, as will be well understood by the person skilled in the art, a processor, memory, and one or more input and/or output (I/O) devices (or peripherals) that are communicatively coupled via a local interface.
The local interface can be, for example, but not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface may have additional elements, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communications. Further, the local interface may include address, control, and/or data connections to enable appropriate communications among the other computer components.
It will be appreciated that the system may be implemented using cloud or local server architecture. In this way it will be understood that the present teaching is to be limited only insofar as is deemed necessary in the light of the appended claims.
The present disclosure provides for the following advantages:
The present example details the optimization of a product stripper in a sustainable diesel unit. The product stripper is a distillation column with a condenser and a reboiler, and the purpose of the stripper is to produce a stabilized diesel product, where all light material like off-gas and naphtha is removed. Typically the target for operating such a stripper is to achieve a certain flash point value of the diesel product. In this example, the column is operated in order to achieve a diesel product flash point of 55° C.
The separation between diesel and naphtha is controlled by the energy input to the stripping column in the reboiler and condenser. The more energy that is supplied to the stripper, the better the separation between the products become, and the more diesel product can be extracted while still meeting the specified flash point. In this example, the three operating cases were considered:
As observed, reducing the total energy input also reduces the amount of diesel product which illustrates the trade-off between energy use (that increases the GHG emissions of the process) and the diesel utilization factor, such as product rate (that increases the plant profitability).
Additionally, co-products such as LPG (liquefied petroleum gas) or naphtha may be recycled to the H2 unit in order to minimize use of natural gas (for example) and therefore minimize the GHG emissions of the hydroprocessing step.
Table 1 illustrates the value of continuous monitoring of carbon intensity CI. The example illustrates the development of CI from start of run (SOR) to end of run (EOR) for a hydroprocessing plant producing renewable diesel. The plant consumes hydrogen, which in part is produced from natural gas. In addition an amount of electricity is also consumed by the plant.
At start of run (SOR) compared to end of run (EOR) more diesel is produced and less natural gas and electricity is consumed, and the conventional approach to this variation in the registration of CI has been to predict the worst case and ensure compliance with that value. In Table 1, this would be the EOR situation.
Therefore by employing a continuous CI monitoring and auditable documentation, it is possible to document a lower CI value, compared to the worst case scenario otherwise required. In the case of Table 1 this would in practice mean an interpolation between the SOR value and the EOR, such that the reported CI would be the average of the two; 13.36 g CO2/MJ, which can be used for achieving additional profit in a plant, based on a carbon credit scheme.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2021/087238 | 12/21/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63130181 | Dec 2020 | US | |
| 63136049 | Jan 2021 | US |
