Patent Application
20220277406
The present disclosure relates to computer-implemented methods of optimizing an industrial process. In non-limiting embodiments, the method includes generating one or more graphical user interfaces. In non-limiting embodiments, the method includes modifying at least one process parameter of a specified type of industrial process based on at least one environmental parameter.
Industrial processes for the manufacture of products may be sensitive to environmental conditions in ways that alter the material properties of the finished product. Some industrial processes, for example the mixing of raw materials to manufacture foam, may be particularly sensitive to environmental conditions such that careful monitoring and accounting of environmental conditions must be undertaken during manufacturing to ensure the finished product has acceptable physical and chemical properties. Particular environmental conditions that may affect industrial processes may include temperature, pressure, humidity, and grains of moisture. In order to account for changes or abnormalities in such environmental conditions, control parameters of the manufacturing process may be altered.
Existing methods for altering such control parameters generally rely on experience of a process operator to configure the control parameters prior to beginning the manufacturing process and make on-the-fly adjustments to the control parameters during the manufacturing process. Such configuration and adjustment to the control parameters may not be repeatable and may vary from operator to operator and/or production run to production run, sometimes leading to unpredictable and unsatisfactory results.
According to a non-limiting embodiment or aspect, provided is a computer-implemented method of optimizing an industrial process based on at least one environmental parameter. The method includes comparing, with at least one processor, current environmental condition data to historic environment condition data for at least one day preceding a specified day. The method also includes determining, with at least one processor, a visual state from a plurality of visual states for the at least one day based on the comparison between the current environmental condition data and the historic environment condition data. The method further includes generating, with at least one processor, a calendar interface including a plurality of days preceding the specified day and corresponding to a plurality of visual representations. At least one visual representation corresponding to the at least one day includes the visual state. The method further includes, in response to receiving a user selection of the at least one day of the plurality of days, generating a graphical user interface including process data for the at least one day, the process data including historical data for at least one type of industrial process.
In some non-limiting embodiments or aspects, the method may further include determining, with at least one processor, the current environmental condition data for the specified day for a region in which the at least one type of industrial process is being performed.
In some non-limiting embodiments or aspects, determining the visual state of the at least one day may include determining a subset of days of the plurality of days based on an availability of data for the at least one specified type of industrial process, and determining a visual state for each day of the subset of days based on the comparison of the current environmental condition data to historic environment condition data for that day. Each visual state of the plurality of visual states is based on a differential between the current environmental condition data and the historic environment condition data.
In some non-limiting embodiments or aspects, the method may further include generating a plurality of visual representations from the plurality of visual states. The plurality of visual states includes a plurality of colors, and each visual representation of the plurality of visual representations represents a different day of the plurality of days.
In some non-limiting embodiments or aspects, the method may further include modifying at least one process parameter for an industrial process based on the process data for the at least one day.
In some non-limiting embodiments or aspects, the method may further include controlling an ingredient addition device based on the at least one process parameter.
In some non-limiting embodiments or aspects, the graphical user interface including process data may include at least one graph showing a plurality of discrete instances of the industrial process according to at least one process parameter. The method may further include receiving a user selection of at least one discrete instance of the industrial process from the at least one graph, and generating a graphical user interface including process parameters for the at least one discrete instance of the industrial process.
According to a non-limiting embodiment or aspect, provided is a computer-implemented method of optimizing an industrial process based on at least one environmental parameter. The method includes receiving, with at least one processor, a specified type of industrial process. The method further includes determining, with at least one processor, a plurality of days preceding a specified day for which process data associated with the specified type of industrial process is stored in a database. The method further includes determining, with at least one processor, historic environment condition data for each day of the plurality of days. The method further includes comparing, with at least one processor, current environmental condition data to the historic environment condition data for each of the plurality of days. The method further includes determining, with at least one processor, a visual state from a plurality of visual states for each day of the plurality of days based on the comparison between the current environmental condition data and the historic environment condition data for each day. The method further includes generating, with at least one processor, a calendar interface including a plurality of visual representations. Each visual representation corresponds to a day of the plurality of days and includes the visual state determined for the corresponding day.
In some non-limiting embodiments or aspects, the method may further include receiving a user selection of at least one visual representation of the plurality of visual representations, and generating a graphical user interface including process data for at least one day corresponding to the at least one visual representation of the user selection. The process data includes historical data for the specified type of industrial process.
In some non-limiting embodiments or aspects, the method may further include determining, with at least one processor, the current environmental condition data for the specified day for a region in which the specified type of industrial process is being performed.
In some non-limiting embodiments or aspects, the plurality of visual states may include a plurality of colors.
In some non-limiting embodiments or aspects, the method may further include modifying at least one process parameter for the specified type of industrial process based on the process data for the at least one day.
In some non-limiting embodiments or aspects, the method may further include controlling an ingredient addition device based on the at least one process parameter.
According to a non-limiting embodiment or aspect, provided is a computer-implemented method of optimizing an industrial process based on at least one environmental parameter. The method includes receiving, with at least one processor, a specified type of industrial process. The method further includes determining, with at least one processor, a plurality of days preceding a specified day for which process data associated with the specified type of industrial process is stored in a database. The method further includes determining, with at least one processor, historic environment condition data for each day of the plurality of days. The method further includes comparing, with at least one processor, current environmental condition data to the historic environment condition data for each of the plurality of days. The method further includes selecting, with at least one processor, at least one day of the plurality of days based on the comparison between the current environmental condition data and the historic environment condition data for each day of the plurality of days. The method further includes retrieving, with at least one processor, process data corresponding to the at least one day from a database. The method further includes configuring process parameters for performing the industrial process based on the process data retrieved from the database.
In some non-limiting embodiments or aspects, the method may further include determining, with at least one processor and during performance of the specified type of industrial process, a change in the current environmental condition data. The method may further include, in response to determining the change, determining, with at least one processor, at least one different day of the plurality of days based on a comparison between the changed current environmental condition data and historic environment condition data for the at least one different day. The method may further include modifying, with at least one processor, at least one of the process parameters for the specified type of industrial process during performance of the specified type of industrial process.
According to a non-limiting embodiment or aspect, provided is a computer program product for optimizing an industrial process based on at least one environmental parameter including at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to compare current environmental condition data to historic environment condition data for at least one day preceding a specified day. The instructions further cause the at least one processor to determine a visual state from a plurality of visual states for the at least one day based on the comparison between the current environmental condition data and the historic environment condition data. The instructions further cause the at least one processor to generate a calendar interface including a plurality of days preceding the specified day and corresponding to a plurality of visual representations. At least one visual representation corresponding to the at least one day includes the visual state. The instructions further cause the at least one processor to, in response to receiving a user selection of the at least one day of the plurality of days, generate a graphical user interface including process data for the at least one day, the process data including historical data for at least one type of industrial process.
In some non-limiting embodiments or aspects, the one or more instructions may further cause the at least one processor to determine the current environmental condition data for the specified day for a region in which the at least one type of industrial process is being performed.
In some non-limiting embodiments or aspects, the one or more instructions that cause the at least one processor to determine the visual state of the at least one day may cause the at least one processor to determine a subset of days of the plurality of days based on an availability of data for the at least one specified type of industrial process, and determine a visual state for each day of the subset of days based on the comparison of the current environmental condition data to historic environment condition data for that day. Each visual state of the plurality of visual states is based on a differential between the current environmental condition data and the historic environment condition data.
In some non-limiting embodiments or aspects, the one or more instructions may further cause the at least one processor to generate a plurality of visual representations from the plurality of visual states. The plurality of visual states includes a plurality of colors, and each visual representation of the plurality of visual representations represents a different day of the plurality of days.
In some non-limiting embodiments or aspects, the one or more instructions may further cause the at least one processor to modify at least one process parameter for an industrial process based on the process data for the at least one day.
In some non-limiting embodiments or aspects, the one or more instructions may further cause the at least one processor to control an ingredient addition device based on the at least one process parameter.
In some non-limiting embodiments or aspects, the graphical user interface including process data may include at least one graph showing a plurality of discrete instances of the industrial process according to at least one process parameter. The one or more instructions further cause the at least one processor to receive a user selection of at least one discrete instance of the industrial process from the at least one graph, and generate a graphical user interface including process parameters for the at least one discrete instance of the industrial process.
According to a non-limiting embodiment or aspect, provided is a system for optimizing an industrial process based on at least one environmental parameter. The system includes at least one processor programmed and/or configured to compare current environmental condition data to historic environment condition data for at least one day preceding a specified day. The at least one processor is further programmed and/or configured to determine a visual state from a plurality of visual states for the at least one day based on the comparison between the current environmental condition data and the historic environment condition data. The at least one processor is further programmed and/or configured to generate a calendar interface including a plurality of days preceding the specified day and corresponding to a plurality of visual representations. At least one visual representation corresponding to the at least one day includes the visual state. The at least one processor is further programmed and/or configured to, in response to receiving a user selection of the at least one day of the plurality of days, generate a graphical user interface including process data for the at least one day, the process data including historical data for at least one type of industrial process.
In some non-limiting embodiments or aspects, the at least one processor may be further programmed and/or configured to determine the current environmental condition data for the specified day for a region in which the at least one type of industrial process is being performed.
In some non-limiting embodiments or aspects, when determining the visual state of the at least one day, the at least one processor may be programmed and/or configured to determine a subset of days of the plurality of days based on an availability of data for the at least one specified type of industrial process, and determine a visual state for each day of the subset of days based on the comparison of the current environmental condition data to historic environment condition data for that day. Each visual state of the plurality of visual states is based on a differential between the current environmental condition data and the historic environment condition data.
In some non-limiting embodiments or aspects, the at least one processor may be further programmed and/or configured to generate a plurality of visual representations from the plurality of visual states. The plurality of visual states includes a plurality of colors, and each visual representation of the plurality of visual representations represents a different day of the plurality of days.
In some non-limiting embodiments or aspects, the at least one processor may be further programmed and/or configured to modify at least one process parameter for an industrial process based on the process data for the at least one day.
In some non-limiting embodiments or aspects, the at least one processor may be further programmed and/or configured to control an ingredient addition device based on the at least one process parameter.
In some non-limiting embodiments or aspects, the graphical user interface including process data may include at least one graph showing a plurality of discrete instances of the industrial process according to at least one process parameter. The at least one processor may be further programmed and/or configured to receive a user selection of at least one discrete instance of the industrial process from the at least one graph, and generate a graphical user interface including process parameters for the at least one discrete instance of the industrial process.
According to a non-limiting embodiment or aspect, provided is a method of producing a chemical product from a reaction mixture containing at least two ingredients. The method includes: generating, with at least one processor, at least one machine learning model configured to determine predicted reaction mixture data based on at least one input environmental parameter and at least one input product property. The predicted reaction mixture data may include at least one of a composition of a reaction mixture and process conditions for a reaction mixture. The method may further include training, with at least one processor, the at least one machine learning model based on a data set including data for a plurality of production instances of producing the chemical product. The data for each production instance may include reaction mixture composition data, at least one environmental parameter for a production site of the chemical product, and at least one product property of the chemical product. The method may further include determining, with at least one processor, the predicted reaction mixture data based on processing input data including a measured environmental parameter and at least one target product property according to the at least one machine learning model. The method may further include producing the chemical product based on the predicted reaction mixture data. The method may further include obtaining at least one measured product property of the chemical product produced based on the predicted reaction mixture data. The method may further include modifying, with at least one processor, the at least one model based on the at least one measured product property and the predicted reaction mixture data.
In some non-limiting embodiments or aspects, the method may further include, prior to training the at least one machine learning model, removing, with at least one processor, outliers from the data set based on a statistical algorithm.
In some non-limiting embodiments or aspects, the method may further include receiving, via a graphical user interface, at least one of the at least one measured environmental parameter and the at least one target product property.
In some non-limiting embodiments or aspects, the method may further include displaying, on a graphical user interface, the predicted reaction mixture data.
In some non-limiting embodiments or aspects, the at least one target product property includes at least two target product properties.
In some non-limiting embodiments or aspects, the at least one measured environmental parameter includes at least two measured environmental parameters.
In some non-limiting embodiments or aspects, the at least one measured environmental parameter includes at least one of the following: an air pressure, an air temperature, an air relative humidity, or combinations thereof.
In some non-limiting embodiments or aspects, the at least one target product property is at least one of a raw density according to DIN EN ISO 845 and a compression load deflection at 40% compression according to EN ISO 3386.
In some non-limiting embodiments or aspects, the chemical product includes a polyurethane foam, and the reaction mixture includes: a polyisocyanate; a polyisocyanate-reactive compound; a blowing agent; or combinations thereof. In an embodiment, the polyisocyanate-reactive compound includes water.
In some non-limiting embodiments or aspects, determining the predicted reaction mixture data includes modifying a predetermined mixture composition by adjusting at least one of: a molar ratio of isocyanate groups to isocyanate-reactive groups; an amount of blowing agent; an amount of physical blowing agent relative to an amount of chemical blowing agent; or combinations thereof.
In some non-limiting embodiments or aspects, the method may further include, while producing the chemical product based on the predicted reaction mixture, receiving an updated measured environmental parameter from the production site of the chemical product. The method may further include updating, with at least one processor, the predicted reaction mixture data based on the updated measured environmental parameter.
In some non-limiting embodiments or aspects, updating the predicted reaction mixture data based on the updated measured environmental parameter includes adjusting at least one of the composition of the reaction mixture and process conditions for the reaction mixture.
In some non-limiting embodiments or aspects, the method may further include, while producing the chemical product based on the predicted reaction mixture, receiving an updated measured environmental parameter from the production site of the chemical product. The method may further include determining not to adjust the predicted reaction mixture data based on the updated measured environmental parameter.
In some non-limiting embodiments or aspects, the method may further include, determining, with at least one processor, that the updated measured environmental parameter is different than the measured environmental parameter. The method may further include adjusting, with at least one processor, at least one of the composition of the reaction mixture and process conditions for the reaction mixture in response to the determination that the updated measured environmental parameter is different than the measured environmental parameter.
In some non-limiting embodiments or aspects, receiving an updated measured environmental parameter includes receiving at least two updated measured environmental parameters.
Further embodiments or aspects are set forth in the following numbered clauses:
Clause 1. A computer-implemented method of optimizing an industrial process based on at least one environmental parameter, comprising: comparing, with at least one processor, current environmental condition data to historic environment condition data for at least one day preceding a specified day; determining, with at least one processor, a visual state from a plurality of visual states for the at least one day based on the comparison between the current environmental condition data and the historic environment condition data; generating, with at least one processor, a calendar interface comprising a plurality of days preceding the specified day and corresponding to a plurality of visual representations, wherein at least one visual representation corresponding to the at least one day comprises the visual state; and in response to receiving a user selection of the at least one day of the plurality of days, generating a graphical user interface comprising process data for the at least one day, the process data including historical data for at least one type of industrial process.
Clause 2. The computer-implemented method of clause 1, further comprising determining, with at least one processor, the current environmental condition data for the specified day for a region in which the at least one type of industrial process is being performed.
Clause 3. The computer-implemented method of clause 1 or 2, wherein determining the visual state of the at least one day comprises: determining a subset of days of the plurality of days based on an availability of data for the at least one specified type of industrial process; and determining a visual state for each day of the subset of days based on the comparison of the current environmental condition data to historic environment condition data for that day, wherein each visual state of the plurality of visual states is based on a differential between the current environmental condition data and the historic environment condition data.
Clause 4. The computer-implemented method of any of clauses 1-3, further comprising generating a plurality of visual representations from the plurality of visual states, wherein the plurality of visual states comprises a plurality of colors, and wherein each visual representation of the plurality of visual representations represents a different day of the plurality of days.
Clause 5. The computer-implemented method of any of clauses 1-4, further comprising modifying at least one process parameter for an industrial process based on the process data for the at least one day.
Clause 6. The computer-implemented method of any of clauses 1-5, further comprising controlling an ingredient addition device based on the at least one process parameter.
Clause 7. The computer-implemented method of any of clauses 1-6, wherein the graphical user interface comprising process data includes at least one graph showing a plurality of discrete instances of the industrial process according to at least one process parameter, the method further comprising: receiving a user selection of at least one discrete instance of the industrial process from the at least one graph; and generating a graphical user interface comprising process parameters for the at least one discrete instance of the industrial process.
Clause 8. A computer-implemented method of optimizing an industrial process based on at least one environmental parameter, comprising: receiving, with at least one processor, a specified type of industrial process; determining, with at least one processor, a plurality of days preceding a specified day for which process data associated with the specified type of industrial process is stored in a database; determining, with at least one processor, historic environment condition data for each day of the plurality of days; comparing, with at least one processor, current environmental condition data to the historic environment condition data for each of the plurality of days; determining, with at least one processor, a visual state from a plurality of visual states for each day of the plurality of days based on the comparison between the current environmental condition data and the historic environment condition data for each day; and generating, with at least one processor, a calendar interface comprising a plurality of visual representations, each visual representation corresponding to a day of the plurality of days and comprising the visual state determined for the corresponding day.
Clause 9. The computer-implemented method of clause 8, further comprising: receiving a user selection of at least one visual representation of the plurality of visual representations; and generating a graphical user interface comprising process data for at least one day corresponding to the at least one visual representation of the user selection, the process data including historical data for the specified type of industrial process.
Clause 10. The computer-implemented method of clause 8 or 9, further comprising determining, with at least one processor, the current environmental condition data for the specified day for a region in which the specified type of industrial process is being performed.
Clause 11. The computer-implemented method of any of clauses 8-10, wherein the plurality of visual states comprises a plurality of colors.
Clause 12. The computer-implemented method of any of clauses 8-11, further comprising modifying at least one process parameter for the specified type of industrial process based on the process data for the at least one day.
Clause 13. The computer-implemented method of any of clauses 8-12, further comprising controlling an ingredient addition device based on the at least one process parameter.
Clause 14. A computer-implemented method of optimizing an industrial process based on at least one environmental parameter, comprising: receiving, with at least one processor, a specified type of industrial process; determining, with at least one processor, a plurality of days preceding a specified day for which process data associated with the specified type of industrial process is stored in a database; determining, with at least one processor, historic environment condition data for each day of the plurality of days; comparing, with at least one processor, current environmental condition data to the historic environment condition data for each of the plurality of days; selecting, with at least one processor, at least one day of the plurality of days based on the comparison between the current environmental condition data and the historic environment condition data for each day of the plurality of days; retrieving, with at least one processor, process data corresponding to the at least one day from a database; and configuring process parameters for performing the industrial process based on the process data retrieved from the database.
Clause 15. The computer-implemented method of clause 14, further comprising: determining, with at least one processor and during performance of the specified type of industrial process, a change in the current environmental condition data; in response to determining the change, determining, with at least one processor, at least one different day of the plurality of days based on a comparison between the changed current environmental condition data and historic environment condition data for the at least one different day; and modifying, with at least one processor, at least one of the process parameters for the specified type of industrial process during performance of the specified type of industrial process.
Clause 16. A computer program product for optimizing an industrial process based on at least one environmental parameter comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to: compare current environmental condition data to historic environment condition data for at least one day preceding a specified day; determine a visual state from a plurality of visual states for the at least one day based on the comparison between the current environmental condition data and the historic environment condition data; generate a calendar interface comprising a plurality of days preceding the specified day and corresponding to a plurality of visual representations, wherein at least one visual representation corresponding to the at least one day comprises the visual state; and in response to receiving a user selection of the at least one day of the plurality of days, generate a graphical user interface comprising process data for the at least one day, the process data including historical data for at least one type of industrial process.
Clause 17. The computer program product of clause 16, wherein the one or more instructions further cause the at least one processor to determine the current environmental condition data for the specified day for a region in which the at least one type of industrial process is being performed.
Clause 18. The computer program product of clause 16 or 17, wherein the one or more instructions that cause the at least one processor to determine the visual state of the at least one day cause the at least one processor to: determine a subset of days of the plurality of days based on an availability of data for the at least one specified type of industrial process; and determine a visual state for each day of the subset of days based on the comparison of the current environmental condition data to historic environment condition data for that day, wherein each visual state of the plurality of visual states is based on a differential between the current environmental condition data and the historic environment condition data.
Clause 19. The computer program product of any of clauses 16-18, wherein the one or more instructions further cause the at least one processor to generate a plurality of visual representations from the plurality of visual states, wherein the plurality of visual states comprises a plurality of colors, and wherein each visual representation of the plurality of visual representations represents a different day of the plurality of days.
Clause 20. The computer program product of any of clauses 16-19, wherein the one or more instructions further cause the at least one processor to modify at least one process parameter for an industrial process based on the process data for the at least one day.
Clause 21. The computer program product of any of clauses 16-20, wherein the one or more instructions further cause the at least one processor to control an ingredient addition device based on the at least one process parameter.
Clause 22. The computer program product of any of clauses 16-21, wherein the graphical user interface comprising process data includes at least one graph showing a plurality of discrete instances of the industrial process according to at least one process parameter, and wherein the one or more instructions further cause the at least one processor to: receive a user selection of at least one discrete instance of the industrial process from the at least one graph; and generate a graphical user interface comprising process parameters for the at least one discrete instance of the industrial process.
Clause 23. A system for optimizing an industrial process based on at least one environmental parameter, the system comprising at least one processor programmed and/or configured to: compare current environmental condition data to historic environment condition data for at least one day preceding a specified day; determine a visual state from a plurality of visual states for the at least one day based on the comparison between the current environmental condition data and the historic environment condition data; generate a calendar interface comprising a plurality of days preceding the specified day and corresponding to a plurality of visual representations, wherein at least one visual representation corresponding to the at least one day comprises the visual state; and in response to receiving a user selection of the at least one day of the plurality of days, generate a graphical user interface comprising process data for the at least one day, the process data including historical data for at least one type of industrial process.
Clause 24. The system of clause 23, wherein the at least one processor is further programmed and/or configured to determine the current environmental condition data for the specified day for a region in which the at least one type of industrial process is being performed.
Clause 25. The system of clause 23 or 24, wherein, when determining the visual state of the at least one day, the at least one processor is programmed and/or configured to: determine a subset of days of the plurality of days based on an availability of data for the at least one specified type of industrial process; and determine a visual state for each day of the subset of days based on the comparison of the current environmental condition data to historic environment condition data for that day, wherein each visual state of the plurality of visual states is based on a differential between the current environmental condition data and the historic environment condition data.
Clause 26. The system of any of clauses 23-25, wherein the at least one processor is further programmed and/or configured to generate a plurality of visual representations from the plurality of visual states, wherein the plurality of visual states comprises a plurality of colors, and wherein each visual representation of the plurality of visual representations represents a different day of the plurality of days.
Clause 27. The system of any of clauses 23-26, wherein the at least one processor is further programmed and/or configured to modify at least one process parameter for an industrial process based on the process data for the at least one day.
Clause 28. The system of any of clauses 23-27, wherein the at least one processor is further programmed and/or configured to control an ingredient addition device based on the at least one process parameter.
Clause 29. The system of any of clauses 23-28, wherein the graphical user interface comprising process data includes at least one graph showing a plurality of discrete instances of the industrial process according to at least one process parameter, and wherein the at least one processor is further programmed and/or configured to: receive a user selection of at least one discrete instance of the industrial process from the at least one graph; and generate a graphical user interface comprising process parameters for the at least one discrete instance of the industrial process.
Clause 30. A method of producing a chemical product from a reaction mixture containing at least two ingredients, comprising: generating, with at least one processor, at least one machine learning model configured to determine predicted reaction mixture data based on at least one input environmental parameter and at least one input product property, the predicted reaction mixture data comprising at least one of a composition of a reaction mixture and process conditions for a reaction mixture; training, with at least one processor, the at least one machine learning model based on a data set comprising data for a plurality of production instances of producing the chemical product, the data for each production instance comprising reaction mixture composition data, at least one environmental parameter for a production site of the chemical product, and at least one product property of the chemical product; determining, with at least one processor, the predicted reaction mixture data based on processing input data comprising a measured environmental parameter and at least one target product property according to the at least one machine learning model; producing the chemical product based on the predicted reaction mixture data; obtaining at least one measured product property of the chemical product produced based on the predicted reaction mixture data; and modifying, with at least one processor, the at least one model based on the at least one measured product property and the predicted reaction mixture data.
Clause 31. The method of clause 30, further comprising: prior to training the at least one machine learning model, removing, with at least one processor, outliers from the data set based on a statistical algorithm.
Clause 32. The method of clause 30 or 31, further comprising receiving, via a graphical user interface, at least one of the at least one measured environmental parameter and the at least one target product property.
Clause 33. The method of any of clauses 30 to 32, further comprising displaying, on a graphical user interface, the predicted reaction mixture data.
Clause 34. The method of any of clauses 30 to 33, wherein the at least one target product property comprises at least two target product properties.
Clause 35. The method of any of clauses 30 to 34, wherein the at least one measured environmental parameter comprises at least two measured environmental parameters.
Clause 36. The method of any of clauses 30 to 35, wherein the at least one measured environmental parameter comprises at least one of the following: an air pressure, an air temperature, an air relative humidity, or combinations thereof.
Clause 37. The method of any of clauses 30 to 36, wherein the at least one target product property is at least one of a raw density according to DIN EN ISO 845 and a compression load deflection at 40% compression according to EN ISO 3386.
Clause 38. The method of any of clauses 30 to 37, wherein the chemical product comprises a polyurethane foam, and wherein the reaction mixture comprises: a polyisocyanate; a polyisocyanate-reactive compound; a blowing agent; or combinations thereof; and optionally water.
Clause 39. The method of any of clauses 30 to 38, wherein determining the predicted reaction mixture data comprises: modifying a predetermined mixture composition by adjusting at least one of: a molar ratio of isocyanate groups to isocyanate-reactive groups; an amount of blowing agent; an amount of physical blowing agent relative to an amount of chemical blowing agent; or combinations thereof.
Clause 40. The method of any of clauses 30 to 39, further comprising: while producing the chemical product based on the predicted reaction mixture, receiving an updated measured environmental parameter from the production site of the chemical product; and updating, with at least one processor, the predicted reaction mixture data based on the updated measured environmental parameter.
Clause 41. The method of any of clauses 30 to 40, wherein updating the predicted reaction mixture data based on the updated measured environmental parameter comprises adjusting at least one of the composition of the reaction mixture and process conditions for the reaction mixture.
Clause 42. The method of any of clauses 30 to 41, further comprising while producing the chemical product based on the predicted reaction mixture, receiving an updated measured environmental parameter from the production site of the chemical product; and determining not to adjust the predicted reaction mixture data based on the updated measured environmental parameter.
Clause 43. The method of any of clauses 30 to 42, further comprising: determining, with at least one processor, that the updated measured environmental parameter is different than the measured environmental parameter, adjusting, with at least one processor, at least one of the composition of the reaction mixture and process conditions for the reaction mixture in response to the determination that the updated measured environmental parameter is different than the measured environmental parameter.
Clause 44. The method of any of clauses 30 to 43, wherein receiving an updated measured environmental parameter comprises receiving at least two updated measured environmental parameters.
These and other features and characteristics of the present invention, as well as the methods of operation and functions of the related elements of structures and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. As used in the specification and the claims, the singular form of “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.
For purposes of the description hereinafter, the terms “end,” “upper,” “lower,” “right,” “left,” “vertical,” “horizontal,” “top,” “bottom,” “lateral,” “longitudinal,” and derivatives thereof shall relate to the invention as it is oriented in the drawing figures. However, it is to be understood that the invention may assume various alternative variations and step sequences, except where expressly specified to the contrary. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply exemplary embodiments or aspects. Hence, specific dimensions and other physical characteristics related to the embodiments or aspects disclosed herein are not to be considered as limiting.
As used herein, the terms “communication” and “communicate” may refer to the reception, receipt, transmission, transfer, provision, and/or the like, of information (e.g., data, signals, messages, instructions, commands, and/or the like). For one unit (e.g., a device, a system, a component of a device or system, combinations thereof, and/or the like) to be in communication with another unit means that the one unit is able to directly or indirectly receive information from and/or transmit information to the other unit. This may refer to a direct or indirect connection (e.g., a direct communication connection, an indirect communication connection, and/or the like) that is wired and/or wireless in nature. Additionally, two units may be in communication with each other even though the information transmitted may be modified, processed, relayed, and/or routed between the first and second unit. For example, a first unit may be in communication with a second unit even though the first unit passively receives information and does not actively transmit information to the second unit. As another example, a first unit may be in communication with a second unit if at least one intermediary unit (e.g., a third unit located between the first unit and the second unit) processes information received from the first unit and communicates the processed information to the second unit. In some non-limiting embodiments, a message may refer to a network packet (e.g., a data packet, and/or the like) that includes data. It will be appreciated that numerous other arrangements are possible.
As used herein, the term “computing device” may refer to one or more electronic devices configured to process data. A computing device may, in some examples, include the necessary components to receive, process, and output data, such as a processor, a display, a memory, an input device, a network interface, and/or the like. A computing device may be a mobile device. As an example, a mobile device may include a cellular phone (e.g., a smartphone or standard cellular phone), a portable computer, a wearable device (e.g., watches, glasses, lenses, clothing, and/or the like), a personal digital assistant (PDA), and/or other like devices. A computing device may also be a desktop computer, server, or other form of non-mobile computer.
As used herein, the term “user interface” or “graphical user interface” refers to a generated display, such as one or more graphical user interfaces (GUIs) with which a user may interact, either directly or indirectly (e.g., through a keyboard, mouse, touchscreen, etc.).
As used herein, the term “application programming interface” (API) may refer to computer code that allows communication between different systems or (hardware and/or software) components of systems. For example, an API may include function calls, functions, subroutines, communication protocols, fields, and/or the like usable and/or accessible by other systems or other (hardware and/or software) components of systems.
As used herein, the term “industrial process” may refer to a process for manufacturing a product. An industrial process may include adding one or more ingredients to a mixture, mixing of one or more ingredients, adding one or more catalysts to the mixture, heating the mixture, conveying the mixture, and/or the like. In some non-limiting embodiments, the industrial process may be a foam manufacturing process, such as a polyurethane foam manufacturing process. The mixture may be a reaction mixture in which two or more ingredients are chemically reacted with one another to produce a chemical product.
As used herein, the term “process data” may refer to data obtained before, after, or during performance of an industrial process. Process data may include data related to historic environment conditions (e.g. temperature, barometric pressure, relative and/or absolute humidity, grains of moisture, and/or the like) observed or measured during past performance of the industrial process. Process data may also include data related to one or more properties of materials (e.g. density, IFD hardness, chemical composition, and/or the like) produced during past performance of the industrial process. Process data may also include data related to one or more process parameters of the industrial process (e.g. ingredient flow rate, ingredient temperature, relative ingredient ratios, catalyst addition, heating parameters, mixing parameters, conveying speed and/or the like) during past performance of the industrial process.
As used herein, the term “product property” may refer to a physical or chemical characteristic of a product. Non-limiting examples of product properties may include density, such as a raw density according to DIN EN ISO 845; an IFD hardness; a load deflection, such as a compression load deflection at 40% compression according to EN ISO 3386; a chemical composition of the product; a reactivity; and/or the like.
As used herein, the term “environmental parameter” may refer to an environmental or climate condition of a location or facility, such as a production site for a chemical product. Non-limiting examples of environmental parameters may include an air temperature; a heat index, an air pressure, a relative and/or absolute humidity, and/or the like. Environmental parameters may be expressed by any conventional measurement techniques. For example, humidity may be expressed in terms of grains of moisture.
As used herein, the term “machine learning algorithm” may refer to an algorithm for applying at least one predictive model to a data set. A machine learning algorithm may train at least one predictive model through expansion of the data set by continually or intermittently updating the data set with results of instances of an industrial process. Examples of machine learning algorithms may include supervised and/or unsupervised techniques such as decision trees, gradient boosting, logistic regression, artificial neural networks, Bayesian statistics, learning automata, Hidden Markov Modeling, linear classifiers, quadratic classifiers, association rule learning, or the like. As used herein, the term “machine learning model” may refer to a predictive model at least partially generated by a machine learning algorithm.
Non-limiting embodiments or aspects of the present disclosure are directed to methods, systems, and computer program products for optimizing an industrial process. The various non-limiting embodiments described herein facilitate comparison of current environmental condition data to historic environment condition data and, based on that comparison, optimize the industrial process. Described embodiments improve upon conventional methods by configuring and/or modifying process parameters of the industrial process based on categorized empirical data from past performances of the industrial process. Disclosed embodiments result in industrial processes which create consistent, repeatable results without relying on the uncertainties of human operator skill and experience. Additionally, disclosed embodiments reduce the need for on-the-fly adjustments necessitated by less than optimal initial configuration of the industrial process. In some embodiments, the use of catalysts, reagents, or other industrial process ingredients conventionally used to mitigate error and/or uncertainty in performance of the industrial process may be reduced as a consequence of the optimized process parameters. In some non-limiting embodiments, one or more user interfaces are generated which allow a user to select at least one day from a plurality of days preceding a specified day. In some non-limiting embodiments, the industrial process is modified based on process data for the at least one day selected by the user. In some non-limiting embodiments, the industrial process is automatically modified based on process data associated with at least one day preceding the specified day, based on the comparison of current environmental condition data to historic environment condition data. As such, the operator may have ultimate control of the process but may be assisted in configuration and/or modification of the process parameters to reduce the prevalence of operator miscalculations and/or estimations of suitable process parameters. In some non-limiting embodiments, the industrial process is automatically modified according to at least one machine learning model in order to produce a product having at least one target property. The at least one machine learning model may predict reaction mixture composition data for the industrial process based on the target property and at least one environmental parameter. The at least one machine learning algorithm may be continuously or periodically retrained by expanding an underlying data set to include measurements obtained from production instances of the industrial process. All of the foregoing improvements result in an industrial process which creates a product having desirable finished characteristics with greater accuracy, improved reliably, and less component waste.
Referring now to
The one or more industrial devices 104 may include one or more modules configured to perform various operations of the industrial process. In non-limiting embodiments, the one or more modules of the one or more industrial devices 104 may include one or more ingredient addition devices 110, one or more mixing devices 112, one or more conveying devices 114, and/or one or more heating devices 116. The one or more industrial devices may include a computing device such as at least one processor programmed or configured to perform a function by executing software instructions stored on a non-transitory computer-readable medium. For example, the at least one processor may be programmed or configured to implement at least one process parameter for controlling the one or more modules. In some non-limiting embodiments, the process parameters may include, for example, ingredient flow rate and/or ingredient temperature controlled by the one or more ingredient addition devices 110 and/or the one or more heating devices 116. Process parameters may also include conveying speed controlled by the one or more conveying devices 114.
The one or more industrial devices 104 may further include one or more process data sensors 117 for measuring and/or gathering process data prior to, during, and/or after performance of the industrial process. The one or more process data sensors 117 may include one or more barometers, thermometers, hydrometers, psychrometers, and/or the like. In non-limiting embodiments, the one or more process data sensors 117 may be configured to measure current environmental condition data, such as temperature, relative and absolute humidity, pressure, grains of moisture, and/or the like, in a region in which the one or more industrial devices 104 performs an industrial process. In non-limiting embodiments, the one or more process data sensors 117 may be configured to gather process parameter data during performance of the industrial process, such as ingredient flow rate, ingredient temperature, conveying speed, and/or the like.
The process data measured and/or gathered by the one or more process data sensors 117 may be communicated to the server computer 108 via the network 102. The process data may be communicated in real-time, at predefined intervals, in batches, and/or in any other like manner. In some examples, the process data communicated to the server computer 108 may include raw sensor data. In other examples, the process data communicated to the server computer 108 may be generated from processed sensor data. The process data may also include a combination of raw and processed sensor data. The server computer 108, in response to receiving process data during performance of the industrial process, may store the process data in a historic process data database 118. The historic process data database 118 may be a secure, read-only database that prevents users from modifying the process data after it has been stored. An example of a table of process data stored in the historic process data database 118 is shown in
With continued reference to
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More particular non-limiting embodiments of the method for optimizing the industrial process will now be described with reference to
Referring now to
In some non-limiting embodiments, step 202 may be preceded by step 204, in which current environmental condition data is determined for the specified day in a region in which at least one type of industrial process is being performed. For example, the current environmental condition data may be determined by receiving and/or aggregating measurement data from one or more process data sensors 117. In other embodiments, the current environmental condition data for the specified day may be acquired from the third party database 124. As noted above, the current environmental condition data for the specified day may include, for example, temperature, relative and/or absolute humidity, barometric pressure, grains of moisture, and/or the like. Determination of the current environmental condition data may be performed by at least one processor of the client device 120 or by at least one processor of the server computer 108.
With continued reference to
In some non-limiting embodiments, the plurality of visual states may include a range of states indicating the differential between the current environmental condition data and the historic environment condition data for each of the plurality of days preceding the specified day. For example, the plurality of visual states may include a plurality of colors, with a first color indicating a differential within a first range (e.g. within 20% of the current environmental condition data), a second color indicating a differential within a second range (e.g. within 40% of the current environmental condition data), a third color indicating a differential with a third range (e.g. within 60% of the current environmental condition data), and so on. The visual state for each of the plurality of days preceding the specified day may thus assist the user of the client device 120, at least one processor of the client device 120, and/or at least one processor of the server computer 108 in identifying the relative differential between the current environmental condition data for the specified day and the historic environment condition data of each of the plurality of days preceding the specified day. For example, if the visual state of a first day of the plurality of days preceding the specified day includes the first color, the historic environment condition data of the first day may have a lesser differential to the current environmental condition data of the specified day than a second day of the plurality of days preceding the specified day which has a visual state including the second color.
It is to be understood that, although colors are specifically discussed herein as examples of visual states, the visual states may also be represented as symbols, tokens, typeface or font attributes, shading, highlighting, cross-hatching, and/or the like.
With continued reference to
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The GUI 6000a may further include one or more graphical representations 650, 660 of predetermined target product properties overlaid with the historical product property data. In the non-limited example shown in
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In some non-limiting embodiments, at least one processor of the client device 120 and/or at least one processor of the server computer 108 may interpolate or extrapolate from the process data of the at least one day selected at step 210 to modify the at least one process parameter based on a differential between the current environmental condition data and the historic environment condition data associated with the at least one day selected at step 210. For example, if the current environmental condition data includes a different value for grains of moisture than the grains of moisture of the selected at least one day, at least one processor of the client device 120 and/or at least one processor of the server computer 108 may modify the at least one process parameter to deviate from the process data associated with the selected day in order to account for the difference in grains of moisture. In some non-limiting embodiments, modification of at least one process parameter may be based on at least one machine learning algorithm trained from a data set including process data associated with past performances of the process. The data set may be updated, and the machine learning model re-trained, with process data from additional performances of the process to improve predictive accuracy. The data set may be updated on a periodic basis, e.g. daily or weekly, with process data from performances having occurred since the last update.
In some non-limiting embodiments, at least one processor of the client device 120 and/or at least one processor of the server computer 108 may modify the at least one process parameter in a manner which deviates from the process data associated with the selected day in order to change a product property of the product produced from the industrial process. For example, the day selected at step 210 may be Jan. 7, 2019 which produced a product having a 25% IFD hardness of 26.91 lb/50 in{circumflex over ( )}2 (as shown in
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At step 304, the method 3000 includes determining a plurality of days preceding the specified day for which process data associated with the specified type of industrial process is accessible. The process data for the plurality of days may be stored in the historic process data database 118. At least one processor of the client device 120 and/or at least one processor of the server computer 108 may parse the historic process data database 118 to determine what of the process data stored in the historic process data database 118 is associated with the specified type of industrial process. For example, the specified type of industrial process from step 302 may include a “GradeA” recipe. The historic process data database 118 is then parsed to find process data for days associated with a “GradeA” recipe.
Referring again to
In some non-limiting embodiments, step 308 may be preceded by step 310, in which current environmental condition data is determined for the specified day in a region in which the specified type of industrial process is being performed. Step 310 may be performed substantially as described herein in connection with step 204 of the method 2000.
At step 312, the method 3000 includes determining a visual state from a plurality of visual states for each day of the plurality of days based on the comparison between the current environmental condition data and the historic environment condition data for each day. In non-limiting embodiments, step 312 may be performed substantially as described herein in connection with step 206 of the method 2000.
At step 314, the method 3000 includes generating a calendar interface including a plurality of visual representations. Each visual representation corresponds to a day of the plurality of days determined at step 304, and each visual representation includes the visual state determined for the corresponding day. In non-limiting embodiments, step 314 may be performed substantially as described herein in connection with step 208 of the method 2000.
In some non-limiting embodiments, the method 3000 may further include, at step 316, generating a GUI including process data for at least one day corresponding to at least one visual representation of the calendar interface selected by the user. The process data included in the GUI may include historical data for the specified type of industrial process. In non-limiting embodiments, step 316 may be performed substantially as described herein in connection with step 210 of the method 2000.
In some non-limiting embodiments, the method 3000 may further include, at step 318, modifying at least one process parameter for the specified type of industrial process based on the process data for the at least one day corresponding to the visual representation selected by the user at step 316. In non-limiting embodiments, step 318 may be performed substantially as described herein in connection with as step 212 of the method 2000.
In some non-limiting embodiments, the method 3000 may further include, at step 320, performing the specified type of industrial process as modified at step 318 to produce a product. In non-limiting embodiments, step 320 may be performed substantially as described herein in connection with step 214 of the method 2000.
In some non-limiting embodiments, the method 3000 may further include, at step 322, obtaining at least one measured product property of the product produced by the industrial process at step 320. In non-limiting embodiments, step 322 may be performed substantially as described herein in connection with step 216 of the method 2000.
In some non-limiting embodiments, the method 3000 may further include, at step 324, training and/or retraining at least one machine learning model based on the measured product property obtained at step 322. In non-limiting embodiments, step 324 may be performed substantially as described herein in connection with step 218 of the method 2000.
Referring now to
With continued reference to
In some non-limiting embodiments, at least one processor of the client device 120 and/or at least one processor of the server computer 108 may interpolate or extrapolate from the process data retrieved at step 414 to configure the at least one process parameter based on a differential between the current environmental condition data and the historic environment condition data associated with the at least one day selected at step 412. For example, if the current environmental condition data includes a different grains of moisture than the grains of moisture of the selected at least one day, at least one processor of the client device 120 and/or at least one processor of the server computer 108 may configure the process parameters to deviate from the process data associated with the selected day in order to account for the difference in grains of moisture. At least one processor of the client device 120 and/or at least one processor of the server computer 108 may implement machine learning, using data from previously-performed iterations of one or more industrial processes in order to interpolate or extrapolate from the process data retrieved at step 414.
In some non-limiting embodiments, the parameters may be configured to deviate from the process data associated with the selected day, in order to change a product property of the product produced from the specified type of industrial process. For example, the day selected at step 412 may be Jan. 7, 2019 which produced a product having a 25% IFD hardness of 26.91 lb/50 in{circumflex over ( )}2 (as shown in
With continued reference to
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In some non-limiting embodiments, the method 4000 may further include, at step 424, obtaining at least one measured product property of the product produced by the industrial process at step 422. In non-limiting embodiments, step 424 may be performed substantially as described herein in connection with step 216 of the method 2000.
In some non-limiting embodiments, the method 4000 may further include, at step 426, training and/or retraining at least one machine learning model based on the measured product property obtained at step 424. In non-limiting embodiments, step 426 may be performed substantially as described herein in connection with step 218 of the method 2000.
As discussed herein, the industrial process in some non-limiting embodiments may be a method of producing a chemical product from a reaction mixture containing two or more ingredients. Referring now to
With continued reference to
With continued reference to
In some non-limiting embodiments, step 806 may include modifying a predetermined mixture composition by adjusting at least at least one of the composition of the reaction mixture and/or process conditions for the reaction mixture. The predetermined mixture composition may be, for example, a reaction mixture including nominal quantities of ingredients standardized for particular environmental conditions. The composition of the reaction mixture may include, for example, a molar ratio of isocyanate groups to isocyanate-reactive groups, an amount of blowing agent, an amount of physical blowing agent relative to an amount of chemical blowing agent, and/or combinations thereof. Process conditions for the reaction mixture may include, for example, ingredient flow rate, ingredient temperature, conveying speed, and/or combinations thereof. Modifying the predetermined reaction mixture at step 816 may be performed by at least one processor of the client device 120 or by at least one processor of the server computer 108.
With continued reference to
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Non-limiting embodiments of the method 8000 may further include, at step 818, determining whether to update the predicted reaction mixture data based on the updated measure environmental parameter received at step 816. The determination at step 818 may be performed by at least one processor of the client device 120 or by at least one processor of the server computer 108. The determination at step 818 may be based on comparing the updated measured environmental parameter received at step 818 to the measured process parameter received at step 806. If it is determined at step 818 to not update the predicted reaction mixture data, production of the chemical product at step 808 may proceed with the prediction reaction mixture determined at step 806.
Alternatively, if it is determined at step 818 to update the predicted reaction mixture data, the method 8000 may further include, at step 820, adjusting at least one of the composition of the reaction mixture and/or process conditions for the reaction mixture. In non-limiting embodiments, adjusting at least one of the composition of the reaction mixture and/or process conditions for the reaction mixture may be performed in response to a determination that the updated measured environmental parameter received at step 816 to the measured environmental parameter received at step 806 are different. After at least one adjustment of the composition of the reaction mixture and/or process conditions for the reaction mixture at step 820, step 808 may resume to produce the product.
Referring now to
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With continued reference to
Device 900 may perform one or more processes described herein. Device 900 may perform these processes based on processor 904 executing software instructions stored by a computer-readable medium, such as memory 906 and/or storage component 908. A computer-readable medium may include any non-transitory memory device. A memory device includes memory space located inside of a single physical storage device or memory space spread across multiple physical storage devices. Software instructions may be read into memory 906 and/or storage component 908 from another computer-readable medium or from another device via communication interface 914. When executed, software instructions stored in memory 906 and/or storage component 908 may cause processor 904 to perform one or more processes described herein. Additionally, or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, embodiments described herein are not limited to any specific combination of hardware circuitry and software. The term “programmed or configured,” as used herein, refers to an arrangement of software, hardware circuitry, or any combination thereof on one or more devices.
Although the invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2020/044825 | 8/4/2020 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 62882638 | Aug 2019 | US |
