Hybrid Chemical and Data-Driven Model for Stiff Chemical Kinetics Using a Physics-Informed Neural Network
Models of chemical kinetic processes, comprising systems of stiff ordinary differential equations (ODEs), are essential for modeling important chemical reactions relevant to drinking water chemistry, such as disinfectant decay and disinfection byproduct formation. However, the accuracy of these mode...
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MDPI AG
2024-09-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/69/1/40 |
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| author | Matthew Frankel Mario De Florio Enrico Schiassi Lina Sela |
| author_facet | Matthew Frankel Mario De Florio Enrico Schiassi Lina Sela |
| author_sort | Matthew Frankel |
| collection | DOAJ |
| description | Models of chemical kinetic processes, comprising systems of stiff ordinary differential equations (ODEs), are essential for modeling important chemical reactions relevant to drinking water chemistry, such as disinfectant decay and disinfection byproduct formation. However, the accuracy of these models can be inhibited by (1) the challenge of fully describing the chemical reaction system, and (2) additional chemical reactions occurring in actual environmental settings that were not accounted for in the laboratory conditions used to develop and calibrate the models. This study proposes the use of a Physics-Informed Neural Network framework, utilizing the eXtreme Theory of Functional Connections (X-TFC) technique to create a hybrid chemical- and data-driven model that incorporates data and the underlying system of ODEs into the trained model in order to increase the accuracy of the predicted chemical concentrations. |
| format | Article |
| id | doaj-art-fd5edadfd7cb4be18ba8b36f7a069ff7 |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-fd5edadfd7cb4be18ba8b36f7a069ff72025-08-20T03:43:15ZengMDPI AGEngineering Proceedings2673-45912024-09-016914010.3390/engproc2024069040Hybrid Chemical and Data-Driven Model for Stiff Chemical Kinetics Using a Physics-Informed Neural NetworkMatthew Frankel0Mario De Florio1Enrico Schiassi2Lina Sela3Fariborz Maseeh Department of Civil Architectural and Environmental Engineering, The University of Texas at Austin, Austin, TX 78712, USADivision of Applied Mathematics, Brown University, Providence, RI 02906, USADepartment of Industrial Engineering, University of Bologna, 40126 Bologna, ItalyFariborz Maseeh Department of Civil Architectural and Environmental Engineering, The University of Texas at Austin, Austin, TX 78712, USAModels of chemical kinetic processes, comprising systems of stiff ordinary differential equations (ODEs), are essential for modeling important chemical reactions relevant to drinking water chemistry, such as disinfectant decay and disinfection byproduct formation. However, the accuracy of these models can be inhibited by (1) the challenge of fully describing the chemical reaction system, and (2) additional chemical reactions occurring in actual environmental settings that were not accounted for in the laboratory conditions used to develop and calibrate the models. This study proposes the use of a Physics-Informed Neural Network framework, utilizing the eXtreme Theory of Functional Connections (X-TFC) technique to create a hybrid chemical- and data-driven model that incorporates data and the underlying system of ODEs into the trained model in order to increase the accuracy of the predicted chemical concentrations.https://www.mdpi.com/2673-4591/69/1/40water quality modelingphysics-informed neural networkstiff differential equations |
| spellingShingle | Matthew Frankel Mario De Florio Enrico Schiassi Lina Sela Hybrid Chemical and Data-Driven Model for Stiff Chemical Kinetics Using a Physics-Informed Neural Network Engineering Proceedings water quality modeling physics-informed neural network stiff differential equations |
| title | Hybrid Chemical and Data-Driven Model for Stiff Chemical Kinetics Using a Physics-Informed Neural Network |
| title_full | Hybrid Chemical and Data-Driven Model for Stiff Chemical Kinetics Using a Physics-Informed Neural Network |
| title_fullStr | Hybrid Chemical and Data-Driven Model for Stiff Chemical Kinetics Using a Physics-Informed Neural Network |
| title_full_unstemmed | Hybrid Chemical and Data-Driven Model for Stiff Chemical Kinetics Using a Physics-Informed Neural Network |
| title_short | Hybrid Chemical and Data-Driven Model for Stiff Chemical Kinetics Using a Physics-Informed Neural Network |
| title_sort | hybrid chemical and data driven model for stiff chemical kinetics using a physics informed neural network |
| topic | water quality modeling physics-informed neural network stiff differential equations |
| url | https://www.mdpi.com/2673-4591/69/1/40 |
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