Hyperparameter Optimization of the Machine Learning Model for Distillation Processes
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| Publicado en: | International Journal of Intelligent Systems vol. 2024 (2024) |
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| Autor Principal: | |
| Outros autores: | , , , , |
| Publicado: |
John Wiley & Sons, Inc.
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| Acceso en liña: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3071322032 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0884-8173 | ||
| 022 | |a 1098-111X | ||
| 024 | 7 | |a 10.1155/2024/5564380 |2 doi | |
| 035 | |a 3071322032 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 163929 |2 nlm | ||
| 100 | 1 | |a Oh, Kwang Cheol |u Agriculture and Life Science Research Institute, Kangwon National University, Chuncheon 24341, Republic of Korea | |
| 245 | 1 | |a Hyperparameter Optimization of the Machine Learning Model for Distillation Processes | |
| 260 | |b John Wiley & Sons, Inc. |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study was conducted to enhance the efficiency of chemical process systems and address the limitations of conventional methods through hyperparameter optimization. Chemical processes are inherently continuous and nonlinear, making stable operation challenging. The efficiency of processes often varies significantly with the operator’s level of expertise, as most tasks rely on experience. To move beyond the constraints of traditional simulation approaches, a new machine learning-based simulation model was developed. This model utilizes a recurrent neural network (RNN) algorithm, which is ideal for analyzing time-series data from chemical process systems, presenting new possibilities for applications in systems with special chemical reactions or those that are continuous and complex. Hyperparameters were optimized using a grid search method, and optimal results were confirmed when the model was applied to an actual distillation process system. By proposing a methodology that utilizes machine learning for the optimization of chemical process systems, this research contributes to solving new problems that were previously unaddressed. Based on these results, the study demonstrates that a machine learning simulation model can be effectively applied to continuous chemical process systems. This application enables the derivation of unique hyperparameters tailored to the specificities of a limited control volume system. | |
| 653 | |a Big Data | ||
| 653 | |a Machine learning | ||
| 653 | |a Simulation | ||
| 653 | |a Chemical reactions | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Simulation models | ||
| 653 | |a Data mining | ||
| 653 | |a Optimization | ||
| 653 | |a Neural networks | ||
| 653 | |a Process controls | ||
| 653 | |a Recurrent neural networks | ||
| 653 | |a Variables | ||
| 653 | |a Distillation | ||
| 653 | |a Feature selection | ||
| 653 | |a Algorithms | ||
| 653 | |a Data collection | ||
| 653 | |a Methods | ||
| 653 | |a System effectiveness | ||
| 653 | |a Data compression | ||
| 653 | |a Computer simulation | ||
| 700 | 1 | |a Kwon, Hyukwon |u Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea; Green Materials & Processes Group, Korea Institute of Industrial Technology, 55 Jongga-ro, Jung-gu, Ulsan 44413, Republic of Korea | |
| 700 | 1 | |a Sun Yong Park |u Department of Biosystems Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea | |
| 700 | 1 | |a Kim, Seok Jun |u Department of Biosystems Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea | |
| 700 | 1 | |a Kim, Junghwan |u Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea | |
| 700 | 1 | |a Kim, DaeHyun |u Department of Biosystems Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea | |
| 773 | 0 | |t International Journal of Intelligent Systems |g vol. 2024 (2024) | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3071322032/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3071322032/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3071322032/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |