Soft Sensor Modeling Method Based on SPA-GWO-SVR for Marine Protease Fermentation Process
The marine protease fermentation process is a highly nonlinear, time-varying, multivariable, and strongly coupled complex biochemical reaction process. Due to the growth and reproduction of living organisms, the internal mechanism is very complicated. Some key variables (such as cell concentration,...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6653503 |
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| author | Zhu Li Khalil Ur Rehman Liu Wenhui Faiza Atique |
| author_facet | Zhu Li Khalil Ur Rehman Liu Wenhui Faiza Atique |
| author_sort | Zhu Li |
| collection | DOAJ |
| description | The marine protease fermentation process is a highly nonlinear, time-varying, multivariable, and strongly coupled complex biochemical reaction process. Due to the growth and reproduction of living organisms, the internal mechanism is very complicated. Some key variables (such as cell concentration, substrate concentration, and enzyme activity) that directly reflect the fermentation process's quality are difficult to measure in real-time by traditional measurement methods. A soft sensor model based on a support vector regression (SVR) is proposed in this paper to resolve this problem. To further improve the model's prediction accuracy, the grey wolf optimization (GWO) algorithm is used to optimize the critical parameters (kernel function width σ, penalty factor c, and insensitivity coefficient ε) of the SVR model. To study the influence of selecting auxiliary variables on soft sensor modeling, the successive projection algorithm (SPA) is used to determine the characteristic variables and compare them with grey relation analysis (GRA) algorithm. Finally, the Excel spreadsheet data was called by MATLAB programming, and the established SPA-GWO-SVR soft sensor model predicted crucial biological variables. The simulation results show that the SPA-GWO-SVR model has higher prediction accuracy and generalization ability than the traditional SPA-SVR model. The real-time monitoring was processed by MATLAB software for the marine protease fermentation process, which met the requirements of optimal control of the marine protease fermentation process. |
| format | Article |
| id | doaj-art-643b4de5fd2d44b48d5428569031e71a |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-643b4de5fd2d44b48d5428569031e71a2025-02-03T01:00:16ZengWileyJournal of Control Science and Engineering1687-52491687-52572021-01-01202110.1155/2021/66535036653503Soft Sensor Modeling Method Based on SPA-GWO-SVR for Marine Protease Fermentation ProcessZhu Li0Khalil Ur Rehman1Liu Wenhui2Faiza Atique3School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, ChinaDepartment of Physiology, GC University Faisalabad (GCUF), Faisalabad, PakistanThe marine protease fermentation process is a highly nonlinear, time-varying, multivariable, and strongly coupled complex biochemical reaction process. Due to the growth and reproduction of living organisms, the internal mechanism is very complicated. Some key variables (such as cell concentration, substrate concentration, and enzyme activity) that directly reflect the fermentation process's quality are difficult to measure in real-time by traditional measurement methods. A soft sensor model based on a support vector regression (SVR) is proposed in this paper to resolve this problem. To further improve the model's prediction accuracy, the grey wolf optimization (GWO) algorithm is used to optimize the critical parameters (kernel function width σ, penalty factor c, and insensitivity coefficient ε) of the SVR model. To study the influence of selecting auxiliary variables on soft sensor modeling, the successive projection algorithm (SPA) is used to determine the characteristic variables and compare them with grey relation analysis (GRA) algorithm. Finally, the Excel spreadsheet data was called by MATLAB programming, and the established SPA-GWO-SVR soft sensor model predicted crucial biological variables. The simulation results show that the SPA-GWO-SVR model has higher prediction accuracy and generalization ability than the traditional SPA-SVR model. The real-time monitoring was processed by MATLAB software for the marine protease fermentation process, which met the requirements of optimal control of the marine protease fermentation process.http://dx.doi.org/10.1155/2021/6653503 |
| spellingShingle | Zhu Li Khalil Ur Rehman Liu Wenhui Faiza Atique Soft Sensor Modeling Method Based on SPA-GWO-SVR for Marine Protease Fermentation Process Journal of Control Science and Engineering |
| title | Soft Sensor Modeling Method Based on SPA-GWO-SVR for Marine Protease Fermentation Process |
| title_full | Soft Sensor Modeling Method Based on SPA-GWO-SVR for Marine Protease Fermentation Process |
| title_fullStr | Soft Sensor Modeling Method Based on SPA-GWO-SVR for Marine Protease Fermentation Process |
| title_full_unstemmed | Soft Sensor Modeling Method Based on SPA-GWO-SVR for Marine Protease Fermentation Process |
| title_short | Soft Sensor Modeling Method Based on SPA-GWO-SVR for Marine Protease Fermentation Process |
| title_sort | soft sensor modeling method based on spa gwo svr for marine protease fermentation process |
| url | http://dx.doi.org/10.1155/2021/6653503 |
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