Research on Soft-Sensing Method Based on Adam-FCNN Inversion in <i>Pichia pastoris</i> Fermentation
To address the challenges in modeling and optimization caused by nonlinear dynamic coupling and real-time measurement difficulties of key biological parameters in <i>Pichia pastoris</i> fermentation processes, this study proposes a soft-sensing method based on Adam-Fully Connected Neural...
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/4105 |
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| author | Bo Wang Wenyu Ma Hui Jiang Shaowen Huang |
| author_facet | Bo Wang Wenyu Ma Hui Jiang Shaowen Huang |
| author_sort | Bo Wang |
| collection | DOAJ |
| description | To address the challenges in modeling and optimization caused by nonlinear dynamic coupling and real-time measurement difficulties of key biological parameters in <i>Pichia pastoris</i> fermentation processes, this study proposes a soft-sensing method based on Adam-Fully Connected Neural Network inverse. Firstly, a non-deterministic mechanism model is constructed to characterize the dynamic coupling relationships among multiple variables in the fermentation process, and the reversibility of the system and the construction method of the inverse extended model are analyzed. Further, by leveraging the nonlinear fitting capabilities of the Fully Connected Neural Network to identify the inverse extended model, an adaptive learning rate optimization algorithm is introduced to dynamically adjust the learning rate of the Fully Connected Neural Network, thereby enhancing the convergence and robustness of the nonlinear system. Finally, a composite pseudo-linear system is formed by cascading the inverse model with the original system, achieving decoupling and the high-accuracy prediction of key parameters. Experimental results demonstrate that the proposed method significantly reduces prediction errors and enhances generalization capabilities compared to traditional models, validating the effectiveness of the proposed method in complex bioprocesses. |
| format | Article |
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| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-4de51d4216da47ff8beffff5468bc81f2025-08-20T02:36:30ZengMDPI AGSensors1424-82202025-06-012513410510.3390/s25134105Research on Soft-Sensing Method Based on Adam-FCNN Inversion in <i>Pichia pastoris</i> FermentationBo Wang0Wenyu Ma1Hui Jiang2Shaowen Huang3School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaTo address the challenges in modeling and optimization caused by nonlinear dynamic coupling and real-time measurement difficulties of key biological parameters in <i>Pichia pastoris</i> fermentation processes, this study proposes a soft-sensing method based on Adam-Fully Connected Neural Network inverse. Firstly, a non-deterministic mechanism model is constructed to characterize the dynamic coupling relationships among multiple variables in the fermentation process, and the reversibility of the system and the construction method of the inverse extended model are analyzed. Further, by leveraging the nonlinear fitting capabilities of the Fully Connected Neural Network to identify the inverse extended model, an adaptive learning rate optimization algorithm is introduced to dynamically adjust the learning rate of the Fully Connected Neural Network, thereby enhancing the convergence and robustness of the nonlinear system. Finally, a composite pseudo-linear system is formed by cascading the inverse model with the original system, achieving decoupling and the high-accuracy prediction of key parameters. Experimental results demonstrate that the proposed method significantly reduces prediction errors and enhances generalization capabilities compared to traditional models, validating the effectiveness of the proposed method in complex bioprocesses.https://www.mdpi.com/1424-8220/25/13/4105Adam optimization<i>Pichia pastoris</i>FCNNfermentationsoft-sensing |
| spellingShingle | Bo Wang Wenyu Ma Hui Jiang Shaowen Huang Research on Soft-Sensing Method Based on Adam-FCNN Inversion in <i>Pichia pastoris</i> Fermentation Sensors Adam optimization <i>Pichia pastoris</i> FCNN fermentation soft-sensing |
| title | Research on Soft-Sensing Method Based on Adam-FCNN Inversion in <i>Pichia pastoris</i> Fermentation |
| title_full | Research on Soft-Sensing Method Based on Adam-FCNN Inversion in <i>Pichia pastoris</i> Fermentation |
| title_fullStr | Research on Soft-Sensing Method Based on Adam-FCNN Inversion in <i>Pichia pastoris</i> Fermentation |
| title_full_unstemmed | Research on Soft-Sensing Method Based on Adam-FCNN Inversion in <i>Pichia pastoris</i> Fermentation |
| title_short | Research on Soft-Sensing Method Based on Adam-FCNN Inversion in <i>Pichia pastoris</i> Fermentation |
| title_sort | research on soft sensing method based on adam fcnn inversion in i pichia pastoris i fermentation |
| topic | Adam optimization <i>Pichia pastoris</i> FCNN fermentation soft-sensing |
| url | https://www.mdpi.com/1424-8220/25/13/4105 |
| work_keys_str_mv | AT bowang researchonsoftsensingmethodbasedonadamfcnninversioninipichiapastorisifermentation AT wenyuma researchonsoftsensingmethodbasedonadamfcnninversioninipichiapastorisifermentation AT huijiang researchonsoftsensingmethodbasedonadamfcnninversioninipichiapastorisifermentation AT shaowenhuang researchonsoftsensingmethodbasedonadamfcnninversioninipichiapastorisifermentation |