Batch-to-Batch Optimization Control of Fed-Batch Fermentation Process Based on Recursively Updated Extreme Learning Machine Models
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| Publicat a: | Algorithms vol. 18, no. 2 (2025), p. 87 |
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| Autor principal: | |
| Altres autors: | |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3170854555 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1999-4893 | ||
| 024 | 7 | |a 10.3390/a18020087 |2 doi | |
| 035 | |a 3170854555 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231333 |2 nlm | ||
| 100 | 1 | |a Moore, Alex | |
| 245 | 1 | |a Batch-to-Batch Optimization Control of Fed-Batch Fermentation Process Based on Recursively Updated Extreme Learning Machine Models | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This paper presents a new method of batch-to-batch optimization control for a fed-batch fermentation process. A recursively updated extreme learning machine (ELM) neural network model is used to model a fed-batch fermentation process. ELM models have advantages over other neural networks in that they can be trained very fast and have good generalization performance. However, the ELM model loses its predictive abilities in the presence of batch-to-batch process variations or disturbances, which lead to a process–model mismatch. The recursive least squares (RLS) technique takes the model prediction error from the previous batch and uses it to update the model parameters for the next batch. This improves the performance of the model and helps it to respond to any changes in process conditions or disturbances. The updated model is used in an optimization control procedure, which generates an improved control profile for the next batch. The update of the RLS model enables the optimization control strategy to maintain a high final product quality in the presence of disturbances. The proposed batch-to-batch optimization control method is demonstrated on a simulated fed-batch fermentation process. | |
| 653 | |a Fermentation | ||
| 653 | |a Neurons | ||
| 653 | |a Performance enhancement | ||
| 653 | |a Disturbances | ||
| 653 | |a Back propagation | ||
| 653 | |a Neural networks | ||
| 653 | |a Oxidation | ||
| 653 | |a Optimization | ||
| 653 | |a Batch processes | ||
| 653 | |a Recursive functions | ||
| 653 | |a Fed batch | ||
| 653 | |a Glucose | ||
| 653 | |a Methods | ||
| 653 | |a Biomass | ||
| 653 | |a Metabolism | ||
| 653 | |a Machine learning | ||
| 653 | |a Control methods | ||
| 653 | |a Ethanol | ||
| 700 | 1 | |a Zhang, Jie | |
| 773 | 0 | |t Algorithms |g vol. 18, no. 2 (2025), p. 87 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3170854555/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3170854555/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3170854555/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |