Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of <i>Pichia pastoris</i>

The problems that the key biomass variables in <i>Pichia pastoris</i> fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm op...

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Autores principales:	Bo Wang, Xingyu Wang, Mengyi He, Xianglin Zhu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	soft sensor piecewise affine improved compression factor particle swarm optimization <i>Pichia pastoris</i> Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/ca87d998ae2b47c8aeae2b7432423ff1
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id	oai:doaj.org-article:ca87d998ae2b47c8aeae2b7432423ff1
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spelling	oai:doaj.org-article:ca87d998ae2b47c8aeae2b7432423ff12021-11-25T18:58:10ZStudy on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of <i>Pichia pastoris</i>10.3390/s212276351424-8220https://doaj.org/article/ca87d998ae2b47c8aeae2b7432423ff12021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7635https://doaj.org/toc/1424-8220The problems that the key biomass variables in <i>Pichia pastoris</i> fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm optimization (PSO) with an improved compression factor (ICF). Firstly, the false nearest neighbor method was used to determine the order of the PWA model. Secondly, the ICF-PSO algorithm was proposed to cooperatively optimize the number of PWA models and the parameters of each local model. Finally, a least squares support vector machine was adopted to determine the scope of action of each local model. Simulation results show that the proposed ICF-PSO-PWA multi-model soft sensor modeling method accurately approximated the nonlinear features of <i>Pichia pastoris</i> fermentation, and the model prediction accuracy is improved by 4.4884% compared with the weighted least squares vector regression model optimized by PSO.Bo WangXingyu WangMengyi HeXianglin ZhuMDPI AGarticlesoft sensorpiecewise affineimproved compression factorparticle swarm optimization<i>Pichia pastoris</i>Chemical technologyTP1-1185ENSensors, Vol 21, Iss 7635, p 7635 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	soft sensor piecewise affine improved compression factor particle swarm optimization <i>Pichia pastoris</i> Chemical technology TP1-1185
spellingShingle	soft sensor piecewise affine improved compression factor particle swarm optimization <i>Pichia pastoris</i> Chemical technology TP1-1185 Bo Wang Xingyu Wang Mengyi He Xianglin Zhu Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of <i>Pichia pastoris</i>
description	The problems that the key biomass variables in <i>Pichia pastoris</i> fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm optimization (PSO) with an improved compression factor (ICF). Firstly, the false nearest neighbor method was used to determine the order of the PWA model. Secondly, the ICF-PSO algorithm was proposed to cooperatively optimize the number of PWA models and the parameters of each local model. Finally, a least squares support vector machine was adopted to determine the scope of action of each local model. Simulation results show that the proposed ICF-PSO-PWA multi-model soft sensor modeling method accurately approximated the nonlinear features of <i>Pichia pastoris</i> fermentation, and the model prediction accuracy is improved by 4.4884% compared with the weighted least squares vector regression model optimized by PSO.
format	article
author	Bo Wang Xingyu Wang Mengyi He Xianglin Zhu
author_facet	Bo Wang Xingyu Wang Mengyi He Xianglin Zhu
author_sort	Bo Wang
title	Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of <i>Pichia pastoris</i>
title_short	Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of <i>Pichia pastoris</i>
title_full	Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of <i>Pichia pastoris</i>
title_fullStr	Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of <i>Pichia pastoris</i>
title_full_unstemmed	Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of <i>Pichia pastoris</i>
title_sort	study on multi-model soft sensor modeling method and its model optimization for the fermentation process of <i>pichia pastoris</i>
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ca87d998ae2b47c8aeae2b7432423ff1
work_keys_str_mv	AT bowang studyonmultimodelsoftsensormodelingmethodanditsmodeloptimizationforthefermentationprocessofipichiapastorisi AT xingyuwang studyonmultimodelsoftsensormodelingmethodanditsmodeloptimizationforthefermentationprocessofipichiapastorisi AT mengyihe studyonmultimodelsoftsensormodelingmethodanditsmodeloptimizationforthefermentationprocessofipichiapastorisi AT xianglinzhu studyonmultimodelsoftsensormodelingmethodanditsmodeloptimizationforthefermentationprocessofipichiapastorisi
_version_	1718410489870942208

Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of <i>Pichia pastoris</i>

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