Nonlinear Soft Sensor Modeling Method Based on Multimode Kernel Partial Least Squares Assisted by Improved KFCM Clustering

2019 ◽  
Author(s):  
Yongxuan Chen ◽  
Xiaogang Deng ◽  
Yuping Cao
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Soft Sensor ◽  
Modeling Method ◽  
Kernel Partial Least Squares ◽  
Sensor Modeling

scholarly journals Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7635
Author(s):  
Bo Wang ◽  
Xingyu Wang ◽  
Mengyi He ◽  
Xianglin Zhu
Keyword(s):  
Pichia Pastoris ◽  
Least Squares ◽  
Fermentation Process ◽  
Model Simulation ◽  
Weighted Least Squares ◽  
Soft Sensor ◽  
Modeling Method ◽  
Local Model ◽  
Support Vector ◽  
Sensor Modeling

The problems that the key biomass variables in Pichia pastoris 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 Pichia pastoris fermentation, and the model prediction accuracy is improved by 4.4884% compared with the weighted least squares vector regression model optimized by PSO.

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