Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of .

Journal: Sensors (Basel, Switzerland)
Published Date: Nov 17, 2021

Abstract

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

Authors

  • Bo Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Xingyu Wang
    1 Key Laboratory for Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai, P. R. China.
  • Mengyi He
    Key Laboratory of Agricultural Measurement and Control Technology and Equipment for Mechanical Industrial Facilities, School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
  • Xianglin Zhu
    a School of Electrical and Information Engineering, JiangSu University , Zhenjiang , China.

Keywords

External Resources

Popular Topics
Recent Journals