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

Abstract

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.

Optimization of cutting parameters based on surface partitioning

“…Juan [6] proposed a multi-objective particle swarm optimization algorithm to determine the optimal machining parameters We combined the particle swarm optimization technique with the weight aggregation method to solve the multi-objective problem and obtain the Pareto optimal solution. In recent years, domestic research in five-axis CNC cutting parameters optimization has also made some rapid progress [7][8][9]. with respect to the natural basis.…”