Development and Optimization of a Novel Soft Sensor Modeling Method for Fermentation Process of Pichia pastoris

Abstract

This paper introduces a novel soft sensor modeling method based on BDA-IPSO-LSSVM designed to address the issue of model failure caused by varying fermentation data distributions resulting from different operating conditions during the fermentation of different batches of Pichia pastoris. First, the problem of significant differences in data distribution among different batches of the fermentation process is addressed by adopting the balanced distribution adaptation (BDA) method from transfer learning. This method reduces the data distribution differences among batches of the fermentation process, while the fuzzy set concept is employed to improve the BDA method by transforming the classification problem into a regression prediction problem for the fermentation process. Second, the soft sensor model for the fermentation process is developed using the least squares support vector machine (LSSVM). The model parameters are optimized by an improved particle swarm optimization (IPSO) algorithm based on individual differences. Finally, the data obtained from the Pichia pastoris fermentation experiment are used for simulation, and the developed soft sensor model is applied to predict the cell concentration and product concentration during the fermentation process of Pichia pastoris. Simulation results demonstrate that the IPSO algorithm has good convergence performance and optimization performance compared with other algorithms. The improved BDA algorithm can make the soft sensor model adapt to different operating conditions, and the proposed soft sensor method outperforms existing methods, exhibiting higher prediction accuracy and the ability to accurately predict the fermentation process of Pichia pastoris under different operating conditions.

Modeling and Optimization of an Enhanced Soft Sensor for the Fermentation Process of Pichia pastoris

“…However, as industrial demands evolve, the working conditions for the fermentation process of Pichia pastoris are subject to change, leading to a noticeable increase in the predictive error of conventional models. Consequently, researchers have proposed soft sensor modeling methods based on transfer learning, which have proven effective in mitigating model errors amidst changing conditions [9][10][11]28,29]. Further, this paper hypothesizes that with limited labeled samples available from a single-source working condition, knowledge transfer from just one source is insufficient to effectively enhance predictive performance under target conditions.…”

Intelligent Soft Sensors

“…To further explore the topic of bioprocesses, the fermentation process of Pichia pastoris is examined and the BDA-IPSO-LSSVM soft sensor modelling method is presented [ 8 ]. This model addresses the recurring problem of model errors due to discrepancies in data distribution.…”