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

• Published in: Sensors
• Main Authors: Bo Wang, Ameng Yu, Haibo Wang, Jun Liu
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2024
• Subjects: soft sensor; Pichia pastoris; transfer component analysis; maximal information coefficient; least squares support vector machine; improved northern goshawk optimization; Chemical technology; TP1-1185; Northern Goshawk
• Online Access: https://doi.org/10.3390/s24103017; https://doaj.org/article/23b9c1f37ffe481db3a714215a0119ab

This paper proposes a novel soft sensor modeling approach, MIC-TCA-INGO-LSSVM, to address the decline in performance of soft sensor models during the fermentation process of Pichia pastoris , caused by changes in working conditions. Initially, the transfer component analysis (TCA) method is utilized to minimize the differences in data distribution across various working conditions. Subsequently, a least squares support vector machine (LSSVM) model is constructed using the dataset adapted by TCA, and strategies for improving the northern goshawk optimization (INGO) algorithm are proposed to optimize the parameters of the LSSVM model. Finally, to further enhance the model’s generalization ability and prediction accuracy, considering the transfer of knowledge from multiple-source working conditions, a sub-model weighted ensemble scheme is proposed based on the maximum information coefficient (MIC) algorithm. The proposed soft sensor model is employed to predict cell and product concentrations during the fermentation process of Pichia pastoris . Simulation results indicate that the RMSE of the INGO-LSSVM model in predicting cell and product concentrations is reduced by 47.3% and 42.1%, respectively, compared to the NGO-LSSVM model. Additionally, TCA significantly enhances the model’s adaptability when working conditions change. Moreover, the soft sensor model based on TCA and the MIC-weighted ensemble method achieves a reduction of 41.6% and 31.3% in the RMSE for predicting cell and product concentrations, respectively, compared to the single-source condition transfer model TCA-INGO-LSSVM. These results demonstrate the high reliability and predictive performance of the proposed soft sensor method under varying working conditions.