Soft Sensor Modeling Method for the Marine Lysozyme Fermentation Process Based on ISOA-GPR Weighted Ensemble Learning

Abstract

Due to the highly nonlinear, multi-stage, and time-varying characteristics of the marine lysozyme fermentation process, the global soft sensor models established using traditional single modeling methods cannot describe the dynamic characteristics of the entire fermentation process. Therefore, this study proposes a weighted ensemble learning soft sensor modeling method based on an improved seagull optimization algorithm (ISOA) and Gaussian process regression (GPR). First, an improved density peak clustering algorithm (ADPC) was used to divide the sample dataset into multiple local sample subsets. Second, an improved seagull optimization algorithm was used to optimize and transform the Gaussian process regression model, and a sub-prediction model was established. Finally, the fusion strategy was determined according to the connectivity between the test samples and local sample subsets. The proposed soft sensor model was applied to the prediction of key biochemical parameters of the marine lysozyme fermentation process. The simulation results show that the proposed soft sensor model can effectively predict the key biochemical parameters with relatively small prediction errors in the case of limited training data. According to the results, this model can be expanded to the soft sensor prediction applications in general nonlinear systems.

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

“…It has been widely utilized globally and extensively researched by scholars, yielding fruitful results. For instance, Lu et al [5] proposed a weighted ensemble learning soft sensor modeling method based on an improved seagull optimization algorithm and Gaussian process regression to address the issue of traditional single methods failing to describe the nonlinear characteristics of the entire fermentation process. Applying this soft sensor method to predict key biochemical parameters in the fermentation process of marine lysozyme, the results indicate relatively small errors.…”

A Soft Sensor for Flow Estimation and Uncertainty Analysis Based on Artificial Intelligence: A Case Study of Water Supply Systems

“…The application of soft sensors is possible in several fields. In biology and environmental studies, an example is the work of Lu et al [9], in which an intelligent algorithm named the improved seagull optimization algorithm (ISOA) and the Gaussian process regression (GPR) were chosen as the central element of a soft sensor able to estimate some key biochemical parameters in marine lysozyme fermentation. ISOA-GPR weighted ensemble learning was shown to perform better (in terms of root mean square and maximum absolute errors) than ISOA-GPR single global predicted value.…”