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

Na Lu¹, Bo Wang¹, Xianglin Zhu¹

¹Key Laboratory of Agricultural Measurement and Control Technology and Equipment for Mechanical Industrial Facilities, School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.

Sensors (Basel, Switzerland)

|November 25, 2023

Summary

This study introduces an improved soft sensor model for marine lysozyme fermentation, enhancing prediction accuracy for key biochemical parameters using ensemble learning and Gaussian process regression (GPR). The model effectively handles nonlinear systems with limited data.

Keywords:

Gaussian process regression grayscale correlation analysis marine lysozyme seagull optimization algorithm soft sensor

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Area of Science:

Biochemical Engineering
Process Control
Machine Learning

Background:

Marine lysozyme fermentation exhibits complex nonlinear, multi-stage, and time-varying dynamics.
Traditional single soft sensor models struggle to capture these dynamic characteristics effectively.

Purpose of the Study:

To develop a novel weighted ensemble learning soft sensor modeling method for marine lysozyme fermentation.
To improve the prediction accuracy of key biochemical parameters in fermentation processes.

Main Methods:

Utilized an improved density peak clustering algorithm (ADPC) for data subset division.
Employed an improved seagull optimization algorithm (ISOA) to optimize Gaussian process regression (GPR) models, creating sub-prediction models.
Developed a fusion strategy based on sample connectivity for model integration.

Main Results:

The proposed soft sensor model accurately predicted key biochemical parameters in marine lysozyme fermentation.
Demonstrated effective performance even with limited training data, showing relatively small prediction errors.
Validated the model's capability in handling nonlinear systems.

Conclusions:

The weighted ensemble learning soft sensor model offers a robust solution for predicting fermentation parameters.
The method shows potential for broader application in soft sensor prediction for general nonlinear systems.
This approach enhances process monitoring and control in biochemical engineering.