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Soft-sensor modeling for L-lysine fermentation process based on hybrid ICS-MLSSVM.

Bo Wang¹, Muhammad Shahzad², Xianglin Zhu¹

¹School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China.

Scientific Reports

|July 17, 2020

Summary

A new hybrid Improved Cuckoo Search-Multi-output Least Squares Support Vector Machine (ICS-MLSSVM) soft-sensor accurately predicts L-lysine fermentation quality. This method enables real-time online detection of critical biochemical variables, improving process control.

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Application of MassSQUIRM for Quantitative Measurements of Lysine Demethylase Activity

Application of MassSQUIRM for Quantitative Measurements of Lysine Demethylase Activity

Published on: March 11, 2012

Area of Science:

Biochemical Engineering
Machine Learning
Process Control

Background:

L-lysine fermentation is a complex, nonlinear dynamic process.
Real-time online measurement of key variables is challenging.
This limits advanced control strategies in biochemical processes.

Purpose of the Study:

To develop a hybrid soft-sensor for online detection of key L-lysine fermentation variables.
To improve the accuracy and adaptability of biochemical process monitoring.
To enable advanced control applications in fermentation.

Main Methods:

Constructed a Multi-output Least Squares Support Vector Machine (MLSSVM) regressor model.
Optimized MLSSVM parameters using the Improved Cuckoo Search (ICS) algorithm.
Developed a hybrid ICS-MLSSVM soft-sensor for online variable prediction.

Main Results:

The ICS-MLSSVM model accurately predicted key biochemical variables (cell, substrate, product concentration).
Achieved superior prediction accuracy and adaptability compared to standard CS, PSO, and GA-MLSSVM models.
Demonstrated the effectiveness of the hybrid soft-sensor for online monitoring.

Conclusions:

The proposed ICS-MLSSVM soft-sensor effectively enables online detection of critical variables in L-lysine fermentation.
This approach enhances process monitoring and control capabilities.
The hybrid model offers significant improvements over existing methods for biochemical process analysis.