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Soft-sensing method based on FDLS-SVM in marine alkaline protease fermentation process.

Bo Wang1, Meifang Yu1, Xianglin Zhu1

  • 1a School of Electrical and Information Engineering, JiangSu University , Zhenjiang , China.

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Summary
This summary is machine-generated.

This study introduces a novel soft-sensing model combining fuzzy c-means clustering (FCM) and least squares support vector machine (LS-SVM) for adaptive bioprocess monitoring. The method enhances model adaptability and generalization by enabling online updates with new operational data.

Keywords:
Dynamic LS-SVMfuzzy c-means clusteringmarine alkaline protease MP fermentation processsoft-sensing

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Area of Science:

  • Bioprocess Engineering
  • Machine Learning
  • Process Control

Background:

  • Soft-sensing models struggle with adaptability to evolving bioprocess conditions.
  • Online updating mechanisms are crucial for maintaining model accuracy in dynamic environments.

Purpose of the Study:

  • To develop an adaptive soft-sensing modeling method for bioprocesses.
  • To enhance the generalization ability of soft-sensing models in changing operational scenarios.

Main Methods:

  • Combined fuzzy c-means clustering (FCM) with least squares support vector machine (LS-SVM) theory.
  • Utilized FCM for data clustering and LS-SVM for developing sub-models.
  • Implemented dynamic learning through fuzzy membership functions for online model updates.

Main Results:

  • The proposed method effectively predicts key biological parameters in marine alkaline protease production.
  • Simulation results demonstrate increased model adaptive abilities across various operational conditions.
  • The soft-sensing model showed improved generalization capabilities.

Conclusions:

  • The integrated FCM-LS-SVM approach provides an effective solution for updating soft-sensing models in dynamic bioprocesses.
  • This method enhances predictive accuracy and operational robustness.
  • The developed soft-sensing strategy is valuable for real-time bioprocess monitoring and control.