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An online soft sensor method for biochemical reaction process based on JS-ISSA-XGBoost.

Ligang Zhang1, Bo Wang2, Yao Shen1

  • 1School of Electrical and Information Engineering, JiangSu University, ZhenJiang, 212013, JiangSu, China.

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|November 8, 2023
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Summary

This study introduces a new soft sensor model combining offline methods and just-in-time learning (JITL) for accurate biochemical process monitoring. The model demonstrates high prediction accuracy for cell and product concentrations in fermentation.

Keywords:
Just-in-time learning strategyOnline soft sensorPichia pastoriseXtreme gradient boosting method

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Area of Science:

  • Biochemical Engineering
  • Process Monitoring
  • Machine Learning

Background:

  • Biochemical reaction processes exhibit dynamic changes in features and parameters over time.
  • Accurate real-time monitoring is crucial for optimizing fermentation processes.
  • Existing methods may struggle with the evolving nature of these systems.

Purpose of the Study:

  • To develop a robust soft sensor model for predicting cell and product concentrations in Pichia pastoris fermentation.
  • To combine offline data processing with online learning for improved predictive accuracy.
  • To address the challenge of time-varying parameters in biochemical reactions.

Main Methods:

  • Offline construction of fermentation sub-databases using an improved fuzzy C-means algorithm with adaptive sample pruning.
  • Online soft sensor model development using an improved eXtreme Gradient Boosting (XGBoost) method.
  • Integration of a multi-similarity-driven just-in-time learning (JITL) strategy and a Stacking integration model for enhanced generalization.

Main Results:

  • The soft sensor model accurately predicted cell concentration with a root mean square error (RMSE) of 0.0260 and R-squared of 0.9945.
  • Product concentration was predicted with an RMSE of 2.6688 and R-squared of 0.9970.
  • The method demonstrated timely prediction and high accuracy, validating its effectiveness.

Conclusions:

  • The proposed JS-ISSA-XGBoost model offers a practical solution for real-time monitoring and control in biochemical reactions.
  • The integration of offline and online learning strategies enhances model adaptability and predictive performance.
  • The model meets the practical needs for monitoring and prediction in complex biochemical processes.