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Modern Soft-Sensing Modeling Methods for Fermentation Processes.

Xianglin Zhu1, Khalil Ur Rehman1, Bo Wang1

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

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|March 27, 2020
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Summary
This summary is machine-generated.

Accurate real-time fermentation monitoring is challenging. This review details data-driven soft-sensing models and optimization techniques to improve process control and variable estimation in industrial fermentation.

Keywords:
fermentation processmonitoring and controloptimizationsoft sensor

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Area of Science:

  • Biotechnology
  • Chemical Engineering
  • Process Control

Background:

  • Real-time measurement of fermentation variables is crucial for process control but is often difficult due to process complexity.
  • Soft sensors are essential for robust and high-performance monitoring in industrial fermentation.
  • Existing soft-sensing methods face challenges in accuracy and real-time applicability.

Purpose of the Study:

  • To provide a comprehensive review of data-driven soft-sensing modeling techniques for fermentation processes.
  • To analyze various data pre-processing, variable selection, and optimization methods relevant to soft sensor development.
  • To serve as a reference for researchers seeking to advance soft-sensing technologies in fermentation.

Main Methods:

  • Review of data-driven (black-box) soft-sensing modeling approaches including Support Vector Machine, Neural Networks, and Deep Learning.
  • Detailed discussion of optimization techniques like Particle Swarm Optimization and Genetic Algorithms for parameter estimation.
  • Analysis of data pre-processing and variable selection strategies for soft sensor construction.

Main Results:

  • A comprehensive overview of soft-sensing modeling methods applied to fermentation processes.
  • Tabular analysis highlighting key methods and their applications in the field.
  • Identification of over 70 relevant research publications for further reference.

Conclusions:

  • Soft-sensing modeling is a critical area for enhancing fermentation process monitoring and control.
  • Data-driven approaches combined with advanced optimization techniques offer significant potential for robust soft sensor development.
  • This review provides a valuable resource for future research and development in industrial fermentation soft sensors.