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Review
. 2020 Mar 23;20(6):1771.
doi: 10.3390/s20061771.

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Xianglin Zhu  1 , Khalil Ur Rehman  1 , Bo Wang  1 , Muhammad Shahzad  1
Affiliations
Review

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Xianglin Zhu et al. Sensors (Basel). .

Abstract

For effective monitoring and control of the fermentation process, an accurate real-time measurement of important variables is necessary. These variables are very hard to measure in real-time due to constraints such as the time-varying, nonlinearity, strong coupling, and complex mechanism of the fermentation process. Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures. In this paper, a comprehensive review of existing data pre-processing approaches, variable selection methods, data-driven (black-box) soft-sensing modeling methods and optimization techniques was carried out. The data-driven methods used for the soft-sensing modeling such as support vector machine, multiple least square support vector machine, neural network, deep learning, fuzzy logic, probabilistic latent variable models are reviewed in detail. The optimization techniques used for the estimation of model parameters such as particle swarm optimization algorithm, ant colony optimization, artificial bee colony, cuckoo search algorithm, and genetic algorithm, are also discussed. A comprehensive analysis of various soft-sensing models is presented in tabular form which highlights the important methods used in the field of fermentation. More than 70 research publications on soft-sensing modeling methods for the estimation of variables have been examined and listed for quick reference. This review paper may be regarded as a useful source as a reference point for researchers to explore the opportunities for further enhancement in the field of soft-sensing modeling.

Keywords: fermentation process; monitoring and control; optimization; soft sensor.

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Conflict of interest statement

The authors declare that they have no competing of interests.

Figures

Figure 1
Figure 1
The concept of a soft sensor as presented in [10]. This figure describes the model of a soft sensor with one hardware sensor and an estimator. Input from a bioprocess is measured through the sensor (in reality this can be a combination of several hardware sensors) and new information is estimated.
Figure 2
Figure 2
Comparison of soft-sensing results.
Figure 3
Figure 3
Error curve comparison diagrams.
Figure 4
Figure 4
Soft-sensing models used in process industry (abbreviations used are listed in Table A1).
Figure 5
Figure 5
SVM based soft-sensing models (abbreviations are listed in Table A1).
Figure 6
Figure 6
NN-based soft-sensing models (abbreviations are listed in Table A1).
Figure 7
Figure 7
FL-based soft-sensing models (abbreviations are listed in Table A1).
Figure 8
Figure 8
Some other useful methods (abbreviations are listed in Table A1).
See this image and copyright information in PMC

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