Application of Gauss process regression modeling based on NN-MIV for marine enzyme fermentation process

Yang, Peisuo; Ding, Yuhan; Mei, Congli; Xu, Chunxiang; Jiang, Hui

doi:10.1109/ccdc.2018.8407458

Cited by 2 publications

(1 citation statement)

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“…The NN-MIV soft-sensing model was compared with the NN model. Another GPR-NNMIV based soft-sensing model was presented in [61]. In this work NN-MIV variable selection method is employed to get the most suitable input variables with the highest contribution rate.…”

Section: Neural Network-based Soft-sensing Modelsmentioning

confidence: 99%

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Zhu

Rehman

Wang

et al. 2020

Sensors

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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.

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Section: Neural Network-based Soft-sensing Modelsmentioning

confidence: 99%