Soft sensor model of marine enzyme fermentation process based on NN-MIV variable selection

Ding, Yuhan; Zeng, Derun; Yang, Peisuo; Huang, Yonghong; Zhu, Xianglin

doi:10.1109/ccdc.2017.7978406

Cited by 2 publications

(2 citation statements)

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“…There are many advantages in a reduced number of variables such as decreasing costs, reduction of model development time, and enabling the feasibility of an application. Many researchers have reported both supervised and unsupervised variable selection methods in the literature such as principal component analysis (PCA) [28,29], filter methods (correlation coefficient), wrapper methods, embedded methods [30], mean impact value (MIV) [31], and uniform incidence degree algorithms [24], to give just a few examples. However, in most of the cases, the selection of most relevant variables for many soft sensor applications is made by system experts.…”

Section: Selection Of Variablesmentioning

confidence: 99%

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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: Selection Of Variablesmentioning

confidence: 99%

“…In [31], the NN-MIV soft-sensing model was presented for the estimation of the key variables (e.g., marine enzyme activity) in a marine enzyme fermentation processf. The NN-MIV soft-sensing model was compared with the NN model.…”

Section: Neural Network-based Soft-sensing Modelsmentioning

confidence: 99%