Title: A review on data-driven approaches for industrial process modelling

Authors: Wei Guo; Tianhong Pan; Zhengming Li; Guoquan Li

Addresses: School of Electrical Engineering and Automation, Anhui University, Hefei, Anhui 230601, China; School of Electrical Information and Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China ' School of Electrical Engineering and Automation, Anhui University, Hefei, Anhui 230601, China; School of Electrical Information and Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China ' School of Electrical Information and Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China ' Jiangsu Hengshun Vinegar Industry Co., Ltd., Zhenjiang, Jiangsu 212043, China

Abstract: Data-driven techniques in industrial processes have been continually attended during the past decades. However, there are many challenging issues in this field when the collected data presents different characteristics. In order to sketch the principle of different modelling methods under various working conditions, data-driven modelling methods from perspectives of data structures and model structures are reviewed in this paper. Firstly, the data collection and preprocessing procedure are inspected. Then, popular methods from linear (including the multivariate linear regression (MLR), to latent variable projection (LVP), etc.) to nonlinear methods (including artificial intelligence, Gaussian process regression (GPR), local model, etc.) are discussed. Finally, the model calibration strategies (including offset-based method, recursive method, moving window method) are also reviewed. The major purpose is to support the industrial process modelling for technical users by providing a set of data-driven methods.

Keywords: data-drive modelling; industrial process; machine learning; data analytics; model structure.

DOI: 10.1504/IJMIC.2020.110352

International Journal of Modelling, Identification and Control, 2020 Vol.34 No.2, pp.75 - 89

Received: 27 Jan 2020
Accepted: 30 Jan 2020
Published online: 15 Oct 2020 *

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