A review on data-driven approaches for industrial process modelling. (13th October 2020)

• Record Type: Journal Article
• Authors: Guo, Wei; Pan, Tianhong; Li, Zhengming; Li, Guoquan
• Is Part Of: International journal of modelling, identification and control. Volume 34:Number 2(2020)
• Page Start: 75
• Page End: 89
• Publication Date: 2020-10-13
• Subjects: data-drive modelling -- industrial process -- machine learning -- data analytics -- model structure; Engineering -- Methodology -- Periodicals; Science -- Methodology -- Periodicals; 001.42
• Languages: English
• ISSNs: 1746-6172

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.