3-D online modeling and assessment of operating performance for nonstationary industrial processes with nonlinearity. (May 2021) - Zou, Xiaoyu; Wang, Fuli; Pan, Jie
Abstract: Industrial processes often show typical nonstationarity, resulting from frequent variation of working condition. It is rather challenging to assess the operating performance of nonstationary processes, since the process property is time-variant and the standard of performance levels may differ from working conditions. Besides the nonstationarity, nonlinearity widely exists in industrial processes, making it hard to analyze process characteristics. To solve the above problems, a three-dimensional (3-D) online modeling and assessment method of operating performance is proposed in the present work. Considering that the inner drive of nonstationarity is often the fluctuation of working condition, 3-D space is established, including sample, variable, and working condition axes. Thus, the process can be modeled and assessed online along direction of the working condition indicator to handle both nonstationarity and nonlinearity. Within the 3-D space, the selection of online training data and establishment of assessing model are solved simultaneously through multi-objective optimization, to make the training data suitable for performance assessment and preserve high assessment accuracy. Bayesian inference based criterion is then defined to determine the performance level. Finally, the validation on a three-phase flow facility and a coal mill of a power plant show feasibility and superiority of the proposed method on performance assessment for nonstationary industrial processes with nonlinearity. Highlights: Performance assessment is solved for general nonlinearly nonstationary processes. 3-D online modeling and assessment method is proposed. Determination of training samples and model are conducted simultaneously. Solution of optimization involves GA and eigenvalue decomposition.