Operational optimal control of industrial evaporation process using noisy and multimode data. (February 2023)

• Record Type: Journal Article
• Authors: Jiang, Peng; Lu, Shaowen; Chen, Zhuoling
• Is Part Of: Journal of process control. Volume 122(2023)
• Page Start: 172
• Page End: 183
• Publication Date: 2023-02
• Subjects: Evaporation process -- Operational optimal control -- Industrial artificial intelligence -- Set point optimization -- Cross-entropy -- Gaussian mixture model; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2023.01.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: Operational optimal control (OOC) aims to maintain the process indexes of an industrial process within the desired range by deciding the control set points. However, the optimization is made difficulty because the key process index cannot be measured online and accurate model for optimization is not available. This work deals with these issues in an industrial multiple-effect evaporation process, with the goal of controlling the product liquor density. The proposed operational optimization framework dynamically finds the optimal control set points through the collaboration of an initial optimizer, a data-driven key process index predictor and a set point compensator. The process index predictor is developed based on the hierarchical Gaussian mixture model, which approximates the distribution of noisy and multimode process data. Then, the same model is used for finding the optimal compensation set points online, where an efficient sampling based stochastic search algorithm is proposed. The proposed method is tested using simulation and in a real industrial evaporation process showing superior performance. Highlights: A solution to the operation optimization of industrial evaporation process. A hierarchal Gaussian mixture model (HGMM) for the prediction of key process index. An adaptive top-down training algorithm of HGMM. A sampling based optimization algorithm to find optimal set-points.