Identification of Hammerstein-Weiner models for nonlinear MPC from infrequent measurements in batch processes. (October 2019)

• Record Type: Journal Article
• Authors: Wang, Zhenyu; Georgakis, Christos
• Is Part Of: Journal of process control. Volume 82(2019)
• Page Start: 58
• Page End: 69
• Publication Date: 2019-10
• Subjects: System identification -- Model-predictive control -- Optimization -- Batch process; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.08.004
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: An approach to identify Hammerstein-Weiner model from infrequent measurement has been proposed. The difficulty of infrequent measurement is bypassed by estimating an intermediate Dynamic Response Surface Model. The proposed method enables the feedback control with infrequent measurement. Abstract: Here we introduce a new approach for the identification of Hammerstein-Wiener (H–W) models from infrequent measurements in batch processes. Because concentration measurements during each batch run are very infrequent, such models cannot be effectively estimated directly from the infrequent measurements. This difficulty is bypassed by first developing a dynamic response surface methodology (DRSM) model. One can also calculate the optimal trajectories using this DRSM model and identify the local H-W model around such optimal or any other operational trajectory for control purposes. This is obtained by frequently "sampling" the DRSM model around these trajectories. We demonstrate the efficacy of the proposed approach using the simulation of an isothermal semi-batch reactor with nonlinear reaction mechanism. The MPC controller using the nonlinear H-W model is able to outperform the same type of controller using a linear recursive model identified directly from the original infrequent measurements.