Globally optimal dynamic real time optimization without model mismatch between optimization and control layer. (2nd September 2017)

• Record Type: Journal Article
• Authors: Wang, Xiaoqiang; Mahalec, Vladimir; Qian, Feng
• Is Part Of: Computers & chemical engineering. Volume 104(2017)
• Page Start: 64
• Page End: 75
• Publication Date: 2017-09-02
• Subjects: Global optimal dynamic real time optimization -- Normalized multi-parametric disaggregation -- Integrated dynamic real time optimization and nonlinear model predictive control; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2017.04.006
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A framework for integration of D-RTO and NMPC. Elimination of the mismatch between D-RTO and NMPC models. Global optimal real time optimization based on modified normalized multi-parametric disaggregation (NMDT). Abstract: Global optimality of dynamic operations ensures maximum economic benefits over time. We introduce Dynamic Real Time Optimization (D-RTO) method which uses the same model for D-RTO and for Nonlinear Model Predictive Controller (NMPC), thereby eliminating the model mismatch between the D-RTO and the NMPC. An integration framework based on two different predictive horizons for time-scale decomposition is proposed. Fast updates and reoptimization of the dynamic trajectories ensure that the dynamic operation is optimal over time. Proposed global optimization algorithm is a variation of normalized multi-parametric disaggregation (NMDT) where NMDT is modified by the use of a bivariate partitioning to solve the D-RTO problem. Elimination of the mismatch between D-RTO and NMPC models significantly reduces the effort required to eliminate the model errors between D-RTO, NMPC and the plant. Three chemical process examples are included to illustrate the proposed framework.