Closed‐loop dynamic real‐time optimization with stabilizing model predictive control. Issue 10 (12th June 2021)

• Record Type: Journal Article
• Authors: Ramesh, Praveen Sundaresan; Swartz, Christopher L. E.; Mhaskar, Prashant
• Is Part Of: AIChE journal. Volume 67:Issue 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-12
• Subjects: closed‐loop dynamic real‐time optimization -- linear model predictive control -- process economics -- stability constraints; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.17308
• Languages: English
• ISSNs: 0001-1541

Abstract

Abstract: Dynamic real‐time optimization (DRTO) is a supervisory strategy at the upper level of the industrial process automation architecture that computes economically optimal set‐point trajectories that are in turn passed on to the lower‐level model predictive control (MPC) for tracking. The economically optimal solution, in several process industries, could lead to operating the plant at or around an unstable steady state. The present article accounts for this by developing a closed‐loop DRTO (CL‐DRTO) formulation that enables handling unstable operating points via an underlying MPC with stability constraints. To this end, a stabilizing MPC that handles trajectory tracking for unstable systems is embedded within the upper‐level DRTO. The resulting CL‐DRTO problem is reformulated by applying a simultaneous solution approach. The economic benefits realized by the proposed strategy are illustrated through applications to both linearized and nonlinear dynamic models for single‐input single‐output and multi‐input multi‐output continuous stirred tank reactor case studies.