Output modifier adaptation with filter-based constraints. (March 2020)

• Record Type: Journal Article
• Authors: Papasavvas, A.; François, G.
• Is Part Of: Journal of process control. Volume 87(2020)
• Page Start: 37
• Page End: 53
• Publication Date: 2020-03
• Subjects: Real-time optimization -- Modifier adaptation -- Model adequacy; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.01.002
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Two extensions (KMAy and ad-KMAy) of output modifier adaptation (MAy) are presented, analyzed, and illustrated. Provided an adequate selection of the filter gain, KMAy provides (at no cost) net safety and stability improvements to MAy. ad-KMAy is KMAy with an gain selector that automatically selects the largest adequate gain. Abstract: Modifier adaptation (MA) and output modifier adaptation (MAy) are iterative model-based real-time optimization (RTO) algorithms that have the proven ability to drive plants to their optimal operating condition upon convergence despite disturbances and modeling uncertainty, provided the model at hand satisfies model adequacy conditions. But there is no guarantee that constraints are satisfied before convergence. In this article, an improvement of the formulation of MA and MAy is proposed that is proven to bring significant improvements w.r.t. these two limitations – model adequacy and feasibility of iterates. While standard MA or MAy suggests to perform optimization and filtering sequentially, it is proposed to integrate the input filtering stage in the modified model-based optimization problem by means of additional filter-based constraints. The corresponding approach, labeled "KMAy", is (i) proven to preserve constraint qualification despite additional constraints, (ii) proven to preserve the property of MA methods to converge to the true plant optimal inputs, (iii) proven to significantly relax the model adequacy condition - leading it to be independent of the constraints of the optimization problem, (iv) shown to increase the chances of converging from the safe side of the plant constraints and (v) shown to support the choice of input filtering, instead of output or modifier filtering, if the input filter is appropriately chosen . A method for the automatic selection of the largest filter gain with the five aforementioned assets, while minimizing the filter-induced conservatism, is also proposed. The performances of KMAy with and without adaptive gain are successfully illustrated by means of the optimization of a benchmark simulated chemical reactor.