We present a framework for the application of design and control optimization via multi‐parametric programming through four case studies. We develop design dependent multi‐parametric model predictive controllers that are able to provide the optimal control actions as functions of the system state and the design of the process at hand, via our recently introduced PAROC framework (Pistikopoulos et al, Chem Eng Sci. 2015;136:115–138). The process and the design dependent explicit controllers undergo a mixed integer dynamic optimization (MIDO) step for the determination of the optimal design. The result of the MIDO is the optimal design of the process under optimal operation. We demonstrate the framework through case studies of a tank, a continuously stirred tank reactor, a binary distillation column and a residential cogeneration unit. © 2017 American Institute of Chemical Engineers AIChE J, 2017

No abstract

As a founder of the Process Systems Engineering (PSE) discipline, Professor Roger W.H. Sargent had set ambitious goals for a systematic new generation of a process design paradigm based on optimization techniques with the consideration of future uncertainties and operational decisions. In this paper, we present a historical perspective on the milestones in model-based design optimization techniques and the developed tools to solve the resulting complex problems. We examine the progress spanning more than five decades, from the early flexibility analysis and optimal process design under uncertainty to more recent developments on the simultaneous consideration of process design, scheduling, and control. This formidable target towards the grand unification poses unique challenges due to multiple time scales and conflicting objectives. Here, we review the recent progress and propose future research directions.

No abstract

We present a systematic framework to derive model-based simultaneous strategies for the integration of scheduling and control via multiparametric programming. We develop offline maps of optimal scheduling actions accounting for the closed-loop dynamics of the process through a surrogate model formulation that incorporates the inherent behavior of the control scheme. The surrogate model is designed to translate the long-term scheduling decisions to time varying set points and operating modes in the time scale of the controller. The continuous and binary scheduling decisions are explicitly taken into account in the multiparametric model predictive controllers. We showcase the framework on a stand-alone three-product continuous stirred tank reactor, and two reactors operating in parallel.