Real-Time Optimization of Uncertain Process Systems via Modifier Adaptation and Gaussian Processes

Ferreira, Tafarel de AvilaShukla, Harsh A.Faulwasser, TimmJones, Colin N.Bonvin, Dominique2019-06-182019-06-182019-06-182018-01-0110.23919/ECC.2018.8550397https://infoscience.epfl.ch/handle/20.500.14299/157326WOS:000467725300077In the context of static real-time optimization, the use of measurements allows dealing with uncertainty in the form of plant-model mismatch and disturbances. Modifier adaptation (MA) is a measurement-based scheme that uses first-order corrections to the model cost and constraint functions so as to achieve plant optimality upon convergence. However, first-order corrections rely crucially on the estimation of plant gradients, which typically requires costly plant experiments.The present paper proposes to implement real-time optimization via MA but use recursive Gaussian processes to represent the plant-model mismatch and estimate the plant gradients. This way, one can (i) attenuate the effect of measurement noise, and (ii) avoid plant-gradient estimation by means finite-difference schemes and, often, additional plant experiments. We use steady-state optimization data to build Gaussian-process regression functions. The efficiency of the proposed scheme is illustrated via a constrained variant of the Williams-Otto reactor problem.Automation & Control SystemsAutomation & Control SystemsReal-Time Optimization of Uncertain Process Systems via Modifier Adaptation and Gaussian Processestext::conference output::conference proceedings::conference paper