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arXiv · 2009.08819

Real-Time Optimization Meets Bayesian Optimization and Derivative-Free Optimization: A Tale of Modifier Adaptation

Ehecatl Antonio del Rio-Chanona·Panagiotis Petsagkourakis·Eric Bradford·Jose Eduardo Alves Graciano·Benoit Chachuat
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Abstract

This paper investigates a new class of modifier-adaptation schemes to overcome plant-model mismatch in real-time optimization of uncertain processes. The main contribution lies in the integration of concepts from the areas of Bayesian optimization and derivative-free optimization. The proposed schemes embed a physical model and rely on trust-region ideas to minimize risk during the exploration, while employing Gaussian process regression to capture the plant-model mismatch in a non-parametric way and drive the exploration by means of acquisition functions. The benefits of using an acquisition function, knowing the process noise level, or specifying a nominal process model are illustrated on numerical case studies, including a semi-batch photobioreactor optimization problem.

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Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis, Eric Bradford, Jose Eduardo Alves Graciano, Benoit Chachuat. 2020-09-18. Real-Time Optimization Meets Bayesian Optimization and Derivative-Free Optimization: A Tale of Modifier Adaptation. https://arxiv.org/abs/2009.08819

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