Real-Time Optimization Meets Bayesian Optimization and Derivative-Free Optimization: A Tale of Modifier Adaptation
This work addresses optimization challenges for uncertain industrial processes, representing an incremental advancement by combining existing concepts from different optimization areas.
The paper tackles plant-model mismatch in real-time optimization of uncertain processes by integrating Bayesian optimization and derivative-free optimization into modifier-adaptation schemes, using Gaussian process regression and trust-region methods to minimize risk and drive exploration, with benefits demonstrated on numerical case studies like a photobioreactor problem.
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.