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Bayesian optimization as a flexible and efficient design framework for sustainable process systems
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Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-evaluate black-box functions, with a broad range of real-world applications in science, engineering, economics, manufacturing, and beyond. In this paper, we provide an overview of recent developments, challenges, and opportunities in BO for design of next-generation process systems. After describing several motivating applications, we discuss how advanced BO methods have been developed to more efficiently tackle important problems in these applications. We conclude the paper with a summary of challenges and opportunities related to improving the quality of the probabilistic model, the choice of internal optimization procedure used to select the next sample point, and the exploitation of problem structure to improve sample efficiency.
Forward citations
Cited by 2 Pith papers
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Distributed Model Predictive Control Design for Multi-agent Systems via Bayesian Optimization
This paper proposes an ADMM-based multi-agent Bayesian optimization algorithm to learn the parameters of distributed model predictive controllers under model mismatch, and claims convergence and optimality guarantees.
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Surrogate-Based Optimization Techniques for Process Systems Engineering
A tutorial-and-benchmark chapter that ranks ten surrogate-based derivative-free optimization algorithms on four synthetic functions and two process engineering case studies, with code released on GitHub.
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