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arXiv · 2408.02551

Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems

Markus Grimm·Sébastien Paul·Pierre Chainais
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Abstract

The optimization of yields in multi-reactor systems, which are advanced tools in heterogeneous catalysis research, presents a significant challenge due to hierarchical technical constraints. To this respect, this work introduces a novel approach called process-constrained batch Bayesian optimization via Thompson sampling (pc-BO-TS) and its generalized hierarchical extension (hpc-BO-TS). This method, tailored for the efficiency demands in multi-reactor systems, integrates experimental constraints and balances between exploration and exploitation in a sequential batch optimization strategy. It offers an improvement over other Bayesian optimization methods. The performance of pc-BO-TS and hpc-BO-TS is validated in synthetic cases as well as in a realistic scenario based on data obtained from high-throughput experiments done on a multi-reactor system available in the REALCAT platform. The proposed methods often outperform other sequential Bayesian optimizations and existing process-constrained batch Bayesian optimization methods. This work proposes a novel approach to optimize the yield of a reaction in a multi-reactor system, marking a significant step forward in digital catalysis and generally in optimization methods for chemical engineering.

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Markus Grimm, Sébastien Paul, Pierre Chainais. 2024-08-05. Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems. https://doi.org/10.1016/j.compchemeng.2024.108779

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