Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems
<|reference_start|>Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems: 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.<|reference_end|>
@article{grimm2024process-constrained,
    title={Process-constrained batch Bayesian approaches for yield optimization in
  multi-reactor systems},
    author={Markus Grimm, S'ebastien Paul, Pierre Chainais},
    journal={Volume 189, October 2024, 108779},
    year={2024},
    doi={10.1016/j.compchemeng.2024.108779},
    archivePrefix={arXiv},
    eprint={2408.02551},
    primaryClass={cs.LG}
}