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.

翻译：多反应器系统作为非均相催化研究中的先进工具，其收率优化因存在层级化技术约束而面临重大挑战。为此，本研究提出了一种称为过程约束汤普森采样批量贝叶斯优化（pc-BO-TS）的新方法及其广义层级化扩展（hpc-BO-TS）。该方法针对多反应器系统的效率需求，在序贯批量优化策略中整合了实验约束，并平衡了探索与利用的关系。相较于其他贝叶斯优化方法，本方法实现了性能提升。pc-BO-TS与hpc-BO-TS的性能在合成案例以及基于REALCAT平台多反应器系统高通量实验数据的真实场景中得到了验证。所提出的方法在多数情况下优于其他序贯贝叶斯优化方法及现有的过程约束批量贝叶斯优化方法。本研究为多反应器系统中的反应收率优化提供了创新方法，标志着数字化催化及化学工程优化方法领域的重要进展。