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

TL;DR

This paper introduces process-constrained batch Bayesian optimization methods tailored for multi-reactor systems, effectively balancing exploration and exploitation to improve yield optimization in complex catalytic processes.

Contribution

It presents novel hierarchical and non-hierarchical Bayesian optimization algorithms that incorporate process constraints for multi-reactor yield optimization, outperforming existing methods.

Findings

Methods outperform other Bayesian optimization techniques.

Validated on synthetic and real high-throughput data.

Significant improvement in multi-reactor yield optimization.

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.