Bayesian based reaction optimization for complex continuous gas-liquid-solid reactions

In recent years, self-optimization strategies have been gradually utilized for the determination of optimal reaction conditions owing to their high convenience and independence from researchers' experience. However, most self-optimization algorithms still focus on homogeneous reactions or simple heterogeneous reactions. Investigations on complex heterogeneous gas-liquid-solid reactions are rare. Based on the Nelder-Mead simplex method and Bayesian optimization, this work proposes a reaction optimization framework for optimizing complex gas-liquid-solid reactions. Three gas-liquid-solid reactions including the hydrogenations of nitrobenzene, 3,4-dichloronitrobenzene, and 5-nitroisoquinoline are investigated, respectively. Reaction parameters (temperature, hydrogen pressure, liquid flow rate, and gas flow rate) are optimized. Compared with the traditional OVAT method, the proposed Bayesian based optimization algorithm exhibits remarkable performance with higher yields (0.998, 0.991 and 0.995, respectively) and computational efficiency.

Automated summary

This study proposes a reaction optimization framework for optimizing complex gas-liquid-solid reactions, based on the Nelder-Mead simplex method and Bayesian optimization. Three gas-liquid-solid reactions were investigated and reaction parameters were optimized. The new Bayesian optimization algorithm shows higher yields and computational efficiency compared to the traditional OVAT method.