Bayesian optimization with search space movement for cooling crystallization process

Experimental automation equipped with data-driven optimization is attracting significant attention as an effective platform for finding optimal operating conditions. The key is to automate the decision-making procedure using Bayesian optimization. However, the optimization performance depends heavily on the search space, which is typically selected manually by an expert with domain knowledge. This study proposes a new Bayesian optimization algorithm with a search space movement strategy to automate the search space selection procedure. Simulation studies of two benchmark problems show that the proposed method can determine the optimal conditions with fewer trials than existing methods. Furthermore, the proposed method was applied to maximize the productivity of batch cooling crystallization. The experimental results indicate that the proposed Bayesian optimization algorithm can automatically and robustly find the proper search space and thus improve productivity by up to 46%.