A Bayesian optimization approach for data-driven Petlyuk distillation column

Recently, the focus on increasing process efficiency to reduce energy consumption has driven the adoption of alternative systems, such as Petlyuk distillation columns.�It has been proven that, when compared to conventional distillation columns, these systems offer significant energy and cost savings. From an economic standpoint, achieving high-purity products alone does not ensure the feasibility of a process. Instead, balancing the trade-off between product purity and cost necessitates multi-objective optimization. While conventional optimization methods are effective, novel strategies like Bayesian optimization offer distinct advantages for handling complex systems. Bayesian optimization requires no explicit mathematical model and can efficiently optimize even when starting from a single initial point. However, as a black-box method, it demands a detailed analysis of hyperparameters, such as the acquisition function and the number of initial points, to ensure optimal performance. This work presents a case study of a Petlyuk distillation column, focusing on the influence of key hyperparameters in Bayesian optimization. Additionally, the role of uncertainty in the optimization process is explored, as it is a critical factor in real-world applications. By simulating uncertainty, this study provides insights into its impact on the optimization process.