Abstract
For the long-cycle process industry, operational cycles can be severely affected by equipment aging, catalyst deactivation, and safety limitations. As illustrated by the residue hydrotreating process, metal impurities gradually deposit on the catalyst during residue purification, leading to catalyst poisoning and eventual process shutdown. Such long-cycle processes require dynamic adjustments of operating conditions to balance immediate economics with long-term sustainability. While current practice relies on empirical tuning based on historical data, this work focuses on studying how to obtain an optimal operating trajectory to guide the monthly adjustments of operating variables. The long-cycle simulation of the residue hydrotreating process can be performed using the commercial software, PetroSIM. After adjusting the feed conditions, its embedded mechanistic model can calculate the deviation of average bed temperature from the set point and output the remaining operating time. Since Bayesian optimization (BO) is well-suited to address complex processes with unknown mechanisms and high computational costs, and can effectively seek optimal solutions, a BO framework incorporating constraints evaluated by PetroSIM is proposed in this work to optimize the monthly feed composition and maximize the total profit over the entire operational cycle. The results indicate that the total profit achieved through BO-optimized operation is 17.8% higher than that from empirical operation. The optimized strategy demonstrates practical rationality: in the early stage of high catalyst activity, more residue can be processed; in the later stage, increasing the light oil ratio helps extend the processing cycle. This strategy provides theoretical support for advancing the research toward industrial applications.