Constrained Operational Optimization of a Distillation Unit in Refineries With Varying Feedstock Properties

This brief studies the challenging operational optimization problem of a distillation unit (DU) under varying feedstock properties (e.g., density and carbon content). This problem, in which changes in the feedstock properties are incorporated, aims to quickly obtain the operating variables that control the operating condition of the DU. To solve this problem, we first model this operational optimization problem considering the ever-changing feedstock properties and practical technological constraints. Then, we propose an efficient soft-sensing strategy to rapidly measure the feedstock properties. Finally, motivated by the challenges caused by the varying feedstock properties, product yield, and tray temperature constraints, we propose an optimization algorithm with global search and self-repair capabilities to optimize the operating variables of the DU. The proposed algorithm integrates the optimization time and survival information of each individual into the proposed mutation strategy to improve its global search capability in the irregular feasible region of the operating variables. Based on the ranking and survival information of each individual, the adaptive strategies of the mutation factor and crossover probability are designed to balance the exploration and exploitation capabilities of the optimization algorithm. Subsequently, we propose an effective correction strategy to correct the infeasible operating variables and improve the optimization efficiency of the algorithm. Computational experiments on practical production data show the accuracy of the soft-sensing model and the superiority of the optimization algorithm for operational optimization of the DU.