An efficient operation optimization method for the series-parallel fractionation system of industrial hydrocracking

Abstract

The fractionation system of industrial hydrocracking aims to fractionate the feedstocks to highly-valued distillates according to different boiling ranges. Therefore, the operation optimization for the system can effectively increase output and profits of factory. However, there is a complicated series-parallel structure between the distillation columns, which makes it very difficult to optimize the system. In this study, a two-stage optimization method for the series-parallel fractionation system of industrial hydrocracking is proposed. Firstly, the fractionation system is divided into an optimal structure through a phased calculation method to cut down unnecessary computation. Then, an adaptive logic of computing resource and a new sampling and evaluation strategy are adopted in candidate adaptive state transition algorithm (CASTA) to solve the optimization problem. CASTA can balance the computation resource and speed up optimization speed. Benchmarks are provided to illustrate the effectiveness of the proposed CASTA. Moreover, the two-stage optimization method was successfully used in the operation optimization of the series-parallel fractionation system of industrial hydrocracking, and achieved more efficient results than other optimization algorithms.