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.

工业加氢裂化的分馏系统旨在根据不同的沸程将原料分馏成高价值的馏出物。因此，系统的运行优化可以有效地增加工厂的产量和利润。但是，在蒸馏塔之间存在复杂的串并联结构，这使得优化系统非常困难。本文针对工业加氢裂化的串并联分馏系统，提出了一种两阶段优化方法。首先，通过分阶段计算方法将分馏系统划分为最佳结构，以减少不必要的计算。然后，候选自适应状态转移算法（CASTA）采用了计算资源的自适应逻辑，并采用了新的采样和评估策略，解决了优化问题。CASTA可以平衡计算资源并加快优化速度。提供基准以说明所提议的CASTA的有效性。此外，该两阶段优化方法已成功地用于工业加氢裂化的串并联分馏系统的运行优化中，并且比其他优化算法获得了更有效的结果。