IEEE Trans Cybern. 2022 Apr;52(4):2249-2262. doi: 10.1109/TCYB.2020.3005893. Epub 2022 Apr 5.
This article studies an operational optimization problem of the fluid catalytic cracking (FCC) unit under uncertainty. The objective of this problem is to quickly reoptimize the operating variables that control the operational condition of the FCC unit when fossil fuel yield constraints or prices change. To solve this problem, based on the challenges caused by the varied constraints, we establish a mathematical model and propose a fast adaptive differential evolution algorithm with an adaptive mutation strategy, a parameter adaptation strategy, a repaired strategy, and an enhanced strategy. In the proposed algorithm, we integrate the status information of each solution into the mutation strategy and parameter adaptation scheme to search for the best solution in the irregular feasible region of the operating variables. In addition, a repaired strategy is proposed to repair the infeasible operating variables with unknown bounds, and an enhanced strategy is presented to further improve the objective function value of the best solution. The experimental results on ten test scenarios with different fossil fuel yield constraints and prices demonstrate the robustness of the proposed algorithm for optimizing the operating variables of the FCC unit under uncertainty.
本文研究了不确定条件下的流化催化裂化（FCC）单元的操作优化问题。该问题的目标是在化石燃料产量约束或价格变化时，快速重新优化控制 FCC 单元操作条件的操作变量。为了解决这个问题，基于约束条件的变化所带来的挑战，我们建立了一个数学模型，并提出了一种快速自适应差分进化算法，该算法具有自适应突变策略、参数自适应策略、修复策略和增强策略。在提出的算法中，我们将每个解的状态信息集成到突变策略和参数自适应方案中，以在操作变量的不规则可行区域中搜索最佳解。此外，还提出了一种修复策略来修复具有未知边界的不可行操作变量，并提出了一种增强策略来进一步提高最佳解的目标函数值。在具有不同化石燃料产量约束和价格的十个测试场景上的实验结果表明，所提出的算法对不确定条件下的 FCC 单元操作变量的优化具有鲁棒性。
