In this work, a Mixed-Integer Nonlinear Programming (MINLP) modeling framework for integrating short-term Crude-oil Scheduling (CS) and mid-term Refinery Planning (RP) has been developed and effectively solved by a proposed Lagrangean Decomposition (LD) algorithm. The principles of this integration are based on the fact that both Crude-oil Scheduling and Refinery Planning have their economic net values as their objectives, and that they are physically linked by the Crude Distillation Unit (CDU). A multi-scale approach is proposed in the framework to aggregate continuous- and discrete-time formulations in CS and RP, respectively. Compared to hierarchically solving the non-integrated CS and RP, computational results show significant improvement regarding the economic objective values. Moreover, the proposed LD approach requires less CPU time converging to a small (1%-5%) optimality gap when compared to the monolithic approach using state-of-the-art MINLP solvers.

在这项工作中，已经开发了一种混合短期整数调度（CS）和中期炼油计划（RP）的混合整数非线性规划（MINLP）建模框架，并通过提出的拉格朗日分解法（LD）有效地解决了该问题。算法。这种整合的原则基于以下事实：原油调度和炼油厂规划均以其经济净值作为目标，并且与原油蒸馏部门（CDU）进行了物理链接。在框架中提出了一种多尺度方法，以分别汇总CS和RP中的连续时间和离散时间公式。与分层求解非集成CS和RP相比，计算结果显示出在经济目标值方面的显着改进。此外，