The paper focuses on the refinery-wide planning operations under uncertainties in product demand and price via robust optimization framework coupled with global optimization. Industrial/simulation data sets are used to build explicit nonlinear surrogate models for correlating the yield/property of products and the operational conditions of secondary processing units. A large-scale nonconvex mixed integer nonlinear programming (MINLP) model is formulated. The product demand and sale price uncertainties are incorporated into the proposed model via the “interval” and “interval + ellipsoidal” uncertainty set, respectively, to obtain the robust counterpart optimization model. A global optimization framework which combines the enhanced normalized multiparametric disaggregation technique (ENMDT) and the optimality-based bound tightening is developed. The proposed model and the global optimization algorithm are applied to an industrial refinery site with three illustrative cases. The results show the value of formulating nonlinear models for secondary processing units and the benefits from applying ENMDT-based algorithm.

本文通过稳健的优化框架和全局优化，着眼于在产品需求和价格不确定的情况下的炼油厂范围内的计划运营。工业/模拟数据集用于建立显式的非线性替代模型，以将产品的产量/性质与二级处理单元的运行条件相关联。建立了一个大规模的非凸混合整数非线性规划模型。产品需求和销售价格的不确定性分别通过“时间间隔”和“时间间隔+椭圆形”不确定性集合合并到建议的模型中，以获得鲁棒的对应优化模型。开发了一个全局优化框架，该框架结合了增强的归一化多参数分解技术（ENMDT）和基于最优性的约束紧缩。提出的模型和全局优化算法在三个示例情况下被应用于工业炼油厂。结果表明，为二次处理单元建立非线性模型的价值以及应用基于ENMDT的算法的好处。