Current practice in refinery planning is to assume that the qualities of the crude oil feedstocks are known, even though they often vary. The uncertainty of the quality properties can significantly impact the profit of the refinery and needs to be considered in purchasing decisions. This work employs the product tri-section CDU model (Li et al. 2020) to build an accurate refinery model and determines the optimal crude selection by two-stage stochastic programming. The uncertainty of the crude oil quality properties is defined via the uncertainty of the TBP curves, which is described by the uncertain parameters of the beta functions approximating the TBP curves. The probabilistic scenarios are generated via random vector sampling method, leading to a relatively small number of scenarios required for the two-stage-stochastic programming model convergence. This enables us to determine the best crude oil choice, while requiring acceptable computational times, as illustrated by computational experiments.

炼油厂规划中的当前实践是假设原油原料的质量是已知的，即使它们经常变化。质量属性的不确定性可能会严重影响炼油厂的利润，因此在购买决策时需要考虑这些因素。这项工作采用了产品三部分CDU模型（Li等，2020）来建立一个精确的炼油厂模型，并通过两阶段随机规划确定最佳的原油选择。原油质量特性的不确定性是通过TBP曲线的不确定性来定义的，该不确定性由近似于TBP曲线的beta函数的不确定性参数来描述。概率场景是通过随机向量采样方法生成的，导致两阶段随机规划模型收敛所需的场景数量相对较少。这使我们能够确定最佳的原油选择，同时需要可接受的计算时间，如计算实验所示。