Refinery planning under uncertainty has gained tremendous attention, and this paper bridges deep learning and robust optimization to address this issue. First, we propose a large-scale mixed-integer linear programming model for refinery planning, where the fixed-yield models of the processing units are used. Prices of final products are considered uncertain parameters in the developed model to enhance the solution's applicability. Second, historical data of different products are collected to construct the uncertainty set characterizing all possible realizations of uncertainty. Third, a deep learning method is employed to capture the uncertainties of product prices, which has been proven to be powerful for high-dimensional price data. Based on the constructed uncertainty set, a data-driven robust optimization model is further developed. Finally, an iterative constraint generation algorithm is applied to solve the data-driven robust optimization problem. Case studies from an actual refinery are presented to showcase the effectiveness of the proposed method, which owes particularly to the representation capability of deep learning.

不确定性下的炼油厂规划获得了极大的关注，本文将深度学习和稳健优化相结合来解决这个问题。首先，我们提出了一个用于炼油厂规划的大规模混合整数线性规划模型，其中使用了处理单元的固定产量模型。在开发的模型中，最终产品的价格被认为是不确定的参数，以提高解决方案的适用性。其次，收集不同产品的历史数据，构建表征不确定性所有可能实现的不确定性集。第三，采用深度学习方法来捕捉产品价格的不确定性，这已被证明对于高维价格数据非常有效。基于构建的不确定性集，进一步开发了数据驱动的鲁棒优化模型。最后，应用迭代约束生成算法解决数据驱动的鲁棒优化问题。来自实际炼油厂的案例研究展示了所提出方法的有效性，这尤其归功于深度学习的表示能力。