Hydrocracking represents a complex and time-consuming chemical process that converts heavy oil fractions into various valuable products with low boiling points. It plays a pivotal role in enhancing the quality of products within the oil refining process. Consequently, the development of efficient surrogate models for simulating the hydrocracking process and identifying appropriate solutions for multi-objective oil refining is now an important area of research. In this study, a novel transferable preference learning-driven evolutionary algorithm is proposed to facilitate multi-objective decision analysis in the oil refining process. Specifically, our approach involves considering user preferences to divide the objective space into a region of interest (ROI) and other subspaces. We then utilize Kriging models to approximate the sub-problems within the ROI. In order to enhance the robustness and generalization capability of the Kriging models during the evolutionary process, we transfer the mutual information between the sub-problems in the ROI. To validate the effectiveness as well as efficiency of our proposed method, we undertake a series of experiments on both benchmarks and the oil refining process. The experimental results conclusively demonstrate the superiority of our approach.

加氢裂化是一种复杂且耗时的化学过程，将重油馏分转化为各种低沸点的有价值的产品。它在提高炼油过程中的产品质量方面发挥着关键作用。因此，开发有效的替代模型来模拟加氢裂化过程并确定多目标炼油的适当解决方案现在是一个重要的研究领域。在本研究中，提出了一种新颖的可转移偏好学习驱动的进化算法，以促进炼油过程中的多目标决策分析。具体来说，我们的方法涉及考虑用户偏好，将目标空间划分为感兴趣区域（ROI）和其他子空间。然后，我们利用克里金模型来近似 ROI 内的子问题。为了增强克里金模型在进化过程中的鲁棒性和泛化能力，我们在ROI中的子问题之间传递互信息。为了验证我们提出的方法的有效性和效率，我们对基准和炼油过程进行了一系列实验。实验结果最终证明了我们方法的优越性。