Abstract
AbstractHydrocracking 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.
Funder
National Natural Science Foundation of China
China Postdoctoral Science Foundation
Publisher
Springer Science and Business Media LLC
Reference56 articles.
1. Zhong W, Qiao C, Peng X, Li Z, Fan C, Qian F (2019) Operation optimization of hydrocracking process based on Kriging surrogate model. Control Eng Pract 85:34–40
2. Zhou H, Lu J, Cao Z, Shi J, Pan M, Li W, Jiang Q (2011) Modeling and optimization of an industrial hydrocracking unit to improve the yield of diesel or kerosene. Fuel 90(12):3521–3530
3. Han D, Du W, Wang X, Du W (2022) A surrogate-assisted evolutionary algorithm for expensive many-objective optimization in the refining process. Swarm Evol Comput 69:100988
4. Ma L, Li N, Guo Y, Wang X, Yang S, Huang M, Zhang H (2021) Learning to optimize: reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system. IEEE Trans Cybern 2:2
5. Ma L, Cheng S, Shi Y (2020) Enhancing learning efficiency of brain storm optimization via orthogonal learning design. IEEE Trans Syst Man Cybern Syst 51(11):6723–6742