{"title":"多目标决策分析中的可转移偏好学习及其在加氢裂化中的应用","authors":"Guo Yu, Xinzhe Wang, Chao Jiang, Yang Liu, Lianbo Ma, Cuimei Bo, Quanling Zhang","doi":"10.1007/s40747-024-01537-6","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Transferable preference learning in multi-objective decision analysis and its application to hydrocracking\",\"authors\":\"Guo Yu, Xinzhe Wang, Chao Jiang, Yang Liu, Lianbo Ma, Cuimei Bo, Quanling Zhang\",\"doi\":\"10.1007/s40747-024-01537-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":10524,\"journal\":{\"name\":\"Complex & Intelligent Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Complex & Intelligent Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s40747-024-01537-6\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Complex & Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s40747-024-01537-6","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
加氢裂化是一种复杂而耗时的化学工艺,可将重油馏分转化为各种有价值的低沸点产品。在炼油过程中,加氢裂化对提高产品质量起着至关重要的作用。因此,开发高效的替代模型来模拟加氢裂化过程并为多目标炼油确定合适的解决方案是目前的一个重要研究领域。本研究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,我们的方法包括考虑用户偏好,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对 ROI 内的子问题进行近似。为了增强 Kriging 模型在演化过程中的稳健性和泛化能力,我们在 ROI 中转移了子问题之间的互信息。为了验证我们提出的方法的有效性和效率,我们在基准和炼油过程中进行了一系列实验。实验结果充分证明了我们方法的优越性。
Transferable preference learning in multi-objective decision analysis and its application to hydrocracking
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.
期刊介绍:
Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.