Multi-objective Design of Blending Fuel by Intelligent Optimization Algorithms

作     者：Ruichen Liu Cong Li Li Wang Xiangwen Zhang Guozhu Li 

作者机构：Key Laboratory for Green Chemical Technology of Ministry of EducationSchool of Chemical Engineering and TechnologyTianjin UniversityTianjin 300072China Collaborative Innovation Center of Chemical Science and Engineering(Tianjin)Tianjin 300072China Haihe Laboratory of Sustainable Chemical TransformationsTianjin 300192China 

出 版 物：《Transactions of Tianjin University》 (天津大学学报（英文版）)

年 卷 期：2024年第30卷第3期

页      面：221-237页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the support from the National Key R&D Program of China(No.2021YFC2103701) the National Natural Science Foundation of China(No.22178248) the Haihe Laboratory of Sustainable Chemical Transformations 

主　　题：Multi-objective optimization Machine learning Blending fuel 

摘      要：Fuel design is a complex multi-objective optimization problem in which facile and robust methods are urgently ***,a complete workflow for designing a fuel blending scheme is presented,which is theoretically supported,efficient,and *** on the data distribution of the composition and properties of the blending fuels,a model of polynomial regression with appropriate hypothesis space was *** parameters of the model were further optimized by different intelligence algorithms to achieve high-precision ***,the design of a blending fuel was described as a multi-objective optimization problem,which was solved using a Nelder–Mead algorithm based on the concept of Pareto ***,the design of a target fuel was fully validated by *** study provides new avenues for designing various blending fuels to meet the needs of next-generation engines.