Data-driven optimal operation of the industrial methanol to olefin process based on relevance vector machine

作     者：Zhiquan Wang Liang Wang Zhihong Yuan Bingzhen Chen Zhiquan Wang;Liang Wang;Zhihong Yuan;Bingzhen Chen

作者机构：Department of Chemical EngineeringTsinghua UniversityBeijing 100084China State Key Laboratory of Chemical EngineeringDepartment of Chemical EngineeringTsinghua UniversityBeijing 100084China 

出 版 物：《Chinese Journal of Chemical Engineering》 (中国化学工程学报（英文版）)

年 卷 期：2021年第34卷第6期

页      面：106-115页

核心收录：

学科分类：0710[理学-生物学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 081702[工学-化学工艺] 08[工学] 0817[工学-化学工程与技术] 081104[工学-模式识别与智能系统] 0703[理学-化学] 0811[工学-控制科学与工程] 

基　　金：financial support for this work from National Natural Science Foundation of China(21978150 21706143)。 

主　　题：Methanol to olefins Relevance vector machine Genetic algorithm Operation optimization Systems engineering Process systems 

摘      要：Methanol to olefin(MTO)technology provides the opportunity to produce olefins from nonpetroleum sources such as coal,biomass and natural gas.More than 20 commercial MTO plants have been put into operation.Till now,contributions on optimal operation of industrial MTO plants from a process systems engineering perspective are rare.Based on relevance vector machine(RVM),a data-driven framework for optimal operation of the industrial MTO process is established to fully utilize the plentiful industrial data sets.RVM correlates the yield distribution prediction of main products and the operation conditions.These correlations then serve as the constraints for the multi-objective optimization model to pursue the optimal operation of the plant.Nondominated sorting genetic algorithmⅡis used to solve the optimization problem.Comprehensive tests demonstrate that the ethylene yield is effectively improved based on the proposed framework.Since RVM does provide the distribution prediction instead of point estimation,the established model is expected to provide guidance for actual production operations under uncertainty.