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 

机构地区：[1]Department of Chemical Engineering,Tsinghua University,Beijing 100084,China [2]State Key Laboratory of Chemical Engineering,Department of Chemical Engineering,Tsinghua University,Beijing 100084,China

出　　处：《Chinese Journal of Chemical Engineering》2021年第6期106-115,共10页中国化学工程学报（英文版）

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

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.

关 键 词：Methanol to olefins Relevance vector machine Genetic algorithm Operation optimization Systems engineering Process systems 

分 类 号：TQ221.2[化学工程—有机化工] TP181[自动化与计算机技术—控制理论与控制工程]