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,con...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.展开更多
Developing innovative capabilities in university students is essential for individual career success and broader societal advancement.This study introduces a predictive Feature Selection(FS)model named bWRBA-SVM-FS,wh...Developing innovative capabilities in university students is essential for individual career success and broader societal advancement.This study introduces a predictive Feature Selection(FS)model named bWRBA-SVM-FS,which combines an enhanced Bat Algorithm(BA)and Support Vector Machine(SVM).To enhance the optimization capability of BA,water follow search and random follow search are introduced to optimize the efficiency and accuracy of the feature subset search.Experimental validation conducted on the IEEE CEC 2017 benchmark functions and the talented innovative capacity dataset demonstrates the efficacy of the proposed method relative to peer and prominent machine learning models.The experimental results reveal that the predictive accuracy of the bWRBA-SVM-FS model is 97.503%,with a sensitivity of 98.391%.Our findings indicate significant predictors of innovation capacity,including project application goals,educational background,and interdisciplinary thinking abilities.The bWRBA-SVM-FS model offers effective strategies for talent selection in higher education,fostering the development of future research leaders.展开更多
基金financial support for this work from National Natural Science Foundation of China(21978150,21706143)。
文摘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.
基金supported by the Zhejiang Province 14th Five Year Plan Teaching Reform Project(jg20220514).
文摘Developing innovative capabilities in university students is essential for individual career success and broader societal advancement.This study introduces a predictive Feature Selection(FS)model named bWRBA-SVM-FS,which combines an enhanced Bat Algorithm(BA)and Support Vector Machine(SVM).To enhance the optimization capability of BA,water follow search and random follow search are introduced to optimize the efficiency and accuracy of the feature subset search.Experimental validation conducted on the IEEE CEC 2017 benchmark functions and the talented innovative capacity dataset demonstrates the efficacy of the proposed method relative to peer and prominent machine learning models.The experimental results reveal that the predictive accuracy of the bWRBA-SVM-FS model is 97.503%,with a sensitivity of 98.391%.Our findings indicate significant predictors of innovation capacity,including project application goals,educational background,and interdisciplinary thinking abilities.The bWRBA-SVM-FS model offers effective strategies for talent selection in higher education,fostering the development of future research leaders.
