典型文献
Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations
文献摘要：
Modeling and optimization is crucial to smart chemical process operations.However,a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations,chemical reactions and separations.This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity.Thus,this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimiza-tion to overcome the aforementioned difficulties.An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method.Firstly,a data set was generated based on process mechanistic simulation validated by industrial data,which provides sufficient and reasonable samples for model training and testing.Secondly,four well-known machine learning methods,namely,K-nearest neighbors,decision tree,support vector machine,and artificial neural network,were compared and used to obtain the prediction models of the processes operation.All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features.Finally,optimal process operations were obtained by using the particle swarm optimization approach.
文献关键词：
中图分类号：
作者姓名：
Haoqin Fang;Jianzhao Zhou;Zhenyu Wang;Ziqi Qiu;Yihua Sun;Yue Lin;Ke Chen;Xiantai Zhou;Ming Pan
作者机构：
School of Chemical Engineering and Technology,Sun Yat-Sen University,Zhuhai 519082,China;School of Mathematics,Sun Yat-Sen University,Zhuhai 519082,China
文献出处：
引用格式：
[1]Haoqin Fang;Jianzhao Zhou;Zhenyu Wang;Ziqi Qiu;Yihua Sun;Yue Lin;Ke Chen;Xiantai Zhou;Ming Pan-.Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations)[J].化学科学与工程前沿,2022(02):274-287
A类：
B类：
Hybrid,integrating,machine,learning,particle,swarm,optimization,smart,chemical,operations,Modeling,crucial,However,large,number,nonlinearities,must,considered,typical,according,unit,reactions,separations,This,leads,great,challenge,implementing,mechanistic,models,into,industrial,scale,problems,due,resulting,computational,complexity,Thus,this,paper,presents,efficient,hybrid,framework,overcome,aforementioned,difficulties,An,propane,dehydrogenation,was,carried,out,demonstrate,validity,efficiency,Firstly,data,set,generated,simulation,validated,by,which,provides,sufficient,reasonable,samples,training,testing,Secondly,four,well,known,methods,namely,nearest,neighbors,decision,tree,support,vector,artificial,neural,network,were,compared,used,prediction,processes,All,these,achieved,highly,accurate,adjusting,parameters,basis,coverage,properly,features,Finally,optimal,obtained,using,approach
AB值：
0.62885
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
