文献类型：期刊文献

中文题名：基于模糊核聚类的乙烯裂解深度DE-LSSVM多模型建模

英文题名：Multiple DE-LSSVM modeling of ethylene cracking severity based on fuzzy kernel clustering

作者：陈贵华[1];王昕[2];王振雷[1];钱锋[1]

机构：[1]化工过程先进控制和优化技术教育部重点实验室(华东理工大学),上海200237;[2]上海交通大学电工与电子技术中心,上海200240

年份：2012

卷号：63

期号：6

起止页码：1790

中文期刊名：化工学报

外文期刊名：CIESC Journal

收录：CSTPCD;;EI(收录号：20122815240305);Scopus;北大核心:【北大核心2011】；CSCD:【CSCD2011_2012】；

基金：国家重点基础研究发展计划项目(2009CB320603);国家自然科学重点基金项目(61134007);国家高技术研究发展计划项目(2012AA040307);上海市科技攻关项目(10dz1121900);高等学校学科创新引智计划(B08021);上海市重点学科建设项目(B504)~~

语种：中文

中文关键词：乙烯裂解深度;模糊核聚类;最小二乘支持向量机;多模型建模

外文关键词：ethylene cracking severity; fuzzy kernel clustering; least squares support vector machine; multiple modeling

摘要：乙烯裂解深度的建模与控制对于裂解炉的实时优化具有重要意义。针对石脑油原料组分复杂、油品特性波动大等状况,采用模糊核聚类对石脑油数据库进行最优划分,建立最小二乘支持向量机的多模型,对于最小二乘支持向量机中模型的参数选取,利用差分进化算法进行参数寻优,提高了模型的精度和泛化能力。通过对现场数据的建模实验,结果表明:基于模糊核聚类的乙烯裂解深度最小二乘支持向量机多模型跟踪性能良好,预测精度较高。
Modeling and control of ethylene cracking severity is very important to the real-time optimization of cracking furnace.To address the problem with the complexity and volatility of naphtha feedstock components,fuzzy kernel clustering method was developed to divide the naphtha database optimally.After establishing multiple models of least squares support vector machine（LSSVM）,in order to improve the model accuracy and generalization ability,differential evolution algorithm was used to determine the proper parameters of the LSSVM model.We established each sub-model based on the sub-condition in ethylene cracking process,also the switching strategy was based on weighted value.The simulation results on the real industrial data showed that DE-LSSVM multiple models of ethylene cracking severity based on fuzzy kernel clustering got good tracking performance and high accuracy.