文献类型：期刊文献

英文题名：Classification and Optimization Model of Mesoporous Carbons Pore Structure and Adsorption Properties Based on Support Vector Machine

作者：Yang, Zhen[1,2];Gu, Xingsheng[3];Liang, Xiaoyi[1,2]

机构：[1]E China Univ Sci & Technol, State Key Lab Chem Engn, Shanghai 200237, Peoples R China;[2]Minist Educ, Key Lab Special Funct Polymer Mat & Their Related, Shanghai 200237, Peoples R China;[3]E China Univ Sci & Technol, Inst Informat Sci, Shanghai 200237, Peoples R China

年份：2011

卷号：74

期号：3-4

起止页码：161

外文期刊名：CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES

收录：;EI(收录号：20113214213605);WOS:【SCI-EXPANDED(收录号:WOS:000293611800001)】；

基金：This work was partly supported by National Science and Technology Ministry (2009BAE72B04), National Science Foundation of China (50730003 and 50672025), National Project of Scientific and Technical Supporting Programs Funded by Ministry of Science & Technology of China (2007BAE55B00), National High Technology Research and Development Program of China (2007AA05Z311).

语种：英文

外文关键词：Mesoporous carbons; pore structure; adsorption properties; genetic algorithm; support vector machine; classification; optimization

摘要：Mesoporous carbons are synthesized by organic organic self-assembly of triblock copolymer F127 and a new type of carbon precursor as resorcinol furfural oligomers. Some factors will impact the mesoporous carbons pore structure and properties were studied. The main factors, such as the ratio of triblock copolymer F127 and oligomers, degree of polymerizstry of resorcinol furfural oligomers, the ratio of resorcinol furfural oligomers - F/R, and their mutual relations were identified. Aimed at balancing the complex characteristic of mesoporous structure and adsorption properties, a classification and optimization model based on support vector machine is developed. The optimal operation conditions of Barret-Joyner-Halenda (BJH) adsorption cumulative volume and average pore diameter are determined by genetic algorithm support vector classification (GA-SVC). Verification results find that GA-SVC provides an effective method to control and optimize operation conditions and is a new promising theoretical method for material design.