Open Access

ARTICLE

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

Zhen Yang¹, Xingsheng Gu², Xiaoyi Liang^1,3

1 State Key Laboratory of Chemical Engineering, East China University of Science and Technology; Key Laboratory for Special functional Polymer Materials and Their Related Technologies, Ministry of Education. Shanghai 200237, PR China.
2 Information Science Institute,East China University of Science and Technology. Shanghai 200237,PR China.
3 Corresponding author e-mail: xyliang73@sina.com

Computer Modeling in Engineering & Sciences 2011, 74(3&4), 161-182. https://doi.org/10.3970/cmes.2011.074.161

Abstract

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.

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Keywords

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