文献类型：期刊文献

英文题名：Prediction of CO2 adsorption on different activated carbons by hybrid group method of data-handling networks and LSSVM

作者：Zhou, Liang

第一作者：周亮

通信作者：Zhou, L[1]

机构：[1]Guizhou Inst Technol, Sch Chem Engn, Guiyang 550003, Guizhou, Peoples R China

第一机构：贵州理工学院化学工程学院

通信机构：corresponding author), Guizhou Inst Technol, Sch Chem Engn, Guiyang 550003, Guizhou, Peoples R China.|贵州理工学院化学工程学院;贵州理工学院;

年份：2019

卷号：41

期号：16

起止页码：1960-1971

外文期刊名：ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000464513200004)】；

基金：This work was supported by the Guizhou Institute of Technology; [XJGC20150102]; Natural Science Research Project of Guizhou Provincial Education Office; Guizhou Province Science and Technology.

语种：英文

外文关键词：Activated carbon; CO2 adsorption; greenhouse gases reduction; neural network; predictive modeling

摘要：CO2 is a ubiquitous species that has received much attention recently. The adsorption of CO2 by means of activated carbons is a well-tried technology that can be used on a large scale. Improvements of the prediction models with more accurate results and lower error are necessary for future development of the projects and the economic dispatch sector. The least square support vector machine, a relatively unexplored neural network known as group method of data handling (GMDH), were implemented to forecast the CO2 adsorption on different activated carbons. This work aims to provide new methods to predict the adsorption equilibrium of pure CO2 on a set of commercial activated carbons and to express it regarding textural properties such as Brunauer-Emmett-Teller (BET) surface area, total pore volume, and micropore volume. Results indicated that the utilized models are very accurate in predicting CO2 adsorption on different activated carbons. Comparison of the outcomes of the two models shows that the GMDH model is more accurate with R-2 and mean squared error values of 0.8915 and 0.0001425, respectively.