期刊
INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL
卷 58, 期 -, 页码 212-222出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ijggc.2017.01.013
关键词
CO2; Carbon capture; Solubility; Choline chloride; CHPSO-ANFIS; GEP
This research presents the application of two predictive models named adaptive neuro fuzzy inference system optimized by combination of hybrid and particle swarm optimization methods (CHPSO-ANFIS) and gene expression programming (GEP) for estimation of CO2 solubility in a deep eutectic solvent based on mixtures of levulinic acid or furfuryl alcohol and choline chloride. The input parameters of both models were temperature, pressure, and ratio of mole of levulinic acid and furfuryl alcohol to mole of choline chloride. The output parameter of model was the solubility of CO2. Results demonstrate that the predictions of developed models are in acceptable agreement with experimental data. However, the CHPSO-ANFIS model provides more accurate results compared to GEP model. The overall R-2 and AARD% values of proposed CHPSO-ANFIS model were 0.999 and 1.07 and for GEP model were 0.995 and 3.63 respectively. The developed model and correlation are effective in providing quick and accurate predictions of CO2 solubility without conducting any time consuming and difficult experimental measurements. (C) 2017 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据