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Prediction of carbon dioxide solubility in aqueous mexture of methyldiethanolamine and N-methylpyrrolidone using intelligent models

By Afshin Tatar, Ali Barati, Ali Yarahmadi, Moonyong Lee and Alireza Bahadori

Abstract

Clear knowledge about the solubility of acid gases such as CO2 in different solvents at different states is very important, especially for carbon capture from flue gases. This study highlights the application of artificial intelligence in prediction of carbon dioxide solubility in a mix solvent of methyldiethanolamine and N-methylpyrrolidone at wide range of temperature and pressure. The input data of the models were temperature, pressure, and saturation pressure and the output parameter was the solubility of CO2. Different intelligent approaches such as MLP-ANN, GA-RBF, CSA-LSSVM, Hybrid-ANFIS, PSO-ANFIS, and CMIS were developed and the reliability of models was investigated through different graphical and statistical methods. Result showed that the developed models are accurate and predictive for estimation of experimental solubility data. However, the CMIS approach exhibited better results compared to other intelligent approaches. Results of this study showed that intelligent based algorithms are powerful alternatives for time-consuming and difficult experimental processes of solubility measurement

Topics: CO2, Solubility, GA-RBF, Modeling, CMIS, Methyldiethanolamine, N-Methylpyrrolidone, Environmental Sciences
Publisher: ePublications@SCU
Year: 2016
DOI identifier: 10.1016/j.ijggc.2016.01.048
OAI identifier: oai:epubs.scu.edu.au:esm_pubs-3692
Provided by: ePublications@SCU
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