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Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide

Solubility of CO2 in brine is one of the contributing trapping mechanisms by which the injected CO2 is sequestrated in aquifers. In the literature, the solubility data on low salinity range are scarce. Thus, in the current study, the CO2 solubility was experimentally obtained in the NaCl brines of l...

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Main Authors:	Mohammadian, Erfan, Motamedi, Shervin, Shamshirband, Shahaboddin, Hashim, Roslan, Junin, Radzuan, Roy, Chandrabhushan, Azdarpour, Amin
Format:	Article
Published:	2016
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/68856/ https://link.springer.com/article/10.1007/s12665-015-4798-4
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spelling	my.utm.688562017-11-20T08:52:15Z http://eprints.utm.my/id/eprint/68856/ Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide Mohammadian, Erfan Motamedi, Shervin Shamshirband, Shahaboddin Hashim, Roslan Junin, Radzuan Roy, Chandrabhushan Azdarpour, Amin TP Chemical technology Solubility of CO2 in brine is one of the contributing trapping mechanisms by which the injected CO2 is sequestrated in aquifers. In the literature, the solubility data on low salinity range are scarce. Thus, in the current study, the CO2 solubility was experimentally obtained in the NaCl brines of low salinity (0–1.5 wt%) at temperature of 333–373 K and pressures up to 280 MPa through the potentiometric titration methods. The short-term, multistep ahead predictive models of aqueous solubility of carbon dioxide were created. The models were developed using a novel method based on the extreme learning machine (ELM). Estimation and prediction results of the ELM model were compared with the genetic programming (GP) and artificial neural networks (ANNs) models. The results revealed enhancement of the predictive accuracy and generalization capability through the ELM method in comparison with the GP and ANN. Moreover, the results indicate that the developed ELM models can be used with confidence for further work on formulating a novel model predictive strategy for the aqueous solubility of carbon dioxide. The experimental results hinted that the current algorithm can present good generalization performance in the majority of cases. Moreover, in comparison with the conventional well-known learning algorithms, it can learn thousands of times faster. In conclusion, it is conclusively found that application of the ELM is particularly promising as an alternative method to estimate the aqueous solubility of carbon dioxide. 2016 Article PeerReviewed Mohammadian, Erfan and Motamedi, Shervin and Shamshirband, Shahaboddin and Hashim, Roslan and Junin, Radzuan and Roy, Chandrabhushan and Azdarpour, Amin (2016) Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide. Environmental Earth Sciences, 75 (3). pp. 1-11. https://link.springer.com/article/10.1007/s12665-015-4798-4
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TP Chemical technology
spellingShingle	TP Chemical technology Mohammadian, Erfan Motamedi, Shervin Shamshirband, Shahaboddin Hashim, Roslan Junin, Radzuan Roy, Chandrabhushan Azdarpour, Amin Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide
description	Solubility of CO2 in brine is one of the contributing trapping mechanisms by which the injected CO2 is sequestrated in aquifers. In the literature, the solubility data on low salinity range are scarce. Thus, in the current study, the CO2 solubility was experimentally obtained in the NaCl brines of low salinity (0–1.5 wt%) at temperature of 333–373 K and pressures up to 280 MPa through the potentiometric titration methods. The short-term, multistep ahead predictive models of aqueous solubility of carbon dioxide were created. The models were developed using a novel method based on the extreme learning machine (ELM). Estimation and prediction results of the ELM model were compared with the genetic programming (GP) and artificial neural networks (ANNs) models. The results revealed enhancement of the predictive accuracy and generalization capability through the ELM method in comparison with the GP and ANN. Moreover, the results indicate that the developed ELM models can be used with confidence for further work on formulating a novel model predictive strategy for the aqueous solubility of carbon dioxide. The experimental results hinted that the current algorithm can present good generalization performance in the majority of cases. Moreover, in comparison with the conventional well-known learning algorithms, it can learn thousands of times faster. In conclusion, it is conclusively found that application of the ELM is particularly promising as an alternative method to estimate the aqueous solubility of carbon dioxide.
format	Article
author	Mohammadian, Erfan Motamedi, Shervin Shamshirband, Shahaboddin Hashim, Roslan Junin, Radzuan Roy, Chandrabhushan Azdarpour, Amin
author_facet	Mohammadian, Erfan Motamedi, Shervin Shamshirband, Shahaboddin Hashim, Roslan Junin, Radzuan Roy, Chandrabhushan Azdarpour, Amin
author_sort	Mohammadian, Erfan
title	Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide
title_short	Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide
title_full	Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide
title_fullStr	Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide
title_full_unstemmed	Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide
title_sort	application of extreme learning machine for prediction of aqueous solubility of carbon dioxide
publishDate	2016
url	http://eprints.utm.my/id/eprint/68856/ https://link.springer.com/article/10.1007/s12665-015-4798-4
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score	13.211869

Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide

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