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

Environmental Earth Sciences volume 75, Article number: 215 (2016) Cite this article

A Correction to this article was published on 12 June 2020

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Abstract

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.

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Change history

  • 12 June 2020

    The Editors-in-Chief of Environmental Earth Sciences are issuing an editorial expression of concern to alert readers that this article [1] shows evidence of substantial text overlap (most notably with the article cited [2]) and authorship manipulation. None of the authors responded to correspondence about this editorial expression of concern.

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Acknowledgments

The authors express their sincere thanks for the funding support received from the HIR-MOHE University of Malaya under Grant No. UM.C/HIR/MOHE/ENG/34.

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Authors and Affiliations

  1. Faculty of Petroleum and Renewable Engineering (FPREE), Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor Bahru, Malaysia

    Erfan Mohammadian & Radzuan Junin

  2. Oil and Gas Engineering Department, Faculty of Chemical Engineering (FKK), University Teknologi Mara (Uitm), 40450, Shah Alam, Selangor, Malaysia

    Erfan Mohammadian & Amin Azdarpour

  3. Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shervin Motamedi, Roslan Hashim & Chandrabhushan Roy

  4. Institute of Ocean and Earth Sciences, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shervin Motamedi & Roslan Hashim

  5. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shahaboddin Shamshirband

Authors
  1. Erfan Mohammadian
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  2. Shervin Motamedi
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  3. Shahaboddin Shamshirband
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  4. Roslan Hashim
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  5. Radzuan Junin
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  6. Chandrabhushan Roy
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  7. Amin Azdarpour
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Correspondence to Shahaboddin Shamshirband.

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Mohammadian, E., Motamedi, S., Shamshirband, S. et al. Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide. Environ Earth Sci 75, 215 (2016). https://doi.org/10.1007/s12665-015-4798-4

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