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Prediction of heat capacity of amine solutions using artificial neural network and thermodynamic models for CO2 capture processes

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Abstract

In this study, artificial neural network (ANN) and thermodynamic models were developed for prediction of the heat capacity (C P ) of amine-based solvents. For ANN model, independent variables such as concentration, temperature, molecular weight and CO2 loading of amine were selected as the inputs of the model. The significance of the input variables of the ANN model on the C P values was investigated statistically by analyzing of correlation matrix. A thermodynamic model based on the Redlich-Kister equation was used to correlate the excess molar heat capacity \( \left({C}_P^E\right) \) data as function of temperature. In addition, the effects of temperature and CO2 loading at different concentrations of conventional amines on the C P values were investigated. Both models were validated against experimental data and very good results were obtained between two mentioned models and experimental data of C P collected from various literatures. The AARD between ANN model results and experimental data of C P for 47 systems of amine-based solvents studied was 4.3%. For conventional amines, the AARD for ANN model and thermodynamic model in comparison with experimental data were 0.59% and 0.57%, respectively. The results showed that both ANN and Redlich-Kister models can be used as a practical tool for simulation and designing of CO2 removal processes by using amine solutions.

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Acknowledgments

We thank the Persian Gulf University and the Converged Energy Materials Research Center, Yonsei University for financial support, for providing various facilities, and for necessary approval.

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Authors and Affiliations

  1. Department of Chemical Engineering, Faculty of Petroleum, Gas and Petrochemical Engineering, Persian Gulf University, P.O. Box 75169-13798, Bushehr, Iran

    Morteza Afkhamipour, Masoud Mofarahi & Masoud Zanganeh

  2. Department of Chemical and Biomolecular Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-749, Republic of Korea

    Masoud Mofarahi

  3. Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, SW72AZ, UK

    Tohid Nejad Ghaffar Borhani

Authors
  1. Morteza Afkhamipour
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  2. Masoud Mofarahi
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  3. Tohid Nejad Ghaffar Borhani
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  4. Masoud Zanganeh
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Correspondence to Masoud Mofarahi.

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Afkhamipour, M., Mofarahi, M., Borhani, T.N.G. et al. Prediction of heat capacity of amine solutions using artificial neural network and thermodynamic models for CO2 capture processes. Heat Mass Transfer 54, 855–866 (2018). https://doi.org/10.1007/s00231-017-2189-y

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  • DOI: https://doi.org/10.1007/s00231-017-2189-y

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