Heat and Mass Transfer

, Volume 54, Issue 3, pp 855–866 | Cite as

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

  • Morteza Afkhamipour
  • Masoud Mofarahi
  • Tohid Nejad Ghaffar Borhani
  • Masoud Zanganeh


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.



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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Panwar N, Kaushik S, Kothari S (2011) Role of renewable energy sources in environmental protection: a review. Renew Sust Energ Rev 15:1513–1524CrossRefGoogle Scholar
  2. 2.
    Afkhamipour M, Mofarahi M (2013) Comparison of rate-based and equilibrium-stage models of a packed column for post-combustion CO2 capture using 2-amino-2-methyl-1-propanol (AMP) solution. Int J Greenhouse Gas Control 15:186–199Google Scholar
  3. 3.
    Afkhamipour M, Mofarahi M (2017) Review on the mass transfer performance of CO2 absorption by amine-based solvents in low-and high-pressure absorption packed columns. RSC Adv 7:17857–17872Google Scholar
  4. 4.
    Borhani TNG, Azarpour A, Akbari V, Wan Alwi SR, Manan ZA (2015) CO2 capture with potassium carbonate solutions: A state-of-the-art review. Int J Greenhouse Gas Control 41:142–162Google Scholar
  5. 5.
    Kohl A, Nielson R (1997) Gas Purification. Gulf Publishing Company Houston, TexasGoogle Scholar
  6. 6.
    Afkhamipour M, Mofarahi M (2014) Sensitivity analysis of the rate-based CO2 absorber model using amine solutions (MEA, MDEA and AMP) in packed columns. Int J Greenhouse Gas Control 25:9–22Google Scholar
  7. 7.
    Smith JM, van Ness HC, Abbott MM (2005) Introduction to chemical engineering thermodynamics. McGraw-Hill, New YorkGoogle Scholar
  8. 8.
    Maham Y, Hepler GL, Mather AE, Hakin AW, Marriott RA (1997) Molar heat capacities of alkanolamines from 299.1 to 397.8 K Group additivity and molecular connectivity analyses. Journal of the Chemical Society. Faraday Trans 93:1747–1750CrossRefGoogle Scholar
  9. 9.
    Chiu L-F, Li M-H (1999) Heat capacity of alkanolamine aqueous solutions. J Chem Eng Data 44:1396–1401CrossRefGoogle Scholar
  10. 10.
    Chen Y-J, Shih T-W, Li M-H (2001) Heat Capacity of Aqueous Mixtures of Monoethanolamine with N-Methyldiethanolamine. J Chem Eng Data 46:51–55CrossRefGoogle Scholar
  11. 11.
    Shih T-W, Li M-H (2002) Heat capacity of aqueous mixtures of diethanolamine with 2-amino-2-methyl-l-propanol. Fluid Phase Equilib 202:233–237CrossRefGoogle Scholar
  12. 12.
    Mundhwa M, Henni A (2007) Molar heat capacity of various aqueous alkanolamine solutions from 303.15 K to 353.15 K. J Chem Eng Data 52:491–498CrossRefGoogle Scholar
  13. 13.
    Weiland RH, Dingman JC, Cronin DB (1997) Heat Capacity of Aqueous Monoethanolamine, Diethanolamine, N-Methyldiethanolamine, and N-Methyldiethanolamine-Based Blends with Carbon Dioxide. J Chem Eng Data 42:1004–1006CrossRefGoogle Scholar
  14. 14.
    Lin S-Y, Leron RB, Li M-H (2014) Molar heat capacities of aqueous binary and ternary mixtures (with piperazine) of two diamines: N,N,N′,N′-Tetramethylethylenediamine and N,N,N′,N′-tetramethyl-1,3-propanediamine. J Taiwan Inst Chem Eng 45:1291–1297CrossRefGoogle Scholar
  15. 15.
    Shaikh IWN (2012) Molar heat capacities and heats of mixing of aqueous solutions of 2-(propylamino)ethanol, 2-(butylamino)ethanol, 1-(2-hydroxyethyl)piperidine, bis(2-methoxyethyl)amine and other alkanolamines of importance to carbon dioxide capture. Master of Applied Science, University of ReginaGoogle Scholar
  16. 16.
    Poozesh S, Rayer AV, Henni A (2013) Molar Heat Capacity (Cp) of Aqueous Cyclic Amine Solutions from (298.15 to 353.15) K. J Chem Eng Data 58:1989–2000CrossRefGoogle Scholar
  17. 17.
    Chen Y-R, Caparanga AR, Soriano AN, Li M-H (2010) Liquid heat capacity of the solvent system (piperazine + 2-amino-2-methyl-l-propanol + water). J Chem Thermodyn 42:518–523CrossRefGoogle Scholar
  18. 18.
    Agbonghae EO, Hughes KJ, Ingham DB, Ma L, Pourkashanian M (2014) A semi-empirical model for estimating the heat capacity of aqueous solutions of alkanolamines for CO2 capture. Ind Eng Chem Res 53:8291–8301Google Scholar
  19. 19.
    Bagheri M, Borhani TNG, Zahedi G (2012) Estimation of flash point and autoignition temperature of organic sulfur chemicals. Energy Convers Manag 58:185–196CrossRefGoogle Scholar
  20. 20.
    Afkhamipour M, Mofarahi M (2016) Modeling and optimization of CO2 capture using 4-diethylamino-2-butanol (DEAB) solution. Int J Greenhouse Gas Control 49:24–33Google Scholar
  21. 21.
    Pouryousefi F, Idem R, Supap T, Tontiwachwuthikul P (2016) Artificial Neural Networks for Accurate Prediction of Physical Properties of Aqueous Quaternary Systems of Carbon Dioxide (CO2)-Loaded 4-(Diethylamino)-2-butanol and Methyldiethanolamine Blended with Monoethanolamine. Ind Eng Chem Res 55:11614–11621Google Scholar
  22. 22.
    Aljahdali SH, Sheta A, Rine D (2001) Prediction of software reliability: A comparison between regression and neural network non-parametric models. Computer Systems and Applications, ACS/IEEE International Conference on. 2001, IEEE, pp 470–473Google Scholar
  23. 23.
    Vaferi B, Rahnama Y, Darvishi P, Toorani A, Lashkarbolooki M (2013) Phase equilibria modeling of binary systems containing ethanol using optimal feedforward neural network. J Supercrit Fluids 84:80–88CrossRefGoogle Scholar
  24. 24.
    Fu K, Chen G, Sema T, Zhang X, Liang Z, Idem R, Tontiwachwuthikul P (2013) Experimental study on mass transfer and prediction using artificial neural network for CO 2 absorption into aqueous DETA. Chem Eng Sci 100:195–202CrossRefGoogle Scholar
  25. 25.
    Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2:303–314MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Chemical Engineering, Faculty of Petroleum, Gas and Petrochemical EngineeringPersian Gulf UniversityBushehrIran
  2. 2.Department of Chemical and Biomolecular EngineeringYonsei UniversitySeoulRepublic of Korea
  3. 3.Centre for Process Systems Engineering, Department of Chemical EngineeringImperial College LondonLondonUK

Personalised recommendations