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A group contribution method for determination of the standard molar chemical exergy of organic compounds

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  • Gharagheizi, Farhad
  • Ilani-Kashkouli, Poorandokht
  • Mohammadi, Amir H.
  • Ramjugernath, Deresh

Abstract

Exergy analysis can be used to achieve the optimal conditions at which a system or a process can precede; as close as possible to the environmental conditions (with minimum loss of energy). In order to do such an analysis, the chemical exergy of each compound should be available. Since there are a limited number of organic compounds for which the chemical exergy values have been reported in the literature, it would be of great interest to have a reliable method for the estimation of this parameter. In this communication, a group contribution method is proposed for the prediction of the chemical exergy of pure organic compounds at the standard condition of 1 atm and 298.15 K for pressure and temperature respectively. In order to develop and validate the model, and also to evaluate its predictive capability, a dataset of 133 pure organic compounds composed of carbon, hydrogen, nitrogen, oxygen, and sulfur was used. The model proposed has a low average absolute relative deviation of 1.6% from literature data and indicates the reliability of the method. It can be used as a predictive tool for the estimation of the standard chemical exergy of pure organic compounds.

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  • Gharagheizi, Farhad & Ilani-Kashkouli, Poorandokht & Mohammadi, Amir H. & Ramjugernath, Deresh, 2014. "A group contribution method for determination of the standard molar chemical exergy of organic compounds," Energy, Elsevier, vol. 70(C), pages 288-297.
    • Handle: RePEc:eee:energy:v:70:y:2014:i:c:p:288-297
    DOI: 10.1016/j.energy.2014.03.124
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    2. Gharagheizi, Farhad & Ilani-Kashkouli, Poorandokht & Hedden, Ronald C., 2018. "Standard molar chemical exergy: A new accurate model," Energy, Elsevier, vol. 158(C), pages 924-935.
    3. Huang, Y.W. & Chen, M.Q. & Li, Y. & Guo, J., 2016. "Modeling of chemical exergy of agricultural biomass using improved general regression neural network," Energy, Elsevier, vol. 114(C), pages 1164-1175.
    4. Geng, Xueli & Yan, Peng & Zhou, Hao & Li, Hong & Gao, Xin, 2023. "Process synthesis and 4E evaluation of hybrid reactive distillation processes for the ethanol and tert-butanol recovery from wastewater," Renewable Energy, Elsevier, vol. 205(C), pages 929-944.

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