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Precise prediction of biogas thermodynamic properties by using ANN algorithm

Author

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  • Farzaneh-Gord, Mahmood
  • Mohseni-Gharyehsafa, Behnam
  • Arabkoohsar, Ahmad
  • Ahmadi, Mohammad Hossein
  • Sheremet, Mikhail A.

Abstract

There are technical problems related to storage and transport of biogas gas that should be addressed before practical injection of these fuels into the existing natural gas networks. In addition, their different final applications resulting in the presence of various components and in various concentrations make the problem harder. Therefore, it is indispensable for designers of the pipeline network to know exactly what the thermodynamic properties of a gas mixture are, especially its density, which would vary a lot. In this work, a MLP (Multi-layer Perceptron) neural network is used for the development of the desired biogas properties predictor model. In order to train the network, the biogas thermodynamic properties created using ISO 20765-2 (2015) (where applicable) and experimental values are employed. Results are compared with the values estimated from the GERG2008 equations of state, which are the reference equations for natural gases and experimental values. The results indicate that the developed MLP model presents a high accuracy in the calculations over a wide range of biogas mixtures and input properties ranges for all the output properties including density, compressibility factor, isochoric heat capacity, isobaric heat capacity, isentropic exponent, internal energy, enthalpy, entropy, Joule-Thomson coefficient, and speed of sound. The Root Mean Square Error (RMSE) of the mentioned properties of test data are 0.00012536, 0.00016593, 0.0025213, 0.0016208, 0.00337, 0.0096329, 0.0099837, 0.0035625, 0.01055, and 0.00039428 respectively.

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  • Farzaneh-Gord, Mahmood & Mohseni-Gharyehsafa, Behnam & Arabkoohsar, Ahmad & Ahmadi, Mohammad Hossein & Sheremet, Mikhail A., 2020. "Precise prediction of biogas thermodynamic properties by using ANN algorithm," Renewable Energy, Elsevier, vol. 147(P1), pages 179-191.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:179-191
    DOI: 10.1016/j.renene.2019.08.112
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    1. Reza Aghayari & Heydar Maddah & Mohammad Hossein Ahmadi & Wei-Mon Yan & Nahid Ghasemi, 2018. "Measurement and Artificial Neural Network Modeling of Electrical Conductivity of CuO/Glycerol Nanofluids at Various Thermal and Concentration Conditions," Energies, MDPI, vol. 11(5), pages 1-16, May.
    2. Ebrahimi-Moghadam, Amir & Mohseni-Gharyehsafa, Behnam & Farzaneh-Gord, Mahmood, 2018. "Using artificial neural network and quadratic algorithm for minimizing entropy generation of Al2O3-EG/W nanofluid flow inside parabolic trough solar collector," Renewable Energy, Elsevier, vol. 129(PA), pages 473-485.
    3. Esonye, Chizoo & Onukwuli, Okechukwu Dominic & Ofoefule, Akuzuo Uwaoma, 2019. "Optimization of methyl ester production from Prunus Amygdalus seed oil using response surface methodology and Artificial Neural Networks," Renewable Energy, Elsevier, vol. 130(C), pages 61-72.
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    5. Hasanpour Seyedlar, Niloufar & Zamir, Seyed Morteza & Nosrati, Mohsen & Rene, Eldon R., 2024. "H2S mitigation for biogas upgrading in a full-scale anaerobic digestion process by using artificial neural network modeling," Renewable Energy, Elsevier, vol. 232(C).

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