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Artificial Neural Network Modeling of Bioethanol Production Via Syngas Fermentation

Author

Listed:
  • Sahar Safarian

    (University of Iceland)

  • Seyed Mohammad Ebrahimi Saryazdi

    (Sharif University of Technologies)

  • Runar Unnthorsson

    (University of Iceland)

  • Christiaan Richter

    (University of Iceland)

Abstract

This paper explores the construction and validation of an artificial neural network (ANN) model to accurately and efficiently predict the performance of a downdraft biomass gasification integrated with syngas fermentation plant for ethanol production. The study aims to predict the specific mass flow rate of bioethanol product from the systems derived by various kinds of biomass feedstocks under atmospheric pressure and various operating conditions. The input parameters used in the models are elemental analysis compositions (C, O, H, N and S), proximate analysis compositions (moisture, ash, volatile material and fixed carbon) and operating parameters (gasifier temperature and air to fuel ratio). The architecture of the model consisted of one input, one hidden and one output layer. 1008 simulated data from 84 different types of biomasses in various operating conditions were used to train the ANN. The developed ANN shows agreement with simulated data with Root Mean Square Error (RMSE) less than 0.05 in the case of product bioethanol. Moreover, the relative influence of biomass characteristics and some specific operating parameters on output are determined. Finally, to have a more detailed assessment, the variations of all input variables with respect to carbon content are compared and analyzed together. The suggested integrated ANN based model can be applied as a very useful tool for optimization and control of the process through the downdraft biomass gasification integrated with bioethanol production unit.

Suggested Citation

  • Sahar Safarian & Seyed Mohammad Ebrahimi Saryazdi & Runar Unnthorsson & Christiaan Richter, 2021. "Artificial Neural Network Modeling of Bioethanol Production Via Syngas Fermentation," Biophysical Economics and Resource Quality, Springer, vol. 6(1), pages 1-13, March.
    • Handle: RePEc:spr:bioerq:v:6:y:2021:i:1:d:10.1007_s41247-020-00083-2
    DOI: 10.1007/s41247-020-00083-2
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    References listed on IDEAS

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    1. Andrea Porcu & Stefano Sollai & Davide Marotto & Mauro Mureddu & Francesca Ferrara & Alberto Pettinau, 2019. "Techno-Economic Analysis of a Small-Scale Biomass-to-Energy BFB Gasification-Based System," Energies, MDPI, vol. 12(3), pages 1-17, February.
    2. Michailos, Stavros & Parker, David & Webb, Colin, 2017. "Design, Sustainability Analysis and Multiobjective Optimisation of Ethanol Production via Syngas Fermentation," MPRA Paper 87640, University Library of Munich, Germany.
    3. Rossana Liguori & Carlos Ricardo Soccol & Luciana Porto de Souza Vandenberghe & Adenise Lorenci Woiciechowski & Vincenza Faraco, 2015. "Second Generation Ethanol Production from Brewers’ Spent Grain," Energies, MDPI, vol. 8(4), pages 1-12, March.
    4. Sahar Safarian & Sorena Sattari & Zeinab Hamidzadeh, 2018. "Sustainability Assessment of Biodiesel Supply Chain from Various Biomasses and Conversion Technologies," Biophysical Economics and Resource Quality, Springer, vol. 3(2), pages 1-15, June.
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    6. Rajaeifar, Mohammad Ali & Akram, Asadolah & Ghobadian, Barat & Rafiee, Shahin & Heijungs, Reinout & Tabatabaei, Meisam, 2016. "Environmental impact assessment of olive pomace oil biodiesel production and consumption: A comparative lifecycle assessment," Energy, Elsevier, vol. 106(C), pages 87-102.
    7. Sahar Safarian & Runar Unnthorsson, 2018. "An Assessment of the Sustainability of Lignocellulosic Bioethanol Production from Wastes in Iceland," Energies, MDPI, vol. 11(6), pages 1-16, June.
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    Cited by:

    1. Niaze, Ambereen A. & Sahu, Rohit & Sunkara, Mahendra K. & Upadhyayula, Sreedevi, 2023. "Model construction and optimization for raising the concentration of industrial bioethanol production by using a data-driven ANN model," Renewable Energy, Elsevier, vol. 216(C).

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