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Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant

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  • Safarian, Sahar
  • Ebrahimi Saryazdi, Seyed Mohammad
  • Unnthorsson, Runar
  • Richter, Christiaan

Abstract

This study is a novel attempt in developing of an Artificial neural network (ANN) model integrated with a thermodynamic equilibrium approach for downdraft biomass gasification integrated power generation unit. The objective of the study is to predict the net output power from the systems derived from 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. 1032 simulated data from 86 different types of biomasses in various operating conditions were used to train the ANN. The developed ANN shows agreement with simulated data with absolute fraction of variance (R2) higher than 0.999 in the case of product power. Moreover, the relative influence of biomass characteristics and some specific operating parameters on output power 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 power generation unit.

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  • Safarian, Sahar & Ebrahimi Saryazdi, Seyed Mohammad & Unnthorsson, Runar & Richter, Christiaan, 2020. "Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant," Energy, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:energy:v:213:y:2020:i:c:s0360544220319071
    DOI: 10.1016/j.energy.2020.118800
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    References listed on IDEAS

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    1. Speirs, Jamie & McGlade, Christophe & Slade, Raphael, 2015. "Uncertainty in the availability of natural resources: Fossil fuels, critical metals and biomass," Energy Policy, Elsevier, vol. 87(C), pages 654-664.
    2. 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.
    3. Dinca, Cristian & Slavu, Nela & Cormoş, Călin-Cristian & Badea, Adrian, 2018. "CO2 capture from syngas generated by a biomass gasification power plant with chemical absorption process," Energy, Elsevier, vol. 149(C), pages 925-936.
    4. Pettinau, Alberto & Ferrara, Francesca & Amorino, Carlo, 2013. "Combustion vs. gasification for a demonstration CCS (carbon capture and storage) project in Italy: A techno-economic analysis," Energy, Elsevier, vol. 50(C), pages 160-169.
    5. Safarian, Sahar & Saboohi, Yadollah & Kateb, Movaffaq, 2013. "Evaluation of energy recovery and potential of hydrogen production in Iranian natural gas transmission network," Energy Policy, Elsevier, vol. 61(C), pages 65-77.
    6. 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.
    7. Elmaz, Furkan & Yücel, Özgün & Mutlu, Ali Yener, 2020. "Predictive modeling of biomass gasification with machine learning-based regression methods," Energy, Elsevier, vol. 191(C).
    8. Sahar Safarian & Runar Unnthorsson & Christiaan Richter, 2020. "Techno-Economic and Environmental Assessment of Power Supply Chain by Using Waste Biomass Gasification in Iceland," Biophysical Economics and Resource Quality, Springer, vol. 5(2), pages 1-13, June.
    9. 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.
    10. Safarian, Sahar & Unnthorsson, Runar & Richter, Christiaan, 2020. "Performance analysis and environmental assessment of small-scale waste biomass gasification integrated CHP in Iceland," Energy, Elsevier, vol. 197(C).
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    Cited by:

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    2. Lv, Xuefei & Lv, Ying & Zhu, Yiping, 2023. "Multi-variable study and MOPSO-based multi-objective optimization of a novel cogeneration plant using biomass fuel and geothermal energy: A complementary hybrid design," Energy, Elsevier, vol. 270(C).
    3. Ayub, Yousaf & Ren, Jingzheng & Shi, Tao & Shen, Weifeng & He, Chang, 2023. "Poultry litter valorization: Development and optimization of an electro-chemical and thermal tri-generation process using an extreme gradient boosting algorithm," Energy, Elsevier, vol. 263(PC).
    4. Fatemehsadat Mirshafiee & Emad Shahbazi & Mohadeseh Safi & Rituraj Rituraj, 2023. "Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study," Energies, MDPI, vol. 16(1), pages 1-20, January.
    5. Guo, Shenghui & Meng, Fanrui & Peng, Pai & Xu, Jialing & Jin, Hui & Chen, Yunan & Guo, Liejin, 2022. "Thermodynamic analysis of the superiority of the direct mass transfer design in the supercritical water gasification system," Energy, Elsevier, vol. 244(PA).
    6. Ascher, Simon & Watson, Ian & You, Siming, 2022. "Machine learning methods for modelling the gasification and pyrolysis of biomass and waste," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    7. Kargbo, Hannah O. & Zhang, Jie & Phan, Anh N., 2021. "Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network," Applied Energy, Elsevier, vol. 302(C).
    8. Pomeroy, Brett & Grilc, Miha & Likozar, Blaž, 2022. "Artificial neural networks for bio-based chemical production or biorefining: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    9. Liu, Shanke & Yang, Yan & Yu, Lijun & Cao, Yu & Liu, Xinyi & Yao, Anqi & Cao, Yaping, 2023. "Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation," Energy, Elsevier, vol. 272(C).
    10. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).

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