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Modelling of Biomass Gasification Through Quasi-Equilibrium Process Simulation and Artificial Neural Networks

Author

Listed:
  • Vera Marcantonio

    (Faculty of Science and Technology for Sustainable Development and One Health, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy)

  • Marcello De Falco

    (Faculty of Science and Technology for Sustainable Development and One Health, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy)

  • Luisa Di Paola

    (Faculty of Science and Technology for Sustainable Development and One Health, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy)

  • Mauro Capocelli

    (Faculty of Science and Technology for Sustainable Development and One Health, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy)

Abstract

In the past two decades, advancements in thermochemical technologies have improved biomass gasification for distributed power generation, enhancing efficiency, scalability, and emission control. This study aims to optimize syngas production from biomass gasification by comparing two computational models: a quasi-equilibrium thermodynamic model implemented in Aspen Plus and an artificial neural network (ANN) model. Operating at 850 °C with varying steam-to-biomass (S/B) ratios, both models were validated against experimental data. Results show that hydrogen concentration in syngas increased from 19.96% to 43.28% as the S/B ratio rose from 0.25 to 0.5, while carbon monoxide concentration decreased from 24.6% to 19.1%, consistent with the water–gas shift reaction. The ANN model provided rapid predictions, showing a mean absolute error of 3% for hydrogen and 2% for carbon monoxide compared to experimental data, though it lacks thermodynamic constraints. Conversely, the Aspen Plus model ensures mass and energy balance compliance, achieving a cold gas efficiency of 95% at an S/B ratio of 0.5. A Multivariate Statistical Analysis (MVA) further clarified correlations between input and output variables, validating model reliability. This combined modelling approach reduces experimental costs, enhances gasification process control and offers practical insights for improving syngas yield and composition.

Suggested Citation

  • Vera Marcantonio & Marcello De Falco & Luisa Di Paola & Mauro Capocelli, 2024. "Modelling of Biomass Gasification Through Quasi-Equilibrium Process Simulation and Artificial Neural Networks," Energies, MDPI, vol. 17(23), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6089-:d:1535905
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    References listed on IDEAS

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    2. Ascher, Simon & Sloan, William & Watson, Ian & You, Siming, 2022. "A comprehensive artificial neural network model for gasification process prediction," Applied Energy, Elsevier, vol. 320(C).
    3. Pala, Laxmi Prasad Rao & Wang, Qi & Kolb, Gunther & Hessel, Volker, 2017. "Steam gasification of biomass with subsequent syngas adjustment using shift reaction for syngas production: An Aspen Plus model," Renewable Energy, Elsevier, vol. 101(C), pages 484-492.
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