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Predictive modeling for energy recovery from sewage sludge gasification

Author

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  • Carotenuto, Alberto
  • Di Fraia, Simona
  • Massarotti, Nicola
  • Sobek, Szymon
  • Uddin, M. Rakib
  • Vanoli, Laura
  • Werle, Sebastian

Abstract

The analysis of the available literature highlights that the models proposed for gasification are calibrated and validated (at a single operating condition) for only one sample of sewage sludge (SS). In the present work, a numerical model of gasification, based on a restricted chemical equilibrium approach, is developed through the software Aspen Plus. The model is calibrated for one SS that is generated through the mechanical-biological-chemical treatment of wastewater (namely SS-A) and validated for another one that is produced by mechanical-biological processes in combination with phosphorous precipitation techniques (namely SS-B). The novelty consists in considering different sludge samples, one for calibration of the model and one for its validation. Calibration and validation (for five different operating conditions) are based on experimental data on syngas generation in a fixed-bed gasifier under laboratory conditions. The developed gasification model is used to identify optimum temperature (900 °C) and equivalence ratio (0.2) through sensitivity analyses. Then the model is used to assess the combined heat and power generation potentiality of SS by integrating a gasifier with an internal combustion engine. This potentiality is predicted to be 2.19 and 2.53 kWh/kg SS as dry solid for SS-A and SS-B respectively. Energy recovery from SS through the proposed solution may supply around 50% of electrical energy demand to run wastewater treatment plants and from 60 to 75% of thermal energy needed for thermal drying of mechanically dewatered SS for gasification.

Suggested Citation

  • Carotenuto, Alberto & Di Fraia, Simona & Massarotti, Nicola & Sobek, Szymon & Uddin, M. Rakib & Vanoli, Laura & Werle, Sebastian, 2023. "Predictive modeling for energy recovery from sewage sludge gasification," Energy, Elsevier, vol. 263(PB).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pb:s0360544222027244
    DOI: 10.1016/j.energy.2022.125838
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    References listed on IDEAS

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    Cited by:

    1. Muhammad Yousaf Arshad & Muhammad Azam Saeed & Muhammad Wasim Tahir & Halina Pawlak-Kruczek & Anam Suhail Ahmad & Lukasz Niedzwiecki, 2023. "Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor," Energies, MDPI, vol. 16(15), pages 1-26, August.
    2. Mohd Zeeshan & Rohan R. Pande & Purnanand V. Bhale, 2024. "A modeling study for the gasification of refuse-derived fuel as an alternative to waste disposal," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(9), pages 23985-24008, September.
    3. Alberto Carotenuto & Simona Di Fraia & Nicola Massarotti & Szymon Sobek & M. Rakib Uddin & Laura Vanoli & Sebastian Werle, 2023. "Sewage Sludge Gasification Process Optimization for Combined Heat and Power Generation," Energies, MDPI, vol. 16(12), pages 1-22, June.
    4. Jena, Priyaranjan & Tirkey, Jeewan Vachan, 2024. "Power and efficiency improvement of SI engine fueled with boosted producer gas-methane blends and LIVC-miller cycle strategy: Thermodynamic and optimization studies," Energy, Elsevier, vol. 289(C).

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