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Assessment of the interchangeability of coal-biomass syngas with natural gas for atmospheric burners and high-pressure combustion applications

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  • Quintero-Coronel, Daniel A.
  • Salazar, Adalberto
  • Pupo-Roncallo, Oscar R.
  • Bula, Antonio
  • Corredor, Lesme
  • Amador, German
  • Gonzalez-Quiroga, Arturo

Abstract

Syngas from biomass-coal co-gasification represents a viable energy vector to incorporate alternative energy sources into electricity production and industrial heating. Although the low energy content of syngas hinders its use, syngas-natural gas blends could overcome this challenge. This study assesses syngas-natural gas interchangeability in atmospheric burners via the combustion potential and the corrected Wobbe index according to Delbourg's approach. Likewise, this research evaluates methane number as an interchangeability indicator for high-pressure combustion. The syngas originated from a Top-Lit UpDraft gasifier, using coal-biomass blends of 0–100, 25–75, and 45–55 wt %, with air as the gasifying agent. The syngas featured a Lower Heating Value ranging from 3.0 to 3.8 MJ Nm-3. Results based on Delbourg's approach indicated that syngas-natural gas blends of up to 15 vol % syngas could operate in atmospheric natural gas burners without modifications. Furthermore, the methane number for syngas-natural gas blends of 15 vol % syngas was 5.8% higher than that of natural gas. The results show slight variations in flue gas composition, adiabatic flame temperature, and laminar flame velocity between natural gas and syngas-natural gas blends. The study showcases the potential of using syngas-natural gas blends in thermal applications and identifies key areas for further research.

Suggested Citation

  • Quintero-Coronel, Daniel A. & Salazar, Adalberto & Pupo-Roncallo, Oscar R. & Bula, Antonio & Corredor, Lesme & Amador, German & Gonzalez-Quiroga, Arturo, 2023. "Assessment of the interchangeability of coal-biomass syngas with natural gas for atmospheric burners and high-pressure combustion applications," Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:energy:v:276:y:2023:i:c:s0360544223009453
    DOI: 10.1016/j.energy.2023.127551
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    1. Zhang, Long & Zhou, Hua & Ren, Zhuyin, 2024. "Physics-guided fuel-switching neural networks for stable combustion of low calorific industrial gas," Energy, Elsevier, vol. 303(C).

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