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Energy content, bulk density, and the latent heat of vaporisation characteristics of abattoir paunch waste

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  • Spence, Jennifer
  • Buttsworth, David
  • Carter, Brad

Abstract

This study investigated the higher heating values, lower heating values, bulk density, energy density, and latent heat of vaporisation characteristics of paunch. The higher heating values (gross calorific value) were 16.9 and 20.3 MJ kg−1 for grass and grain type paunches respectively. Bulk density ranged from 106 to 587 kg m−3 for dry to wet untapped paunch, and 152.04–884.64 kg m−3 for dry to wet tapped paunch. The energy density values for paunch varied from 4865 to −2110 MJ m−3. The latent heat for paunch varied from 3741 to 2519 kJ kg−1 for 6–15% moisture content (MC) with the average latent heat of vaporisation for paunch found to be 2842 kJ kg−1. These values show that paunch has the potential to be a useful waste-to-energy stream for implementation into industry and will help inform future paunch drying designs.

Suggested Citation

  • Spence, Jennifer & Buttsworth, David & Carter, Brad, 2022. "Energy content, bulk density, and the latent heat of vaporisation characteristics of abattoir paunch waste," Energy, Elsevier, vol. 248(C).
    • Handle: RePEc:eee:energy:v:248:y:2022:i:c:s0360544222005485
    DOI: 10.1016/j.energy.2022.123645
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    References listed on IDEAS

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    1. Gebreegziabher, Tesfaldet & Oyedun, Adetoyese Olajire & Hui, Chi Wai, 2013. "Optimum biomass drying for combustion – A modeling approach," Energy, Elsevier, vol. 53(C), pages 67-73.
    2. Spence, Jennifer & Buttsworth, David & McCabe, Bernadette K. & Baillie, Craig & Antille, Diogenes L. & Carter, Brad, 2018. "Investigation into thin layer drying rates and equilibrium moisture content of abattoir paunch waste," Renewable Energy, Elsevier, vol. 124(C), pages 95-102.
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    Cited by:

    1. Yang, Zhaoming & Liu, Zhe & Zhou, Jing & Song, Chaofan & Xiang, Qi & He, Qian & Hu, Jingjing & Faber, Michael H. & Zio, Enrico & Li, Zhenlin & Su, Huai & Zhang, Jinjun, 2023. "A graph neural network (GNN) method for assigning gas calorific values to natural gas pipeline networks," Energy, Elsevier, vol. 278(C).
    2. Yifan Chai & Igor V. Timoshkin & Mark P. Wilson & Martin J. Given & Scott J. MacGregor, 2023. "Free and Wire-Guided Spark Discharges in Water: Pre-Breakdown Energy Losses and Generated Pressure Impulses," Energies, MDPI, vol. 16(13), pages 1-18, June.

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