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Regression Model to Predict the Higher Heating Value of Poultry Waste from Proximate Analysis

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  • Xuejun Qian

    (Industrial and Systems Engineering Department, Morgan State University, 1700 East Cold Spring Lane, Baltimore, MD 21251, USA
    Center for Advanced Energy Systems and Environmental Technologies, School of Engineering, Morgan State University, 5200 Perring Parkway, Baltimore, MD 21239, USA)

  • Seong Lee

    (Industrial and Systems Engineering Department, Morgan State University, 1700 East Cold Spring Lane, Baltimore, MD 21251, USA
    Center for Advanced Energy Systems and Environmental Technologies, School of Engineering, Morgan State University, 5200 Perring Parkway, Baltimore, MD 21239, USA)

  • Ana-maria Soto

    (Department of Chemistry, Towson University, 8000 York Road, Towson, MD 21252, USA)

  • Guangming Chen

    (Industrial and Systems Engineering Department, Morgan State University, 1700 East Cold Spring Lane, Baltimore, MD 21251, USA)

Abstract

Improper land application of excess poultry waste (PW) causes environmental issues and other problems. Meanwhile there is an increasing trend of using PW as an alternative energy resource. The Higher Heating Value (HHV) is critical for designing and analyzing the PW conversion process. Several proximate-based mathematical models have been proposed to estimate the HHV of biomass, coal, and other solid fuels. Nevertheless, only a small number of studies have focused on a subclass of fuels, especially for PW. The aim of this study is to develop proximate-based regression models for an HHV prediction of PW. Sample data of PW were collected from open literature to develop regression models. The resulting models were then validated by additional PW samples and other published models. Results indicate that the most accurate model contains linear (all proximate components), polynomial terms (quadratic and cubic of volatile matter), and interaction effect (fixed carbon and ash). Moreover, results show that best-fit regression model has a higher R 2 (91.62%) and lower estimation errors than the existing proximate-based models. Therefore, this new regression model can be an excellent tool for predicting the HHV of PW and does not require any expensive equipment that measures HHV or elemental compositions.

Suggested Citation

  • Xuejun Qian & Seong Lee & Ana-maria Soto & Guangming Chen, 2018. "Regression Model to Predict the Higher Heating Value of Poultry Waste from Proximate Analysis," Resources, MDPI, vol. 7(3), pages 1-14, June.
    • Handle: RePEc:gam:jresou:v:7:y:2018:i:3:p:39-:d:154537
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    References listed on IDEAS

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    1. Burra, K.G. & Hussein, M.S. & Amano, R.S. & Gupta, A.K., 2016. "Syngas evolutionary behavior during chicken manure pyrolysis and air gasification," Applied Energy, Elsevier, vol. 181(C), pages 408-415.
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    2. Muhammad Usman & Atif Jahanger & Muhammad Sohail Amjad Makhdum & Magdalena Radulescu & Daniel Balsalobre-Lorente & Elena Jianu, 2022. "An Empirical Investigation of Ecological Footprint Using Nuclear Energy, Industrialization, Fossil Fuels and Foreign Direct Investment," Energies, MDPI, vol. 15(17), pages 1-26, September.
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    9. Marreiro, Hívila M.P. & Peruchi, Rogério S. & Lopes, Riuzuani M.B.P. & Rotella Junior, Paulo, 2024. "Briquetting process optimization of poultry litter and urban wood waste," Renewable Energy, Elsevier, vol. 222(C).
    10. Atilgan Atilgan & Anna Krakowiak-Bal & Hasan Ertop & Burak Saltuk & Mateusz Malinowski, 2023. "The Energy Potential of Waste from Banana Production: A Case Study of the Mediterranean Region," Energies, MDPI, vol. 16(14), pages 1-13, July.

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