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Poultry litter valorization: Development and optimization of an electro-chemical and thermal tri-generation process using an extreme gradient boosting algorithm

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  • Ayub, Yousaf
  • Ren, Jingzheng
  • Shi, Tao
  • Shen, Weifeng
  • He, Chang

Abstract

A novel configuration of a tri-generation process for poultry litter valorization, including gasification, solid oxide fuel cell (SOFC), and combined heat and power system was examined in this research. Multi-level factorial, design of experiment methodology has been adopted to extract the simulation data from Aspen Plus simulation model by changing one parameter at a time. Extreme gradient boosting has been applied on the factorial design data to predict and optimize the parametric yield of this model. Results of gasification process sensitivity analysis show that pressure has no significant effect on output yield, but it has a negative effect on SOFC voltage. While gasification process temperature operating condition around 600 °C and 0.25–0.33 biomass to air ratio (BMR) can generate optimum hydrogen yield in syngas. Coefficient of determinant (R2) for Extreme Gradient Booster (XGB) model is greater than 0.97 for all dependent variables. According to XGB results, BMR is the most contributing factor which affects the output of this study. Exergy efficiency of this tri-generation process is 34.6% more than the gasification process. Therefore, based on the findings of this model, it is concluded that this tri-generation process could be the better possible solution for poultry litter valorization.

Suggested Citation

  • Ayub, Yousaf & Ren, Jingzheng & Shi, Tao & Shen, Weifeng & He, Chang, 2023. "Poultry litter valorization: Development and optimization of an electro-chemical and thermal tri-generation process using an extreme gradient boosting algorithm," Energy, Elsevier, vol. 263(PC).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222027256
    DOI: 10.1016/j.energy.2022.125839
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    References listed on IDEAS

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    1. Ayub, Yousaf & Hu, Yusha & Ren, Jingzheng, 2023. "Estimation of syngas yield in hydrothermal gasification process by application of artificial intelligence models," Renewable Energy, Elsevier, vol. 215(C).
    2. Manish Meena & Hrishikesh Kumar & Nitin Dutt Chaturvedi & Andrey A. Kovalev & Vadim Bolshev & Dmitriy A. Kovalev & Prakash Kumar Sarangi & Aakash Chawade & Manish Singh Rajput & Vivekanand Vivekanand , 2023. "Biomass Gasification and Applied Intelligent Retrieval in Modeling," Energies, MDPI, vol. 16(18), pages 1-21, September.
    3. Zhang, Rongquan & Bu, Siqi & Li, Gangqiang, 2024. "Multi-market P2P trading of cooling–heating-power-hydrogen integrated energy systems: An equilibrium-heuristic online prediction optimization approach," Applied Energy, Elsevier, vol. 367(C).
    4. Long Zhang & Jingzheng Ren & Wuliyasu Bai, 2023. "A Review of Poultry Waste-to-Wealth: Technological Progress, Modeling and Simulation Studies, and Economic- Environmental and Social Sustainability," Sustainability, MDPI, vol. 15(7), pages 1-23, March.
    5. Ayub, Yousaf & Zhou, Jianzhao & Shen, Weifeng & Ren, Jingzheng, 2023. "Innovative valorization of biomass waste through integration of pyrolysis and gasification: Process design, optimization, and multi-scenario sustainability analysis," Energy, Elsevier, vol. 282(C).

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