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Application of machine learning to model waste energy recovery for green hydrogen production: A techno-economic analysis

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  • Mojtahed, Ali
  • Lo Basso, Gianluigi
  • Pastore, Lorenzo Mario
  • Sgaramella, Antonio
  • de Santoli, Livio

Abstract

This paper presents an innovative energy recovery approach for hydrogen production in landfill waste disposal plants. The proposed scenario integrates water electrolysis with direct methane reforming into hydrogen at a moderate temperature (500 °C) and incorporates a supercritical CO₂ heat pump. This design achieves reforming without relying on external heat sources, enhancing the system's efficiency. Additionally, the study applies machine learning to model landfill gas with a focus on energy recovery potential. Various machine learning algorithms are assessed for accuracy, and the highest-performing models—achieving R-squared values between 92 % and 99%—are benchmarked against existing landfill models, demonstrating improved precision. The landfill model developed in the initial phase serves as input for the energy model. Results suggest that the levelized cost of hydrogen production could be below 2 €/kg H₂ at stack level, aided by internal energy recovery mechanisms that increase production rates. At 500 °C, the methane conversion efficiency aligns closely with that of conventional systems, making this approach a viable and cost-effective alternative.

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  • Mojtahed, Ali & Lo Basso, Gianluigi & Pastore, Lorenzo Mario & Sgaramella, Antonio & de Santoli, Livio, 2025. "Application of machine learning to model waste energy recovery for green hydrogen production: A techno-economic analysis," Energy, Elsevier, vol. 315(C).
    • Handle: RePEc:eee:energy:v:315:y:2025:i:c:s036054422404115x
    DOI: 10.1016/j.energy.2024.134337
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    1. Beatrice Vincenti & Francesco Gallucci & Enrico Paris & Monica Carnevale & Adriano Palma & Mariangela Salerno & Carmine Cava & Orlando Palone & Giuliano Agati & Michele Vincenzo Migliarese Caputi & Do, 2023. "Syngas Quality in Fluidized Bed Gasification of Biomass: Comparison between Olivine and K-Feldspar as Bed Materials," Sustainability, MDPI, vol. 15(3), pages 1-12, February.
    2. Cudjoe, Dan & Han, Myat Su & Chen, Weiming, 2021. "Power generation from municipal solid waste landfilled in the Beijing-Tianjin-Hebei region," Energy, Elsevier, vol. 217(C).
    3. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
    4. Li, Xiaodong & Jinxi, Wang, 2023. "A novel process for the simultaneous production of methanol, oxygen, and electricity using a PEM electrolyzer and agricultural-based landfill gas-fed oxyfuel combustion power plant," Energy, Elsevier, vol. 284(C).
    5. Sgaramella, Antonio & Pastore, Lorenzo Mario & Lo Basso, Gianluigi & de Santoli, Livio, 2023. "Optimal RES integration for matching the Italian hydrogen strategy requirements," Renewable Energy, Elsevier, vol. 219(P1).
    6. Bhandari, Ramchandra & Shah, Ronak Rakesh, 2021. "Hydrogen as energy carrier: Techno-economic assessment of decentralized hydrogen production in Germany," Renewable Energy, Elsevier, vol. 177(C), pages 915-931.
    7. Korberg, Andrei David & Skov, Iva Ridjan & Mathiesen, Brian Vad, 2020. "The role of biogas and biogas-derived fuels in a 100% renewable energy system in Denmark," Energy, Elsevier, vol. 199(C).
    8. Cormos, Calin-Cristian, 2023. "Green hydrogen production from decarbonized biomass gasification: An integrated techno-economic and environmental analysis," Energy, Elsevier, vol. 270(C).
    9. Khayyam, Hamid & Naebe, Minoo & Milani, Abbas S. & Fakhrhoseini, Seyed Mousa & Date, Abhijit & Shabani, Bahman & Atkiss, Steve & Ramakrishna, Seeram & Fox, Bronwyn & Jazar, Reza N., 2021. "Improving energy efficiency of carbon fiber manufacturing through waste heat recovery: A circular economy approach with machine learning," Energy, Elsevier, vol. 225(C).
    10. Gao, Tian & Niu, Dongxiao & Ji, Zhengsen & Sun, Lijie, 2022. "Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm," Energy, Elsevier, vol. 261(PB).
    11. Bi, Yujing & Ju, Yonglin, 2022. "Design and analysis of an efficient hydrogen liquefaction process based on helium reverse Brayton cycle integrating with steam methane reforming and liquefied natural gas cold energy utilization," Energy, Elsevier, vol. 252(C).
    12. Shahbaz, Muhammad & Taqvi, Syed A. & Minh Loy, Adrian Chun & Inayat, Abrar & Uddin, Fahim & Bokhari, Awais & Naqvi, Salman Raza, 2019. "Artificial neural network approach for the steam gasification of palm oil waste using bottom ash and CaO," Renewable Energy, Elsevier, vol. 132(C), pages 243-254.
    13. Elmaz, Furkan & Yücel, Özgün & Mutlu, Ali Yener, 2020. "Predictive modeling of biomass gasification with machine learning-based regression methods," Energy, Elsevier, vol. 191(C).
    14. Kim, Jun Young & Shin, Ui Hyeon & Kim, Kwangsu, 2023. "Predicting biomass composition and operating conditions in fluidized bed biomass gasifiers: An automated machine learning approach combined with cooperative game theory," Energy, Elsevier, vol. 280(C).
    15. Parente, Marcelo & Soria, M.A. & Madeira, Luis M., 2020. "Hydrogen and/or syngas production through combined dry and steam reforming of biogas in a membrane reactor: A thermodynamic study," Renewable Energy, Elsevier, vol. 157(C), pages 1254-1264.
    16. Ghafoori, Mohammad Samim & Loubar, Khaled & Marin-Gallego, Mylène & Tazerout, Mohand, 2022. "Techno-economic and sensitivity analysis of biomethane production via landfill biogas upgrading and power-to-gas technology," Energy, Elsevier, vol. 239(PB).
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    1. Axel Riccardo Massulli & Fosca Carolina Rosa & Gianluigi Lo Basso, 2025. "Moving Towards Fourth-Generation District Heating as a Power-to-Heat Strategy: Techno-Economic Issues," Sustainability, MDPI, vol. 17(8), pages 1-19, April.

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