IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v278y2023ipbs0360544223012756.html
   My bibliography  Save this article

Predicting municipal solid waste gasification using machine learning: A step toward sustainable regional planning

Author

Listed:
  • Yang, Yadong
  • Shahbeik, Hossein
  • Shafizadeh, Alireza
  • Rafiee, Shahin
  • Hafezi, Amir
  • Du, Xinyi
  • Pan, Junting
  • Tabatabaei, Meisam
  • Aghbashlo, Mortaza

Abstract

The gasification process can treat and valorize municipal solid waste (MSW) in an environmentally and economically friendly way. Using this process, MSW can be safely disposed of and sustainably converted into bioenergy as part of regional planning. Experimental laboratory data is a key component in designing, optimizing, controlling, and scaling up MSW gasifiers. However, most researchers lack the resources and time to conduct experiments. Machine learning (ML) technology can resolve this issue by detecting patterns and hidden information in published data. Hence, the present study aims to construct an inclusive ML model to predict and understand the MSW gasification process. The objective is to establish a consistent and homogeneous database containing MSW sources under different gasification conditions, followed by an analysis of the database using statistical methods. Three ML models are used to predict the distribution of syngas, char, and tar and the quality of syngas in MSW gasification using feedstock characteristics and gasification parameters. When a gradient boost regressor is used to model the process, the prediction accuracy is highest (R2 > 0.926, RMSE <6.318, and RRMSE <0.304). SHAP analysis is successfully used to understand the significance and contribution of descriptors on targets in the modeling process.

Suggested Citation

  • Yang, Yadong & Shahbeik, Hossein & Shafizadeh, Alireza & Rafiee, Shahin & Hafezi, Amir & Du, Xinyi & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2023. "Predicting municipal solid waste gasification using machine learning: A step toward sustainable regional planning," Energy, Elsevier, vol. 278(PB).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pb:s0360544223012756
    DOI: 10.1016/j.energy.2023.127881
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223012756
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127881?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Baruah, Dipal & Baruah, D.C., 2014. "Modeling of biomass gasification: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 806-815.
    2. Aghbashlo, Mortaza & Tabatabaei, Meisam & Soltanian, Salman & Ghanavati, Hossein, 2019. "Biopower and biofertilizer production from organic municipal solid waste: An exergoenvironmental analysis," Renewable Energy, Elsevier, vol. 143(C), pages 64-76.
    3. Ramos, Ana & Monteiro, Eliseu & Silva, Valter & Rouboa, Abel, 2018. "Co-gasification and recent developments on waste-to-energy conversion: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 380-398.
    4. Elmaz, Furkan & Yücel, Özgün & Mutlu, Ali Yener, 2020. "Predictive modeling of biomass gasification with machine learning-based regression methods," Energy, Elsevier, vol. 191(C).
    5. Ascher, Simon & Sloan, William & Watson, Ian & You, Siming, 2022. "A comprehensive artificial neural network model for gasification process prediction," Applied Energy, Elsevier, vol. 320(C).
    6. Soltanian, Salman & Kalogirou, Soteris A. & Ranjbari, Meisam & Amiri, Hamid & Mahian, Omid & Khoshnevisan, Benyamin & Jafary, Tahereh & Nizami, Abdul-Sattar & Gupta, Vijai Kumar & Aghaei, Siavash & Pe, 2022. "Exergetic sustainability analysis of municipal solid waste treatment systems: A systematic critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    7. Elmaz, Furkan & Yücel, Özgün, 2020. "Data-driven identification and model predictive control of biomass gasification process for maximum energy production," Energy, Elsevier, vol. 195(C).
    8. Siwal, Samarjeet Singh & Zhang, Qibo & Devi, Nishu & Saini, Adesh Kumar & Saini, Vipin & Pareek, Bhawna & Gaidukovs, Sergejs & Thakur, Vijay Kumar, 2021. "Recovery processes of sustainable energy using different biomass and wastes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    9. Yoonsuh Jung & Jianhua Hu, 2015. "A K -fold averaging cross-validation procedure," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(2), pages 167-179, June.
    10. Yang, Yadong & Shahbeik, Hossein & Shafizadeh, Alireza & Masoudnia, Nima & Rafiee, Shahin & Zhang, Yijia & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2022. "Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries," Renewable Energy, Elsevier, vol. 201(P2), pages 70-86.
    11. M. Shahabuddin & Tanvir Alam, 2022. "Gasification of Solid Fuels (Coal, Biomass and MSW): Overview, Challenges and Mitigation Strategies," Energies, MDPI, vol. 15(12), pages 1-20, June.
    12. Bhoi, Prakashbhai R. & Huhnke, Raymond L. & Kumar, Ajay & Indrawan, Natarianto & Thapa, Sunil, 2018. "Co-gasification of municipal solid waste and biomass in a commercial scale downdraft gasifier," Energy, Elsevier, vol. 163(C), pages 513-518.
    13. Shahbeik, Hossein & Rafiee, Shahin & Shafizadeh, Alireza & Jeddi, Dorsa & Jafary, Tahereh & Lam, Su Shiung & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2022. "Characterizing sludge pyrolysis by machine learning: Towards sustainable bioenergy production from wastes," Renewable Energy, Elsevier, vol. 199(C), pages 1078-1092.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Qi, Jingwei & Wang, Yijie & Xu, Pengcheng & Hu, Ming & Huhe, Taoli & Ling, Xiang & Yuan, Haoran & Chen, Yong, 2024. "Study on the Co-gasification characteristics of biomass and municipal solid waste based on machine learning," Energy, Elsevier, vol. 290(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Soltanian, Salman & Kalogirou, Soteris A. & Ranjbari, Meisam & Amiri, Hamid & Mahian, Omid & Khoshnevisan, Benyamin & Jafary, Tahereh & Nizami, Abdul-Sattar & Gupta, Vijai Kumar & Aghaei, Siavash & Pe, 2022. "Exergetic sustainability analysis of municipal solid waste treatment systems: A systematic critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    2. Yang, Yadong & Shahbeik, Hossein & Shafizadeh, Alireza & Masoudnia, Nima & Rafiee, Shahin & Zhang, Yijia & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2022. "Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries," Renewable Energy, Elsevier, vol. 201(P2), pages 70-86.
    3. Ascher, Simon & Watson, Ian & You, Siming, 2022. "Machine learning methods for modelling the gasification and pyrolysis of biomass and waste," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    4. Ramos, Ana & Monteiro, Eliseu & Rouboa, Abel, 2019. "Numerical approaches and comprehensive models for gasification process: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 188-206.
    5. Gabbrielli, Roberto & Barontini, Federica & Frigo, Stefano & Bressan, Luigi, 2022. "Numerical analysis of bio-methane production from biomass-sewage sludge oxy-steam gasification and methanation process," Applied Energy, Elsevier, vol. 307(C).
    6. Despina Vamvuka & Petros Tsilivakos, 2024. "Energy Recovery from Municipal Solid Waste through Co-Gasification Using Steam or Carbon Dioxide with Olive By-Products," Energies, MDPI, vol. 17(2), pages 1-13, January.
    7. Qi, Jingwei & Wang, Yijie & Xu, Pengcheng & Hu, Ming & Huhe, Taoli & Ling, Xiang & Yuan, Haoran & Chen, Yong, 2024. "Study on the Co-gasification characteristics of biomass and municipal solid waste based on machine learning," Energy, Elsevier, vol. 290(C).
    8. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    9. Ajorloo, Mojtaba & Ghodrat, Maryam & Scott, Jason & Strezov, Vladimir, 2022. "Modelling and statistical analysis of plastic biomass mixture co-gasification," Energy, Elsevier, vol. 256(C).
    10. Ismail, Tamer M. & Ramos, Ana & Monteiro, Eliseu & El-Salam, M. Abd & Rouboa, Abel, 2020. "Parametric studies in the gasification agent and fluidization velocity during oxygen-enriched gasification of biomass in a pilot-scale fluidized bed: Experimental and numerical assessment," Renewable Energy, Elsevier, vol. 147(P1), pages 2429-2439.
    11. Zhang, Jinchun & Hou, Jinxiu & Zhang, Zichuan, 2022. "Real-time identification of out-of-control and instability in process parameter for gasification process: Integrated application of control chart and kalman filter," Energy, Elsevier, vol. 238(PB).
    12. Mehrpooya, Mehdi & Khalili, Maryam & Sharifzadeh, Mohammad Mehdi Moftakhari, 2018. "Model development and energy and exergy analysis of the biomass gasification process (Based on the various biomass sources)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 869-887.
    13. Mariyam, Sabah & Shahbaz, Muhammad & Al-Ansari, Tareq & Mackey, Hamish. R & McKay, Gordon, 2022. "A critical review on co-gasification and co-pyrolysis for gas production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    14. Fazil, A. & Kumar, Sandeep & Mahajani, Sanjay M., 2023. "Gasification and Co-gasification of paper-rich, high-ash refuse-derived fuel in downdraft gasifier," Energy, Elsevier, vol. 263(PA).
    15. Kartal, Furkan & Özveren, Uğur, 2020. "A deep learning approach for prediction of syngas lower heating value from CFB gasifier in Aspen plus®," Energy, Elsevier, vol. 209(C).
    16. Li, Jinhu & Burra, Kiran Raj G. & Wang, Zhiwei & Liu, Xuan & Gupta, Ashwani K., 2021. "Co-gasification of high-density polyethylene and pretreated pine wood," Applied Energy, Elsevier, vol. 285(C).
    17. Istrate, Ioan-Robert & Medina-Martos, Enrique & Galvez-Martos, Jose-Luis & Dufour, Javier, 2021. "Assessment of the energy recovery potential of municipal solid waste under future scenarios," Applied Energy, Elsevier, vol. 293(C).
    18. Yek, Peter Nai Yuh & Cheng, Yoke Wang & Liew, Rock Keey & Wan Mahari, Wan Adibah & Ong, Hwai Chyuan & Chen, Wei-Hsin & Peng, Wanxi & Park, Young-Kwon & Sonne, Christian & Kong, Sieng Huat & Tabatabaei, 2021. "Progress in the torrefaction technology for upgrading oil palm wastes to energy-dense biochar: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    19. Benim, Ali Cemal & Pfeiffelmann, Björn & Ocłoń, Paweł & Taler, Jan, 2019. "Computational investigation of a lifted hydrogen flame with LES and FGM," Energy, Elsevier, vol. 173(C), pages 1172-1181.
    20. Costantini, Michele & Provolo, Giorgio & Bacenetti, Jacopo, 2024. "The effects of incorporating renewable energy into the environmental footprint of beef production," Energy, Elsevier, vol. 289(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:278:y:2023:i:pb:s0360544223012756. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.