IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i7p2559-d784856.html
   My bibliography  Save this article

Application of Machine Learning to Predict the Performance of an EMIPG Reactor Using Data from Numerical Simulations

Author

Listed:
  • Owen Sedej

    (Department of Systems Engineering and Management, Air Force Institute of Technology, 2950 Hobson Way, Wright Patterson Air Force Base (WPAFB), Dayton, OH 45433, USA)

  • Eric Mbonimpa

    (Department of Systems Engineering and Management, Air Force Institute of Technology, 2950 Hobson Way, Wright Patterson Air Force Base (WPAFB), Dayton, OH 45433, USA)

  • Trevor Sleight

    (Department of Systems Engineering and Management, Air Force Institute of Technology, 2950 Hobson Way, Wright Patterson Air Force Base (WPAFB), Dayton, OH 45433, USA)

  • Jeremy Slagley

    (Department of Systems Engineering and Management, Air Force Institute of Technology, 2950 Hobson Way, Wright Patterson Air Force Base (WPAFB), Dayton, OH 45433, USA)

Abstract

Microwave-driven plasma gasification technology has the potential to produce clean energy from municipal and industrial solid wastes. It can generate temperatures above 2000 K (as high as 30,000 K) in a reactor, leading to complete combustion and reduction of toxic byproducts. Characterizing complex processes inside such a system is however challenging. In previous studies, simulations using computational fluid dynamics (CFD) produced reproducible results, but the simulations are tedious and involve assumptions. In this study, we propose machine-learning models that can be used in tandem with CFD, to accelerate high-fidelity fluid simulation, improve turbulence modeling, and enhance reduced-order models. A two-dimensional microwave-driven plasma gasification reactor was developed in ANSYS (Ansys, Canonsburg, PA, USA) Fluent (a CFD tool), to create 644 (geometry and temperature) datasets for training six machine-learning (ML) models. When fed with just geometry datasets, these ML models were able to predict the proportion of the reactor area with temperature above 2000 K. This temperature level is considered a benchmark to prevent formation of undesirable byproducts. The ML model that achieved highest prediction accuracy was the feed forward neural network; the mean absolute error was 0.011. This novel machine-learning model can enable future optimization of experimental microwave plasma gasification systems for application in waste-to-energy.

Suggested Citation

  • Owen Sedej & Eric Mbonimpa & Trevor Sleight & Jeremy Slagley, 2022. "Application of Machine Learning to Predict the Performance of an EMIPG Reactor Using Data from Numerical Simulations," Energies, MDPI, vol. 15(7), pages 1-22, March.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2559-:d:784856
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/7/2559/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/7/2559/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vecten, S. & Wilkinson, M. & Martin, A. & Dexter, A. & Bimbo, N. & Dawson, R. & Herbert, B., 2020. "Experimental study of steam and carbon dioxide microwave plasma for advanced thermal treatment application," Energy, Elsevier, vol. 207(C).
    2. 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.
    3. Hong, Yong C. & Lee, Sang J. & Shin, Dong H. & Kim, Ye J. & Lee, Bong J. & Cho, Seong Y. & Chang, Han S., 2012. "Syngas production from gasification of brown coal in a microwave torch plasma," Energy, Elsevier, vol. 47(1), pages 36-40.
    4. Lin, Kuang C. & Lin, Yuan-Chung & Hsiao, Yi-Hsing, 2014. "Microwave plasma studies of Spirulina algae pyrolysis with relevance to hydrogen production," Energy, Elsevier, vol. 64(C), pages 567-574.
    5. Sebastian, R.M. & Louis, J., 2021. "Understanding waste management at airports: A study on current practices and challenges based on literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    6. Nick Johnstone & Julien Labonne, 2004. "Generation of Household Solid Waste in OECD Countries: An Empirical Analysis Using Macroeconomic Data," Land Economics, University of Wisconsin Press, vol. 80(4).
    7. 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).
    8. Baruah, Dipal & Baruah, D.C., 2014. "Modeling of biomass gasification: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 806-815.
    9. Ozonoh, M. & Oboirien, B.O. & Higginson, A. & Daramola, M.O., 2020. "Performance evaluation of gasification system efficiency using artificial neural network," Renewable Energy, Elsevier, vol. 145(C), pages 2253-2270.
    10. Li, Jian & Tao, Junyu & Yan, Beibei & Jiao, Liguo & Chen, Guanyi & Hu, Jianli, 2021. "Review of microwave-based treatments of biomass gasification tar," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    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. Yang, Huayu & Yan, Bowen & Chen, Wei & Fan, Daming, 2023. "Prediction and innovation of sustainable continuous flow microwave processing based on numerical simulations: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(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. 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).
    2. Özveren, Uğur & Kartal, Furkan & Sezer, Senem & Özdoğan, Z. Sibel, 2022. "Investigation of steam gasification in thermogravimetric analysis by means of evolved gas analysis and machine learning," Energy, Elsevier, vol. 239(PC).
    3. Liu, Shanke & Yang, Yan & Yu, Lijun & Cao, Yu & Liu, Xinyi & Yao, Anqi & Cao, Yaping, 2023. "Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation," Energy, Elsevier, vol. 272(C).
    4. Kartal, Furkan & Özveren, Uğur, 2022. "Prediction of torrefied biomass properties from raw biomass," Renewable Energy, Elsevier, vol. 182(C), pages 578-591.
    5. Ajorloo, Mojtaba & Ghodrat, Maryam & Scott, Jason & Strezov, Vladimir, 2022. "Modelling and statistical analysis of plastic biomass mixture co-gasification," Energy, Elsevier, vol. 256(C).
    6. Czylkowski, Dariusz & Hrycak, Bartosz & Jasiński, Mariusz & Dors, Mirosław & Mizeraczyk, Jerzy, 2016. "Microwave plasma-based method of hydrogen production via combined steam reforming of methane," Energy, Elsevier, vol. 113(C), pages 653-661.
    7. Marcin Dębowski & Magda Dudek & Marcin Zieliński & Anna Nowicka & Joanna Kazimierowicz, 2021. "Microalgal Hydrogen Production in Relation to Other Biomass-Based Technologies—A Review," Energies, MDPI, vol. 14(19), pages 1-27, September.
    8. Samadi, Seyed Hashem & Ghobadian, Barat & Nosrati, Mohsen, 2020. "Prediction and estimation of biomass energy from agricultural residues using air gasification technology in Iran," Renewable Energy, Elsevier, vol. 149(C), pages 1077-1091.
    9. Hung-Ta Wen & Jau-Huai Lu & Mai-Xuan Phuc, 2021. "Applying Artificial Intelligence to Predict the Composition of Syngas Using Rice Husks: A Comparison of Artificial Neural Networks and Gradient Boosting Regression," Energies, MDPI, vol. 14(10), pages 1-18, May.
    10. Che, Yuechi & Jia, Xiaopeng & Hu, Yongjie & Li, Jian & Wang, Zhi & Yan, Beibei & Chen, Guanyi, 2024. "Microwave driven steam reforming of biomass model tar based on metal organic frameworks (ZIF-67) derived Co/C catalyst," Energy, Elsevier, vol. 304(C).
    11. Carmen van der Merwe & Martin de Wit, 2021. "An In-Depth Investigation into the Relationship Between Municipal Solid Waste Generation and Economic Growth in the City of Cape Town," Working Papers 07/2021, Stellenbosch University, Department of Economics, revised 2021.
    12. Ahmed Eid & May Salah & Mahmoud Barakat & Matevz Obrecht, 2022. "Airport Sustainability Awareness: A Theoretical Framework," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    13. Mazzanti, Massimiliano & Montini, Anna & Zoboli, Roberto, 2006. "Municipal Waste Production, Economic Drivers, and 'New' Waste Policies: EKC Evidence from Italian Regional and Provincial Panel Data," Climate Change Modelling and Policy Working Papers 12053, Fondazione Eni Enrico Mattei (FEEM).
    14. 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.
    15. Zhong, Hanbin & Xiong, Qingang & Yin, Lina & Zhang, Juntao & Zhu, Yuqin & Liang, Shengrong & Niu, Ben & Zhang, Xinyu, 2020. "CFD-based reduced-order modeling of fluidized-bed biomass fast pyrolysis using artificial neural network," Renewable Energy, Elsevier, vol. 152(C), pages 613-626.
    16. Di Foggia, Giacomo & Beccarello, Massimo, 2021. "Drivers of municipal solid waste management cost based on cost models inherent to sorted and unsorted waste," SocArXiv s6q3m, Center for Open Science.
    17. Cecere, Grazia & Mancinelli, Susanna & Mazzanti, Massimiliano, 2014. "Waste prevention and social preferences: the role of intrinsic and extrinsic motivations," Ecological Economics, Elsevier, vol. 107(C), pages 163-176.
    18. Sjöström, Magnus & Östblom, Göran, 2009. "Future Waste Scenarios for Sweden based on a CGE-model," Working Papers 109, National Institute of Economic Research.
    19. Elhambakhsh, Abbas & Van Duc Long, Nguyen & Lamichhane, Pradeep & Hessel, Volker, 2023. "Recent progress and future directions in plasma-assisted biomass conversion to hydrogen," Renewable Energy, Elsevier, vol. 218(C).
    20. Hemmings, Peter & Mulheron, Michael & Murphy, Richard J. & Prescott, Matt, 2023. "Investigating the robustness of UK airport net zero plans," Journal of Air Transport Management, Elsevier, vol. 113(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:gam:jeners:v:15:y:2022:i:7:p:2559-:d:784856. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.