IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v157y2022ics1364032121013149.html
   My bibliography  Save this article

Modeling the impact of some independent parameters on the syngas characteristics during plasma gasification of municipal solid waste using artificial neural network and stepwise linear regression methods

Author

Listed:
  • Chu, C.
  • Boré, A.
  • Liu, X.W.
  • Cui, J.C.
  • Wang, P.
  • Liu, X.
  • Chen, G.Y.
  • Liu, B.
  • Ma, W.C.
  • Lou, Z.Y.
  • Tao, Y.
  • Bary, A.

Abstract

Thermal plasma gasification is considered as an attractive technology to produce high quality syngas from municipal solid waste (MSW). It is imperative to study the effect of operating parameters on syngas quality and find a practical way to predict syngas characteristics. This paper compiled 112 research cases to develop quantitative models for 8 kinds of syngas characteristics and explored the simultaneous effects of input parameters during the plasma gasification by applying stepwise linear regression (SLR) and artificial neural network (ANN) methods. The SLR model has a higher predictive accuracy than the ANN model for gas yield, volume fraction of CH4 and CO2, as well as mechanical gasification efficiency (MGE), with Rtesting2 = 0.659–0.916. The ANN model demonstrates better performance than the SLR model for low heating value (LHV), dry gas ratio, volume fraction of H2 and CO, with Rtesting2 = 0.807–0.939. According to the importance analysis, flow rates of the work gas-N2, feedstock type, flow rates of the work gas-steam, and input power are the most critical parameters for LHV, gas yield, and volume fraction of CH4 and H2, respectively. Input power and specific energy requirements (SER) are the most important factors affecting volume fractions of H2 (25.7–57.3 vol%) and input power plays a dominant role. The models developed in this study could enhance understanding of plasma gasification and are unique to considering multiple input parameters together.

Suggested Citation

  • Chu, C. & Boré, A. & Liu, X.W. & Cui, J.C. & Wang, P. & Liu, X. & Chen, G.Y. & Liu, B. & Ma, W.C. & Lou, Z.Y. & Tao, Y. & Bary, A., 2022. "Modeling the impact of some independent parameters on the syngas characteristics during plasma gasification of municipal solid waste using artificial neural network and stepwise linear regression meth," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    • Handle: RePEc:eee:rensus:v:157:y:2022:i:c:s1364032121013149
    DOI: 10.1016/j.rser.2021.112052
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032121013149
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2021.112052?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. Andrius Tamošiūnas & Ajmia Chouchène & Pranas Valatkevičius & Dovilė Gimžauskaitė & Mindaugas Aikas & Rolandas Uscila & Makrem Ghorbel & Mejdi Jeguirim, 2017. "The Potential of Thermal Plasma Gasification of Olive Pomace Charcoal," Energies, MDPI, vol. 10(5), pages 1-14, May.
    2. Tamošiūnas, Andrius & Gimžauskaitė, Dovilė & Uscila, Rolandas & Aikas, Mindaugas, 2019. "Thermal arc plasma gasification of waste glycerol to syngas," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Teng, Sin Yong & Loy, Adrian Chun Minh & Leong, Wei Dong & How, Bing Shen & Chin, Bridgid Lai Fui & Máša, Vítězslav, 2019. "Catalytic thermal degradation of Chlorella Vulgaris: Evolving deep neural networks for optimization," MPRA Paper 95772, University Library of Munich, Germany.
    4. Parthasarathy, Prakash & Narayanan, K. Sheeba, 2014. "Hydrogen production from steam gasification of biomass: Influence of process parameters on hydrogen yield – A review," Renewable Energy, Elsevier, vol. 66(C), pages 570-579.
    5. Bong, Jang Tyng & Loy, Adrian Chun Minh & Chin, Bridgid Lai Fui & Lam, Man Kee & Tang, Daniel Kuok Ho & Lim, Huei Yeong & Chai, Yee Ho & Yusup, Suzana, 2020. "Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst," Energy, Elsevier, vol. 207(C).
    6. He, Pi-wen & Luo, Si-yi & Cheng, Gong & Xiao, Bo & Cai, Lei & Wang, Jin-bo, 2012. "Gasification of biomass char with air-steam in a cyclone furnace," Renewable Energy, Elsevier, vol. 37(1), pages 398-402.
    7. Kargbo, Hannah O. & Zhang, Jie & Phan, Anh N., 2021. "Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network," Applied Energy, Elsevier, vol. 302(C).
    8. Zhang, Qinglin & Dor, Liran & Fenigshtein, Dikla & Yang, Weihong & Blasiak, Wlodzmierz, 2012. "Gasification of municipal solid waste in the Plasma Gasification Melting process," Applied Energy, Elsevier, vol. 90(1), pages 106-112.
    9. Andrius Tamošiūnas & Dovilė Gimžauskaitė & Mindaugas Aikas & Rolandas Uscila & Marius Praspaliauskas & Justas Eimontas, 2019. "Gasification of Waste Cooking Oil to Syngas by Thermal Arc Plasma," Energies, MDPI, vol. 12(13), pages 1-13, July.
    10. Shie, Je-Lueng & Chen, Li-Xun & Lin, Kae-Long & Chang, Ching-Yuan, 2014. "Plasmatron gasification of biomass lignocellulosic waste materials derived from municipal solid waste," Energy, Elsevier, vol. 66(C), pages 82-89.
    Full references (including those not matched with items on IDEAS)

    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. Tian, Hailin & Li, Jie & Yan, Miao & Tong, Yen Wah & Wang, Chi-Hwa & Wang, Xiaonan, 2019. "Organic waste to biohydrogen: A critical review from technological development and environmental impact analysis perspective," Applied Energy, Elsevier, vol. 256(C).
    2. Chu, Chu & Wang, Ping & Boré, Abdoulaye & Ma, Wenchao & Chen, Guanyi & Wang, Pan, 2023. "Thermal plasma co-gasification of polyvinylchloride and biomass mixtures under steam atmospheres: Gasification characteristics and chlorine release behavior," Energy, Elsevier, vol. 262(PB).
    3. Jie Ma & Ming Zhang & Jianhua Wu & Qiwei Yang & Guangdong Wen & Baogen Su & Qilong Ren, 2017. "Hydropyrolysis of n- Hexane and Toluene to Acetylene in Rotating-Arc Plasma," Energies, MDPI, vol. 10(7), pages 1-12, July.
    4. Fang, Neng & Li, Zhengqi & Liu, Shuxuan & Xie, Cheng & Zeng, Lingyan & Chen, Zhichao, 2021. "Experimental air/particle flow characteristics of an 80,000 Nm3/h fly ash entrained-flow gasifier with different multi-burner arrangements," Energy, Elsevier, vol. 215(PB).
    5. Mallick, Roni & Vairakannu, Prabu, 2024. "Experimental studies on CO2-thermal plasma gasification of refused derived fuel feedstock for clean syngas production," Energy, Elsevier, vol. 288(C).
    6. 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.
    7. Lim, Juin Yau & Teng, Sin Yong & How, Bing Shen & Nam, KiJeon & Heo, SungKu & Máša, Vítězslav & Stehlík, Petr & Yoo, Chang Kyoo, 2022. "From microalgae to bioenergy: Identifying optimally integrated biorefinery pathways and harvest scheduling under uncertainties in predicted climate," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    8. 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.
    9. Banerjee, Debarun & Kushwaha, Nidhi & Shetti, Nagaraj P. & Aminabhavi, Tejraj M. & Ahmad, Ejaz, 2022. "Green hydrogen production via photo-reforming of bio-renewable resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    10. Ansari, Khursheed B. & Gaikar, Vilas G., 2019. "Investigating production of hydrocarbon rich bio-oil from grassy biomass using vacuum pyrolysis coupled with online deoxygenation of volatile products over metallic iron," Renewable Energy, Elsevier, vol. 130(C), pages 305-318.
    11. Arnob Das & Susmita Datta Peu, 2022. "A Comprehensive Review on Recent Advancements in Thermochemical Processes for Clean Hydrogen Production to Decarbonize the Energy Sector," Sustainability, MDPI, vol. 14(18), pages 1-42, September.
    12. Zhang, Guozhao & Liu, Hao & Wang, Jia & Wu, Baojia, 2018. "Catalytic gasification characteristics of rice husk with calcined dolomite," Energy, Elsevier, vol. 165(PB), pages 1173-1177.
    13. Moreira, Rui & Bimbela, Fernando & Gandía, Luis M. & Ferreira, Abel & Sánchez, Jose Luis & Portugal, António, 2021. "Oxidative steam reforming of glycerol. A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    14. Bong, Jang Tyng & Loy, Adrian Chun Minh & Chin, Bridgid Lai Fui & Lam, Man Kee & Tang, Daniel Kuok Ho & Lim, Huei Yeong & Chai, Yee Ho & Yusup, Suzana, 2020. "Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst," Energy, Elsevier, vol. 207(C).
    15. Fugang Zhu & Laihong Shen & Pengcheng Xu & Haoran Yuan & Ming Hu & Jingwei Qi & Yong Chen, 2022. "Numerical Simulation of an Improved Updraft Biomass Gasifier Based on Aspen Plus," IJERPH, MDPI, vol. 19(24), pages 1-11, December.
    16. Detchusananard, Thanaphorn & Im-orb, Karittha & Maréchal, François & Arpornwichanop, Amornchai, 2020. "Analysis of the sorption-enhanced chemical looping biomass gasification process: Performance assessment and optimization through design of experiment approach," Energy, Elsevier, vol. 207(C).
    17. Chen, Zhiyun & Chen, Huashan & Wu, Xieyuan & Zhang, Junhui & Evrendilek, Deniz Eren & Liu, Jingyong & Liang, Guanjie & Li, Weixin, 2021. "Temperature- and heating rate-dependent pyrolysis mechanisms and emissions of Chinese medicine residues and numerical reconstruction and optimization of their non-linear dynamics," Renewable Energy, Elsevier, vol. 164(C), pages 1408-1423.
    18. Nguyen, Nhut M. & Alobaid, Falah & May, Jan & Peters, Jens & Epple, Bernd, 2020. "Experimental study on steam gasification of torrefied woodchips in a bubbling fluidized bed reactor," Energy, Elsevier, vol. 202(C).
    19. Jia, Junxi & Abudula, Abuliti & Wei, Liming & Sun, Baozhi & Shi, Yue, 2015. "Thermodynamic modeling of an integrated biomass gasification and solid oxide fuel cell system," Renewable Energy, Elsevier, vol. 81(C), pages 400-410.
    20. Zheng, Jianpeng & Chen, Liubiao & Liu, Xuming & Zhu, Honglai & Zhou, Yuan & Wang, Junjie, 2020. "Thermodynamic optimization of composite insulation system with cold shield for liquid hydrogen zero-boil-off storage," Renewable Energy, Elsevier, vol. 147(P1), pages 824-832.

    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:rensus:v:157:y:2022:i:c:s1364032121013149. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.