IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v239y2022ipcs0360544221024804.html
   My bibliography  Save this article

Investigation of steam gasification in thermogravimetric analysis by means of evolved gas analysis and machine learning

Author

Listed:
  • Özveren, Uğur
  • Kartal, Furkan
  • Sezer, Senem
  • Özdoğan, Z. Sibel

Abstract

The syngas distribution from steam gasification depends on both the feedstock and the gasification conditions. Therefore, it is of utmost importance to increase the know-how about the overall picture of steam gasification. Thermogravimetric analysis (TGA) is a commonly used method that provides valuable information about the gasification process. The TGA designed for steam gasification and its auxiliary equipment are comparatively expensive, the experiments take a long time and need a qualified operator. Therefore, the development of an easily applicable computational method for thermogravimetric behavior during steam gasification is very important. Although there are some works on predicting the pyrolysis and combustion behavior using artificial neural network (ANN), a model that predicts gasification behavior by TGA has not been studied. In this study, the gasification behavior and gas product characteristics of solid fuels were investigated by TGA coupled with mass spectrometry. Moreover, we report the first comprehensive model to estimate the thermogravimetric behavior of steam gasification using ANN as a machine learning approach. The ANN model provides a reliable estimation with an R2 value of greater than 0.999. Moreover, MAPE values are reported to average less than 1%, while 6.5% for pyrolysis and 33.6% for extrapolated validation conditions.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:239:y:2022:i:pc:s0360544221024804
    DOI: 10.1016/j.energy.2021.122232
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221024804
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2021.122232?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. Song, Weiming & Zhou, Jianan & Li, Yujie & Li, Shu & Yang, Jian, 2021. "Utilization of waste tire powder for gaseous fuel generation via CO2 gasification using waste heat in converter vaporization cooling flue," Renewable Energy, Elsevier, vol. 173(C), pages 283-296.
    2. Xie, Candie & Liu, Jingyong & Zhang, Xiaochun & Xie, Wuming & Sun, Jian & Chang, Kenlin & Kuo, Jiahong & Xie, Wenhao & Liu, Chao & Sun, Shuiyu & Buyukada, Musa & Evrendilek, Fatih, 2018. "Co-combustion thermal conversion characteristics of textile dyeing sludge and pomelo peel using TGA and artificial neural networks," Applied Energy, Elsevier, vol. 212(C), pages 786-795.
    3. 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.
    4. Baruah, Dipal & Baruah, D.C., 2014. "Modeling of biomass gasification: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 806-815.
    5. Tanksale, Akshat & Beltramini, Jorge Norberto & Lu, GaoQing Max, 2010. "A review of catalytic hydrogen production processes from biomass," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 166-182, January.
    6. Yan, Beibei & Jiao, Liguo & Li, Jian & Zhu, Xiaochao & Ahmed, Sarwaich & Chen, Guanyi, 2021. "Investigation on microwave torrefaction: Parametric influence, TG-MS-FTIR analysis, and gasification performance," Energy, Elsevier, vol. 220(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. 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).
    2. Ravi Kumar Kottala & Bharat Kumar Chigilipalli & Srinivasnaik Mukuloth & Ragavanantham Shanmugam & Venkata Charan Kantumuchu & Sirisha Bhadrakali Ainapurapu & Muralimohan Cheepu, 2023. "Thermal Degradation Studies and Machine Learning Modelling of Nano-Enhanced Sugar Alcohol-Based Phase Change Materials for Medium Temperature Applications," Energies, MDPI, vol. 16(5), pages 1-24, February.

    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. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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).
    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. 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.
    8. Wen, Shaoting & Buyukada, Musa & Evrendilek, Fatih & Liu, Jingyong, 2020. "Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models," Renewable Energy, Elsevier, vol. 151(C), pages 463-474.
    9. Han, Yongming & Liu, Shuang & Cong, Di & Geng, Zhiqiang & Fan, Jinzhen & Gao, Jingyang & Pan, Tingrui, 2021. "Resource optimization model using novel extreme learning machine with t-distributed stochastic neighbor embedding: Application to complex industrial processes," Energy, Elsevier, vol. 225(C).
    10. Shahbeig, Hossein & Nosrati, Mohsen, 2020. "Pyrolysis of municipal sewage sludge for bioenergy production: Thermo-kinetic studies, evolved gas analysis, and techno-socio-economic assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    11. González, William A. & Pérez, Juan F. & Chapela, Sergio & Porteiro, Jacobo, 2018. "Numerical analysis of wood biomass packing factor in a fixed-bed gasification process," Renewable Energy, Elsevier, vol. 121(C), pages 579-589.
    12. Nestor Sanchez & Ruth Yolanda Ruiz & Nicolas Infante & Martha Cobo, 2017. "Bioethanol Production from Cachaza as Hydrogen Feedstock: Effect of Ammonium Sulfate during Fermentation," Energies, MDPI, vol. 10(12), pages 1-16, December.
    13. 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.
    14. 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).
    15. Kamel, Salah & El-Sattar, Hoda Abd & Vera, David & Jurado, Francisco, 2018. "Bioenergy potential from agriculture residues for energy generation in Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 28-37.
    16. Yan, Beibei & Li, Songjiang & Cao, Xingsijin & Zhu, Xiaochao & Li, Jian & Zhou, Shengquan & Zhao, Juan & Sun, Yunan & Chen, Guanyi, 2023. "Flue gas torrefaction integrated with gasification based on the circulation of Mg-additive," Applied Energy, Elsevier, vol. 333(C).
    17. Samiee-Zafarghandi, Roudabeh & Karimi-Sabet, Javad & Abdoli, Mohammad Ali & Karbassi, Abdolreza, 2018. "Supercritical water gasification of microalga Chlorella PTCC 6010 for hydrogen production: Box-Behnken optimization and evaluating catalytic effect of MnO2/SiO2 and NiO/SiO2," Renewable Energy, Elsevier, vol. 126(C), pages 189-201.
    18. Sun, Shaohui & Yan, Wei & Sun, Peiqin & Chen, Junwu, 2012. "Thermodynamic analysis of ethanol reforming for hydrogen production," Energy, Elsevier, vol. 44(1), pages 911-924.
    19. Vakalis, Stergios & Moustakas, Konstantinos, 2019. "Modelling of advanced gasification systems (MAGSY): Simulation and validation for the case of the rising co-current reactor," Applied Energy, Elsevier, vol. 242(C), pages 526-533.
    20. Bi, Haobo & Wang, Chengxin & Lin, Qizhao & Jiang, Xuedan & Jiang, Chunlong & Bao, Lin, 2020. "Combustion behavior, kinetics, gas emission characteristics and artificial neural network modeling of coal gangue and biomass via TG-FTIR," Energy, Elsevier, vol. 213(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:239:y:2022:i:pc:s0360544221024804. 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.