IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v213y2020ics0360544220318971.html
   My bibliography  Save this article

Combustion behavior, kinetics, gas emission characteristics and artificial neural network modeling of coal gangue and biomass via TG-FTIR

Author

Listed:
  • Bi, Haobo
  • Wang, Chengxin
  • Lin, Qizhao
  • Jiang, Xuedan
  • Jiang, Chunlong
  • Bao, Lin

Abstract

The combustion behavior and gas product characteristics of coal gangue (CG) and peanut shell (PS) in air atmosphere were studied by thermogravimetry-Fourier transform infrared spectroscopy (TG-FTIR). Artificial neural network (ANN) method was used to establish the optimal prediction model of CG and PS co-combustion. The heating rate of TG-FTIR experiment was set to 10 °C/min, 20 °C/min and 30 °C/min. The mass fractions of PS in the experimental samples were 0%, 25%, 50%, 75% and 100%. Some functional groups in the gas products were detected by Fourier transform infrared spectrometer. Moreover, the apparent activation energy (E) was calculated by Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS). The activation energies of CG and PS mixture combustion are significantly lower than that of pure substance. ANN models have been established to predict the relationship between mass loss and experimental conditions. By comparing errors and correlation coefficients, it is found that the ANN20 model is optimal.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:213:y:2020:i:c:s0360544220318971
    DOI: 10.1016/j.energy.2020.118790
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220318971
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.118790?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. Pallarés, Javier & Herce, Carlos & Bartolomé, Carmen & Peña, Begoña, 2017. "Investigation on co-firing of coal mine waste residues in pulverized coal combustion systems," Energy, Elsevier, vol. 140(P1), pages 58-68.
    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.
    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. Liu, Lang & Ren, Shan & Yang, Jian & Jiang, Donghai & Guo, Junjiang & Pu, Yubao & Meng, Xianpiao, 2022. "Experimental study on K migration, ash fouling/slagging behaviors and CO2 emission during co-combustion of rice straw and coal gangue," Energy, Elsevier, vol. 251(C).
    2. Ma, Jiao & Feng, Shuo & Zhang, Zhikun & Wang, Zhuozhi & Kong, Wenwen & Yuan, Peng & Shen, Boxiong & Mu, Lan, 2022. "Effect of torrefaction pretreatment on the combustion characteristics of the biodried products derived from municipal organic wastes," Energy, Elsevier, vol. 239(PD).
    3. Lei, Yang & Chen, Yuming & Chen, Jinghai & Liu, Xinyan & Wu, Xiaoqin & Chen, Yuqiu, 2023. "A novel modeling strategy for the prediction on the concentration of H2 and CH4 in raw coke oven gas," Energy, Elsevier, vol. 273(C).
    4. Zhao, Shuchun & Guo, Junheng & Dang, Xiuhu & Ai, Bingyan & Zhang, Minqing & Li, Wei & Zhang, Jinli, 2022. "Energy consumption, flow characteristics and energy-efficient design of cup-shape blade stirred tank reactors: Computational fluid dynamics and artificial neural network investigation," Energy, Elsevier, vol. 240(C).
    5. Zhang, Yuanbo & Zhang, Yutao & Li, Yaqing & Shi, Xueqiang & Che, Bo, 2022. "Determination of ignition temperature and kinetics and thermodynamics analysis of high-volatile coal based on differential derivative thermogravimetry," Energy, Elsevier, vol. 240(C).
    6. Guo, Qian & Tang, Yibo, 2022. "Laboratory investigation of the spontaneous combustion characteristics and mechanisms of typical vegetable oils," Energy, Elsevier, vol. 241(C).
    7. Tian, Lu & Lin, Kunsen & Zhao, Youcai & Zhao, Chunlong & Huang, Qifei & Zhou, Tao, 2022. "Combustion performance of fine screenings from municipal solid waste: Thermo-kinetic investigation and deep learning modeling via TG-FTIR," Energy, Elsevier, vol. 243(C).
    8. Chen, Zhiyun & Liu, Jingyong & Chen, Huashan & Ding, Ziyi & Tang, Xiaojie & Evrendilek, Fatih, 2022. "Oxy-fuel and air atmosphere combustions of Chinese medicine residues: Performances, mechanisms, flue gas emission, and ash properties," Renewable Energy, Elsevier, vol. 182(C), pages 102-118.
    9. Tariq, Rumaisa & Mohd Zaifullizan, Yasmin & Salema, Arshad Adam & Abdulatif, Atiqah & Ken, Loke Shun, 2022. "Co-pyrolysis and co-combustion of orange peel and biomass blends: Kinetics, thermodynamic, and ANN application," Renewable Energy, Elsevier, vol. 198(C), pages 399-414.
    10. Ge, Lichao & Zhao, Can & Chen, Simo & Li, Qian & Zhou, Tianhong & Jiang, Han & Li, Xi & Wang, Yang & Xu, Chang, 2022. "An analysis of the carbonization process and volatile-release characteristics of coal-based activated carbon," Energy, Elsevier, vol. 257(C).
    11. Roman Volkov & Timur Valiullin & Olga Vysokomornaya, 2021. "Spraying of Composite Liquid Fuels Based on Types of Coal Preparation Waste: Current Problems and Achievements: Review," Energies, MDPI, vol. 14(21), pages 1-17, November.
    12. Miao, Hengyang & Wang, Zhiqing & Wang, Zhefan & Sun, Haochen & Li, Xiangyu & Liu, Zheyu & Dong, Libo & Zhao, Jiantao & Huang, Jiejie & Fang, Yitian, 2022. "Effects of Na2CO3/Na2SO4 on catalytic gasification reactivity and mineral structure of coal gangue," Energy, Elsevier, vol. 255(C).
    13. Vershinina, Ksenia Yu & Dorokhov, Vadim V. & Romanov, Daniil S. & Strizhak, Pavel A., 2022. "Combustion stages of waste-derived blends burned as pellets, layers, and droplets of slurry," Energy, Elsevier, vol. 251(C).
    14. Xiangbing Gao & Bo Jia & Gen Li & Xiaojing Ma, 2022. "Calorific Value Forecasting of Coal Gangue with Hybrid Kernel Function–Support Vector Regression and Genetic Algorithm," Energies, MDPI, vol. 15(18), pages 1-15, September.
    15. Jiang, Chunlong & Zhou, Wenliang & Bi, Haobo & Ni, Zhanshi & Sun, Hao & Lin, Qizhao, 2022. "Co-pyrolysis of coal slime and cattle manure by TG–FTIR–MS and artificial neural network modeling: Pyrolysis behavior, kinetics, gas emission characteristics," Energy, Elsevier, vol. 247(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. 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.
    2. 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).
    3. Zhao, Jingyu & Deng, Jun & Wang, Tao & Song, Jiajia & Zhang, Yanni & Shu, Chi-Min & Zeng, Qiang, 2019. "Assessing the effectiveness of a high-temperature-programmed experimental system for simulating the spontaneous combustion properties of bituminous coal through thermokinetic analysis of four oxidatio," Energy, Elsevier, vol. 169(C), pages 587-596.
    4. Peña, B. & Pallarés, J. & Bartolomé, C. & Herce, C., 2018. "Experimental study on the effects of co-firing coal mine waste residues with coal in PF swirl burners," Energy, Elsevier, vol. 157(C), pages 45-53.
    5. Vershinina, K. Yu & Shlegel, N.E. & Strizhak, P.A., 2019. "Relative combustion efficiency of composite fuels based on of wood processing and oil production wastes," Energy, Elsevier, vol. 169(C), pages 18-28.
    6. Mlonka-Mędrala, Agata & Dziok, Tadeusz & Magdziarz, Aneta & Nowak, Wojciech, 2021. "Composition and properties of fly ash collected from a multifuel fluidized bed boiler co-firing refuse derived fuel (RDF) and hard coal," Energy, Elsevier, vol. 234(C).
    7. Prabhakaran, SP Sathiya & Swaminathan, Ganapathiraman & Joshi, Viraj V., 2022. "Combustion and pyrolysis kinetics of Australian lignite coal and validation by artificial neural networks," Energy, Elsevier, vol. 242(C).
    8. Wen, Shaoting & Yan, Youping & Liu, Jingyong & Buyukada, Musa & Evrendilek, Fatih, 2019. "Pyrolysis performance, kinetic, thermodynamic, product and joint optimization analyses of incense sticks in N2 and CO2 atmospheres," Renewable Energy, Elsevier, vol. 141(C), pages 814-827.
    9. Jiang, Chunlong & Zhou, Wenliang & Bi, Haobo & Ni, Zhanshi & Sun, Hao & Lin, Qizhao, 2022. "Co-pyrolysis of coal slime and cattle manure by TG–FTIR–MS and artificial neural network modeling: Pyrolysis behavior, kinetics, gas emission characteristics," Energy, Elsevier, vol. 247(C).
    10. 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).
    11. 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.
    12. Wang, Pengqian & Wang, Chang'an & Yuan, Maobo & Wang, Chaowei & Zhang, Jinping & Du, Yongbo & Tao, Zichen & Che, Defu, 2020. "Experimental evaluation on co-combustion characteristics of semi-coke and coal under enhanced high-temperature and strong-reducing atmosphere," Applied Energy, Elsevier, vol. 260(C).
    13. Liu, Chao & Liu, Jingyong & Evrendilek, Fatih & Xie, Wuming & Kuo, Jiahong & Buyukada, Musa, 2020. "Bioenergy and emission characterizations of catalytic combustion and pyrolysis of litchi peels via TG-FTIR-MS and Py-GC/MS," Renewable Energy, Elsevier, vol. 148(C), pages 1074-1093.
    14. Liang Song & Shanjun Liu & Wenwen Li, 2019. "Quantitative Inversion of Fixed Carbon Content in Coal Gangue by Thermal Infrared Spectral Data," Energies, MDPI, vol. 12(9), pages 1-17, May.
    15. Ni, Zhanshi & Bi, Haobo & Jiang, Chunlong & Sun, Hao & Zhou, Wenliang & Qiu, Zhicong & He, Liqun & Lin, Qizhao, 2022. "Research on the co-pyrolysis of coal slime and lignin based on the combination of TG-FTIR, artificial neural network, and principal component analysis," Energy, Elsevier, vol. 261(PA).
    16. Tian, Lu & Lin, Kunsen & Zhao, Youcai & Zhao, Chunlong & Huang, Qifei & Zhou, Tao, 2022. "Combustion performance of fine screenings from municipal solid waste: Thermo-kinetic investigation and deep learning modeling via TG-FTIR," Energy, Elsevier, vol. 243(C).
    17. Zhuang, Xiuzheng & Song, Yanpei & Zhan, Hao & Yin, Xiuli & Wu, Chuangzhi, 2019. "Synergistic effects on the co-combustion of medicinal biowastes with coals of different ranks," Renewable Energy, Elsevier, vol. 140(C), pages 380-389.
    18. Hao, Runlong & Zhang, Zili & Zeng, Qinda & Mao, Yumin & He, Hongzhou & Mao, Xingzhou & Yang, Fan & Zhao, Yi, 2018. "Synergistic behaviors of anthracite and dried sawdust sludge during their co-combustion: Conversion ratio, micromorphology variation and constituents evolutions," Energy, Elsevier, vol. 153(C), pages 776-787.
    19. Sun, Hao & Bi, Haobo & Jiang, Chunlong & Ni, Zhanshi & Tian, Junjian & Zhou, Wenliang & Qiu, Zhicong & Lin, Qizhao, 2022. "Experimental study of the co-pyrolysis of sewage sludge and wet waste via TG-FTIR-GC and artificial neural network model: Synergistic effect, pyrolysis kinetics and gas products," Renewable Energy, Elsevier, vol. 184(C), pages 1-14.

    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:213:y:2020:i:c:s0360544220318971. 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.