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Co-combustion thermal conversion characteristics of textile dyeing sludge and pomelo peel using TGA and artificial neural networks

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  • Xie, Candie
  • Liu, Jingyong
  • Zhang, Xiaochun
  • Xie, Wuming
  • Sun, Jian
  • Chang, Kenlin
  • Kuo, Jiahong
  • Xie, Wenhao
  • Liu, Chao
  • Sun, Shuiyu
  • Buyukada, Musa
  • Evrendilek, Fatih

Abstract

Co-combustion characteristics of textile dyeing sludge (TDS) and pomelo peel (PP) under O2/N2 and O2/CO2 atmospheres were investigated using a thermogravimetric analysis (TGA) and artificial neural networks. 30% O2/70% CO2 and air atmospheres led to a similar co-combustion performance. Increases in O2 concentration and PP significantly improved the oxy-fuel co-combustion performance of TDS. Principal component analysis was applied to reduce the dimensionality of differential TGA curves and to identify the principal reactions. The interaction between TDS and PP occurred mainly at 490–600 °C, thus improving the process of residue co-combustion. Radial basis function was found to have more reliable and robust predictions of TGA under different O2/CO2 atmospheres than did Bayesian regularized network. Regardless of Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) methods used, the lowest mean value of apparent activation energy (155.4 kJ·mol−1 by FWO and 153.2 kJ·mol−1 by KAS) was obtained under the 30% O2/70% CO2 atmosphere.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:212:y:2018:i:c:p:786-795
    DOI: 10.1016/j.apenergy.2017.12.084
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    Cited by:

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    5. 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.
    6. 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).
    7. 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.
    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. 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.
    10. 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.
    11. 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).
    12. 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).
    13. 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).
    14. 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).

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