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Applied energy v.212, 2018년, pp.786 - 795   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

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

Xie, Candie (School of Environmental Science and Engineering, Institute of Environmental-Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China ) ; Liu, Jingyong (School of Environmental Science and Engineering, Institute of Environmental-Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China ) ; Zhang, Xiaochun (School of Environmental Science and Engineering, Institute of Environmental-Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China ) ; Xie, Wuming (School of Environmental Science and Engineering, Institute of Environmental-Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China ) ; Sun, Jian (School of Environmental Science and Engineering, Institute of Environmental-Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China ) ; Chang, Kenlin (School of Environmental Science and Engineering, Institute of Environmental-Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, ) ; Kuo, Jiahong ; Xie, Wenhao ; Liu, Chao ; Sun, Shuiyu ; Buyukada, Musa ; Evrendilek, Fatih ;
  • 초록  

    Abstract Co-combustion characteristics of textile dyeing sludge (TDS) and pomelo peel (PP) under O 2 /N 2 and O 2 /CO 2 atmospheres were investigated using a thermogravimetric analysis (TGA) and artificial neural networks. 30% O 2 /70% CO 2 and air atmospheres led to a similar co-combustion performance. Increases in O 2 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 O 2 /CO 2 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% O 2 /70% CO 2 atmosphere. Highlights Co-combustion of TDS and PP was studied under O 2 /N 2 and O 2 /CO 2 atmospheres. Principal component analysis was used to identify the principal reactions. The interaction of blends occurred mainly between 490 and 600 °C. Bayesian regularized network had a more reliable and robust prediction. The lowest activation energy was obtained under 30% O 2 /70% CO 2 atmosphere.


  • 주제어

    Oxy-fuel combustion .   Textile dyeing sludge .   Pomelo peel .   Thermogravimetric analysis .   Artificial neural networks .   Principal component analysis.  

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