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Co-combustion of sewage sludge and coffee grounds under increased O2/CO2 atmospheres: Thermodynamic characteristics, kinetics and artificial neural network modeling.
Chen, Jiacong; Xie, Candie; Liu, Jingyong; He, Yao; Xie, Wuming; Zhang, Xiaochun; Chang, Kenlin; Kuo, Jiahong; Sun, Jian; Zheng, Li; Sun, Shuiyu; Buyukada, Musa; Evrendilek, Fatih.
Afiliação
  • Chen J; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China.
  • Xie C; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China.
  • Liu J; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: www053991@126.com.
  • He Y; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China.
  • Xie W; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China.
  • Zhang X; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China.
  • Chang K; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China; Institute of Environmental Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan.
  • Kuo J; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China.
  • Sun J; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China.
  • Zheng L; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China.
  • Sun S; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China.
  • Buyukada M; Department of Environmental Engineering, Abant Izzet Baysal University, 14052 Bolu, Turkey.
  • Evrendilek F; Department of Environmental Engineering, Abant Izzet Baysal University, 14052 Bolu, Turkey.
Bioresour Technol ; 250: 230-238, 2018 Feb.
Article em En | MEDLINE | ID: mdl-29174900
ABSTRACT
(Co-)combustion characteristics of sewage sludge (SS), coffee grounds (CG) and their blends were quantified under increased O2/CO2 atmosphere (21, 30, 40 and 60%) using a thermogravimetric analysis. Observed percentages of CG mass loss and its maximum were higher than those of SS. Under the same atmospheric O2 concentration, both higher ignition and lower burnout temperatures occurred with the increased CG content. Results showed that ignition temperature and comprehensive combustion index for the blend of 60%SS-40%CG increased, whereas burnout temperature and co-combustion time decreased with the increased O2 concentration. Artificial neural network was applied to predict mass loss percent as a function of gas mixing ratio, heating rate, and temperature, with a good agreement between the experimental and ANN-predicted values. Activation energy in response to the increased O2 concentration was found to increase from 218.91 to 347.32 kJ·mol-1 and from 218.34 to 340.08 kJ·mol-1 according to the Kissinger-Akahira-Sunose and Flynn-Wall-Ozawa methods, respectively.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Esgotos / Redes Neurais de Computação Tipo de estudo: Prognostic_studies Idioma: En Revista: Bioresour Technol Assunto da revista: ENGENHARIA BIOMEDICA Ano de publicação: 2018 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Esgotos / Redes Neurais de Computação Tipo de estudo: Prognostic_studies Idioma: En Revista: Bioresour Technol Assunto da revista: ENGENHARIA BIOMEDICA Ano de publicação: 2018 Tipo de documento: Article País de afiliação: China