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

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

Applied Energy, 2018, vol. 212, issue C, 786-795

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.

Keywords: Oxy-fuel combustion; Textile dyeing sludge; Pomelo peel; Thermogravimetric analysis; Artificial neural networks; Principal component analysis (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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DOI: 10.1016/j.apenergy.2017.12.084

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