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Investigation of sewage sludge and peanut shells co‐combustion using thermogravimetric analysis and artificial neural network
Author(s) -
Bi Haobo,
Wang Chengxin,
Jiang Xuedan,
Jiang Chunlong,
Bao Lin,
Lin Qizhao
Publication year - 2020
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.6038
Subject(s) - thermogravimetric analysis , combustion , sewage sludge , isothermal process , kinetic energy , materials science , waste management , activation energy , chemical engineering , economic shortage , environmental science , chemistry , environmental engineering , sewage , engineering , thermodynamics , organic chemistry , physics , quantum mechanics , linguistics , philosophy , government (linguistics)
Summary To solve the problems of energy shortage and waste accumulation, the method of co‐combustion of sewage sludge (SS) and peanut shells (PS) was proposed. SS‐PS co‐combustion characteristics in air were investigated using artificial neural networks (ANN) and thermogravimetric analyses (TGA). The proportion of PS in the mixture was 10%‐50%. The temperature range of PS combustion (160‐560°C) is lower than that of PS combustion (170‐600°C). Activation energy was estimated from two non‐isothermal kinetic analysis methods: Kissinger‐Akahira‐Sunose (KAS) and Flynn‐Wall‐Ozawa (FWO). The kinetic mechanism of the combustion process was determined by using the master‐plots method. Multiple ANN models were established to predict TG data of SS‐PS co‐combustion. The best prediction model (ANN21) was obtained. The results showed a good overlap between the predicted and experimental TG data. The ANN model and the master‐plots method are the main innovations of this study. This study can promote the utilization and reduction of solid waste, and give guidance for the large‐scale application of SS‐PS co‐combustion.