Journal
FUEL
Volume 233, Issue -, Pages 529-538Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.fuel.2018.06.089
Keywords
High-ash sewage sludge; Pyrolysis; Thermal decomposition; Kinetics; Thermodynamic; Artificial neural network
Categories
Funding
- National University of Sciences Technology
- Knowledge Foundation (KKS)
- Malarenergi
- Eskilstuna Energi och Miljo
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Pyrolysis of high-ash sewage sludge (HASS) is a considered as an effective method and a promising way for energy production from solid waste of wastewater treatment facilities. The main purpose of this work is to build knowledge on pyrolysis mechanisms, kinetics, thermos-gravimetric analysis of high-ash (44.6%) sewage sludge using model-free methods & results validation with artificial neural network (ANN). TG-DTG curves at 5,10 and 20 degrees C/min showed the pyrolysis zone was divided into three zone. In kinetics, E values of models ranges are; Friedman (10.6-306.2 kJ/mol), FWO (45.6-231.7 kJ/mol), KAS (41.4-232.1 kJ/mol) and Popescu (44.1-241.1 kJ/mol) respectively. Delta H and Delta G values predicted by OFW, KAS and Popescu method are in good agreement and ranged from (41-236 kJ/mol) and 53-304 kJ/mol, respectively. Negative value of Delta S showed the non-spontaneity of the process. An artificial neural network (ANN) model of 2 * 5 * 1 architecture was employed to predict the thermal decomposition of high-ash sewage sludge, showed a good agreement between the experimental values and predicted values (R-2 >= 0.999) are much closer to 1. Overall, the study reflected the significance of ANN model that could be used as an effective fit model to the thermogravimetric experimental data.
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