TY - JOUR
T1 - Pyrolysis of high-ash sewage sludge
T2 - Thermo-kinetic study using TGA and artificial neural networks
AU - Naqvi, Salman Raza
AU - Tariq, Rumaisa
AU - Hameed, Zeeshan
AU - Ali, Imtiaz
AU - Taqvi, Syed A.
AU - Naqvi, Muhammad
AU - Niazi, M. B.K.
AU - Noor, Tayyaba
AU - Farooq, Wasif
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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 °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. ΔH and ΔG 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 ΔS 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 (R2 ⩾ 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.
AB - 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 °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. ΔH and ΔG 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 ΔS 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 (R2 ⩾ 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.
KW - Artificial neural network
KW - High-ash sewage sludge
KW - Kinetics
KW - Pyrolysis
KW - Thermal decomposition
KW - Thermodynamic
UR - http://www.scopus.com/inward/record.url?scp=85048977469&partnerID=8YFLogxK
U2 - 10.1016/j.fuel.2018.06.089
DO - 10.1016/j.fuel.2018.06.089
M3 - Article
AN - SCOPUS:85048977469
VL - 233
SP - 529
EP - 538
JO - Fuel
JF - Fuel
SN - 0016-2361
ER -