Pyrolysis of high-ash sewage sludge: Thermo-kinetic study using TGA and artificial neural networks. (1st December 2018)

• Record Type: Journal Article
• Authors: Naqvi, Salman Raza; Tariq, Rumaisa; Hameed, Zeeshan; Ali, Imtiaz; Taqvi, Syed A.; Naqvi, Muhammad; Niazi, M.B.K.; Noor, Tayyaba; Farooq, Wasif
• Is Part Of: Fuel. Volume 233(2018)
• Page Start: 529
• Page End: 538
• Publication Date: 2018-12-01
• Subjects: High-ash sewage sludge -- Pyrolysis -- Thermal decomposition -- Kinetics -- Thermodynamic -- Artificial neural network; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2018.06.089
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: A thermo-kinetic study is performed of high ash sewage sludge. ANN, for the first time, is applied for sewage sludge pyrolysis process. Kinetics is estimated using model-free methods as a function of conversion. Results provide reference to promote pilot scale high-ash sewage sludge. Abstract: 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 (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.