Pyrolysis of high-ash sewage sludge: Thermo-kinetic study using TGA and artificial neural networks

• Main Authors: Naqvi, S.R., Tariq, R., Hameed, Z., Ali, I., Taqvi, S.A., Naqvi, M., Niazi, M.B.K., Noor, T., Farooq, W.
• Format: Article
• Published: Elsevier Ltd 2018
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048977469&doi=10.1016%2fj.fuel.2018.06.089&partnerID=40&md5=f2adb815363ca6145ead05ea20541371; http://eprints.utp.edu.my/21393/
• Institution: Universiti Teknologi Petronas

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. �H and �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 �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 (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. © 2018 Elsevier Ltd