@article{scholars9559,
             doi = {10.1016/j.fuel.2018.06.089},
            year = {2018},
           title = {Pyrolysis of high-ash sewage sludge: Thermo-kinetic study using TGA and artificial neural networks},
            note = {cited By 136},
       publisher = {Elsevier Ltd},
         journal = {Fuel},
          volume = {233},
           pages = {529--538},
        keywords = {Enzyme kinetics; Gravimetric analysis; Kinetics; Pyrolysis; Sewage sludge; Thermodynamics; Thermolysis; Waste treatment; Wastewater treatment, Artificial neural network models; Energy productions; Experimental values; Model-free method; Negative values; Pyrolysis mechanism; Thermo-gravimetric; Wastewater treatment facilities, Neural networks},
        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 {\^A}oC/min showed the pyrolysis zone was divided into three zone. In kinetics, E values of models ranges are; Friedman (10.6{\^a}??306.2 kJ/mol), FWO (45.6{\^a}??231.7 kJ/mol), KAS (41.4{\^a}??232.1 kJ/mol) and Popescu (44.1{\^a}??241.1 kJ/mol) respectively. {\^I}?H and {\^I}?G values predicted by OFW, KAS and Popescu method are in good agreement and ranged from (41{\^a}??236 kJ/mol) and 53{\^a}??304 kJ/mol, respectively. Negative value of {\^I}?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 {\^a}{\copyright}3/4 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. {\^A}{\copyright} 2018 Elsevier Ltd},
            issn = {00162361},
          author = {Naqvi, S. R. and Tariq, R. and Hameed, Z. and Ali, I. and Taqvi, S. A. and Naqvi, M. and Niazi, M. B. K. and Noor, T. and Farooq, W.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048977469&doi=10.1016\%2fj.fuel.2018.06.089&partnerID=40&md5=f2adb815363ca6145ead05ea20541371}
}