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

Salman Raza Naqvi*, Rumaisa Tariq, Zeeshan Hameed, Imtiaz Ali, Syed A. Taqvi, Muhammad Naqvi, M. B.K. Niazi, Tayyaba Noor, Wasif Farooq

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    81 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)529-538
    Number of pages10
    Publication statusPublished - 1 Dec 2018


    • Artificial neural network
    • High-ash sewage sludge
    • Kinetics
    • Pyrolysis
    • Thermal decomposition
    • Thermodynamic


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