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Pyrolysis of high-ash sewage sludge: Thermo-kinetic study using TGA and artificial neural networks
Univ Twente, Fac Engn Technol, Enschede, Netherlands; Natl Univ Sci & Technol, Islamabad, Pakistan.
Natl Univ Sci & Technol, Islamabad, Pakistan.
Natl Univ Sci & Technol, Islamabad, Pakistan.
King Abdulaziz Univ, Dept Chem, Rabigh, Saudi Arabia; King Abdulaziz Univ, Dept Mat Engn, Rabigh, Saudi Arabia.
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2018 (English)In: Fuel, ISSN 0016-2361, E-ISSN 1873-7153, Vol. 233, p. 529-538Article in journal (Refereed) Published
Abstract [en]

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. © 2018 Elsevier Ltd

Place, publisher, year, edition, pages
Oxon, UK: Elsevier Ltd , 2018. Vol. 233, p. 529-538
Keywords [en]
Artificial neural network, High-ash sewage sludge, Kinetics, Pyrolysis, Thermal decomposition, Thermodynamic
National Category
Water Engineering Chemical Sciences
Research subject
Chemistry
Identifiers
URN: urn:nbn:se:kau:diva-68997DOI: 10.1016/j.fuel.2018.06.089ISI: 000441893200056Scopus ID: 2-s2.0-85048977469OAI: oai:DiVA.org:kau-68997DiVA, id: diva2:1245514
Available from: 2018-09-05 Created: 2018-09-05 Last updated: 2018-10-25Bibliographically approved

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Naqvi, Muhammad

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