eprintid: 9559
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/95/59
datestamp: 2023-11-09 16:36:12
lastmod: 2023-11-09 16:36:12
status_changed: 2023-11-09 16:29:17
type: article
metadata_visibility: show
creators_name: Naqvi, S.R.
creators_name: Tariq, R.
creators_name: Hameed, Z.
creators_name: Ali, I.
creators_name: Taqvi, S.A.
creators_name: Naqvi, M.
creators_name: Niazi, M.B.K.
creators_name: Noor, T.
creators_name: Farooq, W.
title: Pyrolysis of high-ash sewage sludge: Thermo-kinetic study using TGA and artificial neural networks
ispublished: pub
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
note: cited By 136
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. �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
date: 2018
publisher: Elsevier Ltd
official_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
id_number: 10.1016/j.fuel.2018.06.089
full_text_status: none
publication: Fuel
volume: 233
pagerange: 529-538
refereed: TRUE
issn: 00162361
citation:   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.  (2018) Pyrolysis of high-ash sewage sludge: Thermo-kinetic study using TGA and artificial neural networks.  Fuel, 233.  pp. 529-538.  ISSN 00162361