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Pyrolysis of high-a...
Pyrolysis of high-ash sewage sludge : Thermo-kinetic study using TGA and artificial neural networks
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- Naqvi, Salman Raza (författare)
- Univ Twente, Fac Engn Technol, Enschede, Netherlands; Natl Univ Sci & Technol, Islamabad, Pakistan
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- Tariq, R. (författare)
- Natl Univ Sci & Technol, Islamabad, Pakistan
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- Hameed, Z. (författare)
- Natl Univ Sci & Technol, Islamabad, Pakistan
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- Ali, I. (författare)
- King Abdulaziz Univ, Dept Chem, Rabigh, Saudi Arabia; King Abdulaziz Univ, Dept Mat Engn, Rabigh, Saudi Arabia
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- Taqvi, S. A. (författare)
- Univ Teknol PETRONAS, Dept Chem Engn, Seri Iskandar, Malaysia; NED Univ Engn & Technol, Chem Engn Dept, Karachi 75270, Pakistan
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- Naqvi, Muhammad (författare)
- Karlstads universitet,Institutionen för ingenjörs- och kemivetenskaper (from 2013)
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- Niazi, M. B. K. (författare)
- Natl Univ Sci & Technol, Sch Chem & Mat Engn, Islamabad, Pakistan
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- Noor, T. (författare)
- Natl Univ Sci & Technol, Sch Chem & Mat Engn, Islamabad, Pakistan
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- Farooq, W. (författare)
- KFUPM, Dept Chem Engn, Dhahran, Saudi Arabia
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(creator_code:org_t)
- Oxon, UK : Elsevier Ltd, 2018
- 2018
- Engelska.
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Ingår i: Fuel. - Oxon, UK : Elsevier Ltd. - 0016-2361 .- 1873-7153. ; 233, s. 529-538
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Vattenteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Water Engineering (hsv//eng)
- NATURVETENSKAP -- Kemi (hsv//swe)
- NATURAL SCIENCES -- Chemical Sciences (hsv//eng)
Nyckelord
- Artificial neural network
- High-ash sewage sludge
- Kinetics
- Pyrolysis
- Thermal decomposition
- Thermodynamic
- Kemi
- Chemistry
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- art (ämneskategori)
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