Co-combustion thermal conversion characteristics of textile dyeing sludge and pomelo peel using TGA and artificial neural networks

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dc.authorid0000-0003-1099-4363en_US
dc.authorid0000-0001-6841-6457en_US
dc.authorid0000-0003-1099-4363en_US
dc.authorid0000-0002-8148-5163en_US
dc.contributor.authorXie, Candie
dc.contributor.authorLiu, Jingyong
dc.contributor.authorZhang, Xiaochun
dc.contributor.authorXie, Wuming
dc.contributor.authorSun, Jian
dc.contributor.authorChang, Kenlin
dc.contributor.authorKuo, Jiahong
dc.contributor.authorBüyükada, Musa
dc.contributor.authorEvrendilek, Fatih
dc.date.accessioned2021-06-23T19:49:56Z
dc.date.available2021-06-23T19:49:56Z
dc.date.issued2018
dc.departmentBAİBÜ, Mühendislik Fakültesi, Çevre Mühendisliği Bölümüen_US
dc.description.abstractCo-combustion characteristics of textile dyeing sludge (TDS) and pomelo peel (PP) under O-2/N-2 and O-2/CO2 atmospheres were investigated using a thermogravimetric analysis (TGA) and artificial neural networks. 30% O-2/70% CO2 and air atmospheres led to a similar co-combustion performance. Increases in O-2 concentration and PP significantly improved the oxy-fuel co-combustion performance of TDS. Principal component analysis was applied to reduce the dimensionality of differential TGA curves and to identify the principal reactions. The interaction between TDS and PP occurred mainly at 490-600 degrees C, thus improving the process of residue co combustion. Radial basis function was found to have more reliable and robust predictions of TGA under different O-2/CO2 atmospheres than did Bayesian regularized network. Regardless of Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) methods used, the lowest mean value of apparent activation energy (155.4 kJ.mol(-1) by FWO and 153.2 kJ.mol(-1) by KAS) was obtained under the 30% O-2/70% CO2 atmosphere.en_US
dc.identifier.doi10.1016/j.apenergy.2017.12.084
dc.identifier.endpage795en_US
dc.identifier.issn0306-2619
dc.identifier.issn1872-9118
dc.identifier.scopus2-s2.0-85039745183en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage786en_US
dc.identifier.urihttps://doi.org/10.1016/j.apenergy.2017.12.084
dc.identifier.urihttps://hdl.handle.net/20.500.12491/9659
dc.identifier.volume212en_US
dc.identifier.wosWOS:000425200700059en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBüyükada, Musa
dc.institutionauthorEvrendilek, Fatih
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofApplied Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectOxy-fuel Combustionen_US
dc.subjectTextile Cyeing Sludgeen_US
dc.subjectPomelo Peelen_US
dc.subjectThermogravimetric Analysisen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectPrincipal Component Analysisen_US
dc.titleCo-combustion thermal conversion characteristics of textile dyeing sludge and pomelo peel using TGA and artificial neural networksen_US
dc.typeArticleen_US

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