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dc.contributor.authorZhang, Junhui
dc.contributor.authorLiu, Jingyong
dc.contributor.authorEvrendilek, Fatih
dc.contributor.authorZhang, Xiaochun
dc.contributor.authorBuyukada, Musa
dc.date.accessioned2021-06-23T19:51:02Z
dc.date.available2021-06-23T19:51:02Z
dc.date.issued2019
dc.identifier.issn0196-8904
dc.identifier.issn1879-2227
dc.identifier.urihttps://doi.org/10.1016/j.enconman.2019.05.019
dc.identifier.urihttps://hdl.handle.net/20.500.12491/9911
dc.descriptionWOS:000482244300028en_US
dc.description.abstractThe increased amounts of manure have become an issue of environmental management due to the rapid growth of livestock industry. This study quantified the pyrolytic performance and gaseous products of cattle manure using (derivative) thermogravimetric ((D)TG), Fourier transform infrared spectrometry (FTIR) and pyrolysis-gas chromatography and mass spectrometry (Py-GC/MS) analyses. The pyrolysis process of cattle manure was determined to occur in three stages, with the main reaction in the range of 161-600 degrees C. The N-2 atmosphere was found to be more favorable for the release of volatiles according to a higher comprehensive pyrolysis index in the range of 30 - 600 degrees C. The lower activation energies were shown to be required in the CO2 than N-2 atmosphere. Random forests algorithm outperformed multiple linear regression, gradient boosting machine, and artificial neural networks for the prediction of mass loss due to the cattle manure pyrolysis. The main gaseous products were CO2, phenol (23.23%), and furans (12.98%). The theoretical and practical guidance for the energy and resource utilization of cattle manure was provided by this study.en_US
dc.language.isoengen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCattle manureen_US
dc.subjectKinetic analysisen_US
dc.subjectRandom forestsen_US
dc.subjectTG-FTIRen_US
dc.subjectPy-GC/MSen_US
dc.titleTG-FTIR and Py-GC/MS analyses of pyrolysis behaviors and products of cattle manure in CO2 and N-2 atmospheres: Kinetic, thermodynamic, and machine-learning modelsen_US
dc.typearticleen_US
dc.contributor.department[0-Belirlenecek]en_US
dc.contributor.authorID0000-0001-5555-7509en_US
dc.contributor.authorID0000-0003-1099-4363en_US
dc.contributor.authorID0000-0001-6841-6457en_US
dc.contributor.institutionauthor[0-Belirlenecek]
dc.identifier.doi10.1016/j.enconman.2019.05.019
dc.identifier.volume195en_US
dc.identifier.startpage346en_US
dc.identifier.endpage359en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.relation.ispartofEnergy Conversion And Managementen_US


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