Zobrazit minimální záznam

dc.contributor.authorAhmad, Uzair
dc.contributor.authorNaqvi, Salman Raza
dc.contributor.authorAli, Imtiaz
dc.contributor.authorSaleem, Faisal
dc.contributor.authorMehran, Muhammad Taqi
dc.contributor.authorSikandar, Umair
dc.contributor.authorJuchelková, Dagmar
dc.date.accessioned2022-09-06T12:14:39Z
dc.date.available2022-09-06T12:14:39Z
dc.date.issued2022
dc.identifier.citationFuel. 2022, vol. 324, art. no. 124565.cs
dc.identifier.issn0016-2361
dc.identifier.issn1873-7153
dc.identifier.urihttp://hdl.handle.net/10084/148587
dc.description.abstractThe study presents the production of biolubricant from castor oil using Fe3O4 nanoparticles and ethylene glycol in a transesterification process, as an additive. Operational parameters such as FAME/alcohol, catalyst loading, and temperature were optimized. The reaction was complete after two hours at 160 degrees C, giving a yield of 94 %. To enhance the physiochemical properties of modified castor seed oil (MCSO), Fe3O4 nanoparticles and ethylene glycol were used. The biolubricant yield was also predicted using artificial neural networks (ANN). The multilayer perceptron (MLP)-based ANN showed a linear correlation between the output and target values at different temperatures, the amount of catalyst, and the alcohol/FAME ratios during training, testing, and validation. The tribological properties of the biolubricant produced (MCSO + ethylene glycol + 0.5 % Fe3O4 nanoparticles) showed the lowest coefficient of friction (almost 50%) and 40% decreased wear compared to raw castor seed oil and other biolubricant samples produced.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesFuelcs
dc.relation.urihttps://doi.org/10.1016/j.fuel.2022.124565cs
dc.rights© 2022 Elsevier Ltd. All rights reserved.cs
dc.subjectbiolubricantcs
dc.subjectiron oxidecs
dc.subjectnanoparticlescs
dc.subjectartificial neural networkcs
dc.subjecttribological propertiescs
dc.titleBiolubricant production from castor oil using iron oxide nanoparticles as an additive: Experimental, modelling and tribological assessmentcs
dc.typearticlecs
dc.identifier.doi10.1016/j.fuel.2022.124565
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume324cs
dc.description.firstpageart. no. 124565cs
dc.identifier.wos000807571500002


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