Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/1877
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSujesh Ganitha
dc.contributor.authorSubbiah Ganesan
dc.contributor.authorSengottuvelu Ramesh
dc.date.accessioned2022-05-24T10:13:02Z-
dc.date.available2022-05-24T10:13:02Z-
dc.date.issued2020
dc.identifier.citationIET Renewable Power Generation
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/1877-
dc.description.abstractThis study introduces a new biodiesel blend as an alternative for diesel using waste cooking oil methyl ester by adding tyre pyrolysis oil and cerium oxide. Despite the conventional biodiesel blending models, this study made an effort to efficiently measure the prediction rate of these blended fuels by modelling through the deep belief network (DBN). To attain the accurate prediction, this study moves on with the new logic of optimal tuning of the count of hidden neurons in DBN. The optimal selection is carried out by introducing a new algorithm named lioness updated crow search algorithm (LCSA), which hybrids the concept of the lion algorithm (LA) and crow search algorithm (CSA). Finally, the proposed work is analysed and compared over other conventional models with respect to emission analysis and error analysis. From the analysis, the proposed model in terms of mean deviation (MD) measure has gained betterment and is 75.57, 17.71, 85.55, and 74.19% better than grey wolf optimiser (GWO), whale optimisation algorithm (WOA), LA, and CSA, respectively. For the mean absolute error measure, the implemented model is 42.38, 24.42, 43.53 and 36.72% improved than GWO, WOA, LA, and CSA, respectively.
dc.format.extent14
dc.language.isoen
dc.publisherThe Institution of Engineering and Technology
dc.titleModelling of biodiesel blend using optimised deep belief network: blending waste cooking oil methyl ester with tyre pyrolysis oil
dc.typeArticle
Appears in Collections:Mechanical Department

Files in This Item:
File SizeFormat 
SOE-Mech-08.pdf91.75 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.