Hybrid optimization and modelling of CI engine performance and emission characteristics of novel hybrid biodiesel blends

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dc.authoridSaboor, Shaik/0000-0002-0490-4766
dc.authoridSaid, Zafar/0000-0003-2376-9309
dc.authoridAfzal, Asif/0000-0003-2961-6186
dc.authoridSaid, Zafar/0000-0003-2376-9309
dc.authoridAğbulut, Ümit/0000-0002-6635-6494
dc.authoridVeza, Ibham/0000-0002-1674-4798
dc.authorwosidSaboor, Shaik/M-8170-2018
dc.authorwosidSaid, Zafar/ABC-1650-2021
dc.authorwosidSaid, Zafar/C-4086-2016
dc.authorwosidAfzal, Asif/U-3071-2017
dc.contributor.authorViswanathan, Vinoth Kannan
dc.contributor.authorKaladgi, Abdul Razak
dc.contributor.authorThomai, Pushparaj
dc.contributor.authorAğbulut, Ümit
dc.contributor.authorAlwetaishi, Mamdooh
dc.contributor.authorSaid, Zafar
dc.contributor.authorShaik, Saboor
dc.date.accessioned2023-07-26T11:50:57Z
dc.date.available2023-07-26T11:50:57Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümüen_US
dc.description.abstractDifferent meta-heuristic optimization algorithms have been used in a variety of fields due to their intelligent behavior and fast convergence. However, use of these algorithms in the engine behavior optimization is very-limited. The development of so-called hybrid optimization technique when these algorithms are combined with experimental design technique is an upcoming method in the field of renewable energy. Hence in this research, meta-heuristic optimization algorithms and experimental design methods were combined to optimize the engine behavior. Additionally, artificial neural networks (ANN) were employed to forecast the performance and emission behaviors of a CI engine running on a novel hybrid biodiesel blend of Cucurbita pepo. L (pumpkin) and Prosopis juliflora, mixed with a novel Elaeocarpus ganitrus (Rudraksha) additive. To assess the success of the ANN, four statistical benchmarks (R-2, and MSE) were used. Experiments were designed according to Design of Experiments (DOE) rules with performance and emission parameters as outputs. Response surface methodology (RSM) was employed to find the effect of interaction factors. Single objective and multi-objective optimization using highly efficient hybrid RSM-particle swarm optimization (RPSO) and dragon fly algorithm (RMODA) were employed to optimize the response of the obtained RSM equations. The outcomes demonstrated that RSM and ANN were excellent modelling techniques for these kinds of situations, with good accuracy. In addition, ANN's prediction performance (R-2 = 0.978 for BTE) was somewhat better than RSM's (R-2 = 0.960 for BTE). On the other hand, the PJB20 blend with 5 mL additive increased BTE by 52.8% and reduced BSFC by 34.9% at maximum load. The smoke opacity was lowered by 7.1% when compared to pure diesel, without any engine modifications. CO2 emission was seen to be shortened by 19.14%. Finally, it can be concluded that this novel biodiesel can be possibly utilized in CI engines with no modification and the engine characteristics can be controlled by optimization and prediction models.en_US
dc.description.sponsorshipTaif University Researchers Supporting Project, Taif University, Taif, Saudi Arabia [TURSP-2020/196]; Taif Uni- versity, Taif, Saudi Arabiaen_US
dc.description.sponsorshipThe authors acknowledge the support received by Taif University Researchers Supporting Project number (TURSP-2020/196) , Taif Uni- versity, Taif, Saudi Arabia.en_US
dc.identifier.doi10.1016/j.renene.2022.08.008
dc.identifier.endpage567en_US
dc.identifier.issn0960-1481
dc.identifier.issn1879-0682
dc.identifier.scopus2-s2.0-85136507302en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage549en_US
dc.identifier.urihttps://doi.org/10.1016/j.renene.2022.08.008
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12462
dc.identifier.volume198en_US
dc.identifier.wosWOS:000863047800002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAğbulut, Ümit
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofRenewable Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectPumpkin; Prosopis Juliflora; Dragon Fly Algorithm; Rudraksha; Particle Swarm Optimizationen_US
dc.subjectResponse-Surface Methodology; Extreme Learning-Machine; Diesel-Engine; Combustion Characteristics; Vegetable-Oil; Exhaust Emissions; Rapeseed Oil; Fuel; Transesterification; Karanjaen_US
dc.titleHybrid optimization and modelling of CI engine performance and emission characteristics of novel hybrid biodiesel blendsen_US
dc.typeArticleen_US

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