Prediction, optimization, and validation of the combustion effects of diisopropyl ether-gasoline blends: a combined application of artificial neural network and response surface methodology

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dc.authorscopusidEdwin Geo Varuvel / 25225283500
dc.authorscopusidFemilda Josephin Joseph Shobana Bai / 59417834100
dc.authorwosidEdwin Geo Varuvel / AAE-5222-2022
dc.authorwosidFemilda Josephin Joseph Shobana Bai / JTQ-1812-2023
dc.contributor.authorSeetharaman, Sathyanarayanan
dc.contributor.authorSuresh, S.
dc.contributor.authorShivaranjani, R. S.
dc.contributor.authorDhamodaran, Gopinath
dc.contributor.authorBai, Femilda Josephin Joseph Shobana
dc.contributor.authorAlharbi, Sulaiman Ali
dc.contributor.authorPugazhendhi, Arivalagan
dc.contributor.authorVaruvel, Edwin Geo
dc.date.accessioned2025-04-18T10:25:16Z
dc.date.available2025-04-18T10:25:16Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThis research study mainly focuses on identifying the significant factors to be considered to discover the accuracy and reliability of the predictive models. The experimental results were employed to develop three different models: an artificial neural network (ANN), a response surface methodology (RSM), and a hybrid model. Brake thermal efficiency, specific fuel consumption, and regulated emissions were predicted using ANN, and inputs such as fuel blend concentration, CR, and engine speed were optimized using the RSM and hybrid models. The accuracy and reliability of the model results were validated with the least mean square error, mean absolute percentage error, and a higher signal-to-noise ratio. The higher R 2 between 0.99426 and 0.9998 was observed by ANN whereas R 2 by RSM and the hybrid model were relatively less. Similarly, the mean square error of ANN was relatively less compared to RSM and hybrid. However, the mean absolute percentage error observed in the validation test results for the optimized input parameters discovered by RSM, was less than 5 % for all the responses and higher in the hybrid model. Thus, the authors concluded that the ANN 's predictive ability was much higher and RSM is the best suited for optimizing the engine parameters compared to the hybrid model.
dc.description.sponsorshipEaswari Engineering College King Saud University
dc.identifier.citationSeetharaman, S., Suresh, S., Shivaranjani, R. S., Dhamodaran, G., Js, F. J., Alharbi, S. A., ... & Varuvel, E. G. (2024). Prediction, optimization, and validation of the combustion effects of diisopropyl ether-gasoline blends: A combined application of artificial neural network and response surface methodology. Energy, 305, 132185.
dc.identifier.doi10.1016/j.energy.2024.132185
dc.identifier.endpage16
dc.identifier.issn0360-5442
dc.identifier.issn1873-6785
dc.identifier.scopus2-s2.0-85197314379
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.energy.2024.132185
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7057
dc.identifier.volume305
dc.identifier.wosWOS:001267847900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBai, Femilda Josephin Joseph Shobana
dc.institutionauthorVaruvel, Edwin Geo
dc.institutionauthoridEdwin Geo Varuvel / 0000-0002-7303-3984
dc.institutionauthoridFemilda Josephin Joseph Shobana Bai / 0000-0003-0249-9506
dc.language.isoen
dc.publisherPergamon-elsevier science
dc.relation.ispartofEnergy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGasoline Engine Emission
dc.subjectClean Environment
dc.subjectHybrid Model
dc.subjectEngine Optimization Techniques
dc.titlePrediction, optimization, and validation of the combustion effects of diisopropyl ether-gasoline blends: a combined application of artificial neural network and response surface methodology
dc.typeArticle

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