Investigations on biomass gasification derived producer gas and algal biodiesel to power a dual-fuel engines: Application of neural networks optimized with Bayesian approach and K-cross fold

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dc.authoridSHARMA, PRABHAKAR/0000-0002-7585-6693en_US
dc.authoridAlruqi, Mansoor/0000-0002-0740-3967en_US
dc.authoridAgbulut, Umit/0000-0002-6635-6494en_US
dc.authorscopusid57225072010en_US
dc.authorscopusid58961316700en_US
dc.authorscopusid57202959651en_US
dc.authorwosidSHARMA, PRABHAKAR/ISU-9669-2023en_US
dc.contributor.authorAlruqi, Mansoor
dc.contributor.authorSharma, Prabhakar
dc.contributor.authorAgbulut, Uemit
dc.date.accessioned2024-08-23T16:04:49Z
dc.date.available2024-08-23T16:04:49Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThe adoption of sustainable energy sources is a crucial step towards achieving a low-carbon economy and mitigating the effects of climate change. One promising approach is the use of Producer Gas (PG) derived from solid biomass materials, which can be burned as fuel in internal combustion engines to generate power. Biomass gasification, the process of converting solid biomass into PG through thermochemical means, offers a sustainable alternative to traditional fossil fuels. However, using PG in dual-fuel engines poses a significant challenge due to its complex combustion characteristics. Fortunately, modern machine learning techniques offer a promising solution to this problem. In this study, we propose a Bayesian optimized neural network (BONN) to predict the performance and emissions of PG-algal biodiesel (ABD) -powered dual-fuel engines. The BONN is trained using experimental data obtained from a single-cylinder diesel engine retrofitted to run on PG as the primary fuel and algal biodiesel as the pilot fuel. The performance and emissions data are collected under various operating conditions, such as engine load, fuel injection pressure, biodiesel blending ratio, and injection timings. K-cross fold validation was used to reduce the chances of model overfitting while the Bayesian approach was used for hyperparameters finetuning. This strategy helped in the reduction of predicting errors and improved the accu-racy of the model. The coefficient of determination was in the range of 0.9421-0.9989 and mean squared errors were in the range of 0.0026-15.77. The mean absolute errors in model-predicted values were in the range of 0.0027-2.945. In all the cases of the prediction results during the model, the test improved upon the model training predictions, indicating a robust generalization and negated the chances of model overfitting. The results demonstrate that the BONN can accurately predict the performance and emissions of the engine, making it a valuable tool for engine optimization and control. This approach offers a promising pathway toward achieving net-zero targets and a sustainable future.en_US
dc.description.sponsorshipDeanship of Scientific Research at Shaqra Universityen_US
dc.description.sponsorshipThe author (Mansoor Alruqi) would like to thank the Deanship of Scientific Research at Shaqra University for supporting this work.en_US
dc.identifier.doi10.1016/j.energy.2023.128336
dc.identifier.issn0360-5442
dc.identifier.issn1873-6785
dc.identifier.scopus2-s2.0-85165381432en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.energy.2023.128336
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14377
dc.identifier.volume282en_US
dc.identifier.wosWOS:001147631800001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEnergyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiomass gasificationen_US
dc.subjectSustainabilityen_US
dc.subjectProducer gasen_US
dc.subjectEmissionen_US
dc.subjectMachine learningen_US
dc.subjectDiesel-Enginesen_US
dc.subjectPerformanceen_US
dc.subjectBiogasen_US
dc.titleInvestigations on biomass gasification derived producer gas and algal biodiesel to power a dual-fuel engines: Application of neural networks optimized with Bayesian approach and K-cross folden_US
dc.typeArticleen_US

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