Photothermal solar assisted Madhuca diethyl ether fuel processing for LHR engines with AI-based performance and yield prediction

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dc.contributor.authorDubey, Rakesh
dc.contributor.authorPrajapati, Ajeet Kumar
dc.contributor.authorBharadwaj, Shruti
dc.contributor.authorKamchoom, Viroon
dc.contributor.authorOnyelowe, Kennedy C.
dc.contributor.authorArunachalam, Krishna Prakash
dc.date.accessioned2026-08-06T10:56:31Z
dc.date.available2026-08-06T10:56:31Z
dc.date.issued2026-12-01
dc.description.abstractThis study investigates the combustion, performance, and emission characteristics of biodiesel blends derived from Madhuca longifolia oil with diethyl ether (DEE) as an oxygenated additive in a diesel engine. Prior to fuel preparation, Fourier Transform Infrared (FTIR) analysis was conducted to verify the chemical composition of the extracted oil, confirming the presence of triglyceride structures and long-chain fatty acids characteristic of Madhuca longifolia oil. A solar-assisted preheating mechanism was incorporated during oil extraction to reduce energy consumption and improve yield consistency. The system was further integrated with a 250 Wp solar photovoltaic (PV) panel (efficiency ~ 17%, Voc = 37 V, Isc = 8.5 A, MPPT = 30 V/8 A) to power auxiliary loads such as the fuel metering unit, sensors, and control panel. This renewable integration enabled 100% solar contribution for auxiliary components, saving approximately 1.04 kWh/day of grid electricity and achieving an estimated reduction of about 151 kg of CO<inf>2</inf> emissions annually. Four fuel types were evaluated: Diesel, MB100 (pure biodiesel), MB20D80 (20% biodiesel, 80% diesel), and MB5DEE5D90 (5% biodiesel, 5% DEE, 90% diesel). Among these, MB5DEE5D90 demonstrated comparatively improved performance, showing an 8% increase in Brake Thermal Efficiency (BTE) and a 10% reduction in Brake-Specific Fuel Consumption (BSFC) compared with diesel. Emission analysis indicated reductions of approximately 20% in CO and 18% in HC emissions, while life-cycle assessment suggested around 40% lower combustion-phase CO<inf>2</inf> emissions. Heat release rate analysis indicated earlier and more efficient combustion behavior. Additionally, LSTM-based predictive modeling showed lower error margins compared with RNN, demonstrating improved prediction accuracy for engine performance parameters.
dc.identifier.citationScientific Reports, 16(1), 2026
dc.identifier.doi10.1038/s41598-026-44697-w
dc.identifier.issn20452322
dc.identifier.other2-s2.0-105039595264
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/18355
dc.sourceScientific Reports
dc.subjectBiodiesel
dc.subjectCI engine
dc.subjectDiethyl ether
dc.subjectNeural network
dc.subjectOxygenated additives
dc.titlePhotothermal solar assisted Madhuca diethyl ether fuel processing for LHR engines with AI-based performance and yield prediction
dc.typeArticle

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