Optimizing Thermal Efficiency in Diesel Engines: Predicting Performance with Ternary Blends, Variable Injection Pressures and EGR Using LSTM Machine Learning

Abstract

Modern society prioritizes Sustainable Development Goals (SDGs 7 and 13) to address the fuel requirements of transportation and agriculture, concentrating on clean energy and climate change mitigation. This study examines the combination of Simmondsia chinensis (jojoba) biodiesel and methyl acetate (MA) to improve combustion efficiency and decrease emissions in a CRDi engine. The test fuels comprised diesel, biodiesel (SCB), and MA additives, formulated as DB50 (50% diesel + 50% biodiesel), DBMA10 (50% diesel + 40% biodiesel + 10% MA), and DBMA20 (50% diesel + 30% biodiesel + 20% MA). Tests performed at 21º CA for fuel injection time, with varied fuel injection pressures (FIP: 400, 500, 600 bar) and exhaust gas recirculation (EGR: 0, 10, 20%), demonstrated that DBMA20 enhanced brake thermal efficiency by 1.02% relative to DB50. NOx emissions decreased by 32.3% and 18.23% in DB50 relative to diesel at 400 bar fuel injection pressure and 20% exhaust gas recirculation. DBMA20 elevated smoke opacity and CO/HC emissions while decreasing FIP and augmenting EGR. A Long Short-Term Memory (LSTM) neural network accurately forecasted ideal circumstances (R² = 0.91–0.991). The best configuration for CRDi engines was determined to be DBMA20 at 600 bar FIP with 10% EGR.

Keywords

• ternary blends
• fuel injection pressure
• EGR
• machine learning
• performance
• optimization

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