Effect of Pongamia pinnata leaves extracted natural antioxidant doped in biodiesel–diesel blend run low heat rejection engine

Pandey, Kiran; Murugan, S.

doi:10.1007/s13762-023-04777-0

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…The JME20 PLA4 engine's maximum load reduces emissions by 17.5% for unburned hydrocarbon (HC) and 16.3% for smoke. In the coated engine for JME20 with 2000 ppm, nitric oxide (NO) emissions are decreased by 16% [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Impact of additives from moringa stenopetela leaf extract and ethanol on the emission characteristics and performance of soybean biodiesel for single cylinder CI engine

Zeleke,

Bezabih

2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Impact of additives from moringa stenopetela leaf extract and ethanol on the emission characteristics and performance of soybean biodiesel for single cylinder CI engine

Zeleke,

Bezabih

2024

Heliyon

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Performance Evaluation of C I Engine Using Neem Biodiesel with Additive and Blends of Diesel

Jaiswal,

Tirole

2024

Springer Proceedings in Energy

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Comparison of machine learning algorithms on a low heat rejection diesel engine running on ternary blends

Pandey,

Khayum,

Shaik

2024

Journal of Renewable and Sustainable Energy

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Depletion of fossil fuels and increasing the energy demand are the critical concerns for the sustainable growth of every country. These challenges highlight the need for a cleaner fuel globally. In particular, substituting the petroleum fuels with biofuels could significantly support sustainability. Hence, this study explores the impact of acetylene induction in a low heat rejection (LHR) diesel engine with ternary blend (TB) as a pilot fuel. TB includes 10%methanol + 20%WCOB + 70%diesel. During the dual fuel operation, the acetylene induction was varied (12, 18, and 24 lpm) with different pilot fuel combinations in a LHR engine. From the experimental results, it was revealed that brake thermal efficiency (BTE) is improved by 10.3% for TB with acetylene at 24 lpm and increased the exhaust gas temperature (EGT) by 10.3% at full load conditions. This study further evaluates the implementation of machine learning algorithms, namely, Random Forest regression (RFR) and Polynomial regression (PR) in predicting the performance parameters [BTE, brake-specific fuel consumption (BSFC), and EGT]. The results indicate that RFR outperforms PR in accurately predicting engine performance characteristics with a coefficient of determination R2 = 0.97, R2 = 0.98 for BTE, whereas for BSFC the R2 = 0.96, R2 = 0.95, and for EGT, the R2 = 0.95, R2 = 0.97 for the test fuels diesel and TBA3, respectively. These findings suggest that the combination of LHR technology, TB fuel, and acetylene can enhance engine efficiency and performance characteristics, and machine learning models can effectively predict these outcomes.

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Effect of Pongamia pinnata leaves extracted natural antioxidant doped in biodiesel–diesel blend run low heat rejection engine

Cited by 3 publications

References 45 publications

Impact of additives from moringa stenopetela leaf extract and ethanol on the emission characteristics and performance of soybean biodiesel for single cylinder CI engine

Impact of additives from moringa stenopetela leaf extract and ethanol on the emission characteristics and performance of soybean biodiesel for single cylinder CI engine

Performance Evaluation of C I Engine Using Neem Biodiesel with Additive and Blends of Diesel

Comparison of machine learning algorithms on a low heat rejection diesel engine running on ternary blends

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