Experimental and prediction model of low compression marine diesel engine fuelled with palm biodiesel blends
Marine diesel engines provide primary power sources for sea transportation, but emissions from the engines are a major contributor to air pollution especially in port and coastal areas. The introduction of alternative fuels such as biodiesel is seen as a promising solution to reduce harmful gas emis...
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Main Author: | |
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Format: | Thesis |
Language: | English |
Published: |
2019
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Subjects: | |
Online Access: | http://umpir.ump.edu.my/id/eprint/27991/1/Experimental%20and%20prediction%20model%20of%20low%20compression%20marine%20diesel%20engine%20fuelled.pdf |
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Summary: | Marine diesel engines provide primary power sources for sea transportation, but emissions from the engines are a major contributor to air pollution especially in port and coastal areas. The introduction of alternative fuels such as biodiesel is seen as a promising solution to reduce harmful gas emission from the engine. However, biodiesel fuel studies on marine diesel engines are still limited where most previous studies focus on high compression ratio engines. How the biodiesel effect on the performance of the low compression marine engine is unknown and should be investigated in order to determine its suitability. Therefore, this project aims to investigate the effects of palm biodiesel blends on low compression marine diesel engines focusing on fuel combustion, engine cyclic variations, engine performance and emissions characteristics. Apart from experiment, an approach by using artificial neural network simulation model was also developed to predict the performance parameters. Engine testing was performed using four strokes, in-line six-cylinder marine diesel engine (Cummin NT-855M) in the laboratory. The engine has 14.5:1 compression ratio and 14 litres displacement volume. All tests were performed under steady-state condition at 10%, 30% and 50% engine loads by varying the engine speed between 800–1600 rpm and fuelled with different palm biodiesel blends (B10, B20 and B30). The test results reveal that palm biodiesel blends are successfully used in low compression marine diesel engine and comparable to diesel fuel. Increase the concentration of palm biodiesel in blends has reduced the rate of heat release and the rate of pressure rise up to 2.62% and 2.77% respectively, during the combustion proses. Mass fraction burned progress indicated the combustion duration was longer by 1.50–2.14 °CA relative to diesel fuel. Low heating value and high viscosity of palm biodiesel coupled with low compression cylinder have reduced the premix combustion rate hence prolonged the biodiesel combustion duration. Lesser energy content in palm biodiesel blends has slightly reduced the engine thermal efficiency (8.31%). However, on the positive side, the use of palm biodiesel blends in low compression marine diesel engine has reduced harmful gas emissions of nitrogen oxides and carbon monoxide up to 13.02% and 66.67%, respectively. Increased palm biodiesel percentage in the blend also lowered the engine cyclic variations where the B30 blend produces the minimum variations among the tested fuels. The wavelet power spectrum plot shows lower frequency oscillations of IMEP when operated with palm biodiesel blends. The engine prediction model of using artificial neural network approach provides excellent correlation between predicted and experimental data as indicated by higher R-value of 0.9987–0.9999. An artificial neural network model is suitable for use in non-linear problems as the model provides accurate engine performance prediction with minimal errors (0.20–2.79%). Experimental and prediction model of using palm biodiesel blends in the unmodified low compression marine diesel engine has contributed to the body of knowledge in understanding the combustion and performance behaviours of the marine engine. Reduction in premixed combustion rate, engine cyclic variations and exhaust emission obtained from palm biodiesel blends are the most important findings in this study. |
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