Assessment of performance, emission and combustion characteristics of palm, jatropha and Calophyllum inophyllum biodiesel blends

Monirul, I.M.; Masjuki, H.H.; Kalam, M.A.; Mosarof, M.H.; Zulkifli, Nurin Wahidah Mohd; Teoh, Yew Heng; How, Heoy Geok

doi:10.1016/j.fuel.2016.05.010

Cited by 107 publications

(46 citation statements)

References 59 publications

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“…Also, a 14% lesser fuel consumption is seen for CIB25 (at 18:1 CR and 6 kg) than the CIB with BHT 500 ppm fueled 18 . JCB10 and CPB10 consume 1 and 5% more fuel than the CIB25, respectively 23 . Addition of TBHQ, BHA, and BHT consumes lesser fuel than CIB25 24 …”

Section: Resultsmentioning

confidence: 94%

Developing a smart fuel using artificial neural network for compression ignition engine fueled with Calophyllum inophyllum diesel blend at various compression ratio

Venugopal

Kasimani

Chinnasamy

2020

Env Prog and Sustain Energy

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In machinery, it is evident that the computing system for self‐automated machinery derives nonlinear and complex equations by comparing the machinery's different input parameters with their corresponding performance output parameters. In order to operate the machinery with good performance and better efficiency, the computing system needs a machine‐learning algorithm. Most recent researchers have concentrated more on self‐driving vehicle, which seems to be lack of developing a strong algorithm for compression ignition (CI) engines to predict the performance and emission output parameter. Thus, this article deals with the prediction of performance and emission characteristics of CI engine fueled with 25% Calophyllum inophyllum and 75% diesel blend (CIB25) at various compression ratios using artificial neural network (ANN). Performance and emission tests were conducted in a single‐cylinder four‐stroke variable‐compression‐ratio CI engine fueled with CIB25 with varying loads and at a constant speed of operation. Experimental investigation indicates that 18:1 compression ratio gives better performance results when CIB25 is used as the fuel. Emission test results show better emission characteristics at 17:1 compression ratio. These results show that some input factors affect the output factors under some set of operating conditions, while some input factors improve them. ANN developed for the CI engine learns how the input factors affect and improve the output factors. Also, developed neural network is found to be satisfactory, and it predicts the output at a regression value of 0.998 with an average error of 1.77% in the case of CIB25.

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Section: Resultsmentioning

confidence: 94%

Developing a smart fuel using artificial neural network for compression ignition engine fueled with Calophyllum inophyllum diesel blend at various compression ratio

Venugopal

Kasimani

Chinnasamy

2020

Env Prog and Sustain Energy

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“…The carbon monoxide emission shows increasing trend while increasing the load of the engine for all the fuel type tested due to the reducing trend of the air-fuel ratio. At higher engine load, burning of richer air-fuel mixture generally produced higher CO emissions [18]. The measured CO emissions of algae biofuel blends were found to be an average of 22% lower than that of diesel fuel.…”

Section: Carbon Monoxidementioning

confidence: 89%

Combustion Analysis of Ci Engine Fuelled With Algae Biofuel Blends

Joshi

2019

Journal of Thermal Engineering

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Algae-based biofuels are the most capable solutions to energy catastrophe and global warming for years to come due to its potentially high yield, higher growth rate, biodegradable, nontoxic, carbon neutral, low emission profile, use of non-arable land and non-potable water with less attention and resource consumption. A biofuel produced from chlorella algae oil through a transesterification process was used in this study. Experimental tests were performed on a single cylinder, four stroke, direct injection, naturally aspirated diesel engine at constant engine speed of 1500 rpm and compression ratio of 18 at various loading conditions to evaluate the performance, emission, and combustion characteristics using algae biofuel blends of 5%, 10%, 20%, and 30%, moreover the results are compared with those of standard diesel. The brake thermal efficiency showed decreasing trend (upto 5%) whereas specific fuel consumption (upto 7%) and exhaust gas temperature (upto 3%) showed increasing trend for algae biofuel blends compared to diesel. Reduction in hydrocarbon (upto 28%) and carbon monoxide (upto 22%) emission was noted for algae biofuel blends along with a marginal increase in NOx (upto 13%) emissions. Also, algae biofuel blends showed almost comparable combustion results compared to conventional diesel, however, the blends confirm shortened ignition delay due to their earlier start of combustion. From this study, we can conclude that small fraction by volume (20%) of algae biofuel addition with diesel has exhibited satisfactory results and could be used as a good substitute for petrodiesel fuel.

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“…Raw Punnai oil was obtained from the kernel of Punnai tree (Calophyllum Inophyllum) is one of the available sources for the preparation of biodiesel because of large quantities available in Africa and Asia [17][18][19]. Punnai biodiesel has higher oil yielding capacity and higher heating value than other methyl esters like Pongamia, Neem, and Jatropha [20]. The objective of the present study is to experimentally investigate performance, combustion and emission characteristics of a direct injection (DI) Diesel engine operated with biodiesel derived from Punnai oil.…”

Section: Introductionmentioning

confidence: 99%

Performance, emission and combustion characteristics of a direct injection diesel engine using blends of punnai oil biodiesel and diesel as fuel

2020

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Fast-growing demand for automobile vehicles and depletion of fossil fuel forced the researchers to think for alternative fuel which can replace the diesel fuel. From this perspective, Punnai oil which is non-edible in nature is chosen as a feedstock for producing methyl ester. Punnai oil can be converted into biodiesel/methyl ester by transesterification process. From gas chromatography analysis it is found that biodiesel of Punnai oil contains linoleic, oleic and palmitic fatty acids. Presence of these fatty acids and in the Punnai oil biodiesel will enhance the combustion characteristics. To ascertain the suitability of Punnai oil biodiesel as a fuel for direct injection Diesel engine, the experimental work was carried out using a constant speed, four-stroke single-cylinder Diesel engine. Experimental results show that there is a decrease in brake thermal efficiency and an increase in NOx and CO2 emissions with increased concentration of biodiesel in the blend. Smoke, CO, and HC emissions were reduced significantly. At rated power, brake thermal effi

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Assessment of performance, emission and combustion characteristics of palm, jatropha and Calophyllum inophyllum biodiesel blends

Cited by 107 publications

References 59 publications

Developing a smart fuel using artificial neural network for compression ignition engine fueled with Calophyllum inophyllum diesel blend at various compression ratio

Developing a smart fuel using artificial neural network for compression ignition engine fueled with Calophyllum inophyllum diesel blend at various compression ratio

Combustion Analysis of Ci Engine Fuelled With Algae Biofuel Blends

Performance, emission and combustion characteristics of a direct injection diesel engine using blends of punnai oil biodiesel and diesel as fuel

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