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RSS FeedsEnergies, Vol. 16, Pages 1470: An Assessment of the Operation and Emission Characteristics of a Diesel Engine Powered by a New Biofuel Prepared Using In Situ Transesterification of a Dry Spirogyra Algae–Jatropha Powder Mixture (Energies)

 
 

2 february 2023 10:53:50

 
Energies, Vol. 16, Pages 1470: An Assessment of the Operation and Emission Characteristics of a Diesel Engine Powered by a New Biofuel Prepared Using In Situ Transesterification of a Dry Spirogyra Algae–Jatropha Powder Mixture (Energies)
 


The present work deals with the optimization of the process parameters of in situ transesterification of dry spirogyra Algae–Jatropha powder along with engine efficiency and combustion analysis of the prepared biofuel. Three operational parameters, namely catalyst concentration (0–5 wt.%), methanol to dry algae–Jatropha curcas powder (v/v) (20–60%), and reaction time (60–180 min) at a constant reaction temperature of 50 °C, were selected. Response surface methodology (RSM) was used to design the experiments. The maximum biodiesel yield of 88.5% was obtained under the optimized conditions of a catalyst concentration of 3.396% (w/w), methanol/oil ratio of 19.86, and reaction time of 180 min. At varying loads, the performance and emissions of a diesel engine linked to a power source and fueled with various biodiesel mixes (Diesel, B5, B10, and B20) were tested. It was found that BSFC decreased as the applied load increased for all of the evaluated fuels. All of the biodiesel blends had greater BSFC than the diesel fuel. However, a substantial decrease in the emissions, including hydrocarbon (HC) and carbon monoxide (CO), was observed with the increase in NOx emissions. This method of preparing biodiesel will be beneficial in order to cater to the needs of the transportation sector because it has a lower energy consumption and less engine emissions.


 
89 viewsCategory: Biophysics, Biotechnology, Physics
 
Energies, Vol. 16, Pages 1475: Cross-Well Lithology Identification Based on Wavelet Transform and Adversarial Learning (Energies)
Energies, Vol. 16, Pages 1477: Condition Monitoring Accuracy in Inverter-Driven Permanent Magnet Synchronous Machines Based on Motor Voltage Signature Analysis (Energies)
 
 
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