Application of Correlation and Regression Analysis to the Optimized Biodiesel Yield from Used Cooking Oil Via Acid-Catalyzed Esterification
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Abstract
This research examines the physicochemical characteristics of waste vegetable oil biodiesel blends based on viscosity, density,
flash point, cloud point, pour point, and sulfur content. B20 to B100 blends were subjected to testing to determine their suitability
as alternative fuels against regular diesel standards. An acid-catalyzed esterification and base-catalyzed transesterification in
two steps were employed to decrease free fatty acid (FFA) content and enhance biodiesel yield. At optimized reaction conditions,
a biodiesel yield of 96.3% was achieved, indicating the efficiency of the process. Experimental findings showed that viscosity of
blends of biodiesel decreased consistently with an increase in temperature from 7.212 mm²/s at 10°C for B100 to 3.415 mm²/s
at 50°C for B20. All the blends met the ASTM D445 standard for 40°C viscosity. At higher biodiesel blends the flash point
increased indicating fuel safety. Whereas, the associated rise in pour and cloud points signified a reduced low-temperature
fluidity. Through heatmap analysis, it was found that viscosity is strongly negatively correlated with temperature, while flash
point, pour point, and blend concentration were positively correlated. Regression analysis shows strong linear relationships
between viscosity, flash point, and blend composition, further establishing their temperature dependence and their suitability
for predictive modeling. It is concluded that there is a need for control of production parameters and optimization of the blend
ratios to realize maximum engine efficiencies and product conformity to standards. Biodiesel blends from waste vegetable oil
are thus efficient alternatives to conventional diesel, whose various physicochemical properties have been successfully analyzed
and optimized using statistical approaches.
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