Abstract
The study examined the extraction of bio-oil from pumpkin seed and compared the optimization of the production of Fatty acid ethyl ester (FAEE) via the transesterification process using Response Surface Methodology (RSM), and Artificial Neural Network (ANN). This research uniquely highlights the utilization of pumpkin seed oil, a non-edible and sustainable feedstock, and combines RSM and ANN methodologies to enhance the precision of biodiesel optimization. The transesterification experiment was conducted at 60 min reaction time under varying temperature ranges, catalyst weight, stirrer speed, and ethanol-oil molar ratio. The RSM optimized conditions for maximum production were determined to be a 1.3% catalyst concentration, 6:1 ethanol-to-oil molar ratio, 50 °C temperature, and 550 rpm stirrer speed, resulting in a 90% biodiesel yield. The statistical evaluation metrics confirmed the neural network predictions were compromised to 80% yield due to limitations such as insufficient data size and the inherent complexity of the ANN model. The biofuel produced satisfied ASTM specifications peculiar to sustainable environmental applications. The mechanistic parameters revealed variations in thermodynamic stability and feasibility across reaction orders (zero, pseudo-first, and pseudo-second), emphasizing the temperature-dependent effects of the transesterification kinetic pathway on yield, design, and efficiency.
Published Version
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