RSM modelling and yield prediction of base-catalyzed transesterification of pumpkin-seed oil to biodiesel: artificial neural network, kinetics, and thermodynamics DOI

Chika Vincent Isichei,

Prosper Eguono Ovuoraye, Chinenye Adaobi Igwegbe

et al.

Biofuels, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: Jan. 13, 2025

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

Language: Английский

RSM modelling and yield prediction of base-catalyzed transesterification of pumpkin-seed oil to biodiesel: artificial neural network, kinetics, and thermodynamics DOI

Chika Vincent Isichei,

Prosper Eguono Ovuoraye, Chinenye Adaobi Igwegbe

et al.

Biofuels, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: Jan. 13, 2025

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

Language: Английский

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