The study investigates the optimization of biodiesel production from waste vegetable oils using synthesized ionic liquids as catalysts, with a focus on enhancing process efficiency and yield. The key process variables the reaction temperature, time, molar ratio, and catalyst concentration exert that significant influence on the biodiesel yield obtained with ionic liquid catalysts. Systematic optimization of these variables proved crucial, as improvements in ionic liquid yields directly translated to higher biodiesel outputs. Advanced optimization techniques, specifically response surface methodology (RSM) and artificial neural networks (ANN), were employed to model and analyze the complex interactions among process parameters. Both RSM and ANN provided robust frameworks for predicting optimal conditions and maximizing biodiesel production. The findings highlight that optimizing ionic liquid yields not only boosts process performance but also enhances the reliability and accuracy of predictive modeling tools. The study concluded that the combined application of these optimization techniques reveals that ionic liquids can be strategically tailored and efficiently utilized to overcome the limitations traditionally associated with waste vegetable oil feedstocks. The study recommended that future work place strong emphasis on the precise control and systematic evaluation of critical process variables
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Nneka Perpetua (Ph. D.) Onuoha
Enugu State University of Science and Technology
Monday (Ph. D.) Omotiomo
Delta State University
G. O. Prof Mba
Enugu State University of Science and Technology
Delta State University
Enugu State University of Science and Technology
University of Delta Agbor
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Onuoha et al. (Wed,) studied this question.
synapsesocial.com/papers/69d895046c1944d70ce05f64 — DOI: https://doi.org/10.5281/zenodo.19456662