article · Energy
This research explores the optimisation of biodiesel production from waste cooking soybean oil using a novel titanium-supported zinc oxide catalyst. Utilising waste cooking oil addresses concerns regarding the use of food crops for renewable energy generation. The study applied a Box-Behnken design through response surface methodology alongside artificial neural network modelling to evaluate the effects of catalyst loading, reaction temperature, and the methanol-to-oil ratio. Experimental testing achieved a peak biodiesel yield of 94.93 per cent under optimal conditions of 21.9 weight per cent catalyst loading, a reaction temperature of 55 degrees Celsius, and an 8:1 methanol-to-oil ratio. Response surface methodology demonstrated superior predictive accuracy compared to the neural network model. Furthermore, the resulting fuel satisfied standard specifications set for biodiesel.
Using waste cooking oil for fuel generation offers a sustainable alternative to fossil fuels without diverting crops from the food supply. Enhancing production efficiency through advanced computational and statistical models lowers the barriers to generating high-quality biodiesel, contributing directly to climate change mitigation and cleaner energy supplies.
This research provides optimised processing parameters and catalyst data relevant to biofuel producers and chemical process engineers seeking to process waste cooking oil. As an experimental optimisation study conducted under controlled conditions, the technology remains at an early laboratory stage, requiring scale-up evaluation and continuous-flow testing before industrial adoption.
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Global utilization of Green fuels mitigates the harmful effects of climate change caused by greenhouse gas emissions from fossil fuels. Biodiesel was identified as an alternative fuel to satisfy the growing need for energy. Waste cooking oil is applied as a feedstock due to legitimate worries about using food crops to produce fuel. In this study, optimization of biodiesel synthesized from waste cooking soybean oil in the presence of a novel enhanced titanium-supported zinc oxide (ZnO/TiO2) catalyst had been reported. The aim was to use DOE to correlate relationships between optimal biodiesel productivity and the operating parameters. The influence of catalyst loading, methanol-to-oil ratio, and reaction temperature were investigated using a time-efficient Box-Behnken design of response surface methodology and artificial neural network. The predicted and experimental yield was comparable with 94.04 % (BBD-RSM), 93.99 % (ANN), and 94.42 % respectively. A significant biodiesel yield of 94.93 % was obtained at optimal operating conditions of catalyst loading (21.9 wt%), reaction temperature of 55 OC, and methanol oil ratios of 8:1. Comparative analysis indicates higher prediction capabilities for RSM than the ANN model in terms of lowest error functionality and highest correlation coefficient. However, the obtained FAME has properties within the standard limits set for biodiesel.
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DOI: 10.1016/j.energy.2024.132765
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