Sustainable biodiesel production from waste cooking oil: ANN modeling and environmental factor assessment

Biodiesel, the most suitable diesel fuel alternative, is currently gaining popularity due to its advantages and potential environmental protection. Besides, one of the environmental sustainability approaches in biodiesel production process would be the reuse of biomass waste. In this regard, this work focuses on sustainability and environmental factor assessment of waste cooking oil biodiesel production and the application of artificial neural networks to predict the biodiesel yield and its engine characteristics. The artificial neural network model was developed to predict the biodiesel yield, engine performance, and emission characteristics. It was observed that biodiesel production with 1% (wt/wt) catalyst concentration, 9:1 methanol to waste cooking oil molar ratio, 60 min reaction time with 500 rpm mixing intensity generates lesser wastes, and the process exhibits positive environmental impacts. In the process of predicting the biodiesel yield, the overall regression coefficient was calculated as 0.98. Moreover, in case of biodiesel fueled engine characteristics, the overall regression coefficient was computed as 0.99. It is disclosed from the analysis that artificial neural networks are excellent tools to simulate and predict the biodiesel produced from waste cooking oil and its engine characteristics.

Automated summary

This study focused on assessing the sustainability and environmental impact of waste cooking oil biodiesel production, as well as using artificial neural networks to predict biodiesel yield and engine characteristics. The research found that using a 1% catalyst concentration, 9:1 methanol to waste oil molar ratio, 60 minutes reaction time, and 500 rpm mixing intensity can lead to reduced waste generation and positive environmental impacts. The use of artificial neural networks was shown to be effective in simulating and predicting biodiesel yield and engine characteristics.