Process parameter optimization of biodiesel production from algal oil by response surface methodology and artificial neural networks

By A. Garg, Siddharth Jain. 2020

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Abstract Biodiesel production from algae oil at low temperature as well as a comparative study of response surface methodology (RSM) and artificial neural networks (ANN) for the modeling of yield and process parameters was carried out in this research work. Box–Behnken experimental design was adopted and the three process parameters considered were methanol to oil percentage (v/v) (20–60%), catalyst concentration (0–2 wt%) and reaction time (60–180 min) at constant temperature of 50 °C. The results of the present work indicate that ANN has good predictability as compared to RSM. A significant quadratic regression model with value of R2 of 0.99 and 0.96 was obtained in case of ANN and RSM respectively.

Subfields: Biomedical Engineering, Mechanical Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/f46c9ab4692083bc63d4615ebe6c7fc375e8a670