Abstract
The present work describes the comparison of biodiesel yield prediction by Response Surface Methodology (RSM) and Artificial Neural Network (ANN). The prediction models were developed based on three-level design of experiments conducted with waste cooking oil transesterified by varying four process parameters such as catalyst concentration, molar ratio, reaction time, and stirrer speed. The optimum reaction conditions were found to be 0.75% wt/wt catalyst concentration, 9:1 M ratio, 60 min reaction time and 500 rpm stirrer speed. For these optimum conditions, experimental fatty acid methyl ester (FAME) content of 95.05 ± 0.26% was obtained, which was in good agreement with the predicted yield. The RSM model was developed using Box-Behnken design and the ANN predictive model was developed using a feed-forward backpropagation neural network algorithm with 14 neurons in the hidden layer. The mathematical models of RSM and developed ANN were compared for biodiesel yield. The higher value of correlation coefficient (R2 = 0.99) and lower value of root mean square error (RMSE = 1.97) for ANN compared to RSM (R2 = 0.95 and RMSE = 2.71) evidently proved that ANN model is far better in predicting FAME content compared to the RSM model.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
