Artificial Neural Network Approach to Multi-Response Prediction of Transesterification

Abstract

This study investigates the impact of transesterification parameters on biodiesel yield and kinematic viscosity, using an Artificial Neural Network (ANN) with the Levenberg-Marquardt algorithm to predict these outcomes. Predictions are validated against experimental results using established metrics like RMSE, MAPE, and R2. The experiments follow a Design of Experiment (DOE) approach, enabling accurate yield and viscosity measurements for each biodiesel sample. Results show that the ANN model strongly aligns with experimental results, showing lower RMSE and MAPE and higher R², outperforming RSM. Consequently, the ANN model is recommended for reliable biodiesel yield and viscosity prediction.