Response Surface Methodology and Artificial Neural Networks for Optimization of Catalytic Esterification of Lactic Acid
Vishal S. Chandane, Ajit P. Rathod, Kailas L. Wasewar, Prakash. G. Jadhav
Abstract
Abstract Response surface methodology (RSM) and artificial neural network (ANN) models were employed to study the esterification of lactic acid and isoamyl alcohol. A carbon‐based solid acid catalyst prepared by wet impregnation was used in the esterification reaction. Experimental characterization revealed its potential to serve as catalyst for the esterification reaction. The experiments were performed based on the design of experiments provided by RSM and ANN models. Both models were compared on the basis of prediction efficacies and deviation from actual data. The prediction data results demonstrated that the ANN model gave better prediction efficiency and lower prediction deviation than the RSM model. The ANN model provided a higher coefficient of determination and lower error values than the RSM model. Moreover, the catalyst exhibited a good stability and recyclability up to four reaction cycles.