Modeling of process indices of Aloe vera gel extraction machine using artificial neural network

Abstract

AbstractThe performance evaluation of Aloe vera gel extraction machine in terms of extraction efficiency, gel recovery, and extraction loss were evaluated at three level of roller speeds (11.40, 15.10, and 18.80 rpm) and roller clearance (4, 5, and 6 mm). The data obtained were used for development of artificial neural network (ANN) model. The roller speed, roller clearance, extraction efficiency, gel recovery, and extraction loss were used as the inputs and the gel extraction efficiency, gel recovery, and extraction loss were the output from the model. New feed forward neural network was used for the model building and trained using Bayesian regularization training algorithm. Optimum ANN architecture was obtained by trial and error method. The results showed that the correlation coefficient for calibration and validation was .9751 and .9982, respectively, the root mean square error for calibration and validations was .4746 and .1258, respectively, and the ANN efficiency for calibration and validation was .9507 and .9964, respectively, were recorded for ANN model 5‐10‐3. Results indicated that ANN can be used efficiently for the modeling of the process indices of the Aloe vera gel extraction machine.Practical applicationsUsing a set of experimental data from Aloe vera gel extraction machine artificial neural network (ANN) model was developed to evaluate and predict the performance of Aloe vera gel extraction machine in terms of extraction efficiency, gel recovery, and extraction loss. The evaluation of ANN model with test data proved that model has very good accuracy. Using this model one can search for optimum operation conditions of Aloe vera gel extraction machine to maximize the efficiency and gel recovery and minimize the extraction loss.