Oil Extraction from Pistacia Khinjuk - Experimental and Prediction by Computational Intelligence Models

ABSTRACT: This study investigates the oil extraction from Pistacia Khinjuk by the application of enzyme. Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) were applied for modeling and prediction of oil extraction yield. 16 data points were collected and the ANN was trained with one hidden layer using various numbers of neurons. A two-layered ANN provides the best results, using application of ten neurons in the hidden layer. Moreover, process optimization were carried out by using both methods to predict the best operating conditions which resulted in the maximum extraction yield of the Pistacia Khinjuk. The maximum extraction yield of Pistacia Khinjuk was estimated by ANN method to be 56.52% under the operational conditions of temperature and enzyme concentration of 0.27, pH of 6, and the Ultrasonic time of 4.23 h, while the optimum oil extraction yield by ANFIS method was 55.8% by applying the operational circumstances of enzyme concentration of 0.30, pH of 6.5, and the Ultrasonic time of 4.55 h. In addition, mean-squared-error (MSE) and relative error methods were utilized to compare the predicted values of the oil extraction yield obtained for both models with the experimental data. The results of the comparisons revealed the superiority of ANN model as compared to ANFIS model.