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Vasseghian, Y., Zahedi, Gh, Ahmadi, M. (1394). Oil Extraction from Pistacia Khinjuk - Experimental and Prediction by Computational Intelligence Models. , 06(1), 1-12.
Y. Vasseghian; Gh Zahedi; M Ahmadi. "Oil Extraction from Pistacia Khinjuk - Experimental and Prediction by Computational Intelligence Models". , 06, 1, 1394, 1-12.
Vasseghian, Y., Zahedi, Gh, Ahmadi, M. (1394). 'Oil Extraction from Pistacia Khinjuk - Experimental and Prediction by Computational Intelligence Models', , 06(1), pp. 1-12.
Vasseghian, Y., Zahedi, Gh, Ahmadi, M. Oil Extraction from Pistacia Khinjuk - Experimental and Prediction by Computational Intelligence Models. , 1394; 06(1): 1-12.

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

Journal of Food Biosciences and Technology
مقاله 1، دوره 06، شماره 1، فروردین 2016، صفحه 1-12  XML اصل مقاله (356.03 K)
نوع مقاله: Research Paper
نویسندگان
Y. Vasseghian* 1؛ Gh Zahedi2؛ M Ahmadi3
1Ph. D. Research Student of the Department of Chemical Engineering, Faculty of Engineering, Razi University, Kermanshah, Iran.
2Associate Professor of the Department of Chemical and Biological Engineering, Missouri University of Science and Technology, 65409, Rolla, USA.
3Assistant Professor of the Department of Chemical Engineering, Faculty of Engineering, Razi University, Kermanshah, Iran.
چکیده
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, meansquared-
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.
کلیدواژه‌ها
Artificial Neural Network؛ Adaptive neuro fuzzy inference system؛ Modeling؛ Optimization؛ Pistacia Khinjuk
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