A Neuro-Fuzzy Model for a Dynamic Prediction of Milk Ultrafiltration Flux and Resistance

Document Type: Research Article

Authors

1 Department of Chemical Engineering, University of Ferdowsi, P.O. Box 9177948944 Mashhad, I. R. IRAN

2 Department of Chemical Engineering, University of Sistan and Baluchestan, P.O. Box 98164 -161 Zahedan, I. R. IRAN

Abstract

A neuro-fuzzy modeling tool (ANFIS) has been used to dynamically model cross flow ultrafiltration of milk. It aims to predict permeate flux and total hydraulic resistance as a function of transmembrane pressure, pH, temperature, fat, molecular weight cut off, and processing time. Dynamic modeling of ultrafiltration performance of colloidal systems (such as milk) is very important for designing of a new process and better understanding of the present process. Such processes show complex non-linear behavior due to unknown interactions between compounds of a colloidal system. In this paper, ANFIS, Multilayer Perceptron (MLP) and FIS were applied to compare results. The ANFIS approximation gave some advantage over the other methods. The results reveal that there is an excellent agreement between the tested (not used in training) and modeled data, with a good degree of accuracy. Furthermore, the trained ANFIS are capable of accurately capture the non-linear dynamics of milk ultrafiltration even for a new condition that has not been used in the training process (tested data). In addition, ANFIS and Multilayer Perceptron (MLP) are compared and the Matlab software was adopted to implement the method.  

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[1] Zadeh, L.A., Fuzzy Sets, Information and Control, 8, 338(1965).

[2] Takagi, T. and Sugeno, M., Structure Identification of Systems and Its Application to Modeling and Control, IEEE Trans. Systems Man Cybern., 15, 116(1985).

[3] Lin, C.T. and Lee, C., Neural-Network-Based Fuzzy Logic Control and Decision Systems, IEEE Trans. Comput., (special issue on artificial neural networks), 40, 1320 (1991).

[4] Amano, A. and Arisuka, T., On the Use of Neural Networks and Fuzzy Logic in Speech Recognition, in: Proceedings of the International Joint Conference on Neural Networks, Tokyo, Japan, 301(1989).

[5] Lin, Y. and Cunningham, G.A., A New Approach to Fuzzy-Neural System Modeling, IEEE Trans. Fuzzy Systems, 3, 190 (1995).

[6] Wong, C. and Chen, C.C., A Hybrid Clustering and Gradient Descent Approach for Fuzzy Modeling, IEEE Trans. Systems, Man Cybern., 29, 686 (1999).

[7] Linkens, D.A. and Chen, M.Y., Input Selection and Partition Validation for Fuzzy Modeling using Neural Network, Fuzzy Sets Systems, 107, 299 (1999).

[8] Chen, M.Y. and Linkens, D.A., A Systematic Neuro-Fuzzy Modeling Framework with Application to Material Property Prediction, IEEE Trans. Systems, Man Cybern., 31 (5), 781 (2001).

[9] Kohonen, T., The Self-Organizing Map, Proc. IEEE, 78 (9), 1464 (1990).

[10] Jyh-Shing, Jang, R., ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Trans. Systems, Man Cybern., 23 (3), 665 (1993).

[11] Li-Xin  Wang, Training of Fuzzy Logic Systems using Nearest Neighborhood Clustering, in: Proceedings of the Second IEEE International Conference on Fuzzy Systems,USA, 1, 93 (1993).

[12] Adaptive Fuzzy Inference Neural Network, http:// www.elsevier.com/locate/patcog, (2005).

[13] Iyatomi, H. and  Hagiwara,  M., Knowledge Extraction from Scenery Images and the Recognition using Fuzzy Inference Neural Networks, Trans. IEICE (D-II) J82-D-II (4), 685 (1999).

[14] Iyatomi,  H.  and  Hagiwara, M., Scenery Image Recognition and Interpretation using Fuzzy Inference Neural Networks, Pattern Recognition 35, 8, 1793 (2002).

[15] Chiu, S.L., Selecting  Input  Variables for Fuzzy Models, Journal Intelligent Fuzzy Systems, 4, 243 (1996).

[16] Juang, C.F. and Lin, C.T., An On-Line Self-Constructing Neural Fuzzy Inference Network and Its Applications, IEEE Trans. Fuzzy Systems, 6, 12 (1998).

[17] Jang, J.S.R. and Sun, C.T., Neuro-Fuzzy Modeling and Control, IEEE Trans. Fuzzy Systems, 3, 378 (1995).

[18] Nauck, D., Klawonn, F. and Kruse, R., Foundation of Neuro-Fuzzy Systems, Wiely, New York, (1997).

[19] Grandison, A.S., Youravong, W. and Lewis, M.J., Hydrodynamic Factors Affecting Flux and Fouling During Ultrafiltration of Skimmed Milk, Lait 80, p. 165 (2000).