Modeling of milk ultrafiltration permeate flux under various operating conditions and physicochemical properties using Nero–Fuzzy method

Shahidi Noghabi, Mahnaz; Razavi, Seyed Mohammad Ali; Shahidi Noghabi, Mostafa

doi:10.22101/JRIFST.2014.10.23.337

Modeling of milk ultrafiltration permeate flux under various operating conditions and physicochemical properties using Nero–Fuzzy method

Document Type : Original Paper

Authors

¹ MSc. Graduated Student, Department of Food Science and Technology, Islamic Azad University–Quchan Branch, Razavi Khorasan Province, Iran

² Professor, Department of Food Science and Technology, Ferdowsi University of Mashhad (FUM), Mashhad, Iran

³ Assistant Professor, Department of Food Chemistry, Research Institute of Food Science and Technology, Mashhad, Iran

10.22101/JRIFST.2014.10.23.337

Abstract

In this study, an adaptive neuro-fuzzy inference system (ANFIS) used for the prediction of permeate flux as a function of the physico-chemical and operating parameters during ultrafiltration of milk. An ultrafiltration pilot plant equipped with hollow fiber module and polyethersulfone membrane (MWCO 10 kDa) was used to do the milk ultrafiltration with various physico-chemical properties, consists of five levels of pH (5.6 , 6, 6.6, 6.9 and 7.6) and three levels of ionic strength (0.03, 0.06 and 0.12) and under different operating conditions including transmembrane pressure (TMP) at three levels (0.1, 0.3 and 1 atm), temperature at three levels ( 30 , 40 and 50 °C ) and the flow rate at three levels (10, 30 and 46 m/s). In order to model the effects of operating parameters and physicochemical properties of milk on permeate flux, the experimental data was randomized. 30 % of the data for learning, 30% of the data for evaluation and 40 % of the data was used to test the model. The results showed that the Nero–Fuzzy modeling approach is capable to predict the permeate flux under various operating conditions and physiochemical characteristics of milk, and modeling results represented there was an excellent correlation (average R = 0.93) between the predicted data and experimental data.

Keywords

References

Decloux, M., Tatoud, L., Mersad, A. 2000. Removal of colorants an polysaccharides from raw cane sugar remelts by ultrafiltration. Zuckerindustrie, 125: 106.

Eckner, K.F. & E.A. Zottola. 1992. Partitioning of skim milk components as a function of pH, acidulant and temperature during membrane processing. Journal of Dairy Science, 75(8): 2092-2097.

Eerikainen, T., Linko, P., Linko, C., Siimes, T., & Zhu, Y. H. 1993. Fuzzy logic and neural network applications in food science and technology. Trends in Food Science and Technology, 4: 237-242.

Entchev, E., & Yang, L. 2007. Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation. Journal of Power Sources, 170: 122-129

Hertz, J., Krogh, A., & Palmer, R.G. 1991. Introduction to the Theory of Neural Computation. Addison-Wesley publisher.

Hilal, N., H. Al-Zoubi, N.A. Darwish, A.W. Mohammad, M. Abu Arabi .2004. A comprehensive review of nanofiltration membranes: Treatment, pretreatment, modelling, and atomic force microscopy. Desalination, 170: 281-308.

Mashrei, M. A., Abdulrazzaq, N. Andalla, T.Y. and Rahman, M.S., 2010. Neural net works model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members. Engineering Structures, 32: 1723-1734.

Mohebbi, M., Barouei, J., Akbarzadeh-T, M.R., Rowhanimanesh, A.R., Habibi-Najafi, M.B., & Yavarmanesh, M. 2008. Modeling and optimization of viscosity in enzyme-modified cheese by fuzzy and genetic algorithm. Computers and Electronics in Agriculture, 62: 260–265.

Patel, R.S. & H. Reuter. 1985. Fouling of hollow fiber membrane during ultrafiltration of skim milk. Milchwissenschaft, 40 (12): 731-733.

Perrot, N., Ioannou, I., Allais, I., Curt, C., Hossenlopp, J., & Trystram, G. 2006. Fuzzy concepts applied to food product quality control: a review. Fuzzy Sets and Systems, 157: 1145–1154.

Pouliot, 2008. Membrane processes in dairy technology- From a simple idea to world wide panacea. International Dairy Journal, 18: 735-740.

Razavi, S.M.A., 2005, The study of ultrafiltration performance as a function of milk pH, Iranian Journal of Agriculture Science, 36 (2): 415-424.

Razavi, M. A. 2006. Effect of process temperature on milk ultrafiltration performance. The Agricultural science, 16: 85-94.

Razavi, M.A., Mortazavi, A.& Mousavi, M. 2004. Application of neural networks for crossflow milk ultration sim uktration. International Dairy Journal, 14: 69-80.

Razavi, M.A., Mortazavi, A., Mousavi, M. 2003. Dynamic modeling of milk ultrafiltration by artifical neural networks. Journal of Membrane Science, 220: 47-58.

Rinaldoni, A., Tarazaga, C., Campderros, M.E. & Padilla, A. 2009. Assessing performance of skim milk ultrafiltration by using technical parameters. Journal of Food Engineering, 92: 226-232.