Compare Modeling the Effect of Flow Parameters on the Efficiency of Membrane Clarification of Pomegranate Juice Regression Method With Artificial Intelligence Methods

Objective: pomegranate juice is a fruit native to iran because of attractive color, smell and value of mineral water is popular fruits in the world. pomegranate juice has more nutritional value is due to a combination of anthocyanins that reduce the risk of diseases such as cancer. processes feta membrane such as microfiltration 1 and 2 are used to clarify beer industry. methods: the advantage of this method compared to traditional methods require less labor, higher yields and the process is low. making a mathematical model or artificial intelligence to predict the juice clarification process in membrane systems is a valuable tool in the field of membrane science and technology. results: these models play an important role in the simulation and optimization of transparency in membrane systems in order to achieve an economic and efficient design play. many of the older models polar models, the osmotic pressure and boundary layer model have been used to simulate the performance tuning 1 fruit juices. conclusion: in this study, we tried to take advantage of four regression system, fuzzy inference, neural networks, and fuzzy-neural adaptive method for predicting the flow of water permeate the membrane pomegranate transparency in the system assessed.