Study On The Models For Prediction Of Desalination Rate Using Electrodialysis

Ion-exchange membrane is the crital medium of electrodialysis technology, under the directly electric field, with the potential difference as the driving force, effectively removed inorganic salts ionize in water solution. This paper presents experimental data, a fuzzy logic (FL) model, a mathematical model (MM) for a laboratory scale electrodialysis (ED) cell. The aim was to predict Removal rate as a function of concentration, temperature, ?ow rate and voltage.At first, according to the property that ion electromigration happens on the external electric field, using an electrodialysis(ED) channel mass balance and fundamental electrochemical equations, a mathematical modeling was applied to model sepration from brine waste using ED. Experiments were conducted to investigate the effect of voltages, flow rates, temperature and feed concentrations on ED cell performance and the model parameter was calculated using sepration in the dilute compartment. Using experimental data, the equation was fitted forαas a function of voltage, flow rate, temperature and feed concentrations. Using multiple regression procedure, the model gives the values of sepration in the dilute compartment for various voltages, flow rates, temerature and feed concentrations.Regarding to the existing MM for predicting, and analyzing the factors that affecting ED process demineralization, a Sugeno type FL inference system was applied to model NaCl ions separation using ED, training and testing was accomplished with the aid of MATLAB software, ANFIS methodology whit hybrid learning method was applied to identify membership function, estimate the parameters c andσof the applied two-parametric Gaussian membership function and the consequent functions. Compared with experimental data and MM, predictive power of a model was tested and analysed, FL modeling results showed that there is an excellent agreement between the experimental data and the predicted values, with mean squared relative error (MSRE) of less than 0.01. Expeiments show the model pccupies preferable predication performance and practicability, ANFIS is very well suited for the parameters mapping modeling of electrodialysis, Then, the results of a previously developed MM were presented. The MM related SP to hydrodynamic dimension of the ED cell and operation conditions via two distinct parametersαandβ. This ability favored the MM for scale-up applications. However, based on MSRE of the MM (about 0.0397), it could not obviously predict the experimental data as well as FL.