Study the transport properties of anion and cation exchange membranes toward various ions using chronopotentiometry
The transport properties of various anion and cation exchange membranes were studied in different electrolyte solutions using chronopotentiometry technique to get insight about the influence of the counter ion on the transport properties of the membranes. The investigated samples include heterogeneous ion exchange membranes varying in the functionality of fixed charged groups, physical properties of the polymer and the preparation procedure. Chloride and nitrate were investigated as counter-ion in their sodium salts for anion exchange membranes while sodium, calcium, potassium, and magnesium were selected as counter-ion in their chloride salts to study cation exchange membranes transport properties. First, the current – voltage curve of an ion exchange membrane in a given solution was recorded to obtain the limiting current, then a constant current above the limiting current was used for chronopotentiometry measurement. In all the studied membranes the potential across the membrane remained constant for a time interval called the transition time , when a constant current was applied. Then the potential increased rapidly to reach the steady state potential. The permselectivty and the transport number of a membrane were calculated using the corresponding transition time.
Effect of current-induced coion transfer on the shape of chronopotentiograms of cation-exchange membranes
Abstract Chronopotentiometry using pulses of a constant current of density j is a powerful method for characterizing ion-exchange membranes (IEMs). We report on the influence of coion transport on the shape of chronopotentiogram (ChP) and the consequent new possibility of quantifying coion transport based on ChP analysis. We show that in the case where the bathing solution contains Ca2+ or Mg2+ ions, the ChPs of the homogeneous (CMX) and heterogeneous (MK-40) cation-exchange membranes at overlimiting current densities have a maximum (a peak), which appears a few seconds after the transition time. The time required to reach a stationary state is of the order of d 2 / D ‾ 2 (where d is the membrane thickness and D ‾ 2 is the coion diffusion coefficient in the membrane); this time under our experiment conditions is about 300–400 s. We show that the cause of the maximum is the increase in coion transfer caused by the current-induced concentration polarization of the bathing solution. This increase in coion transfer results in increasing the limiting current density jlim, which at j = const leads to a reduction in the resistance of the depleted diffusion layer over time and the appearance of a maximum on the ChP. 1D mathematical modeling is based on the Nernst-Planck-Poisson equations. The main assumption inspired by the works of Levich and Amatore is that the apparent electrolyte diffusion coefficient in the depleted solution increases with increasing electroconvection. The stationary value of this diffusion coefficient is found from the I–V curve. The only fitting parameter is the critical potential difference, which refers to the onset of intensive electroconvection.
Application of neural networks for crossflow milk ultrafiltration simulation
The ability of neural network approach was investigated for the dynamic simulation of crossflow milk ultrafiltration. It aims to model the permeate flux and total hydraulic resistance as a function of pH, fat per cent and operation time. Feed forward perceptron networks with a single hidden layer were used to simulate the time-dependent rate of ultrafiltration from a few experimental data. The effect of the number of training points, the number of hidden neurons and training data arrangements on the accuracy of simulation are studied in this work. The results showed that the quality of simulation could be improved using appropriate selection of training points and small network. The best network was able to accurately capture the non-linear dynamics of milk ultrafiltration, so that the agreement between the actual data and simulated values was excellent with maximum and average errors less than 3.61% and 1.06%, respectively.