Iranian Journal of Chemical Engineering (IJChE)

Abstract In this work, the surface tension (σ ) of aqueous solutions of PEGs (poly ethylene glycol) with molecular weights of 200, 300 and 6000 in water, and poly (propylene glycol) (PPG) with a molecular weight of 2000 in ethanol, were measured in the (293.2-338.2) K temperature range and atmospheric pressure. The results of the measurements were modeled by an Artificial Neural Network(ANN) with input of mass fraction of polymer and solution temperature. The predictions of the artificial neural network model fit the experimental data perfectly.

Abstract Cadmium is a hazardous non-biodegradable material that enters the food chain. In this study, the removal of cadmium from aqueous solutions by Canola residues (a natural biosorbent) was investigated. The effect of parameters such as contact time, pH, cadmium concentration and adsorbent dose were evaluated, the results showed that increasing of pH, contact time and adsorbent caused increasing of efficiency of removal cadmium from aqueous solutions. A comparison between non-linear and linear of estimating the isotherm and kinetic parameters was discussed. The results confirmed that the non-linear method was a more efficient method to gain isotherm and kinetic parameters.

Abstract In this study, adsorption data for the case of energetically heterogeneous solid surface are modeled using artificial neural network. A neural network with three hidden neurons, including the bias, was able to predict very accurately the temperature dependency of adsorption data. The results were compared with experimental data (over temperature range 273-313 K and 0-2 MPa pressure) and it was found that the predictions of the artificial neural network model fit the experimental data very accurately.