Estimation of Surface Tension of Aqueous Polymer Solutions Using Soft Computing Approaches

The surface tension of aqueous polymer solutions is an important property that plays a vital role in mass and heat transfer. In this study, the surface tension of a polymer mixture is modeled using four algorithms (Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and  Adaptive group of Ink Drop Spread (AGIDS) ) which has been developed in the soft-computing domain. In this paper, four models for predicting the surface tension are applied and the results were compared with our published experimental data and it was found that the predictions of these models fit the experimental data very accurately. Also, a comparison has been done to evaluate the effectiveness of the relevant four algorithms in the current problem. The simulation results have shown that ANFIS and RBF model predictions are more accurate than the two others in the current problem.

Keywords:

Soft-computing , prediction , Surface tension , Polymer solution