Abstract
Artificial neural networks (ANN) were used to predict the viscosity of epoxy resin modified by natural waste material (pomegranate peel) powder. This waste material, which has a high pollution capacity for the environment, could be used as an improvement to the properties of a weaken material such as epoxy resin In reservoir engineering computations the viscosity parameter is a very important fluid property. The data is not either reliable or unavailable most of the time; It should be specified in the lab. Levenberg-Marquardt backpropagation of artificial neural network based model (ANNs) was evolved to predict the viscosity of modified pomegranate peel powder of epoxy resin. Three parameters affecting the viscosity of mixtures based on epoxy resin containing pomegranate peel powder and pure epoxy resin were studied. These are temperature, concentration of pomegranate peel, and shear rate. The viscosity as output was predicted by training the network. A network was built up and trained using experimental information. The effects of temperature (30-50 °C), concentration of pomegranate peel powder (0-3)wt% and shear rate (4.35-15.95 1/sec) on the epoxy resin were modeled by ANNs as well. The expected values were in excellent agreement with the measured ones, showing that the developed model is really accurate and has the great ability for predicting the viscosity. The linear regressions R2 0.9994 and 0. 9998 are the values of the ANN viscosity model for training and testing data set, respectively, 2.7175*10-4 and 1.2441*10-4 are the values of mean square error respectively.
