Rheological wall slip velocity prediction model based on artificial neural network

Wall slip is a phenomenon in which particles migrate from solid boundaries, leaving a thin liquid rich layer adjacent to a wall, which can affect the measurement of the rheological properties. Currently, analyses of wall slip are normally carried out through experimental study using a rheometer. These traditional methods are generally time consuming, as several experiment sets are usually required. The aim of this research is to develop an alternative, more efficient approach, by formulating a mathematical model able to predict the wall slip velocity with an acceptable level of accuracy. Specifically, this study investigates a Multi-Layer Perceptron Neural Network (MLP-NN) as an advanced method to predict wall slip velocity. It develops and tests several MLP-NN architectures that accommodate a range of fixed input variables including shear stress, concentration, temperature and particle sizes, with estimated wall slip velocity as the output variable. Using this method, users can perform wall slip velocity analyses by simply plugging different patterns of the proposed input variables into the recommended architecture. Our tests show an MLP-NN model with one hidden layer consisting of nine hidden neurons to be the best architecture for such purposes, producing a strong overall performance with an R-2 value of 0.9994 and maximum error of 28%. This research study is innovative in its use of artificial intelligence to predict wall slip velocity in rheological applications.