New approach to mimic rheological actual shear rate under wall slip condition

Abstract

The presence of wall slip in concentrated suspensions affect the rheological measurements such as shear stress, shear rate, and viscosity. The measured shear rate will have a value lower than the actual shear rate. Therefore, it is important to have a study on such scenario as the concentrated suspensions have a wide industrial application. The current method for actual shear rate prediction is a challenging task and quite time-consuming as several experimental works are required. Therefore, the development of a mathematical model with an acceptable accuracy is required. Multi-layer perceptron neural network (MLP-NN) is employed to develop the prediction model by applying shear stress, volumetric concentration, particle size, and temperature as the input parameters while the actual shear rate is kept as the output variable. MLP-NN model with 9 hidden neurons is the model that has achieved the best performance in term of statistical analyses. Besides, MLP-NN models for five different temperature ranges (i.e., 11–20 °C, 21–30 °C, 31–40 °C, 41–50 °C, and 51–60 °C) are also proposed to investigate the potential of the developed model to achieve a better accuracy and ease the user while dealing with the specified temperature range. It is found that the MLP-NN models with the best performance for each temperature range are the models with 8 hidden neurons, 10 hidden neurons, 8 hidden neurons, 9 hidden neurons, and 9 hidden neurons respectively. The novelty of this research study is the application of artificial intelligence method to mimic rheological actual shear rate under wall slip condition.