Prediction of the Dynamic Viscosity of N-Alcohol by Three Intelligent Models (ANN, LSSVM, and ANFIS) in Operation Conditions

Abstract

Viscosity is an essential property in chemical engineering for different applications. If predicted accurately, the viscosity has a significant effect on chemical applications. In this paper, the capability of the three intelligence models, artificial neural network (ANN), least squares support vector machine (LSSVM), and adaptive neuro-fuzzy inference system (ANFIS), were evaluated to model the dynamic viscosity of n-alcohol at the different operational conditions. The models were improved based on a 237 data set collected from reliable articles. The used databank contains temperature (T), pressure (P), and carbon number of n-alcohols (n-C) were chosen as the input of models. The result of these models was studied by Statistical parameters such as Mean of the squares errors (MSE), Root mean of the squares errors (RMSE), Maximum absolute error (MAAE %), Mean absolute error (MEAE %), correlation coefficient and graphical technique like Taylor diagram and William plot. The proposed models are known to appropriately estimate the viscosity of n-alcohol at the different operational conditions. It was found that the ANN with R2= 0.999, MSE=0.000017, MAAE %= 1.6, MEAE %=0.32 for Test, and R2= 0.999, MSE=0.0000094, MAAE %= 0.83, MEAE %=0.23 for Train exhibited a high performance than LSSVM and ANFIS for predicting dynamic viscosity of n-alcohol at the operational conditions.