Prediction of Kinematic Viscosity of Binary Mixture of Poly (Ethylene Glycol) in Water using Artificial Neural Networks
نویسندگان
چکیده
An artificial neural network (ANN) model is presented for the prediction of kinematic viscosity of binary mixtures of poly (ethylene glycol) (PEG) in water as a function of temperature, number-average molecular weight and mass fraction. Kinematic viscosities data of aqueous solutions for PEG (0.55419×10 – 9.875×10 m2/s) were obtained from the literature for a wide range of temperatures (277.15 338.15 K), number-average molecular weight (200 -10000), and mass fraction (0.0 – 1.0). A three layer feed-forward artificial neural network was employed. This model predicts the kinematic viscosity with a mean square error (MSE) of 0.281 and the coefficient of determination (R) of 0.983. The results show that the kinematic viscosity of binary mixture of PEG in water could be successfully predicted using an artificial neural network model. Keywords—Artificial neural network, kinematic viscosity, poly ethylene glycol (PEG)
منابع مشابه
Study of Surface Tension of Binary Mixtures of Poly (Ethylene Glycol) in Water and Poly (Propylene Glycol) in Ethanol and its Modeling Using Neural Network
متن کامل
Prediction of Kinematic Viscosity of Petroleum Fractions Using Artificial Neural Networks
In this work, artificial neural network (ANN) was utilized to develop a new model for the prediction of the kinematic viscosity of petroleum fractions. This model was generated as a function of temperature (T), normal boiling point temperature (Tb), and specific gravity (S). In order to develop the new model, different architectures of feed-forward type were examined. Finally, the optimum struc...
متن کاملPrediction of monthly rainfall using artificial neural network mixture approach, Case Study: Torbat-e Heydariyeh
Rainfall is one of the most important elements of water cycle used in evaluating climate conditions of each region. Long-term forecast of rainfall for arid and semi-arid regions is very important for managing and planning of water resources. To forecast appropriately, accurate data regarding humidity, temperature, pressure, wind speed etc. is required.This article is analytical and its database...
متن کاملPrediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine
Two main objectives have been considered in this paper: providing a good model to predict the critical temperature and pressure of binary hydrocarbon mixtures, and comparing the efficiency of the artificial neural network algorithms and the support vector regression as two commonly used soft computing methods. In order to have a fair comparison and to achieve the highest efficiency, a comprehen...
متن کاملViscosity Prediction of Different Ethylene Glycol/Water Based Nanofluids Using a RBF Neural Network
In this study, a radial basis function (RBF) neural network with three-layer feed forward architecture was developed to effectively predict the viscosity ratio of different ethylene glycol/water based nanofluids. A total of 216 experimental data involving CuO, TiO2, SiO2, and SiC nanoparticles were collected from the published literature to train and test the RBF neural network. The parameters ...
متن کامل