Viscosity prediction by computational method and artificial neural networkapproach: The case of six refrigerants (original) (raw)
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International Journal of Refrigeration-revue Internationale Du Froid, 2008
Mixture Modelling Calculation Density Liquid a b s t r a c t In this study, a new approach for the auto-design of a neural network based on genetic algorithm (GA) has been used to predict saturated liquid density for 19 pure and 6 mixed refrigerants. The experimental data including Pitzer's acentric factor, reduced temperature and reduced saturated liquid density have been used to create a GA-ANN model. The results from the model are compared with the experimental data, Hankinson and Thomson and Riedel methods, and Spencer and Danner modification of Rackett methods. GA-ANN model is the best for the prediction of liquid density with an average of absolute percent deviation of 1.46 and 3.53 for 14 pure and 6 mixed refrigerants, respectively. ª 2008 Elsevier Ltd and IIR. All rights reserved. Ré seau neuronal utilisé afin de pré voir la densité des frigorigè nes purs et mé langé s, à l'aide d'un algorithme gé né tique (A. Mohebbi). w w w . ii fi i r .o r g a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / i j r e f r i g 0140-7007/$ -see front matter ª
Expert Systems with Applications, 2009
This study proposes a alternative approach based on artificial neural networks (ANNs) to determine the thermodynamic propertiesspecific volume, enthalpy and entropy -of an alternative refrigerant (R508b) for both saturated liquid-vapor region (wet vapor) and superheated vapor region. In the ANN, the back-propagation learning algorithm with two different variants, namely scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM), and Logistic Sigmoid transfer function were used to determine the best approach. The most suitable algorithm and with appropriate number of neurons (i.e. 7) in the hidden layer is found to be the LM algorithm which has provided the minimum error. For wet vapor region, R 2 values -which are errors known as absolute fraction of variance -are 0.983495, 0.969027, 0.999984, 0.999963, 0.999981, and 0.999975, for specific volume, enthalpy and entropy for training and testing, respectively. Similarly, for superheated vapor, they are: 0.995346, 0.996947, 0.999996, 0.999997, 0.999974, and 0.999975, for training and testing, respectively. According to the regression analysis results, R 2 values are 0.9312, 0.9708, 0.9428, 0.9343, 0.967 and 0.9546 for specific volume, enthalpy and entropy for wet vapor region and superheated vapor, respectively. The comparisons of the results suggest that, ANN provided results comfortably within the acceptable range. This study, deals with the potential application of the ANNs to represent PVTx (pressure-specific volume-temperature-vapor quality) data. Therefore, reducing the risk of experimental uncertainties and also removing the need for complex analytic equations requiring long computational time and efforts.
Thermodynamic analyses of refrigerant mixtures using artificial neural networks
Applied Energy, 2004
The aim of this study is to make a contribution towards the efforts of reducing the use of CFCs by finding a drop-in replacement for pure refrigerants used in domestic and industrial appliances. The suggested solution is the use of HFC and HC based refrigerant mixtures. In this study, we investigate different possible ratios of these mixtures and their corresponding performances by using Artificial Neural-Networks (ANNs). We believe this dramatically reduces the times and efforts required to achieve these targets. Coefficients of Performances (COPs) and Total Irreversibilities (TIs) of refrigerants and their mixtures have been calculated for a vapor-compression refrigeration system with a liquid/suction line heat-exchanger. The constant cooling-load method is taken as a reference. The thermodynamic properties of refrigerants have been taken from REFPROP 6.01. To train the network, based on Scaled Conjugate Gradient (SCG), Pola-Ribiere Conjugate Gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function, we have used various ratios of 7 refrigerant mixtures of HFCs and HCs along with three CFCs (R12, R22, and R502). They were used as inputs while the COP and TI values, calculated as above, were the outputs. The network has yielded R 2 values of 0.9999 and maximum errors for training and test data were found to be 2 and 3%, respectively.
Prediction of the Viscosity of Heavy Petroleum Fractions and Crude Oils by Neural Networks
Journal of The Japan Petroleum Institute, 1996
In this work, the prediction of heavy petroleum fractions was significantly improved by using a backpropagation neural network model. It was found that scaling the data, fed to the neural net, improved the convergence of the estimated parameter (viscosity) in reasonable time with acceptable accuracy. An absolute error of 3.4% was achieved which is found to be better than those by other conventional methods.
Fluid Phase Equilibria, 2003
Thermodynamic and transport property data on environmentally acceptable refrigerant fluids are of the utmost interest for the refrigeration industry and, in particular, for designing and optimising refrigeration equipment: heat exchangers and compressors. Up to now, the simultaneous representation of vapour-liquid equilibria (VLE) and pressure-volume-temperature (PVT) data is not satisfactory enough with respect to experimental accuracies. New models are then highly required. Therefore, an effort has been made to develop an alternative to a classical equation of state. This work deals with the potential application of artificial neural networks to represent PVT data within their experimental uncertainty. The second aim of the work is to obtain, by numerical derivatives, other properties such as enthalpies, entropies, heat capacities, expansion coefficients, speed of sounds, etc. Tests presented here were performed on data corresponding to six refrigerants from 240 to 340 K at pressures up to 20 MPa.
Applied thermal engineering, 2005
This paper presents a new approach based on artificial neural networks (ANNs) to determine the properties of liquid and two phase boiling and condensing of two alternative refrigerant/absorbent couples (methanol/LiBr and methanol/LiCl). These couples do not cause ozone depletion and use in the absorption thermal systems (ATSs). ANNs are able to learn the key information patterns within multidimensional information domain. ANNs operate such as a Ôblack boxÕ model, requiring no detailed information about the system. On the other hand, they learn the relationship between the input and the output. In order to train the neural network, limited experimental measurements were used as training data and test data. In this study, in input layer, there are temperatures in the range of 298-498 K, pressures (0.1-40 MPa) and concentrations of 2%, 7%, 12% of the couples; specific volume is in output layer. The back-propagation learning algorithm with three different variants, namely scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM), and logistic sigmoid transfer function were used in the network so that the best approach can find. The most suitable algorithm and neuron number in the hidden layer are found as SCG with 8 neurons. For this number level, after the training, it is found that maximum error is less than 3%, average error is about 1% and R 2 value are 99.999%. As seen from the 1359-4311/$ -see front matter Ó Applied Thermal Engineering 25 (2005) 1808-1820 results obtained the thermodynamic equations for each pair by using the weights of network have been obviously predicted within acceptable errors. This paper shows that values predicted with ANN can be used to define the thermodynamic properties instead of approximate and complex analytic equations.
Estimating the Dynamic Viscosity of Vegetable Oils Using Artificial Neural Networks
Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2014
In this study, viscosities of raw sunflower and corn oils are measured at 1°C intervals between 0-100°C. Experimental results are fitted to six equations that are used in viscosity estimation and the correlation coefficients are determined. The best correlation coefficient is obtained using In(µ)=a+b/(T+c) equation with 0.99972 and 0.99974 for sunflower and corn oil, respectively. In addition to this, viscosity values are obtained using artificial neural networks and the results are compared to the equation leading to the best correlation coefficient. Using artificial neural networks, the correlation coefficients are obtained as 0.999907 and 0.999925 for raw sunflower and corn oil respectively.
2015
In this study, an artificial neural network (ANN) application which predicts of factor refrigerating capacity in a mechanical compression refrigeration system was developed. Mechanical compression refrigeration cycle, the most common cooling cycle. Element that provides heat by the evaporation of the refrigerant evaporator at low pressure environment. The Network, which has three layers as input, output, and hidden layer, has four input and one output cells. Six cells were used in hidden layers. Which back propagation algorithm was used for training. Desired error value was achieved in ANN and, ANN was tested with both data used for training ANN and data not used. Resultant low relative error value of the test indicates the usability of ANNs in this area. Capacity of change in a refrigeration system can be studied by different proceeding. Changing condenser temperature also changes the capacity of the refrigeration system. In this study, a series of experiments were performed in ord...