Modeling of thermodynamic properties using neural networks

Chouai, Assia; Laugier, Serge; Richon, Dominique

doi:10.1016/s0378-3812(01)00801-9

Cited by 183 publications

(144 citation statements)

References 7 publications

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“…The ANN approach has been applied to predict the performance of various thermal systems [15][16][17][18][19][20][21][22][23].An artificial neural network (ANN) was used for the long-term performance prediction of thermosyphonic type solar domestic water heating (SDWH) systems. Results indicated that the proposed method can successfully be used for the prediction of the solar energy output of the system for a draw-off equal to the volume of the storage tank or for the solar energy output of the system and the average quantity of the hot water per month for the two demand water temperatures considered [24].…”

Section: Neural Network Designmentioning

confidence: 99%

Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

et al. 2009

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This study deals with artificial neural network (ANN) modeling a diesel engine using waste cooking biodiesel fuel to predict the brake power, torque, specific fuel consumption and exhaust emissions of engine. To acquire data for training and testing the proposed ANN, two cylinders, four-stroke diesel engine was fuelled with waste vegetable cooking biodiesel and diesel fuel blends and operated at different engine speeds. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The experimental results reveal that blends of waste vegetable oil methyl ester with diesel fuel provide better engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model based on standard Back-Propagation algorithm for the engine was developed. Multi layer perception network (MLP) was used for nonlinear mapping between the input and the output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. It was observed that the ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficient (R) were 0.9487, 0.999, 0.929 and 0.999 for the engine torque, SFC, CO and HC emissions, respectively. The prediction MSE (Mean Square Error) error was between the desired outputs as measured values and the simulated values by the model was obtained as 0.0004. Keywords

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Section: Neural Network Designmentioning

confidence: 99%

Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

et al. 2009

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“…Artificial neural networks have large numbers of computational units called neurons, connected in a massively parallel structure and do not need an explicit formulation of the mathematical or physical relationships of the handled problem [5,6,[8][9][10][11]. The most commonly used ANNs are the feed-forward neural networks [11], which are designed with one input layer, one output layer and hidden layers [8][9][10].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The most commonly used ANNs are the feed-forward neural networks [11], which are designed with one input layer, one output layer and hidden layers [8][9][10]. The number of neurons in the input and output layers equals to the number of inputs and outputs physical quantities, respectively.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Predicting the Hydrate Stability Zones of Natural Gases Using Artificial Neural Networks

Chapoy

Mohammadi

Richon

2007

Oil & Gas Science and Technology - Rev. IFP

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207

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Résumé -Prévision des zones de stabilité d'hydrates de gaz naturels par utilisation de réseaux de neurones artificiels -Une méthode par réseaux de neurones artificiels avec alimentation dans le sens direct faisant appel à 19 variables d'entrée (température, structure de l'hydrate, composition du gaz et concentration de l'inhibiteur en phase aqueuse) et 35 neurones dans une phase cachée unique, a été développée pour estimer les pressions de dissociation d'hydrates de gaz naturels en présence ou non d'inhibiteur en solution aqueuse. Le modèle a été développé en faisant appel à 3296 données de dissociation d'hydrates collectées dans la littérature. La fiabilité de la méthode a été vérifiée par utilisation de données expérimentales indépendantes (non utilisées pour l'apprentissage du réseau de neurones et le développement du modèle). On montre que les résultats de calculs (prédictions) sont en accord acceptable avec les valeurs expérimentales prouvant ainsi la capacité des réseaux de neurones pour l'estimation des zones de stabilité d'hydrates de gaz naturels. Abstract -Predicting the Hydrate Stability Zones of Natural Gases Using Artificial Neural Networks

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“…18 ANN has been widely applied to predict the physical and thermodynamic properties of chemical compounds. Recently a few researches have been performed by artificial neural networks for prediction of pure substances and petroleum fraction's properties 19 ; activity coefficients of isobaric binary systems 20 ; thermodynamic properties of refrigerants [21][22][23][24] ; activity coefficient ratio of electrolytes in amino acid's solutions 25 ; the phase stability problem 26 ; and dew point pressure for retrograde gases. 27 Other ANN applications include density predication of ionic liquids 28 ; modeling flow boiling heat transfer of pure fluids 29 ; predicting slag viscosity over a broad range of temperatures and slag compositions 30 ; -T-P prediction for ionic liquids 31 ; prediction of simple physical properties of mixed solvent systems, 32 etc.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction

Miao¹,

Rooney²,

Gan³

2012

IJCIS

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Being a new generation of green solvents and high-tech reaction media of the future, ionic liquids have increasingly attracted much attention. Of particular interest in this context are room temperature ionic liquids (in short as ILs in this paper). Due to the relatively high viscosity, ILs is expected to be used in the form of solvent diluted mixture with reduced viscosity in industrial application, where predicting the viscosity of IL mixture has been an important research issue. Different IL mixture and many modelling approaches have been investigated. The objective of this study is to provide an alternative model approach using soft computing technique, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of ILs [C n -mim][NTf 2 ] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0-328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity taking account of IL alkyl chain length, as well as temperature and compositions simultaneously, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. This illustrates the potential application of ANN in the case that the physical and thermodynamic properties are highly non-linear or too complex.

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Modeling of thermodynamic properties using neural networks

Cited by 183 publications

References 7 publications

Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

Predicting the Hydrate Stability Zones of Natural Gases Using Artificial Neural Networks

Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction

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