Modeling and prediction of activity coefficient ratio of electrolytes in aqueous electrolyte solution containing amino acids using artificial neural network

Dehghani, Mohammadreza; Modarress, Hamid; Bakhshi, Ali

doi:10.1016/j.fluid.2006.04.003

Cited by 25 publications

(9 citation statements)

References 27 publications

(34 reference statements)

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“…Although, ANN is an applicable 58 tool to predict the properties of mixtures such as heat capacity, viscosity 59 and liquid-liquid extraction data [12,13]. However, a survey of literatures 60 shows that limited publications have been made on the use of ANN to 61 prediction for the activity coefficients [14][15][16][17]. However, it is essential 62 to use predicting technique such as ANN method when the common 63 methods are difficult to use to produce the experimental data.…”

mentioning

confidence: 99%

Thermodynamic modeling of the KCl+formamide/glucose/proline+water ternary systems and activity coefficient prediction based on artificial neural network

Ghalami‐Choobar

Choobar

2015

Journal of Molecular Liquids

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confidence: 99%

Thermodynamic modeling of the KCl+formamide/glucose/proline+water ternary systems and activity coefficient prediction based on artificial neural network

Ghalami‐Choobar

Choobar

2015

Journal of Molecular Liquids

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“…[21,22] The advantages of the ANNs, even if the exact relationship between sets of inputs and outputs data is unknown but is acknowledged to exist, are that the ability to represent both linear and nonlinear relationships, the ability to learn these relationships directly from the data used, need not take into account the detailed information of structures and interactions in the systems, and they are regarded as ultimate black-box models. [23][24][25] At least in some cases if not always, i.e., for prediction by the trained network, the ANN systems are alternative to experimentation and save a lot of time which may have been consumed. [26] So far, different types of neural network architectures and their performances have been studied.…”

Section: Methods Artificial Neural Networkmentioning

confidence: 99%

Prediction of Chemical Inhibitors Efficiency for Reducing Deposition Thickness Using Artificial Neural Network

Lashkarbolooki

Seyfaee

Esmaeilzadeh

et al. 2014

Journal of Dispersion Science and Technology

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Prediction of efficiency of chemical inhibitors to mitigation of deposition thickness is a key to developing crude oil transportation process. In this work, a feed-forward artificial neural network (ANN) algorithm has been applied to predict the influence of the mitigation effect of ethylene-covinyl acetate (EVA) copolymer and its combination with chloroform (C), acetone (A), P-xylene (PX), and petroleum ether (PE) on the deposition thickness in the pipeline. An optimized three-layer feed-forward ANN model using properties of the oil pipeline such as: inlet oil temperature, environmental (coolant mixture) temperature, oil Reynolds numbers; properties of injected inhibitor such as molecular weight, boiling point, and amount of injection; and time is presented. Different networks are considered and trained using 62661 data sets; the accuracy of the network is validated by 20888 testing data sets. To verify the network generalization, 29 different experiment data sets of four different set of inhibitors have been considered. It is found that the proposed ANN model is an alternative to experimentation and predicts deposition thickness without experimentation, vast information, and tedious and time-consuming calculations.

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“…In order to find relationship between the input and output data derived from experimental work, a more powerful method than the traditional ones are necessary. ANN is an especially efficient algorithm to approximate any function with finite number of discontinuities by learning the relationships between input and output vectors (Sozen et al, 2005; Dehghani et al, 2006). These algorithms can learn from the experiments, and also are fault tolerant in the sense that they are able to handle noisy and incomplete data.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

An expert model for estimation of the performance of direct dimethyl ether synthesis from synthesis gas

Moradi

Parvizian

2011

Can J Chem Eng

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In this work, an artificial neural network (ANN) has been trained and tested for estimation of the performance of direct synthesis of dimethyl ether (DME) from synthesis gas. Yield and selectivity of DME production and also conversion of CO could be predicted when temperature and pressure of reactor and H 2 /CO molar ratio in feed have been specified. The results of ANN estimation for yield of DME, selectivity of DME and CO conversion are in very good agreement with experimental values. For this development, database was collected from our previous experiment. The accuracy and trend stability of the trained networks were tested against unseen data. Different training schemes for the back-propagation learning algorithm, such as: Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), Gradient Descent with Momentum (GDM), variable learning rate Back propagation (GDA) and Resilient back Propagation (RP) methods were used. The SCG algorithm with seven neurons in the hidden layer shows the best suitable algorithm with the minimum average absolute relative error 0.05231.Dans ce travail, une réseau de neurones artificiels (RNA) aété instruit et testé pour l'estimation du rendement de la synthèse directe d'oxyde de diméthyle (ODM)à partir de gaz de synthèse. On peut prédire le rendement et la sélectivité de la production de ODM et la conversion de CO lorsque la température et la pression du réacteur ainsi que le rapport molaire H2/CO de la charge de départ ontété spécifiés. Les résultats de l'estimation RNA du rendement de ODM, de la sélectivité de la production de ODM et la conversion de CO sont en bon accord avec les valeurs expérimentales. Pour ce dévelopment, une base de données de notre précédente expérimentation aété collectée. L'exactitude et la stabilité des tendances des réseaux instruits ontété testées par rapportà des données anonymes. Différents systèmes d'instruction pour l'algorithme de rétropropagation d'apprentissage ontété utilisés, par exemple, le gradient conjugué pondéré (SCG), Levenberg-Marquardt (LM), la descente de gradient avec momentum (GDM), la rétropropagationà taux d'apprentissage variable (GDA) et la rétropropagation résiliente (RP). L'algorithme SCG avec sept neurones dans la couche cachée se démontre commeétant l'algorithme le mieux approprié avec une erreur relative absolue moyenne minimale de 0,05231.

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Modeling and prediction of activity coefficient ratio of electrolytes in aqueous electrolyte solution containing amino acids using artificial neural network

Cited by 25 publications

References 27 publications

Thermodynamic modeling of the KCl+formamide/glucose/proline+water ternary systems and activity coefficient prediction based on artificial neural network

Thermodynamic modeling of the KCl+formamide/glucose/proline+water ternary systems and activity coefficient prediction based on artificial neural network

Prediction of Chemical Inhibitors Efficiency for Reducing Deposition Thickness Using Artificial Neural Network

An expert model for estimation of the performance of direct dimethyl ether synthesis from synthesis gas

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