A new correlation for predicting hydrate formation conditions for various gas mixtures and inhibitors

Elgibaly, Ahmed A.; Elkamel, Ali

doi:10.1016/s0378-3812(98)00368-9

Cited by 92 publications

(37 citation statements)

References 21 publications

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“…Elgibaly and Elkamel [12] used a one-hidden layer network for predicting hydrate formation conditions, which is composed of 16 input variables, 50 neurons in the hidden layer and one output (hydrate dissociation pressure). The network was used to correlate 2387 experimental data with 19% average error.…”

Section: Resultsmentioning

confidence: 99%

“…Their results apparently show signs of "over-fitting" or "over-training" of the network. Elgibaly and Elkamel [12] however mentioned that, because of the lack of sufficient experimental data especially for some binary and multicomponent hydrate systems, the developed model have to be updated by being retrained using extra collected data. We therefore followed a similar approach.…”

Section: Resultsmentioning

confidence: 99%

“…We therefore followed a similar approach. However, the number of input variables in our method is different from Elgibaly and Elkamel's model [12], i.e., 19 input variables and only 35 neurons in the single hidden layer were used (Fig. 2).…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

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

Chapoy

Mohammadi

Richon

2007

Oil & Gas Science and Technology - Rev. IFP

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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Chapoy

Mohammadi

Richon

2007

Oil & Gas Science and Technology - Rev. IFP

207

View full text Add to dashboard Cite

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“…Neural networks performed all predictions in a few minutes and required only a conventional computer. The only physical expertise needed to prepare an artificial neural network is to define the important inputs and outputs of the system (Elgibaly and Elkamel, 1998). For these reasons, a percentage of 80% correct classifications for the validation data set can be considered very good, indicating that the neural network is able to make confident predictions for new blends.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of the effect of dry-film biocides on paint film preservation using neural networks

Contant

Junior

Machado

et al. 2010

Braz. J. Chem. Eng.

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-Biocides play an important role in the preservation of a variety of products susceptible to microbiological growth such as paint, a material that can undergo microbial deterioration both in storing (inside the can) and after the application on a surface. In this work, artificial neural networks were used to predict the level of fungal growth on surfaces painted with water-based paints with biocide formulations containing different concentrations of ten kinds of commercial and experimental chemical agents. The use of neural networks is well known in chemical processes and they are a powerful tool for discovering relationships between sets of data. Industrial Environmental Tropical Chamber tests were used as the network training set. The importance of the each additive of the dry-film biocide formulation in prevention of biodeterioration was also examined.

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“…It is also called (Holder and Makogon) method, is specially selected for pure gases correlations those depends on the range of temperature for each gas [8,9].…”

Section: Empirical Correlationmentioning

confidence: 99%

A review on the Application of Empirical Models to Hydrate Formation Prediction

Abbasi

Hashim

2014

MATEC Web of Conferences

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Abstract. In deepwater hydrocarbon transportation pipeline, the production may decrease and operational cost and time are increasing due to the growth rate of hydrate. The pressure of deepwater pipeline is comparatively high, so it is entirely possible to form the hydrate formation conditions and pose a major operational and safety challenge. This work provides a review on empirical models for hydrate formation prediction in deepwater gas pipeline. The correlation and empirical models are presented with the hydrate formation temperature described as a function of pressure and gas gravity. This study could possibly provide a milestone for predicting PVT and heat transfer effects on hydrate formation in deepwater pipeline.

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A new correlation for predicting hydrate formation conditions for various gas mixtures and inhibitors

Cited by 92 publications

References 21 publications

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

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

Evaluation of the effect of dry-film biocides on paint film preservation using neural networks

A review on the Application of Empirical Models to Hydrate Formation Prediction

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