Determination of the gas hydrate formation limits to isenthalpic Joule–Thomson expansions

Yarveicy, Hamidreza; Ghiasi, Mohammad M.; Mohammadi, Amir H.

doi:10.1016/j.cherd.2017.12.046

Cited by 44 publications

(23 citation statements)

References 20 publications

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“…Following expressions define the mathematical formula of the employed statistical parameters :

R^{2} = 1 - \frac{\sum_{i}^{n} {(\exp_{i} - {pred}_{i})}^{2}}{\sum_{i}^{n} {({pred}_{i} - average (\exp_{i}))}^{2}} \times 100

ARD % = \frac{100}{n} \sum_{i}^{n} \frac{(\exp_{i} - {pred}_{i})}{\exp_{i}}

AARD % = \frac{100}{n} \sum_{i}^{n} \frac{|\exp_{i} - {pred}_{i}|}{\exp_{i}}

where R 2 , ARD%, and AARD% are coefficient of determination, average relative deviation percent, and average absolute relative deviation percent, respectively. AARD% is a measure of the accuracy of the model.…”

Section: Resultsmentioning

confidence: 99%

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Modeling Equilibrium Systems of Amine‐Based CO₂ Capture by Implementing Machine Learning Approaches

Ghiasi

Abedi‐Farizhendi

Mohammadi

2019

Env Prog and Sustain Energy

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Precise calculation of carbon dioxide equilibrium solubility in aqueous amine solutions is decisive in the success of establishment or maintenance of amine‐based absorptive carbon dioxide capture processes. To implement the AdaBoost algorithm in conjunction with the classification and regression tree (AdaBoost‐CART) aimed at developing models to accurately estimate the equilibrium absorption of carbon dioxide in ethanolamine solutions, experimental data for monoethanolamine (MEA), diethanolamine (DEA), and triethanolamine (TEA) systems were gathered from the literature. Furthermore, neural‐based models were developed using the collected databank as the basis of comparison. The results of the presented models were compared to the results of the available models in the literature. It was found that the proposed AdaBoost‐CART models for the investigated amine systems present more precise and reliable outputs compared to the results of the neural‐based and literature models. In a respective order, the introduced AdaBoost‐CART models for MEA, DEA, and TEA solutions show average absolute relative deviation percent of 0.51, 2.76, and 1.41 which indicate their reliability and superiority over other models. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13146, 2019

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“…Following expressions define the mathematical formula of the employed statistical parameters :

R^{2} = 1 - \frac{\sum_{i}^{n} {(\exp_{i} - {pred}_{i})}^{2}}{\sum_{i}^{n} {({pred}_{i} - average (\exp_{i}))}^{2}} \times 100

ARD % = \frac{100}{n} \sum_{i}^{n} \frac{(\exp_{i} - {pred}_{i})}{\exp_{i}}

AARD % = \frac{100}{n} \sum_{i}^{n} \frac{|\exp_{i} - {pred}_{i}|}{\exp_{i}}

Section: Resultsmentioning

confidence: 99%

“…Following expressions define the mathematical formula of the employed statistical parameters [38,39]:…”

Section: Parameters Of Error Analysismentioning

confidence: 99%

Modeling Equilibrium Systems of Amine‐Based CO₂ Capture by Implementing Machine Learning Approaches

Ghiasi

Abedi‐Farizhendi

Mohammadi

2019

Env Prog and Sustain Energy

Self Cite

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“…26 Several researchers tried to reduce the complexity of the ML estimation methods and reduce their computational times. [28][29][30] The artificial intelligence (AI) techniques have reliably been utilized to monitor the effect of influential variables on the response factor. [31][32][33][34] Ghiasi et al estimated the solubility of methanol in liquid-phase paraffin hydrocarbons using a support vector machine, radial basis function, and multilayer perceptron neural networks (MLPNNs).…”

Section: Introductionmentioning

confidence: 99%

Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy

Khandakar

2022

Energy Science & Engineering

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Artificial neural networks (ANNs) can understand the behavior of a given system from the historical measurements of its associated variables. Adjusting the weight and bias of the ANN model using an optimization algorithm is known as the training process. The ANN reliability is directly related to the success of the training process. Therefore, this study investigates the effect of optimization algorithms on the prediction accuracy of the multilayer perceptron neural networks (MLPNNs). The complex gas hydrate prevention unit is simulated using the MLPNN model trained by 20 different optimization algorithms. This study investigates the gradient‐based, evolutionary, and Bayesian‐based optimization algorithms. Combining statistical and ranking analyses confirms that the Levenberg–Marquardt (LM) is the most efficient optimization technique for training the MLPNN model. This training algorithm adjusts the weight and bis parameters of the MLPNN so that the highest accurate predictions have been achieved. On the other hand, the trained MLPNN by imperialist competitive algorithm shows the lowest accuracy for the considered task. The trained MLPNN by the LM algorithm predicts 239 laboratory‐measured data sets about the methanol (MeOH) loss with the absolute average relative deviation of 6.4% and regression coefficient of 0.9643. Coupling the developed MLPNN and differential evolution optimization algorithm shows that temperature = 263 K and pressure = 6.92 MPa are the optimum condition for minimizing the MeOH loss in the gas hydrate prevention unit. Economic analysis confirms that the annual cost of methanol loss for the daily processing of 100 × 106 m3 of gas is ~17 million US dollars.

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“…The aims of some reservoir engineering problems [1][2][3][4][5][6][7], such as optimal control of reservoir production and history matching, are to determine the optimal parameter sets (well controls or physical properties). Several methodologies [8][9][10][11][12][13][14][15][16][17][18] including ensemblebased optimization methodologies, quadratic interpolation, and gradient-based methodologies have been applied to determine the optimal parameter sets. The gradient-based algorithms [19] have been by far the most classical methods for calculating the optimal parameter sets, in which gradually approaching the optimal values is achieved via constantly calculating new gradients.…”

Section: Introductionmentioning

confidence: 99%

Proper Orthogonal Decomposition-Based Method for Predicting Flow and Heat Transfer of Oil and Water in Reservoir

Sun

et al. 2019

Journal of Energy Resources Technology

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Calculation process of some reservoir engineering problems involves several passes of full-order numerical reservoir simulations, and this makes it a time-consuming process. In this study, a fast method based on proper orthogonal decomposition (POD) was developed to predict flow and heat transfer of oil and water in a reservoir. The reduced order model for flow and heat transfer of oil and water in the hot water-drive reservoir was generated. Then, POD was used to extract a reduced set of POD basis functions from a series of “snapshots” obtained by a finite difference method (FDM), and these POD basis functions most efficiently represent the dynamic characteristics of the original physical system. After injection and production parameters are changed constantly, the POD basis functions combined with the reduced order model were used to predict the new physical fields. The POD-based method was approved on a two-dimensional hot water-drive reservoir model. For the example of this paper, compared with FDM, the prediction error of water saturation and temperature fields were less than 1.3% and 1.5%, respectively; what is more, it was quite fast, where the increase in calculation speed was more than 70 times.

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Determination of the gas hydrate formation limits to isenthalpic Joule–Thomson expansions

Cited by 44 publications

References 20 publications

Modeling Equilibrium Systems of Amine‐Based CO₂ Capture by Implementing Machine Learning Approaches

Modeling Equilibrium Systems of Amine‐Based CO₂ Capture by Implementing Machine Learning Approaches

Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy

Proper Orthogonal Decomposition-Based Method for Predicting Flow and Heat Transfer of Oil and Water in Reservoir

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Determination of the gas hydrate formation limits to isenthalpic Joule–Thomson expansions

Cited by 44 publications

References 20 publications

Modeling Equilibrium Systems of Amine‐Based CO2 Capture by Implementing Machine Learning Approaches

Modeling Equilibrium Systems of Amine‐Based CO2 Capture by Implementing Machine Learning Approaches

Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy

Proper Orthogonal Decomposition-Based Method for Predicting Flow and Heat Transfer of Oil and Water in Reservoir

Contact Info

Product

Resources

About

Modeling Equilibrium Systems of Amine‐Based CO₂ Capture by Implementing Machine Learning Approaches

Modeling Equilibrium Systems of Amine‐Based CO₂ Capture by Implementing Machine Learning Approaches