Prediction of methanol loss in vapor phase during gas hydrate inhibition using Arrhenius-type functions

Bahadori, Alireza; Vuthaluru, Hari

doi:10.1016/j.jlp.2010.01.002

Cited by 14 publications

(12 citation statements)

References 14 publications

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“…One of the efficient ways not to deal with computationally expensive systems of equations is utilization of the Lagrange interpolation technique. If the total number of data points is (n + 1), the Lagrange polynomial is n‐degree as shown below:

{normalΨ}_{i} true(x_{j} true) = true{\begin{array}{c} center & 1 & i f & i = j \\ center & 0 & i f & i \neq j \end{array}

…”

Section: Methodology For Model Developmentmentioning

confidence: 99%

Systematic investigation of asphaltene precipitation by experimental and reliable deterministic tools

2017

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Asphaltene precipitation in oil operations contributes to serious technical and non-technical issues, which is affected by reservoir conditions such as pressure, temperature, dilution ratio, and type of diluent. In this work, a mathematical correlation is introduced to forecast the weight percent (wt%, g/g) of precipitated asphaltene in light oils. Experimental data are obtained based on high-resolution images taken from high-pressure cell processed by the image analysis method. Employing experimental data, a simple and precise correlation on the basis of the scaling/fractal theory is developed. In this study, the amount of asphaltene precipitation is considered in terms of pressure and dilution ratio in which the coefficients are simply calculated through the Vandermone matrix. Comparison between the model results and real data reveals a good match where the average coefficient of correlation (R 2 ) is above 0.95. Additionally, two previous predictive methods, namely the Bayesian belief network and scaling models, are utilized and the outputs are compared (e.g. the magnitudes of mean squared error (MSE) are 0.0065 and 0.038 for C6 and C7, respectively), implying the effectiveness of the new deterministic tool in terms of accuracy and reliability such that the average MSE is 0.006 for both C 6 and C 7 . The trustworthiness of the real asphaltene precipitation data is also assessed by the statistical leverage approach based on the hat matrix, standardized residuals, and William plot to specify the applicability domain (AD) of the predictive models. It was found that all deterministic techniques presented in this study result in satisfactory accuracy, since the entire collected experimental data are reliable within AD.

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{normalΨ}_{i} true(x_{j} true) = true{\begin{array}{c} center & 1 & i f & i = j \\ center & 0 & i f & i \neq j \end{array}

…”

Section: Methodology For Model Developmentmentioning

confidence: 99%

Systematic investigation of asphaltene precipitation by experimental and reliable deterministic tools

2017

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“…It is necessary to reduce methanol loss (ML) by the gas stream in the hydrate prevention unit. This loss is directly related to methanol liquid–liquid equilibria (LLE) and vapor–liquid equilibria (VLE) behaviors in potential hydrate systems 26 . A straightforward method should be developed to approximate ML with reasonable accuracy before planning a scenario for mitigating or minimizing ML.…”

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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“…In view of this, for researches associated with hydrate issue such investigations can be considered. In this regard, Bahadori and Vuthaluru (2010) presented a model for the prediction of methanol vaporization and vapor pressure of aqueous methanol solution as a function of temperature and methanol mass fraction in aqueous solution based-on vandermonde matrix. In another effort by Bahadori and Vuthaluru (2009) the investigations around proposed correlation for estimation of hydrate formation condition of sweet natural gases can be noted.…”

Section: Introductionmentioning

confidence: 99%

Prediction of carbon dioxide separation from gas mixtures in petroleum industries using the Levenberg–Marquardt algorithm

Amin

Bahadori

Lee

et al. 2016

Petroleum Science and Technology

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Prediction of methanol loss in vapor phase during gas hydrate inhibition using Arrhenius-type functions

Cited by 14 publications

References 14 publications

Systematic investigation of asphaltene precipitation by experimental and reliable deterministic tools

Systematic investigation of asphaltene precipitation by experimental and reliable deterministic tools

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

Prediction of carbon dioxide separation from gas mixtures in petroleum industries using the Levenberg–Marquardt algorithm

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