Connectionist approaches for solubility prediction of n-alkanes in supercritical carbon dioxide

Daryasafar, Amin; Daryasafar, Navid; Madani, Mohammad; Meybodi, Mahdi Kalantari; Joukar, Mohammad

doi:10.1007/s00521-016-2793-7

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…ML and DL models have been introduced in various fields [1][2][3][4][5][6][7][8] to make decisions using available data and domain knowledge. It is crucial to consider both accuracy and reliability when evaluating such models.…”

Section: Introductionmentioning

confidence: 99%

Applying Monte Carlo Dropout to Quantify the Uncertainty of Skip Connection-Based Convolutional Neural Networks Optimized by Big Data

et al. 2023

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Although Deep Learning (DL) models have been introduced in various fields as effective prediction tools, they often do not care about uncertainty. This can be a barrier to their adoption in real-world applications. The current paper aims to apply and evaluate Monte Carlo (MC) dropout, a computationally efficient approach, to investigate the reliability of several skip connection-based Convolutional Neural Network (CNN) models while keeping their high accuracy. To do so, a high-dimensional regression problem is considered in the context of subterranean fluid flow modeling using 376,250 generated samples. The results demonstrate the effectiveness of MC dropout in terms of reliability with a Standard Deviation (SD) of 0.012–0.174, and of accuracy with a coefficient of determination (R2) of 0.7881–0.9584 and Mean Squared Error (MSE) of 0.0113–0.0508, respectively. The findings of this study may contribute to the distribution of pressure in the development of oil/gas fields.

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Section: Introductionmentioning

confidence: 99%

Applying Monte Carlo Dropout to Quantify the Uncertainty of Skip Connection-Based Convolutional Neural Networks Optimized by Big Data

et al. 2023

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“…Another limitation of applying CO 2 extraction phenomenon in EOR is asphaltene precipitation that creates major problems for oil recovery and production. Therefore, finding the solubility of oil fractions in supercritical CO 2 is very important (Daryasafar et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Daryasafar et al investigated the viability of using artificial techniques for predicting the solubility of n-alkane in supercritical CO 2 . Based on the results of their study, intelligent tools can be good candidates for predicting n-alkane solubility (Daryasafar et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Modeling n-alkane solubility in supercritical CO2 via intelligent methods

Songolzadeh

Shahbazi

Madani

2020

J Petrol Explor Prod Technol

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Injection of carbon dioxide is a familiar, cost-effective and influential technology of enhancing oil recovery whose application has been limited owing to the low n-alkane solubility in supercritical CO2. Thus, determining the amount of dissolved n-alkane in supercritical CO2 is of importance. Accordingly, in this study, least-squares support vector machine (LSSVM), tuned with two different optimizing algorithms, namely particle swarm optimization (PSO) and cross-validation-assisted Simplex algorithm (CV-Simplex), has been used for this simulation process. Based on the results, the predicted values for dissolved n-alkane mole fraction in supercritical CO2 by PSO–LSSVM model were quite in line with experimental data. Furthermore, the accuracy of these models was compared with Chrastil correlation. Absolute average relative error for PSO–LSSVM, CV-Simplex–LSSVM and Chrastil was calculated to be 3.88%, 13.49% and 18.22% for total dataset, respectively, which leaves PSO–LSSVM as the superior model with the highest accuracy. Finally, the statistical parameters of absolute average relative error, mean square error and determination coefficient equal to 3.88%, 0.0164 and 0.994 for total dataset, respectively, proved that PSO–LSSVM model is an efficient method that can predict n-alkane solubility in supercritical CO2 with high precision within 8.99–45.90 MPa pressure and 308.15–344.15 K temperature range.

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“…In the recent years, supercritical fluid has become one of the interests of chemical engineers and chemists as a novel and extensive applicable technology. The synthesis and generating of nanomaterials and extraction process of different materials are the popular applications of supercritical fluids [1][2][3][4][5][6][7][8][9][10][11][12][13]. One of the supercritical fluids which have wide applications in the extraction of various metals from solid and liquid phases [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2

Bemani¹,

Baghban²,

Shamshirband³

et al. 2019

Preprint

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In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.

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Connectionist approaches for solubility prediction of n-alkanes in supercritical carbon dioxide

Cited by 11 publications

References 31 publications

Applying Monte Carlo Dropout to Quantify the Uncertainty of Skip Connection-Based Convolutional Neural Networks Optimized by Big Data

Applying Monte Carlo Dropout to Quantify the Uncertainty of Skip Connection-Based Convolutional Neural Networks Optimized by Big Data

Modeling n-alkane solubility in supercritical CO2 via intelligent methods

Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2

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