Modeling of viscosity for mixtures of Athabasca bitumen and heavy n-alkane with LSSVM algorithm

Baghban, Alireza Akbarzadeh; Abbasi, Peyman; Rostami, Peyman

doi:10.1080/10916466.2016.1219748

Cited by 36 publications

(6 citation statements)

References 27 publications

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“…Their proposed model showed superiority in predicting bitumen density at different conditions. Baghban et al 10 modeled the viscosity of Athabasca bitumen with n -tetradecane using the least square support vector machine method. Finally, Rostami et al 11 modeled bitumen/ n -tetradecane mixture density using gene expression programming as a function of solvent composition, pressure, and temperature.…”

Section: Introductionmentioning

confidence: 99%

Development of Generalized Correlations for Thermophysical Properties of Light Hydrocarbon Solvents (C₁–C₅)/Bitumen Systems Using Genetic Programming

2019

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Accurate modeling of thermophysical properties of solvent/bitumen mixtures is critical for proper design and implementation of thermal- and solvent-based bitumen recovery processes. In this study, three generalized correlations were developed for prediction of solubility, density, and viscosity of light hydrocarbon/bitumen mixtures. The generalized correlations were developed using symbolic regression based on genetic programming and employing a 10-year set of comprehensive phase behavior experimental studies conducted under the SHARP research program on solvent-aided thermal recovery of bitumen. The data set comprised Surmont, JACOS, Mackay River, and Cold Lake bitumen samples and five light hydrocarbon solvents including methane, ethane, propane, n -butane, and n -pentane. The developed correlations are valid for gaseous solvents. Finally, the developed correlations for solubility, density, and viscosity were validated against a large data set of experimental measurements collected from the literature. The validation demonstrates that the developed correlations are able to accurately predict the available experimental data of solubility, density, and viscosity reported in the literature.

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Section: Introductionmentioning

confidence: 99%

Development of Generalized Correlations for Thermophysical Properties of Light Hydrocarbon Solvents (C₁–C₅)/Bitumen Systems Using Genetic Programming

2019

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“…Developing alternative desirable refrigerants is necessary because of increasing attention to the greenhouse efect and depletion of the ozone layer [10]. In the computer-aided molecular design (CAMD) process for refrigerants, predicting approaches for estimation of their properties are important, so highly accurate prediction models are needed for engineers and scientists working on this issue [11][12][13][14][15]. One of the important thermal properties of refrigerants is their normal boiling point (T b ), which has applications in the prediction of other thermal properties.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligent Approach to Predict the Normal Boiling Point of Refrigerants

Liu

Nouroddin

2023

International Journal of Chemical Engineering

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Due to the desirable and interesting applications of refrigerants in organic Rankine cycles, heat pumps, and refrigeration, engineers and researchers are becoming more interested in refrigerant properties. One of the most dominant thermophysical properties of these fluids is their normal boiling point (Tb). In the current study, a novel extreme learning method (ELM) and ensemble decision tree boosted algorithm (EDT Boosted) are proposed to forecast the normal boiling point from 16 different molecular groups and one topological index. To this end, a total of 334 data points of Tb are gathered to prepare and test ELM and EDT boosted algorithms. The visual and mathematical comparisons of model outputs and real Tb express that proposed models have great potential to predict Tb of refrigerant. Moreover, sensitivity analysis is applied to explain the effectiveness of input parameters on the determination of Tb for refrigerants.

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“…The model was then evaluated using dynamic data from actual production wells, and the validation results indicated that the model had a high degree of applicability. Baghban et al [7] and colleagues derived the pressure calculation equation and production prediction equation for gas wells in the presence of gas-water two-phase flow, providing theoretical evidence for dynamic production forecast of gas wells when the gas phase and water phase are identical. Although numerous research on the flow law and capacity evaluation of carbonate reservoirs have been done, the majority of them have focused on a single contributing factor.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Prediction of Oil-Water Flow Dynamics in Carbonate Reservoirs

Yue¹,

Luo²

2022

Fluid Dynamics &Amp; Materials Processing

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Because carbonate rocks have a wide range of reservoir forms, a low matrix permeability, and a complicated seam hole formation, using traditional capacity prediction methods to estimate carbonate reservoirs can lead to significant errors. We propose a machine learning-based capacity prediction method for carbonate rocks by analyzing the degree of correlation between various factors and three machine learning models: support vector machine, BP neural network, and elastic network. The error rate for these three models are 10%, 16%, and 33%, respectively (according to the analysis of 40 training wells and 10 test wells).

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Modeling of viscosity for mixtures of Athabasca bitumen and heavy n-alkane with LSSVM algorithm

Cited by 36 publications

References 27 publications

Development of Generalized Correlations for Thermophysical Properties of Light Hydrocarbon Solvents (C₁–C₅)/Bitumen Systems Using Genetic Programming

Development of Generalized Correlations for Thermophysical Properties of Light Hydrocarbon Solvents (C₁–C₅)/Bitumen Systems Using Genetic Programming

Application of Artificial Intelligent Approach to Predict the Normal Boiling Point of Refrigerants

Machine Learning-Based Prediction of Oil-Water Flow Dynamics in Carbonate Reservoirs

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Modeling of viscosity for mixtures of Athabasca bitumen and heavy n-alkane with LSSVM algorithm

Cited by 36 publications

References 27 publications

Development of Generalized Correlations for Thermophysical Properties of Light Hydrocarbon Solvents (C1–C5)/Bitumen Systems Using Genetic Programming

Development of Generalized Correlations for Thermophysical Properties of Light Hydrocarbon Solvents (C1–C5)/Bitumen Systems Using Genetic Programming

Application of Artificial Intelligent Approach to Predict the Normal Boiling Point of Refrigerants

Machine Learning-Based Prediction of Oil-Water Flow Dynamics in Carbonate Reservoirs

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Development of Generalized Correlations for Thermophysical Properties of Light Hydrocarbon Solvents (C₁–C₅)/Bitumen Systems Using Genetic Programming

Development of Generalized Correlations for Thermophysical Properties of Light Hydrocarbon Solvents (C₁–C₅)/Bitumen Systems Using Genetic Programming