On the estimation of CO2-brine interfacial tension

Partovi, Mohammad; Mosalanezhad, Masoud; Lotfi, Safa; Barati-Harooni, Ali; Najafi‐Marghmaleki, Adel; Mohammadi, Amir H.

doi:10.1016/j.molliq.2017.08.027

Cited by 20 publications

(5 citation statements)

References 36 publications

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“…This model is developed using 378 data points, encompassing various temperatures, pressures, and salinities. Partovi et al 49 harnessed 1716 data points of CO 2 -brine IFT and leveraged computer-based models to formulate hybrid models. These models yielded notably more accurate results compared with empirical correlations.…”

Section: Introductionmentioning

confidence: 99%

Toward Predicting Interfacial Tension of Impure and Pure CO₂-Brine Systems Using Robust Correlative Approaches

Wei-yu,

Sun,

Zhou

et al. 2024

ACS Omega

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In the context of global climate change, significant attention is being directed toward renewable energy and the pivotal role of carbon capture and storage (CCS) technologies. These innovations involve secure CO 2 storage in deep saline aquifers through structural and capillary processes, with the interfacial tension (IFT) of the CO 2 -brine system influencing the storage capacity of formations. In this study, an extensive data set of 2811 experimental data points was compiled to model the IFT of impure and pure CO 2 -brine systems. Three white-box machine learning (ML) methods, namely, genetic programming (GP), gene expression programming (GEP), and group method of data handling (GMDH) were employed to establish accurate mathematical correlations. Notably, the study utilized two distinct modeling approaches: one focused on impurity compositions and the other incorporating a pseudocritical temperature variable (Tcm) offering a versatile predictive tool suitable for various gas mixtures. Among the correlation methods explored, GMDH, employing five inputs, exhibited exceptional accuracy and reliability across all metrics. Its mean absolute percentage error (MAPE) values for testing, training, and complete data sets stood at 7.63, 7.31, and 7.38%, respectively. In the case of six-input models, the GEP correlation displayed the highest precision, with MAPE values of 9.30, 8.06, and 8.31% for the testing, training, and total data sets, respectively. The sensitivity and trend analyses revealed that pressure exerted the most significant impact on the IFT of CO 2 -brine, showcasing an adverse effect. Moreover, an impurity possessing a critical temperature below that of CO 2 resulted in an elevated IFT. Consequently, this relationship leads to higher impurity concentrations aligning with lower Tcm values and subsequently elevated IFT. Also, monovalent and divalent cation molalities exhibited a growing influence on the IFT, with divalent cations exerting approximately double the influence of monovalent cations. Finally, the Leverage approach confirmed both the reliability of the experimental data and the robust statistical validity of the best correlations established in this study.

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Section: Introductionmentioning

confidence: 99%

Toward Predicting Interfacial Tension of Impure and Pure CO₂-Brine Systems Using Robust Correlative Approaches

Wei-yu,

Sun,

Zhou

et al. 2024

ACS Omega

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“…However, these algorithms use the three-degrees-of-freedom UAV model and do not consider the attitude characteristics of the UAV itself. Because most of the maneuvers used by the target in the training process are basic maneuvers, the particle swarm optimization radial basis function (PSO-RBF) algorithm [38,39] was used in this study to make the target aircraft generate simulated manual operation commands in air combat, so that the air combat decision module trained achieved a high air combat efficiency. At the same time, the existing DDPG algorithm was improved to improve its convergence.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Maneuver Decision of Air Combat Based on Simulated Operation Command and FRV-DDPG Algorithm

Lyu²,

Shi³

et al. 2022

Aerospace

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With the improvement of UAV performance and intelligence in recent years, it is particularly important for unmanned aerial vehicles (UAVs) to improve the ability of autonomous air combat. Aiming to solve the problem of how to improve the autonomous air combat maneuver decision ability of UAVs so that it can be close to manual manipulation, this paper proposes an autonomous air combat maneuvering decision method based on the combination of simulated operation command and the final reward value deep deterministic policy gradient (FRV-DDPG) algorithm. Firstly, the six-degree-of-freedom (6-DOF) model is established based on the air combat process, UAV motion, and missile motion. Secondly, a prediction method based on the Particle swarm optimization radial basis function (PSO-RBF) is designed to simulate the operation command of the enemy aircraft, which makes the training process more realistic, and then an improved DDPG strategy is proposed, which returns the final reward value to the previous reward value in a certain proportion of time for offline training, which can improve the convergence speed of the algorithm. Finally, the effectiveness of the algorithm is verified by building a simulation environment. The simulation results show that the algorithm can improve the autonomous air combat maneuver decision-making ability of UAVs.

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“…In recent years, Niroomand-Toomaj et al, Partovi et al, and Chen and Yang have adopted a multilayer perceptron model, least squares support vector machine, adaptive neurofuzzy inference system, radial basis function network optimized by particle swarm optimization method, adaptive neurofuzzy inference system trained by a hybrid method, and mutual solubility model; the models are established for temperature, pressure, salinity/molality concentration, and gas mole fraction, all of which have achieved high prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Four Neural Network Models on the Estimation of CO₂–Brine Interfacial Tension

et al. 2021

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During the CO 2 injection of geological carbon sequestration and CO 2enhanced oil recovery, the contact of CO 2 with underground salt water is inevitable, where the interfacial tension (IFT) between gas and liquid determines whether the projects can proceed smoothly. In this paper, three traditional neural network models, the wavelet neural network (WNN) model, the back propagation (BP) model, and the radical basis function model, were applied to predict the IFT between CO 2 and brine with temperature, pressure, monovalent cation molality, divalent cation molality, and molar fraction of methane and nitrogen impurities. A total of 974 sets of experimental data were divided into two data groups, the training group and the testing group. By optimizing the WNN model (I_WNN), a most stable and precise model is established, and it is found that temperature and pressure are the main parameters affecting the IFT. Through the comparison of models, it is found that I_WNN and BP models are more suitable for the IFT evaluation between CO 2 and brine.

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On the estimation of CO2-brine interfacial tension

Cited by 20 publications

References 36 publications

Toward Predicting Interfacial Tension of Impure and Pure CO₂-Brine Systems Using Robust Correlative Approaches

Toward Predicting Interfacial Tension of Impure and Pure CO₂-Brine Systems Using Robust Correlative Approaches

Autonomous Maneuver Decision of Air Combat Based on Simulated Operation Command and FRV-DDPG Algorithm

Comparative Analysis of Four Neural Network Models on the Estimation of CO₂–Brine Interfacial Tension

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On the estimation of CO2-brine interfacial tension

Cited by 20 publications

References 36 publications

Toward Predicting Interfacial Tension of Impure and Pure CO2-Brine Systems Using Robust Correlative Approaches

Toward Predicting Interfacial Tension of Impure and Pure CO2-Brine Systems Using Robust Correlative Approaches

Autonomous Maneuver Decision of Air Combat Based on Simulated Operation Command and FRV-DDPG Algorithm

Comparative Analysis of Four Neural Network Models on the Estimation of CO2–Brine Interfacial Tension

Contact Info

Product

Resources

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Toward Predicting Interfacial Tension of Impure and Pure CO₂-Brine Systems Using Robust Correlative Approaches

Toward Predicting Interfacial Tension of Impure and Pure CO₂-Brine Systems Using Robust Correlative Approaches

Comparative Analysis of Four Neural Network Models on the Estimation of CO₂–Brine Interfacial Tension