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Kiani S, Hadavimoghaddam F, Atashrouz S, Nedeljkovic D, Hemmati-Sarapardeh A, Mohaddespour A. Modeling of ionic liquids viscosity via advanced white-box machine learning. Sci Rep 2024; 14:8666. [PMID: 38622138 PMCID: PMC11018629 DOI: 10.1038/s41598-024-55147-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 02/20/2024] [Indexed: 04/17/2024] Open
Abstract
Ionic liquids (ILs) are more widely used within the industry than ever before, and accurate models of their physicochemical characteristics are becoming increasingly important during the process optimization. It is especially challenging to simulate the viscosity of ILs since there is no widely agreed explanation of how viscosity is determined in liquids. In this research, genetic programming (GP) and group method of data handling (GMDH) models were used as white-box machine learning approaches to predict the viscosity of pure ILs. These methods were developed based on a large open literature database of 2813 experimental viscosity values from 45 various ILs at different pressures (0.06-298.9 MPa) and temperatures (253.15-573 K). The models were developed based on five, six, and seven inputs, and it was found that all the models with seven inputs provided more accurate results, while the models with five and six inputs had acceptable accuracy and simpler formulas. Based on GMDH and GP proposed approaches, the suggested GMDH model with seven inputs gave the most exact results with an average absolute relative deviation (AARD) of 8.14% and a coefficient of determination (R2) of 0.98. The proposed techniques were compared with theoretical and empirical models available in the literature, and it was displayed that the GMDH model with seven inputs strongly outperforms the existing approaches. The leverage statistical analysis revealed that most of the experimental data were located within the applicability domains of both GMDH and GP models and were of high quality. Trend analysis also illustrated that the GMDH and GP models could follow the expected trends of viscosity with variations in pressure and temperature. In addition, the relevancy factor portrayed that the temperature had the greatest impact on the ILs viscosity. The findings of this study illustrated that the proposed models represented strong alternatives to time-consuming and costly experimental methods of ILs viscosity measurement.
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Affiliation(s)
- Sajad Kiani
- Faculty of Science and Engineering, Swansea University, Swansea, SA1 8EN, UK
| | - Fahimeh Hadavimoghaddam
- Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development (Northeast Petroleum University), Ministry of Education, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China
- Institute of Unconventional Oil & Gas, Northeast Petroleum University, Daqing, 163318, China
| | - Saeid Atashrouz
- Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
| | - Dragutin Nedeljkovic
- College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait
| | - Abdolhossein Hemmati-Sarapardeh
- Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
- College of Construction Engineering, Jilin University, Changchun, China.
| | - Ahmad Mohaddespour
- Department of Chemical Engineering, McGill University, Montreal, QC, H3A 0C5, Canada.
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Dabiri MS, Hadavimoghaddam F, Ashoorian S, Schaffie M, Hemmati-Sarapardeh A. Modeling liquid rate through wellhead chokes using machine learning techniques. Sci Rep 2024; 14:6945. [PMID: 38521803 PMCID: PMC10960849 DOI: 10.1038/s41598-024-54010-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 02/07/2024] [Indexed: 03/25/2024] Open
Abstract
Precise measurement and prediction of the fluid flow rates in production wells are crucial for anticipating the production volume and hydrocarbon recovery and creating a steady and controllable flow regime in such wells. This study suggests two approaches to predict the flow rate through wellhead chokes. The first is a data-driven approach using different methods, namely: Adaptive boosting support vector regression (Adaboost-SVR), multivariate adaptive regression spline (MARS), radial basis function (RBF), and multilayer perceptron (MLP) with three algorithms: Levenberg-Marquardt (LM), bayesian-regularization (BR), and scaled conjugate gradient (SCG). The second is a developed correlation that depends on wellhead pressure (Pwh), gas-to-liquid ratio (GLR), and choke size (Dc). A dataset of 565 data points is available for model development. The performance of the two suggested approaches is compared with earlier correlations. Results revealed that the proposed models outperform the existing ones, with the Adaboost-SVR model showing the best performance with an average absolute percent relative error (AAPRE) of 5.15% and a correlation coefficient of 0.9784. Additionally, the results indicated that the developed correlation resulted in better predictions compared to the earlier ones. Furthermore, a sensitivity analysis of the input variable was also investigated in this study and revealed that the choke size variable had the most significant effect, while the Pwh and GLR showed a slight effect on the liquid rate. Eventually, the leverage approach showed that only 2.1% of the data points were in the suspicious range.
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Affiliation(s)
- Mohammad-Saber Dabiri
- Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
| | | | - Sefatallah Ashoorian
- Institute of Petroleum Engineering, School of Chemical Engineering, University of Tehran, P.O. Box: 11155-4563, Tehran, Iran
| | - Mahin Schaffie
- Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Abdolhossein Hemmati-Sarapardeh
- Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
- State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing, China.
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Wei-yu C, Sun L, Zhou J, Li X, Huang L, Xia G, Meng X, Wang K. Toward Predicting Interfacial Tension of Impure and Pure CO 2-Brine Systems Using Robust Correlative Approaches. ACS OMEGA 2024; 9:7937-7957. [PMID: 38405476 PMCID: PMC10882694 DOI: 10.1021/acsomega.3c07956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 02/27/2024]
Abstract
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 CO2 storage in deep saline aquifers through structural and capillary processes, with the interfacial tension (IFT) of the CO2-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 CO2-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 CO2-brine, showcasing an adverse effect. Moreover, an impurity possessing a critical temperature below that of CO2 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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Affiliation(s)
- Chen Wei-yu
- CNOOC
EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China
| | - Lin Sun
- CNOOC
EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China
| | - Jiyong Zhou
- CNOOC
EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China
| | - Xuguang Li
- CNOOC
EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China
| | - Liping Huang
- CNOOC
EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China
| | - Guang Xia
- CNOOC
EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China
| | - Xiangli Meng
- CNOOC
EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China
| | - Kui Wang
- State
Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China
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Zou X, Zhu Y, Lv J, Zhou Y, Ding B, Liu W, Xiao K, Zhang Q. Toward Estimating CO 2 Solubility in Pure Water and Brine Using Cascade Forward Neural Network and Generalized Regression Neural Network: Application to CO 2 Dissolution Trapping in Saline Aquifers. ACS OMEGA 2024; 9:4705-4720. [PMID: 38313487 PMCID: PMC10831835 DOI: 10.1021/acsomega.3c07962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/28/2023] [Accepted: 01/04/2024] [Indexed: 02/06/2024]
Abstract
Predicting carbon dioxide (CO2) solubility in water and brine is crucial for understanding carbon capture and storage (CCS) processes. Accurate solubility predictions inform the feasibility and effectiveness of CO2 dissolution trapping, a key mechanism in carbon sequestration in saline aquifers. In this work, a comprehensive data set comprising 1278 experimental solubility data points for CO2-brine systems was assembled, encompassing diverse operating conditions. These data encompassed brines containing six different salts: NaCl, KCl, NaHCO3, CaCl2, MgCl2, and Na2SO4. Also, this databank encompassed temperature spanning from 273.15 to 453.15 K and a pressure range spanning 0.06-100 MPa. To model this solubility databank, cascade forward neural network (CFNN) and generalized regression neural network (GRNN) were employed. Furthermore, three optimization algorithms, namely, Bayesian Regularization (BR), Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton, and Levenberg-Marquardt (LM), were applied to enhance the performance of the CFNN models. The CFNN-LM model showcased average absolute percent relative error (AAPRE) values of 5.37% for the overall data set, 5.26% for the training subset, and 5.85% for the testing subset. Overall, the CFNN-LM model stands out as the most accurate among the models crafted in this study, boasting the highest overall R2 value of 0.9949 among the other models. Based on sensitivity analysis, pressure exerts the most significant influence and stands as the sole parameter with a positive impact on CO2 solubility in brine. Conversely, temperature and the concentration of all six salts considered in the model exhibited a negative impact. All salts exert a negative impact on CO2 solubility due to their salting-out effect, with varying degrees of influence. The salting-out effects of the salts can be ranked as follows: from the most pronounced to the least: MgCl2 > CaCl2 > NaCl > KCl > NaHCO3 > Na2SO4. By employing the leverage approach, only a few instances of potential suspected and out-of-leverage data were found. The relatively low count of identified potential suspected and out-of-leverage data, given the expansive solubility database, underscores the reliability and accuracy of both the data set and the CFNN-LM model's performance in this survey.
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Affiliation(s)
- Xinyuan Zou
- State
Key Laboratory of Enhanced Oil Recovery, Research Institute of Petroleum
Exploration and Development, CNPC, Beijing 100083, China
- Research
Institute of Petroleum Exploration & Development, Beijing 100083, China
| | - Yingting Zhu
- Research
Institute of Petroleum Exploration & Development, Beijing 100083, China
- Key
Laboratory of Oilfield Chemistry of CNPC, Beijing 100083, China
| | - Jing Lv
- Research
Institute of Petroleum Exploration & Development, Beijing 100083, China
- Key
Laboratory of Oilfield Chemistry of CNPC, Beijing 100083, China
| | - Yuchi Zhou
- Oil
and Gas engineering research Institute, Petrochina Jilin Oilfield Company, Songyuan 138000, China
| | - Bin Ding
- Research
Institute of Petroleum Exploration & Development, Beijing 100083, China
- Key
Laboratory of Oilfield Chemistry of CNPC, Beijing 100083, China
| | - Weidong Liu
- Research
Institute of Petroleum Exploration & Development, Beijing 100083, China
- Key
Laboratory of Oilfield Chemistry of CNPC, Beijing 100083, China
| | - Kai Xiao
- State
Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China
| | - Qun Zhang
- State
Key Laboratory of Enhanced Oil Recovery, Research Institute of Petroleum
Exploration and Development, CNPC, Beijing 100083, China
- Research
Institute of Petroleum Exploration & Development, Beijing 100083, China
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Abdollahi SA, Ranjbar SF, Razeghi Jahromi D. Applying feature selection and machine learning techniques to estimate the biomass higher heating value. Sci Rep 2023; 13:16093. [PMID: 37752284 PMCID: PMC10522575 DOI: 10.1038/s41598-023-43496-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/25/2023] [Indexed: 09/28/2023] Open
Abstract
The biomass higher heating value (HHV) is an important thermal property that determines the amount of recoverable energy from agriculture byproducts. Precise laboratory measurement or accurate prediction of the HHV is essential for designing biomass conversion equipment. The current study combines feature selection scenarios and machine learning tools to establish a general model for estimating biomass HHV. Multiple linear regression and Pearson's correlation coefficients justified that volatile matter, nitrogen, and oxygen content of biomass samples have a slight effect on the HHV and it is better to ignore them during the HHV modeling. Then, the prediction performance of random forest, multilayer and cascade feedforward neural networks, group method of data handling, and least-squares support vector regressor are compared to determine the intelligent estimator with the highest accuracy toward biomass HHV prediction. The ranking test shows that the multilayer perceptron neural network better predicts the HHV of 532 biomass samples than the other intelligent models. This model presents the outstanding absolute average relative error of 2.75% and 3.12% and regression coefficients of 0.9500 and 0.9418 in the learning and testing stages. The model performance is also superior to a recurrent neural network which was recently developed in the literature using the same databank.
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