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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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Mousavi SP, Nakhaei-Kohani R, Atashrouz S, Hadavimoghaddam F, Abedi A, Hemmati-Sarapardeh A, Mohaddespour A. Modeling of H 2S solubility in ionic liquids: comparison of white-box machine learning, deep learning and ensemble learning approaches. Sci Rep 2023; 13:7946. [PMID: 37193679 DOI: 10.1038/s41598-023-34193-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 04/25/2023] [Indexed: 05/18/2023] Open
Abstract
In the context of gas processing and carbon sequestration, an adequate understanding of the solubility of acid gases in ionic liquids (ILs) under various thermodynamic circumstances is crucial. A poisonous, combustible, and acidic gas that can cause environmental damage is hydrogen sulfide (H2S). ILs are good choices for appropriate solvents in gas separation procedures. In this work, a variety of machine learning techniques, such as white-box machine learning, deep learning, and ensemble learning, were established to determine the solubility of H2S in ILs. The white-box models are group method of data handling (GMDH) and genetic programming (GP), the deep learning approach is deep belief network (DBN) and extreme gradient boosting (XGBoost) was selected as an ensemble approach. The models were established utilizing an extensive database with 1516 data points on the H2S solubility in 37 ILs throughout an extensive pressure and temperature range. Seven input variables, including temperature (T), pressure (P), two critical variables such as temperature (Tc) and pressure (Pc), acentric factor (ω), boiling temperature (Tb), and molecular weight (Mw), were used in these models; the output was the solubility of H2S. The findings show that the XGBoost model, with statistical parameters such as an average absolute percent relative error (AAPRE) of 1.14%, root mean square error (RMSE) of 0.002, standard deviation (SD) of 0.01, and a determination coefficient (R2) of 0.99, provides more precise calculations for H2S solubility in ILs. The sensitivity assessment demonstrated that temperature and pressure had the highest negative and highest positive affect on the H2S solubility in ILs, respectively. The Taylor diagram, cumulative frequency plot, cross-plot, and error bar all demonstrated the high effectiveness, accuracy, and reality of the XGBoost approach for predicting the H2S solubility in various ILs. The leverage analysis shows that the majority of the data points are experimentally reliable and just a small number of data points are found beyond the application domain of the XGBoost paradigm. Beyond these statistical results, some chemical structure effects were evaluated. First, it was shown that the lengthening of the cation alkyl chain enhances the H2S solubility in ILs. As another chemical structure effect, it was shown that higher fluorine content in anion leads to higher solubility in ILs. These phenomena were confirmed by experimental data and the model results. Connecting solubility data to the chemical structure of ILs, the results of this study can further assist to find appropriate ILs for specialized processes (based on the process conditions) as solvents for H2S.
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Affiliation(s)
- Seyed-Pezhman Mousavi
- Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Reza Nakhaei-Kohani
- Department of Chemical and Petroleum Engineering, Shiraz University, Shiraz, Iran
| | - Saeid Atashrouz
- Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
| | - Fahimeh Hadavimoghaddam
- Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing, 163318, China
- Ufa State Petroleum Technological University, Ufa, 450064, Russia
| | - Ali Abedi
- 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.
- State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing, China.
| | - Ahmad Mohaddespour
- Department of Chemical Engineering, McGill University, Montreal, QC, H3A 0C5, Canada.
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Viscosity prediction of ionic liquids using NLR and SVM approaches. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.120610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Abdi J, Mazloom G. Machine learning approaches for predicting arsenic adsorption from water using porous metal-organic frameworks. Sci Rep 2022; 12:16458. [PMID: 36180503 PMCID: PMC9525301 DOI: 10.1038/s41598-022-20762-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/19/2022] [Indexed: 11/21/2022] Open
Abstract
Arsenic in drinking water is a serious threat for human health due to its toxic nature and therefore, its eliminating is highly necessary. In this study, the ability of different novel and robust machine learning (ML) approaches, including Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Decision Tree, and Random Forest was implemented to predict the adsorptive removal of arsenate [As(V)] from wastewater over 13 different metal–organic frameworks (MOFs). A large experimental dataset was collected under various conditions. The adsorbent dosage, contact time, initial arsenic concentration, adsorbent surface area, temperature, solution pH, and the presence of anions were considered as input variables, and adsorptive removal of As(V) was selected as the output of the models. The developed models were evaluated using various statistical criteria. The obtained results indicated that the LightGBM model provided the most accurate and reliable response to predict As(V) adsorption by MOFs and possesses R2, RMSE, STD, and AAPRE (%) of 0.9958, 2.0688, 0.0628, and 2.88, respectively. The expected trends of As(V) removal with increasing initial concentration, solution pH, temperature, and coexistence of anions were predicted reasonably by the LightGBM model. Sensitivity analysis revealed that the adsorption process adversely relates to the initial As(V) concentration and directly depends on the MOFs surface area and dosage. This study proves that ML approaches are capable to manage complicated problems with large datasets and can be affordable alternatives for expensive and time-consuming experimental wastewater treatment processes.
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Affiliation(s)
- Jafar Abdi
- Faculty of Chemical and Materials Engineering, Shahrood University of Technology, Shahrood, Iran.
| | - Golshan Mazloom
- Department of Chemical Engineering, Faculty of Engineering, University of Mazandaran, Babolsar, Iran
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Nakhaei-Kohani R, Atashrouz S, Hadavimoghaddam F, Abedi A, Jabbour K, Hemmati-Sarapardeh A, Mohaddespour A. Chemical Structure and Thermodynamic Properties Based Models for Estimating Nitrous Oxide Solubility in Ionic Liquids: Equations of State and Machine Learning Approaches. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.120445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Nakhaei-Kohani R, Atashrouz S, Hadavimoghaddam F, Bostani A, Hemmati-Sarapardeh A, Mohaddespour A. Solubility of gaseous hydrocarbons in ionic liquids using equations of state and machine learning approaches. Sci Rep 2022; 12:14276. [PMID: 35995904 PMCID: PMC9395420 DOI: 10.1038/s41598-022-17983-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 08/03/2022] [Indexed: 11/09/2022] Open
Abstract
Ionic liquids (ILs) have emerged as suitable options for gas storage applications over the past decade. Consequently, accurate prediction of gas solubility in ILs is crucial for their application in the industry. In this study, four intelligent techniques including Extreme Learning Machine (ELM), Deep Belief Network (DBN), Multivariate Adaptive Regression Splines (MARS), and Boosting-Support Vector Regression (Boost-SVR) have been proposed to estimate the solubility of some gaseous hydrocarbons in ILs based on two distinct methods. In the first method, the thermodynamic properties of hydrocarbons and ILs were used as input parameters, while in the second method, the chemical structure of ILs and hydrocarbons along with temperature and pressure were used. The results show that in the first method, the DBN model with root mean square error (RMSE) and coefficient of determination (R2) values of 0.0054 and 0.9961, respectively, and in the second method, the DBN model with RMSE and R2 values of 0.0065 and 0.9943, respectively, have the most accurate predictions. To evaluate the performance of intelligent models, the obtained results were compared with previous studies and equations of the state including Peng-Robinson (PR), Soave-Redlich-Kwong (SRK), Redlich-Kwong (RK), and Zudkevitch-Joffe (ZJ). Findings show that intelligent models have high accuracy compared to equations of state. Finally, the investigation of the effect of different factors such as alkyl chain length, type of anion and cation, pressure, temperature, and type of hydrocarbon on the solubility of gaseous hydrocarbons in ILs shows that pressure and temperature have a direct and inverse effect on increasing the solubility of gaseous hydrocarbons in ILs, respectively. Also, the evaluation of the effect of hydrocarbon type shows that increasing the molecular weight of hydrocarbons increases the solubility of gaseous hydrocarbons in ILs.
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Affiliation(s)
- Reza Nakhaei-Kohani
- Department of Chemical and Petroleum Engineering, Shiraz University, Shiraz, Iran
| | - Saeid Atashrouz
- Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
| | - 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 and Gas, Northeast Petroleum University, Daqing, 163318, China
| | - Ali Bostani
- College of Engineering and Applied Sciences, American University of Kuwait, AUK, P.O. Box 3323, Salmiya, Kuwait
| | | | - Ahmad Mohaddespour
- Department of Chemical Engineering, McGill University, Montreal, QC, H3A 0C5, Canada.
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Alsaihati A, Abughaban M, Elkatatny S, Shehri DA. Application of Machine Learning Methods in Modeling the Loss of Circulation Rate while Drilling Operation. ACS OMEGA 2022; 7:20696-20709. [PMID: 35755391 PMCID: PMC9218981 DOI: 10.1021/acsomega.2c00970] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
Fluid losses into formations are a common operational issue that is frequently encountered when drilling across naturally or induced fractured formations. This could pose significant operational risks, such as well control, stuck pipe, and wellbore instability, which, in turn, lead to an increase in well time and cost. This research aims to use and evaluate different machine learning techniques, namely, support vector machine (SVM), random forest (RF), and K nearest neighbor (K-NN) in predicting the loss of circulation rate (LCR) while drilling using solely mechanical surface parameters and interpretation of the active pit volume readings. Actual field data of seven wells, which had suffered partial or severe loss of circulation, were used to build predictive models with an 80:20 training-to-test data ratio, while Well No. 8 was used to compare the performance of the developed models. Different performance metrics were used to evaluate the performance of the developed models. The root-mean-square error (RMSE) and correlation coefficient (R) were used to evaluate the performance of the models in predicting the LCR while drilling. The results showed that K-NN outperformed other models in predicting the LCR in Well No. 8 with an R of 0.90 and an RMSE of 0.17.
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Affiliation(s)
- Ahmed Alsaihati
- Department of Petroleum Engineering, King Fahd University of Petroleum &Minerals, Dhahran 31261, Saudi Arabia
| | - Mahmoud Abughaban
- Drilling Technology Division, EXPEC Advanced Research Center, Dhahran 34466, Saudi Arabia
| | - Salaheldin Elkatatny
- Department of Petroleum Engineering, King Fahd University of Petroleum &Minerals, Dhahran 31261, Saudi Arabia
| | - Dhafer Al Shehri
- Department of Petroleum Engineering, King Fahd University of Petroleum &Minerals, Dhahran 31261, Saudi Arabia
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Nakhaei-Kohani R, Ali Madani S, Mousavi SP, Atashrouz S, Abedi A, Hemmati-Sarapardeh A, Mohaddespour A. Machine Learning Assisted Structure-based Models for Predicting Electrical Conductivity of Ionic Liquids. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.119509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Mousavi SP, Atashrouz S, Nakhaei-Kohani R, Hadavimoghaddam F, Shawabkeh A, Hemmati-Sarapardeh A, Mohaddespour A. Modeling of H2S solubility in ionic liquids using deep learning: A chemical structure-based approach. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2021.118418] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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How Sustainable Is People’s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships. SUSTAINABILITY 2022. [DOI: 10.3390/su14073989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Several previous studies examined the variables of public-transit-related walking and privately owned vehicles (POVs) to go to work. However, most studies neglect the possible non-linear relationships between these variables and other potential variables. Using the 2017 U.S. National Household Travel Survey, we employ the Bayesian Network algorithm to evaluate the non-linear and interaction impacts of health condition attributes, work trip attributes, work attributes, and individual and household attributes on walking and privately owned vehicles to reach public transit stations to go to work in California. The authors found that the trip time to public transit stations is the most important factor in individuals’ walking decision to reach public transit stations. Additionally, it was found that this factor was mediated by population density. For the POV model, the population density was identified as the most important factor and was mediated by travel time to work. These findings suggest that encouraging individuals to walk to public transit stations to go to work in California may be accomplished by adopting planning practices that support dense urban growth and, as a result, reduce trip times to transit stations.
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Mohammadi MR, Hadavimoghaddam F, Atashrouz S, Abedi A, Hemmati-Sarapardeh A, Mohaddespour A. Modeling hydrogen solubility in alcohols using machine learning models and equations of state. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2021.117807] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Mousavi SP, Atashrouz S, Nait Amar M, Hadavimoghaddam F, Mohammadi MR, Hemmati-Sarapardeh A, Mohaddespour A. Modeling surface tension of ionic liquids by chemical structure-intelligence based models. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116961] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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14
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Polarization of ionic liquid and polymer and its implications for polymerized ionic liquids: An overview towards a new theory and simulation. JOURNAL OF POLYMER SCIENCE 2021. [DOI: 10.1002/pol.20210330] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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