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Sharifzadegan A, Behnamnia M, Dehghan Monfared A. Artificial intelligence-based framework for precise prediction of asphaltene particle aggregation kinetics in petroleum recovery. Sci Rep 2023; 13:18525. [PMID: 37898668 PMCID: PMC10613205 DOI: 10.1038/s41598-023-45685-0] [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: 06/09/2023] [Accepted: 10/23/2023] [Indexed: 10/30/2023] Open
Abstract
The precipitation and deposition of asphaltene on solid surfaces present a significant challenge throughout all stages of petroleum recovery, from hydrocarbon reservoirs in porous media to wellbore and transfer pipelines. A comprehensive understanding of asphaltene aggregation phenomena is crucial for controlling deposition issues. In addition to experimental studies, accurate prediction of asphaltene aggregation kinetics, which has received less attention in previous research, is essential. This study proposes an artificial intelligence-based framework for precisely predicting asphaltene particle aggregation kinetics. Different techniques were utilized to predict the asphaltene aggregate diameter as a function of pressure, temperature, oil specific gravity, and oil asphaltene content. These methods included the adaptive neuro-fuzzy interference system (ANFIS), radial basis function (RBF) neural network optimized with the Grey Wolf Optimizer (GWO) algorithm, extreme learning machine (ELM), and multi-layer perceptron (MLP) coupled with Bayesian Regularization (BR), Levenberg-Marquardt (LM), and Scaled Conjugate Gradient (SCG) algorithms. The models were constructed using a series of published data. The results indicate the excellent correlation between predicted and experimental values using various models. However, the GWO-RBF modeling strategy demonstrated the highest accuracy among the developed models, with a determination coefficient, average absolute relative deviation percent, and root mean square error (RMSE) of 0.9993, 1.1326%, and 0.0537, respectively, for the total data.
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Affiliation(s)
- Ali Sharifzadegan
- Department of Petroleum Engineering, Faculty of Petroleum, Gas and Petrochemical Engineering, Persian Gulf University, Bushehr, 75169-13817, Iran
| | - Mohammad Behnamnia
- Department of Petroleum Engineering, Faculty of Petroleum, Gas and Petrochemical Engineering, Persian Gulf University, Bushehr, 75169-13817, Iran
| | - Abolfazl Dehghan Monfared
- Department of Petroleum Engineering, Faculty of Petroleum, Gas and Petrochemical Engineering, Persian Gulf University, Bushehr, 75169-13817, Iran.
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Abdi J, Mazloom G, Hadavimoghaddam F, Hemmati-Sarapardeh A, Esmaeili-Faraj SH, Bolhasani A, Karamian S, Hosseini S. Estimation of the flow rate of pyrolysis gasoline, ethylene, and propylene in an industrial olefin plant using machine learning approaches. Sci Rep 2023; 13:14081. [PMID: 37640807 PMCID: PMC10462638 DOI: 10.1038/s41598-023-41273-4] [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: 11/14/2022] [Accepted: 08/24/2023] [Indexed: 08/31/2023] Open
Abstract
Light olefins, as the backbone of the chemical and petrochemical industries, are produced mainly via steam cracking route. Prediction the of effects of operating variables on the product yield distribution through the mechanistic approaches is complex and requires long time. While increasing in the industrial automation and the availability of the high throughput data, the machine learning approaches have gained much attention due to the simplicity and less required computational efforts. In this study, the potential capability of four powerful machine learning models, i.e., Multilayer perceptron (MLP) neural network, adaptive boosting-support vector regression (AdaBoost-SVR), recurrent neural network (RNN), and deep belief network (DBN) was investigated to predict the product distribution of an olefin plant in industrial scale. In this regard, an extensive data set including 1184 actual data points were gathered during four successive years under various practical conditions. 24 varying independent parameters, including flow rates of different feedstock, numbers of active furnaces, and coil outlet temperatures, were chosen as the input variables of the models and the outputs were the flow rates of the main products, i.e., pyrolysis gasoline, ethylene, and propylene. The accuracy of the models was assessed by different statistical techniques. Based on the obtained results, the RNN model accurately predicted the main product flow rates with average absolute percent relative error (AAPRE) and determination coefficient (R2) values of 1.94% and 0.97, 1.29% and 0.99, 0.70% and 0.99 for pyrolysis gasoline, propylene, and ethylene, respectively. The influence of the various parameters on the products flow rate (estimated by the RNN model) was studied by the relevancy factor calculation. Accordingly, the number of furnaces in service and the flow rates of some feedstock had more positive impacts on the outputs. In addition, the effects of different operating conditions on the propylene/ethylene (P/E) ratio as a cracking severity factor were also discussed. This research proved that intelligent approaches, despite being simple and straightforward, can predict complex unit performance. Thus, they can be efficiently utilized to control and optimize different industrial-scale units.
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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
| | - Fahimeh Hadavimoghaddam
- Institute of Unconventional Oil & Gas, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China
- Ufa State Petroleum Technological University, Ufa, 450064, Russia
| | - 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.
| | | | - Akbar Bolhasani
- Research and Development Center, Jam Petrochemical Company, Bushehr, 1434853114, Iran
| | - Soroush Karamian
- Research and Development Center, Jam Petrochemical Company, Bushehr, 1434853114, Iran
| | - Shahin Hosseini
- Research and Development Center, Jam Petrochemical Company, Bushehr, 1434853114, Iran
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Bakhtyari A, Rasoolzadeh A, Vaferi B, Khandakar A. Application of machine learning techniques to the modeling of solubility of sugar alcohols in ionic liquids. Sci Rep 2023; 13:12161. [PMID: 37500713 PMCID: PMC10374917 DOI: 10.1038/s41598-023-39441-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023] Open
Abstract
The current trend of chemical industries demands green processing, in particular with employing natural substances such as sugar-derived compounds. This matter has encouraged academic and industrial sections to seek new alternatives for extracting these materials. Ionic liquids (ILs) are currently paving the way for efficient extraction processes. To this end, accurate estimation of solubility data is of great importance. This study relies on machine learning methods for modeling the solubility data of sugar alcohols (SAs) in ILs. An initial relevancy analysis approved that the SA-IL equilibrium governs by the temperature, density and molecular weight of ILs, as well as the molecular weight, fusion temperature, and fusion enthalpy of SAs. Also, temperature and fusion temperature have the strongest influence on the SAs solubility in ILs. The performance of artificial neural networks (ANNs), least-squares support vector regression (LSSVR), and adaptive neuro-fuzzy inference systems (ANFIS) to predict SA solubility in ILs were compared utilizing a large databank (647 data points of 19 SAs and 21 ILs). Among the investigated models, ANFIS offered the best accuracy with an average absolute relative deviation (AARD%) of 7.43% and a coefficient of determination (R2) of 0.98359. The best performance of the ANFIS model was obtained with a cluster center radius of 0.435 when trained with 85% of the databank. Further analyses of the ANFIS model based on the leverage method revealed that this model is reliable enough due to its high level of coverage and wide range of applicability. Accordingly, this model can be effectively utilized in modeling the solubilities of SAs in ILs.
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Affiliation(s)
- Ali Bakhtyari
- Department of Chemical Engineering, Shiraz University, Shiraz, Iran
| | - Ali Rasoolzadeh
- Faculty of Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran
| | - Behzad Vaferi
- Department of Chemical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
- Department of Advanced Calculations, Chemical, Petroleum, and Polymer Engineering Research Center, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
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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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Moradkhani MA, Hosseini SH, Ranjbar K, Moradi M. Intelligent modeling of hydrogen sulfide solubility in various types of single and multicomponent solvents. Sci Rep 2023; 13:3777. [PMID: 36882537 PMCID: PMC9992357 DOI: 10.1038/s41598-023-30777-8] [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: 09/18/2022] [Accepted: 02/28/2023] [Indexed: 03/09/2023] Open
Abstract
This study aims to study the solubility of acid gas, i.e., hydrogen sulfide (H2S) in different solvents. Three intelligent approaches, including Multilayer Perceptron (MLP), Gaussian Process Regression (GPR) and Radial Basis Function (RBF) were used to construct reliable models based on an extensive databank comprising 5148 measured samples from 54 published sources. The analyzed data cover 95 single and multicomponent solvents such as amines, ionic liquids, electrolytes, organics, etc., in broad pressure and temperature ranges. The proposed models require just three simple input variables, i.e., pressure, temperature and the equivalent molecular weight of solvent to determine the solubility. A competitive examination of the novel models implied that the GPR-based one gives the most appropriate estimations with excellent AARE, R2 and RRMSE values of 4.73%, 99.75% and 4.83%, respectively for the tested data. The mentioned intelligent model also performed well in describing the physical behaviors of H2S solubility at various operating conditions. Furthermore, analyzing the William's plot for the GPR-based model affirmed the high reliability of the analyzed databank, as the outlying data points comprise just 2.04% of entire data. In contrast to the literature models, the newly presented approaches proved to be applicable for different types of single and multicomponent H2S absorbers with AAREs less than 7%. Eventually, a sensitivity analysis based on the GPR model reflected the fact that the solvent equivalent molecular weight is the most influential factor in controlling H2S solubility.
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Affiliation(s)
- M A Moradkhani
- Department of Chemical Engineering, Ilam University, Ilam, 69315-516, Iran
| | - S H Hosseini
- Department of Chemical Engineering, Ilam University, Ilam, 69315-516, Iran.
| | - K Ranjbar
- Department of Chemical Engineering, Ilam University, Ilam, 69315-516, Iran
| | - M Moradi
- Department of Chemical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, 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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Asensio-Delgado S, Pardo F, Zarca G, Urtiaga A. Machine learning for predicting the solubility of high-GWP fluorinated refrigerants in ionic liquids. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.120472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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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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Nait Amar M, Ghriga MA, Ben Seghier MEA, Ouaer H. Predicting solubility of nitrous oxide in ionic liquids using machine learning techniques and gene expression programming. J Taiwan Inst Chem Eng 2021. [DOI: 10.1016/j.jtice.2021.08.042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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10
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Towards estimating absorption of major air pollutant gasses in ionic liquids using soft computing methods. J Taiwan Inst Chem Eng 2021. [DOI: 10.1016/j.jtice.2021.07.032] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Nait Amar M. Towards improved genetic programming based-correlations for predicting the interfacial tension of the systems pure/impure CO2-brine. J Taiwan Inst Chem Eng 2021. [DOI: 10.1016/j.jtice.2021.08.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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Alizadeh R, Abad JMN, Ameri A, Mohebbi MR, Mehdizadeh A, Zhao D, Karimi N. A machine learning approach to the prediction of transport and thermodynamic processes in multiphysics systems - heat transfer in a hybrid nanofluid flow in porous media. J Taiwan Inst Chem Eng 2021. [DOI: 10.1016/j.jtice.2021.03.043] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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13
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Esmaeili-Faraj SH, Vaferi B, Bolhasani A, Karamian S, Hosseini S, Rashedi R. Design of a Neuro‐Based Computing Paradigm for Simulation of Industrial Olefin Plants. Chem Eng Technol 2021. [DOI: 10.1002/ceat.202000442] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
| | - Behzad Vaferi
- Islamic Azad University Department of Advanced Calculations, Chemical, Petroleum, and Polymer Engineering Research Center, Shiraz Branch 7198774731 Shiraz Iran
| | - Akbar Bolhasani
- Jam Petrochemical Company Research and Development Center 1434853114 Bushehr Iran
| | - Soroush Karamian
- Jam Petrochemical Company Research and Development Center 1434853114 Bushehr Iran
| | - Shahin Hosseini
- Jam Petrochemical Company Research and Development Center 1434853114 Bushehr Iran
| | - Reza Rashedi
- Jam Petrochemical Company Research and Development Center 1434853114 Bushehr Iran
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