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Gao N, Yang Y, Wang Z, Guo X, Jiang S, Li J, Hu Y, Liu Z, Xu C. Viscosity of Ionic Liquids: Theories and Models. Chem Rev 2024; 124:27-123. [PMID: 38156796 DOI: 10.1021/acs.chemrev.3c00339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Ionic liquids (ILs) offer a wide range of promising applications due to their unique and designable properties compared to conventional solvents. Further development and application of ILs require correlating/predicting their pressure-viscosity-temperature behavior. In this review, we firstly introduce methods for calculation of thermodynamic inputs of viscosity models. Next, we introduce theories, theoretical and semi-empirical models coupling various theories with EoSs or activity coefficient models, and empirical and phenomenological models for viscosity of pure ILs and IL-related mixtures. Our modelling description is followed immediately by model application and performance. Then, we propose simple predictive equations for viscosity of IL-related mixtures and systematically compare performances of the above-mentioned theories and models. In concluding remarks, we recommend robust predictive models for viscosity at atmospheric pressure as well as proper and consistent theories and models for P-η-T behavior. The work that still remains to be done to obtain the desired theories and models for viscosity of ILs and IL-related mixtures is also presented. The present review is structured from pure ILs to IL-related mixtures and aims to summarize and quantitatively discuss the recent advances in theoretical and empirical modelling of viscosity of ILs and IL-related mixtures.
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Affiliation(s)
- Na Gao
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Ye Yang
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Zhiyuan Wang
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Xin Guo
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Siqi Jiang
- Sinopec Engineering Incorporation, Beijing 100195, P. R. China
| | - Jisheng Li
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Yufeng Hu
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum Beijing at Karamay, Karamay 834000, China
| | - Zhichang Liu
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Chunming Xu
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
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Rostami A, Kordavani A, Parchekhari S, Hemmati-Sarapardeh A, Helalizadeh A. New insights into permeability determination by coupling Stoneley wave propagation and conventional petrophysical logs in carbonate oil reservoirs. Sci Rep 2022; 12:11618. [PMID: 35804036 PMCID: PMC9270338 DOI: 10.1038/s41598-022-15869-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 06/30/2022] [Indexed: 11/09/2022] Open
Abstract
The need to determine permeability at different stages of evaluation, completion, optimization of Enhanced Oil Recovery (EOR) operations, and reservoir modeling and management is reflected. Therefore, various methods with distinct efficiency for the evaluation of permeability have been proposed by engineers and petroleum geologists. The oil industry uses acoustic and Nuclear Magnetic Resonance (NMR) loggings extensively to determine permeability quantitatively. However, because the number of available NMR logs is not enough and there is a significant difficulty in their interpreting and evaluation, the use of acoustic logs to determine the permeability has become very important. Direct, continuous, and in-reservoir condition estimation of permeability is a unique feature of the Stoneley waves analysis as an acoustic technique. In this study, five intelligent mathematical methods, including Adaptive Network-Based Fuzzy Inference System (ANFIS), Least-Square Support Vector Machine (LSSVM), Radial Basis Function Neural Network (RBFNN), Multi-Layer Perceptron Neural Network (MLPNN), and Committee Machine Intelligent System (CMIS), have been performed for calculating permeability in terms of Stoneley and shear waves travel-time, effective porosity, bulk density and lithological data in one of the naturally-fractured and low-porosity carbonate reservoirs located in the Southwest of Iran. Intelligent models have been improved with three popular optimization algorithms, including Coupled Simulated Annealing (CSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Among the developed models, the CMIS is the most accurate intelligent model for permeability forecast as compared to the core permeability data with a determination coefficient (R2) of 0.87 and an average absolute deviation (AAD) of 3.7. Comparing the CMIS method with the NMR techniques (i.e., Timur-Coates and Schlumberger-Doll-Research (SDR)), the superiority of the Stoneley method is demonstrated. With this model, diverse types of fractures in carbonate formations can be easily identified. As a result, it can be claimed that the models presented in this study are of great value to petrophysicists and petroleum engineers working on reservoir simulation and well completion.
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Affiliation(s)
- Alireza Rostami
- Department of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran.
| | - Ali Kordavani
- Department of Petrophysics Engineering, National Iranian South Oil Company (NISOC), Ahwaz, Iran
| | - Shahin Parchekhari
- Department of Petroleum Engineering, Kish International Campus, University of Tehran, Kish, Iran
| | - Abdolhossein Hemmati-Sarapardeh
- Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. .,Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing, 163318, China.
| | - Abbas Helalizadeh
- Department of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran
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Bavoh CB, Nashed O, Rehman AN, Othaman NAAB, Lal B, Sabil KM. Ionic Liquids as Gas Hydrate Thermodynamic Inhibitors. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c01401] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Cornelius B. Bavoh
- Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia
- Research Centre for CO2 Capture (RCCO2C), Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia
| | - Omar Nashed
- Petroleum Engineering Department, College of Energy Technologies, Jikharrah, Libya
| | - Amirun Nissa Rehman
- Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia
- Research Centre for CO2 Capture (RCCO2C), Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia
| | | | - Bhajan Lal
- Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia
- Research Centre for CO2 Capture (RCCO2C), Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia
| | - Khalik M. Sabil
- PETRONAS Research Sdn Bhd, Kawasan Institusi Bangi, Lot 3288 3289 Off Jalan Ayer Itam, Kajang 43000 Selangor, Malaysia
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On the Evaluation of Rhamnolipid Biosurfactant Adsorption Performance on Amberlite XAD-2 Using Machine Learning Techniques. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5530093. [PMID: 33996999 PMCID: PMC8096566 DOI: 10.1155/2021/5530093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/06/2021] [Accepted: 03/31/2021] [Indexed: 11/17/2022]
Abstract
Biosurfactants are a series of organic compounds that are composed of two parts, hydrophobic and hydrophilic, and since they have properties such as less toxicity and biodegradation, they are widely used in the food industry. Important applications include healthy products, oil recycling, and biological refining. In this research, to calculate the curves of rhamnolipid adsorption compared to Amberlite XAD-2, the least-squares vector machine algorithm has been used. Then, the obtained model is formed by 204 adsorption data points. Various graphical and statistical approaches are applied to ensure the correctness of the model output. The findings of this study are compared with studies that have used artificial neural network (ANN) and data group management method (GMDH) models. The model used in this study has a lower percentage of absolute mean deviation than ANN and GMDH models, which is estimated to be 1.71%.The least-squares support vector machine (LSSVM) is very valuable for investigating the breakthrough curve of rhamnolipid, and it can also be used to help chemists working on biosurfactants. Moreover, our graphical interface program can assist everyone to determine easily the curves of rhamnolipid adsorption on Amberlite XAD-2.
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A compare review about equilibrium conditions of semi-clathrate hydrate: experimental measurements visions and thermodynamic modeling aspects. J INCL PHENOM MACRO 2021. [DOI: 10.1007/s10847-021-01062-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Ghiasi MM, Mohammadi AH, Zendehboudi S. Modeling stability conditions of methane Clathrate hydrate in ionic liquid aqueous solutions. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2020.114804] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Koi ZK, Yahya WZN, Kurnia KA. Prediction of ionic conductivity of imidazolium-based ionic liquids at different temperatures using multiple linear regression and support vector machine algorithms. NEW J CHEM 2021. [DOI: 10.1039/d1nj01831k] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The conductivity of various imidazolium-based ILs has been predicted via QSPR approach using MLR and SVM regression coupled with stepwise model-building. This will aid the screening of suitable ILs with desired conductivity for specific applications.
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Affiliation(s)
- Zi Kang Koi
- Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia
| | - Wan Zaireen Nisa Yahya
- Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia
- Center of Research in Ionic Liquids, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia
| | - Kiki Adi Kurnia
- Department of Chemical Engineering, Faculty of Industrial Technology, Institut Teknologi Bandung, Jl. Ganesha No. 10, Bandung 40132, Indonesia
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Hybrid intelligence methods for modeling the diffusivity of light hydrocarbons in bitumen. Heliyon 2020; 6:e04936. [PMID: 32995622 PMCID: PMC7502588 DOI: 10.1016/j.heliyon.2020.e04936] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 08/22/2020] [Accepted: 09/10/2020] [Indexed: 11/29/2022] Open
Abstract
The solvent diffusivity is considered as a key factor in the design of solvent assisted processes in the bitumen field. In this study, a novel Adaptive neuro-fuzzy interference system (ANFIS) is employed to evaluate the diffusivity of the light hydrocarbons in the bitumen system. The particle swarm optimization (PSO) and genetic algorithm (GA) are adopted to promote ANFIS efficiency. The proposed models are established by a prepared dataset from multiple papers in the literature. Temperature (T), pressure (P) and molecular weight of alkanes (Mw) were considered as the input variables and on the other hand, Statistical parameters and graphical methods were used to appraise ANFIS, ANFIS-PSO, and ANFIS-GA performance. The results demonstrated that the highest correlation coefficient is related to ANFIS-PSO with R2 = 0.991 and 0.987 for train and test data, respectively. In the end, the results indicated that the ANFIS-PSO model has a higher level of desirability based on statistical parameters.
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Li L, Zhao S, Wang S, Rao Y. CO2 hydrate formation kinetics based on a chemical affinity model in the presence of GO and SDS. RSC Adv 2020; 10:12451-12459. [PMID: 35497591 PMCID: PMC9051314 DOI: 10.1039/c9ra10073c] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 02/26/2020] [Indexed: 11/21/2022] Open
Abstract
Hydrate generation promotion and kinetic models are key issues in the hydrate utilization technology. The formation kinetics of CO2 hydrates in a graphene oxide (GO) and sodium dodecyl sulfate (SDS) compounding accelerator system was studied experimentally, and the influences of different concentrations on the hydrate formation time and gas consumption were revealed. The results show that with the combination of GO and SDS, the formation rate of CO2 hydrates was accelerated, the induction time and generation time were shortened, and the gas consumption increased. The optimal compounding concentration was 0.005% GO and 0.2% SDS. Compared with the observations for pure water and a single 0.005% GO system, the hydrate formation time was shortened by 69.7% and 12.2%, respectively, and the gas consumption increased by 11.24% and 3.2%. A chemical affinity model of CO2 hydrate formation was established for this system. The effects of the GO and SDS compound ratio, temperature and pressure on the chemical affinity model parameters were studied from the model point of view. On using Matlab to program the model and compare it with the experimental results, very good agreement was achieved. Through research, the chemical affinity model can accurately predict the formation of hydrates in complex systems. Hydrate generation promotion and kinetic models are key issues in the hydrate utilization technology.![]()
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Affiliation(s)
- Lijun Li
- School of Petroleum Engineering
- Changzhou University
- China
| | - Shuhua Zhao
- School of Petroleum Engineering
- Changzhou University
- China
| | - Shuli Wang
- School of Petroleum Engineering
- Changzhou University
- China
| | - Yongchao Rao
- School of Petroleum Engineering
- Changzhou University
- China
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Raji M, Dashti A, Amani P, Mohammadi AH. Efficient estimation of CO2 solubility in aqueous salt solutions. J Mol Liq 2019. [DOI: 10.1016/j.molliq.2019.02.090] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Baghban A, Sasanipour J, Habibzadeh S, Zhang Z. Sulfur dioxide solubility prediction in ionic liquids by a group contribution — LSSVM model. Chem Eng Res Des 2019. [DOI: 10.1016/j.cherd.2018.11.026] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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15
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Thermodynamical and artificial intelligence approaches of H2S solubility in N-methylpyrrolidone. Chem Phys Lett 2018. [DOI: 10.1016/j.cplett.2018.07.032] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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16
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Golmohammadi H, Dashtbozorgi Z, Khooshechin S. Modeling and predicting the solute polarity parameter in reversed-phase liquid chromatography using quantitative structure-property relationship approaches. J Sep Sci 2017; 40:4495-4502. [DOI: 10.1002/jssc.201700603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 09/13/2017] [Accepted: 09/14/2017] [Indexed: 11/07/2022]
Affiliation(s)
- Hassan Golmohammadi
- Young Researchers and Elite Club, Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch; Islamic Azad University; Tehran Iran
| | - Zahra Dashtbozorgi
- Young Researchers and Elite Club, Central Tehran Branch; Islamic Azad University; Tehran Iran
| | - Sajad Khooshechin
- Young Researchers and Elite Club, Central Tehran Branch; Islamic Azad University; Tehran Iran
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Baghban A, Sasanipour J, Haratipour P, Alizad M, Vafaee Ayouri M. ANFIS modeling of rhamnolipid breakthrough curves on activated carbon. Chem Eng Res Des 2017. [DOI: 10.1016/j.cherd.2017.08.007] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Haratipour P, Baghban A, Mohammadi AH, Nazhad SHH, Bahadori A. On the estimation of viscosities and densities of CO 2 -loaded MDEA, MDEA + AMP, MDEA + DIPA, MDEA + MEA, and MDEA + DEA aqueous solutions. J Mol Liq 2017. [DOI: 10.1016/j.molliq.2017.06.123] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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19
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Baghban A, Kardani MN, Habibzadeh S. Prediction viscosity of ionic liquids using a hybrid LSSVM and group contribution method. J Mol Liq 2017. [DOI: 10.1016/j.molliq.2017.04.019] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Golmohammadi H, Dashtbozorgi Z. QSPR studies for predicting polarity parameter of organic compounds in methanol using support vector machine and enhanced replacement method. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:977-997. [PMID: 27658742 DOI: 10.1080/1062936x.2016.1233138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2016] [Accepted: 09/02/2016] [Indexed: 06/06/2023]
Abstract
In the present work, enhanced replacement method (ERM) and support vector machine (SVM) were used for quantitative structure-property relationship (QSPR) studies of polarity parameter (p) of various organic compounds in methanol in reversed phase liquid chromatography based on molecular descriptors calculated from the optimized structures. Diverse kinds of molecular descriptors were calculated to encode the molecular structures of compounds, such as geometric, thermodynamic, electrostatic and quantum mechanical descriptors. The variable selection method of ERM was employed to select an optimum subset of descriptors. The five descriptors selected using ERM were used as inputs of SVM to predict the polarity parameter of organic compounds in methanol. The coefficient of determination, r2, between experimental and predicted polarity parameters for the prediction set by ERM and SVM were 0.952 and 0.982, respectively. Acceptable results specified that the ERM approach is a very effective method for variable selection and the predictive aptitude of the SVM model is superior to those obtained by ERM. The obtained results demonstrate that SVM can be used as a substitute influential modeling tool for QSPR studies.
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Affiliation(s)
- H Golmohammadi
- a Young Researchers and Elite Club , Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch, Islamic Azad University , Tehran , Iran
| | - Z Dashtbozorgi
- b Young Researchers and Elite Club, Central Tehran Branch , Islamic Azad University , Tehran , Iran
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Amedi HR, Baghban A, Ahmadi MA. Evolving machine learning models to predict hydrogen sulfide solubility in the presence of various ionic liquids. J Mol Liq 2016. [DOI: 10.1016/j.molliq.2016.01.060] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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22
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Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2015.10.027] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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23
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Experimental and modeling study on the phase equilibria for hydrates of gas mixtures in TBAB solution. Chem Eng Sci 2015. [DOI: 10.1016/j.ces.2015.07.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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