Prediction of phase equilibrium of CO2/cyclic compound binary mixtures using a rigorous modeling approach |
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| Affiliation: | 1. Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran;2. Department of Chemical Engineering, Sahand University of Technology, Tabriz, Iran;3. Southern Cross University, School of Environment, Science and Engineering, Lismore, NSW, Australia;1. School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China;2. Parchn Sodium Isovitamin C Co. Ltd., Dexing 334221, PR China;3. Jiangxi Provincial Engineering and Technology Center for Food Additives Bio-production, Dexing 334221, PR China;4. Department of Pharmaceutical, Hebei Chemical and Pharmaceutical College, Shijiazhuang 050026, PR China;5. School of Biotechnology, Jiangnan University, Wuxi 214122, PR China;1. Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore;2. Department of Nephrology, National University Hospital, Singapore, Singapore;1. Institute of Electronic Systems, Warsaw University of Technology, Nowowiejska 15/19, Warsaw 00-665, Poland;2. Albert-Ludwigs-Universität-Freiburg- IMTEK, Freiburg, Germany;1. Department of Chemical Engineering, Faculty of Engineering, University of Kashan, 87317-53153, Kashan, Iran;2. Laboratory of Supercriritcal Fluids and Nanotechnology, University of Kashan, 87317-53153, Kashan, Iran;3. Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, 87317-53153, Kashan, Iran;4. South Zagros Oil and Gas Production, National Iranian Oil Company, 7135717991, Shiraz, Iran |
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| Abstract: | Vapor liquid equilibrium (VLE) data has significant role in designing processes which include vapor and liquid in equilibrium. Since it is impractical to measure equilibrium data at any desired temperature and pressure, particularly near critical region, thermodynamic models based on equation of state (EOS) are usually used for VLE estimating. In recent years due to the development of numerical tools like artificial intelligence methods, VLE prediction has been find new alternatives.In the present study a novel method called Least-Squares Support Vector Machine (LSSVM) used for predicting bubble/dew point pressures of binary mixtures containing carbon dioxide (CO2) + cyclic compounds as function of reduced temperature of the system, critical pressure, acentric factor of the cyclic compound, and CO2 composition. A 333 binary equilibrium data points of CO2 and six cyclic compounds within temperature and pressure ranges of 308.15–473.15 K and 0.5–27.71 MPa were used to develop the model. Results show that the proposed model is able to predict VLE data for binary systems containing supercritical or near-critical CO2/cyclic compounds with an acceptable average absolute relative deviation percent (AARD%) of 3.9381% and the coefficient of determination (R2) value of 0.9980. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for VLE prediction and the whole phase equilibrium data points are in applicability domain of the model. |
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| Keywords: | Supercritical phase equilibrium Least-Squares Support Vector Machine Outlier diagnostics Leverage approach Bubble point pressure Dew point pressure |
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