Estimation of vapour liquid equilibria of binary systems,carbon dioxide–ethyl caproate,ethyl caprylate and ethyl caprate using artificial neural networks |
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| Institution: | 1. Department of Metallurgy and Ceramics Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro, Tokyo 152-8550, Japan;2. Semiconductor and MEMS Processing Center, Tokyo Institute of Technology, 4259, Nagatsuda, Midori, Yokohama, Kanagawa 226-8503, Japan;1. Ecole Supérieure du Matériel ESM, BP 188 Beau-Lieu, Alger, Algeria;2. Ecole Militaire Polytechnique EMP, BP 17 Bordj-El-Bahri, Alger, Algeria;3. Laboratoire des Sciences Analytiques UMR 5280, Université de Lyon-UCB Lyon 1, 43 Bd du 11 Novembre 1918, 69622 Villeurbanne, France;4. Faculté de chimie, USTHB, BP. 32 El-Alia, 16111 Bab-Ezzouar, Alger, Algeria;5. Université Paris-Sud, EA 401, IFR 141, Faculté de pharmacie, F-92296 Châtenay Malabry, France;1. Department of Pharmaceutics, Faculty of Pharmacy, Umm Al-Qura University, Makkah 21955, Saudi Arabia;2. Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Minia University, Minia 61519, Egypt;3. Science and Technology Unit (STU), Umm Al-Qura University, Makkah 21955, Saudi Arabia;4. Department of Pharmaceutical Chemistry, College of Pharmacy, King Khalid University, Abha 62529, KSA, Saudi Arabia;5. Department of Pharmaceutics, College of Pharmacy, Qassim University, Buraidah 52571, Saudi Arabia;6. Department of Electrical Power and Machines, Faculty of Engineering, Alexndria University, Alexandria, Egypt;1. Computational Transport Phenomena Laboratory (CTPL), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia;2. Petrochina Beijing Oil and Gas Pipeline Control Center, 9 Dongzhimen North Street, Dongcheng District, Beijing, 100007, China;1. School of Mechatronic Engineering, Xi''an Technological University, Xi''an 710021, China;2. Institute of Engineering and Technology, Department of Hydraulics and Hydraulic and Pneumatic Systems, South Ural State University, Lenin Prospect 76, Chelyabinsk, 454080, Russian Federation;3. Department of Process Engineering, Research and Development Department, Shazand-Arak Oil Refinery Company, Arak 381314-3553, Iran;4. Laboratory of Theoretical and Computational Biophysics, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;5. Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Viet Nam |
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| Abstract: | Vapour liquid equilibrium (VLE) data are important for designing and modeling of process equipments. Since it is not always possible to carry out experiments at all possible temperatures and pressures, generally thermodynamic models based on equations of state are used for estimation of VLE. In this paper, an alternate tool, i.e. the artificial neural network technique has been applied for estimation of VLE for three binary systems viz. carbon dioxide–ethyl caproate, ethyl caprylate and ethyl caprate which are of importance in supercritical extraction. The temperature range in which these models are valid is 308.2–328.2 K and the pressure range is 1.6–9.2 MPa. The average absolute deviation for all the three systems in the estimation of liquid phase mole fraction was 3% or less and less than 0.02% for the vapour phase mole fraction. The error was less compared to that estimated by SRK or Peng Robinsons equation of state. |
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