Korean Chemical Engineering Research

  • 제55권5호
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  • Pages.652-659
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  • 2017
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  • 0304-128X(pISSN)
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  • 2233-9558(eISSN)
한국화학공학회 (The Korean Institute of Chemical Engineers)
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Optimization of Extended UNIQUAC Parameter for Activity Coefficients of Ions of an Electrolyte System using Genetic Algorithms

  • 투고 : 2017.01.13
  • 심사 : 2017.06.07
  • 발행 : 2017.10.01
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초록

In the present research, in order to predict activity coefficient of inorganic ions in electrolyte solution of a petroleum system, we studied 13 components in the electrolyte solution, including $H_2O$, $CO_2$ (aq), $H^+$, $Na^+$, $Ba^{2+}$, $Ca^{2+}$, $Sr^{2+}$, $Mg^{2+}$, $SO_4$, $CO_3$, $OH^-$, $Cl^-$, and $HCO_3$. To predict the activity coefficient of the components of the petroleum system (a solid/liquid equilibrium system), activity coefficient model of Extended UNIQUAC was studied, along with its adjustable parameters optimized based on a genetic algorithm. The total calculated error associated with optimizing the adjustable parameters of Extended UNIQUAC model considering the 13 components under study at three temperature levels (298.15, 323.15, and 373.15 K) using the genetic algorithm is found to be 0.07.

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참고문헌

  1. Muhlenbein, H. and Schlierkamp-Voosen, D., "Predictive Models for the Breeder Genetic Algorithm," Evolutionary Computation, 1(1), 25-49(1993). https://doi.org/10.1162/evco.1993.1.1.25
  2. Fathian, M., Amiri, B. and Maroosi, A., "Application of Honey-bee Mating Optimization Algorithm on Clustering," Applied Mathematics and Computation, 190, 1502-1503(2007). https://doi.org/10.1016/j.amc.2007.02.029
  3. Fontane, D., "Multi-objective Simulation and Optimization of Reservoir Operation Using Excel.," Proceeding of the Second Federal Interagency Hydrologic Modeling Conference. Las Vegas, NV, USA(2001).
  4. Thomsen, K. and Rasmussen, P., "Modeling of Vapor-liquid-Solid Equilibrium in Gas-aqueous Electrolytesystems," Chemical Engineering Science, 54, 1787-1802(1999). https://doi.org/10.1016/S0009-2509(99)00019-6
  5. Abrams, D. and Prausnitz, J., "Statistical Thermodynamics of Liquid Mixtures: A New Expression for the Excess Gibbs Energy of Partly or Completely Miscible Systems," AIChE J. 21(1) 116-128(1975). https://doi.org/10.1002/aic.690210115
  6. Garcia, A., Thomsen, K. and Stenby, E., "Prediction of Mineral Scale Formation in Geothermal and Oilfield Operations Using the Extended UNIQUAC Model Part I. Sulfate Scaling Minerals," Geothermics, 34, 61-97(2005). https://doi.org/10.1016/j.geothermics.2004.11.002
  7. Voss, M. and Foley, C., "Evolutionary Algorithm for Structural Optimization," Proceedings Genetic and Evolutionary Computation Conference (GECCO 99)(1999).
  8. Whitley, D., "A Genetic Algorithm Tutorial," Statistics and computer, 4, 65-85(1994).
  9. Krishnamoorthy, C., Venkatesh, P. and Sudarshan, R., "Object-Oriented Framework for Genetic Algorithm Engineering, with Application to Space Truss Optimization," Journal of Computer in Civil Engineering, 16(1), 66-75(2002). https://doi.org/10.1061/(ASCE)0887-3801(2002)16:1(66)
  10. Mitsuo, G. and Runwei, C., Genetic algorithms and engineering design. Wiley(1997).
  11. Goldberg, D., Genetic Algorithm in search, Optimization and Machine Learning, Addison-Wesley(1989).
  12. Agrawal, N., Rangaiah, G., Ray, A. K. and Gupta, S. K., "Multiobjective Optimization of the Operation of an Industrial Lowdensity Polyethylene Tubular Reactor Using Genetic Algorithm and Its Jumping Gene Adaptations," Industrial and Engineering Chemistry Research, 45(9), 3182-3199(2006). https://doi.org/10.1021/ie050977i
  13. Cao, K., Feng, X. and Ma, H., "Pinch Multi-agent Genetic Algorithm for Optimizing Water-using Networks," Computers and Chemical Engineering, 31(12), 1565-1575(2007). https://doi.org/10.1016/j.compchemeng.2007.01.004
  14. Elliott, L., Ingham, D., Kyne, A., Mera, N., Pourkashanian, M. and Wilson, C., "A Novel Approach to Mechanism Reduction Optimization for an Aviation Fuel/air Reaction Mechanism Using a Genetic Algorithm," Journal of Engineering for Gas Turbines and Power, 128(2), 255-263(2006). https://doi.org/10.1115/1.2131887
  15. Jezowski, J., Bochenek, R. and Poplewski, G., "On Application of Stochastic Optimization Techniques to Designing Heat Exchangerand Water Networks," Chemical Engineering and Processing: Process Intensification 46(11), 1160-1174(2007). https://doi.org/10.1016/j.cep.2007.02.024
  16. Masoori, M., Boozarjomehry, R. and dlobshiat, A., "Which Method is Better for the Kinetic Modeling: Decimal Encoded or Binary Genetic Algorithm?," Chemical Engineering Journal, 130(1), 29-37 (2007). https://doi.org/10.1016/j.cej.2006.11.017
  17. Till, J., Sand, G., Urselmann, M. and Engell, S., A Hybrid Evolutionary Algorithm for Solving Two-stage Stochastic Integer Programs in Chemical Batch Scheduling," Computers and Chemical Engineering, 31(5-6), 630-647(2007). https://doi.org/10.1016/j.compchemeng.2006.09.003
  18. Young, C.-T., Zheng, Y., Yeh, C.-W. and Jang, S.-S., "Informationguided Genetic Algorithm Approach to the Solution of Minlp Problems," Industrial and Engineering Chemistry Research, 46(5), 1527-1537(2007). https://doi.org/10.1021/ie060727h
  19. Lepar, Y. Y., Wang, Y,-C., Chang, C.-T., "Automatic Generation of Interlock Designs Using Genetic Algorithms," Computers and Chemical Engineering, 101(9), 167-192(2017). https://doi.org/10.1016/j.compchemeng.2017.02.042
  20. Garcia, A., Thomsen, K. and Stenby, E., "Prediction of Mineral Scale Formation in Geothermal and Oilfield Operations Using the Extended UNIQUAC Model Part II. Carbonate-scaling Minerals," Geothermics, 35, 239-284(2006). https://doi.org/10.1016/j.geothermics.2006.03.001
  21. Ghasemi, M., Ghavidel, S., Rahmani, S., Roosta, A., Falah, H., "A Novel Hybrid Algorithm of Imperialist Competitive Algorithm and Teaching Learning Algorithm for Optimal Power Flow Problem with Non-smooth Cost Functions," Eng. Appl. Artif. Intell., 29, 54-69(2014). https://doi.org/10.1016/j.engappai.2013.11.003
  22. Yonggang, C., Fengjie, Y. and Jigui, S., "A New Particle Swam Optimization Algorithm," Journal of Jilin University, 24(2), 181-183(2006).
  23. Clerc, M. and Kennedy, J., "The Particle Swarm-explosion Stability, and Convergence in a Multidimensional Complex Space," IEEE Transactions on Evolutionary Computation, 6(1), 58-73(2002). https://doi.org/10.1109/4235.985692
  24. Basturk, B. and Karaboga, D., "An Artificial Bee Colony (ABC) Algorithm for Numeric Function Optimization," IEEE Swarm Intelligence Symposium, May 12-14, Indianapolis, IN, USA(2006).
  25. Karaboga, D. and Basturk, B., "A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm," Journal of Global Optimization, 39, 459-471(2007). https://doi.org/10.1007/s10898-007-9149-x
  26. Karaboga, D. and Basturk, B., "On the Performance of Artificial Bee Colony (ABC) Algorithm," Applied Soft Computing, 8, 687-697(2008). https://doi.org/10.1016/j.asoc.2007.05.007
  27. Templeton, C., "Solubility of Barium Sulfate in Sodium Chloride Solutions from 25 to 958C," J. Chem. Eng. Data, 514-516(1960).
  28. Vetter, O., Vandenbroeck, I. and Nayberg, J., "$SrSO_4$: the Basic Solubility Data," Society of Petroleum Engineers, 271-281(1983).

피인용 문헌

  1. Performance comparison of GRG algorithm with evolutionary algorithms in an aqueous electrolyte system vol.6, pp.4, 2020, https://doi.org/10.1007/s40808-020-00818-6
  2. Thermodynamic study of the effect of concentration and ionic strength on osmotic coefficient of aqueous sulfate and chloride solutions at 298.15 K vol.6, pp.4, 2017, https://doi.org/10.1007/s40808-020-00834-6