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Analysis of the design and optimization of preparative chromatography on the basis of the separation of a real post-reaction mixture

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The operating conditions for preparative chromatography, as for any industrial process, must be optimized. Such optimization is based on thorough understanding of process variables and economics. Optimization of the operating conditions is the best justification for detailed study of the fundamentals of nonlinear chromatography. It is difficult to optimize a se-paration without a clear understanding of how the thermodynamics of com-petitive phase equilibria, the finite rate of mass transfer, and dispersion phenomena combine to affect the individual band profiles of the compo-nents to be separated. The operating conditions determine the objectives of the process – yield, productivity, and, ultimately, the cost of the separation. Because of the severe nonlinearity of the chromatography model, the problem of optimization is difficult to solve, and because of the large number of operating variables and the complexity of the objective fun-ctions, the solution found can easily be the result of trapping in a local opti-mum. It is, therefore, necessary to use an effective mathematical tool for global optimization of nonlinear problems. In this work a chromatographic process for separation of the cis and trans isomers of furyl analogues of natural plant terpenes from a real post-synthesis mixture has been optimized. Typical problems during the optimization, which are discussed below, were: (a) formulation of a model of the process dynamics; (b) specification of model variables such as isotherm data, system effi-ciency, and physicochemical properties of the system; (c) specification of the objectives of the separation process and the pro-cess operating variables; and (d) selection of the optimization procedure.
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Bibliogr. 38 poz., rys., tab.
  • Rzeszów University of Technology, Chemical and Process Engineering, al. Powstańców Warszawy 6, 35-959 Rzeszów, Poland
  • [1] G. Guiochon, S. Golshan-Shirazi, and A.M. Katti, Fundamentals of Preparative and Nonlinear Chromatography, Academic Press, Boston, 1994
  • [2] G. Guiochon and B. Lin, Modeling for Preparative Chromatography, Academic Press, Amsterdam, 2003
  • [3] A. Felinger and G. Guiochon, J. Chromatogr. A, 752, 31 (1996)
  • [4] A. Felinger and G. Guiochon, J. Chromatogr. A, 796, 59 (1998)
  • [5] P. Jandera, D. Komers, and G. Guiochon, J. Chromatogr. A, 796, 115 (1998)
  • [6] Z. Zhang, K. Hidajat, and A.K. Ray, Ind. Eng. Chem Res., 41, 3213 (2002)
  • [7] Z. Zhang, M. Mazzotti, and M. Morbidelli, AIChE. J., 48, 2800 (2002)
  • [8] Z. Zhang, M. Mazzotti, and M. Morbidelli, J. Chromatogr. A, 1006, 87 (2003)
  • [9] G. Ziomek, Y. Shan, D. Antos, and A. Seidel-Morgenstern, Comparing different options how to apply five identical columns in preparative chromatography, PREP2005, Philadelphia USA, L-206
  • [10] G. Ziomek, D. Antos, L. Tobiska, and A. Seidel-Morgenstern, J. Chromatogr. A, (2005) submitted for publication
  • [11] Y. Shan and A. Seidel-Morgenstern, J. Chromatogr. A, 1041, 53 (2004)
  • [12] G. Ziomek, M. Kaspereit, J. Jeżowski, A. Seidel-Morgenstern, and D. Antos, J. Chromatogr. A, 1070, 111 (2005)
  • [13] G. Ziomek and D. Antos, Comput. Chem. Eng., 29, 1577 (2005)
  • [14] K. Kaczmarski and D. Antos, Application of Simulated Annealing and Random Search Method for Optimization of Periodic and Continuous Chromatography Separation, PREP2005, Philadelphia USA, L-204
  • [15] D.M. Bates and D.G. Watts, Nonlinear Regression and its Applications, Wiley, New York, 1988
  • [16] P.R. Gillhill, W. Murray, M.H. Wright, The Levenberg–Marquardt Method, §4.7.3 in Practical Optimization, Academic Press, London, 1981
  • [17] J.A. Nelder and R. Mead, Comp. J., 7, 308 (1965)
  • [18] L.T. Biegler and I.E. Grossmann, Comput. Chem. Eng., 28, 1169 (2004)
  • [19] A. Nemirovsky and N. Yudin, Interior-Point Polynomial Methods in Convex Programming PA: SIAM , Philadelphia, 1994
  • [20] R. Bochenek and J. Jeżowski, Inż. Chem. Proc., 25, 721 (2004)
  • [21] R. Bochenek, J. Jeżowski, G. Poplewski, and A. Jeżowska, Studies in Adaptive Random Search Optimization for MINLP Problems, Comput. Chem. Eng., S483–S486 (1999)
  • [22] J. Jeżowski, R. Bochenek, A. Jeżowska, G. Poplewski, and R. Słoma, Inż. Apar. Chem., 43, 3 (2004)
  • [23] J. Jeżowski, A. Jeżowska, and G. Poplewski, Inż. Chem. Proc., 4, 47 (2003)
  • [24] M.A. Luersen and R. Le Riche, Comput. Struct., 82, 2251 (2004)
  • [25] R. Chelouah and P. Siary, Eur. J. Oper. Res., 161, 636 (2005)
  • [26] W. Piątkowski, Inż. Chem. Proc., 26, 605 (2005)
  • [27] M. Suzuki, Adsorption Engineering, Elsevier, Amsterdam, 1990
  • [28] D.M. Ruthven, Principles of Adsorption and Adsorption Processes, John Wiley, New York, 1989
  • [29] R. Petrus, G. Aksielrud, J. Gumnicki, and W. Piątkowski, Wymiana masy w układzie ciało stałe-ciecz, Of. Wyd. PRz., Rzeszów, 1988
  • [30] L.C. Craig, J. Biol. Chem., 155, 519 (1944)
  • [31] A.J.P. Martin and R.L.M. Synge, Biol. Chem., 35, 1358 (1941)
  • [32] D. Antos, K. Kaczmarski, W. Piątkowski, and A. Seidel-Morgenstern A., J. Chromatogr. A, 1006, 61 (2003)
  • [33] Y. Shan and A. Seidel-Morgenstern, J. Chromatogr. A, 1093, 47 (2005)
  • [34] L.R. Snyder and J.W. Dolan, J. Chromatogr. A, 540, 21 (1991)
  • [35] M.J. Ościk, Adsorption, Ellis Horwood Limited, Chichester, 1982
  • [36] E. Soczewiński, Anal. Chem., 41, 179 (1969)
  • [37] L.R. Snyder, Anal. Chem., 46, 1384 (1974)
  • [38] J. Kula, M. Sikora, D. Hammad, R. Bonikowski, M. Balawajder, and J. Nowicki, Flav. Fragr. J., 20, 487 (2005)
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