Abstract
An important problem in designing technical systems under partial uncertainty of the initial physical, chemical, and technological data is the determination of a design in which the technical system is flexible, i.e., its control system is capable of guaranteeing that the constraints hold even under changes in external and internal factors and application of fuzzy mathematical models in its design. Three flexibility problems, viz., the flexibility of a technical system of given structure, structural flexibility of a technical system, and the optimal design guaranteeing the flexibility of a technical system, are studied. Two approaches to these problems are elaborated. Results of a computation experiment are given.
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REFERENCES
Biegler, L.T., Grossmann, I.E., and Westerberg, A.W., Systematic Methods of Chemical Process Design, Upper Saddle River: Prentice Hall, 1997.
Polak, E., An Implemented Algorithm for the Optimal Design Centering, Tolerancing and Tuning, J.Optim.Theory Appl., 1982, vol. 37, pp. 45–66.
Yudin, D.B., Zadachi i metody stokhasticheskogo programmirovaniya (Stochastic Programming Problems and Methods), Moscow: Sovetskoe Radio, 1979.
Birge, J.R. and Louveaux, F., Introduction to Stochastic Programming, New York: Springer, 1997.
Halemane, K.P. and Grossmann, I.E., Optimal Process Design under Uncertainty, AIChE J., 1983, vol. 29, no. 3, pp. 425–433.
Bakhvalov, N.S., Chislennye metody (Numerical Methods), Moscow: Fizmatgiz, 1987.
Pistikopoulos, E.N. and Grossmann, I.E., Optimal Retrofit Design for Improving Process exibility in Nonlinear Systems: Fixed Degree of Flexibility, Comp.Chem.Eng., 1989, vol. 12, pp. 1003–1016.
Reemtsen, R. and Gorner, S., Numerical Methods for Semiinfinite Programming: A Survey, in Semiinfinite Programming, Reemtsen, R. and Ruckman, J.-J., Eds, London: Kluwer, 1998, pp. 195–275.
Ermol'ev, Yu.M., Metody stokhasticheskogo programmirovaniya (Stochastic Programming Methods), Moscow: Sovetskoe Radio, 1979.
Perevozchikov, A.G., A Stochastic Clarke Generalized Method for Two-Stage Stochastic Programming Problems of Related Variables, Zh.Vychisl.Mat.Mat.Fiz., 1993, vol. 33, no. 3, pp. 453–458.
Lepp, R.E., Discrete Approximation and Stability of Complete-Recusion Stochastic Programming Problems, Zh.Vychisl.Mat.Mat.Fiz., 1987, vol. 27, no. 7, pp. 993–1004.
Kall, P., Approximation Methods in Stochastic Programming: A Survey, in Optimizatsiya: modeli, metody, resheniya.Sb.nauch.tr., (Optimization: Models, Methods, and Solutions. Collected Papers), Novosibirsk: Sib. Energ. Inst., 1992, pp. 125–133.
Volin, Yu.M. and Ostrovskii, G.M., Optimization of Technological Processes under Partial Uncertainty of Initial Information, Avtom.Telemekh., 1995, no. 12, pp. 85–98.
Ostrovsky, G.M., Volin, Yu.M., and Senyavin, M.M., An Approach to Solving the Optimization Problem under Uncertainty, Int.J.Syst.Sci., 1997, vol. 28, no. 4, pp. 379–390.
Grossmann, I.E. and Floudas, C.A., Active Constraint Strategy for Flexibility Analysis in Chemical Processes, Comput.Chem.Eng., 1987, vol. 11, no. 6, pp. 675–693.
Swaney, R.E. and Grossmann, I.E., An Index for Operational Flexibility in Chemical Process Design, AIChE J., 1985, vol. 31, no. 4, pp. 621–630.
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Volin, Y.M., Ostrovskii, G.M. Flexibility Analysis of Complex Technical Systems under Uncertainty. Automation and Remote Control 63, 1123–1136 (2002). https://doi.org/10.1023/A:1016111031968
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DOI: https://doi.org/10.1023/A:1016111031968