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Authors
Mario Francisco; Silvana Revollar; Pastora Vega; Rosalba Lamanna

Digital Object Identifier (DOI)
10.3182/20050703-6-CZ-1902.00917

Page Numbers:
916-916

Index Terms
integrated design,sequential quadratic programming,genetic algorithms,stochastic optimization,controllability

Abstract
This paper focuses on the application of stochastic (genetic algorithms, simulated annealing) and deterministic (sequential quadratic programming) optimization methods for the Integrated Design of processes considering dynamical non-linear models. Moreover, a hybrid methodology that combines both types of methods is proposed, showing an improvement on performance. Controllability indexes such as disturbance sensitivity gains, the Hα norm, and the ISE were considered to obtain optimum disturbance rejection. In order to illustrate and validate our proposal, an activated sludge process with PI schemes is taken. The problem is stated as a multiobjective non-linear optimization problem with non-linear constraints. The application of the mentioned strategies is discussed. The results are encouraging for future application of these techniques to solve synthesis MINLP problems.

References

[1] Costa, L. and Oliveira, P. (2001). Evolutionary
    Algorithms Approach to the Solution of Mixed
    Integer Non-Linear Programming Problems.
    Comp. Chem. Eng., 25 257-266.
[2] Edgar, T. and Himmelblau, D. (2001) Optimization
    of Chemical Processes. Mc Graw Hill.
[3] Gen, M. and Chen, R. (2000). Genetic algorithms
    and engineering optimisation. John Wiley and
    Sons.
[4] Gill, P., Murray, W. and Wright, M. (1981) Practical
    Optimization. Academic Press.
[5] Kookos I. and Perkins J. (2001). An algorithm for
    simultaneous process design and control. Ind.
    Eng. Chem. Res., 40, pp. 4079-4088.
[6] Laarhoven, P. and Aarts, E. (1987) Simulated
    Annealing: Theory and Applications. Kluwer
    Academic Publishers.
[7] Luyben, M. L. and Floudas, C. A (1994). Analyzing
    the interaction of design and control -1. A
    multiobjective framework and application to
    binary distillation synthesis, Comp. Chem. Eng.,
    18, No. 10, pp. 933-969.
[8] Luyben, W. (1993). Trade-offs Betwen Design and
    Control in Chemical Reactor Systems. J. Proc.
    Cont. 3, pp. 17-40.
[9] Revollar, S; Vega, P. and Lamanna, R. (2004)
    Algorithmic synthesis and Integrated Design of
    chemical reactor systems using genetic
    algorithms. World Automation Congress (Sevilla,
    Spain).
[10] Salamon, P. Sibani, P. and Frost, R. (2002). Facts,
     Conjectures, and Improvements for Simulated
     Annealing. SIAM monographs on Mathematical
     Modelling and Computation.
[11] Tsai, M. and Chang, Ch. (2001). Water usage and
     treatment network design using genetic
     algorithms Ind. Eng. Chem. Res., 40, pp. 4874-
     4888.
[12] Vega, P. and Gutiérrez, G. (1999). Optimal Design
     Control and Operation of wastewater treatment
     plants. European Control Conference (Germany).