A Novel Application of Evolutionary Computing in Process Systems Engineering

  • Jessica Andrea Carballido
  • Ignacio Ponzoni
  • Nélida Beatriz Brignole
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3448)

Abstract

In this article we present a Multi-Objective Genetic Algorithm for Initialization (MOGAI) that finds a starting sensor configuration for Observability Analysis (OA), this study being a crucial stage in the design and revamp of process-plant instrumentation. The MOGAI is a binary-coded genetic algorithm with a three-objective fitness function based on cost, reliability and observability metrics. MOGAI’s special features are: dynamic adaptive bit-flip mutation and guided generation of the initial population, both giving a special treatment to non-feasible individuals, and an adaptive genotypic convergence criterion to stop the algorithm. The algorithmic behavior was evaluated through the analysis of the mathematical model that represents an ammonia synthesis plant. Its efficacy was assessed by comparing the performance of the OA algorithm with and without MOGAI initialization. The genetic algorithm proved to be advantageous because it led to a significant reduction in the number of iterations required by the OA algorithm.

Keywords

Combinatorial Optimization Problem PSE Process-Plant Instrumentation Design Multi-Objective Genetic Algorithm Observability Analysis 

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References

  1. 1.
    Vazquez, G.E., Ferraro, S.J., Carballido, J.A., Ponzoni, I., Sánchez, M.C., Brignole, N.B.: The Software Architecture of a Decision Support System for Process Plant Instrumentation. WSEAS Transactions on Computers 4, 2, 1074–1079 (2003)Google Scholar
  2. 2.
    Ponzoni, I., Sánchez, M.C., Brignole, N.B.: A New Structural Algorithm for Observability Classification. Ind. Eng. Chem. Res. 38, 8, 3027–3035 (1999)CrossRefGoogle Scholar
  3. 3.
    Ferraro, S.J., Ponzoni, I., Sánchez, M.C., Brignole, N.B.: A Symbolic Derivation Approach for Redundancy Analysis. Ind. Eng. Chem. Res. 41, 23, 5692–5701 (2002)CrossRefGoogle Scholar
  4. 4.
    Osyczka, A.: Multicriterion Optimization in Engineering with FORTRAN Programs. Ellis Horwood Limited (1984)Google Scholar
  5. 5.
    Toscano Pulido, G.: Optimización Multiobjetivo Usando Un Micro Algoritmo Genético. Tesis de Maestría en Inteligencia Artificial, Universidad Veracruzana LANIA (2001)Google Scholar
  6. 6.
    Fonseca, C.M.: Multiobjective Genetic Algorithms with Application to Control Engineering Problems. PhD Thesis, Department of Automatic Control and Systems Engineering University of Sheeld (1995)Google Scholar
  7. 7.
    Coello Coello, C.A.: A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques. Knowledge and Information Systems, An International Journal 1, 3, 269–308 (1999)Google Scholar
  8. 8.
    Rosenberg, R.S.: Simulation of Genetic Populations with Biochemical Properties. PhD thesis, University of Michigan, Ann Harbor, Michigan (1967)Google Scholar
  9. 9.
    Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter Control in Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 3, 2, 124–141 (1999)CrossRefGoogle Scholar
  10. 10.
    Bäck, T., Hammel, U., Schwefel, H.P.: Evolutionary Computation: Comments on the History and Current State. IEEE Transactions on Evolutionary Computation 1, 1, 3–17 (1997)CrossRefGoogle Scholar
  11. 11.
    Safe, M., Carballido, J., Ponzoni, I., Brignole, N.B.: On Stopping Criteria for Genetic Algorithms. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 405–413. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Radcliffe, N.J.: Equivalence Class Analysis of Genetic Algorithms. Complex Systems 5 (1991)Google Scholar
  13. 13.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)MATHGoogle Scholar
  14. 14.
    Bike, S.: Design of an Ammonia Synthesis Plant, CACHE Case Study. In: Department of Chemical Engineering, Carnegie Mellon University (1985)Google Scholar
  15. 15.
    Vazquez, G.E., Ponzoni, I., Sánchez, M.C., Brignole, N.B.: ModGen: A Model Generator for Instrumentation Analysis. Advances in Engineering Software 32, 37–48 (2001)CrossRefGoogle Scholar
  16. 16.
    Ponzoni, I., Brignole, N.B., Bandoni, J.A.: Estudio de Instrumentación para una Planta de Producción de Amoníaco empleando un Nuevo Algoritmo de Clasificación. In: AADECA 1998, vol. 1, pp. 59–64 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jessica Andrea Carballido
    • 1
    • 2
  • Ignacio Ponzoni
    • 1
    • 2
  • Nélida Beatriz Brignole
    • 1
    • 2
  1. 1.Laboratorio de Investigación y Desarrollo en Computación Científica (LIDeCC) Departamento de Ciencias e Ingeniería de la ComputaciónUniversidad Nacional del SurBahía BlancaArgentina
  2. 2.Planta Piloto de Ingeniería Química – CONICETComplejo CRIBABBBahía BlancaArgentina

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