A Novel Application of Evolutionary Computing in Process Systems Engineering

  • Jessica Andrea Carballido
  • Ignacio Ponzoni
  • Nélida Beatriz Brignole
Conference paper

DOI: 10.1007/978-3-540-31996-2_2

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3448)
Cite this paper as:
Carballido J.A., Ponzoni I., Brignole N.B. (2005) A Novel Application of Evolutionary Computing in Process Systems Engineering. In: Raidl G.R., Gottlieb J. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2005. Lecture Notes in Computer Science, vol 3448. Springer, Berlin, Heidelberg

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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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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