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Start-Up Optimisation of a Combined Cycle Power Plant with Multiobjective Evolutionary Algorithms

  • Ilaria Bertini
  • Matteo De Felice
  • Fabio Moretti
  • Stefano Pizzuti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6025)

Abstract

In this paper we present a study of the application of Evolutionary Computation methods to the optimisation of the start-up of a combined cycle power plant. We propose a multiobjective approach considering different objectives for the optimisation in order to reduce the pollution emissions and to maximise the efficiency of the plant. We compare a multiobjective evolutionary algorithm (NSGA-II) with 2 and 5 objectives on a software simulator and then we use different metrics to measure the performances. We show that NSGA-II algorithm is able to provide a set of solutions, defined as Pareto Front, that represent the best trade-off on the different objectives among those the decision maker can choose.

Keywords

Pareto Front Multiobjective Optimisation Steam Turbine Multiobjective Evolutionary Algorithm Multiobjective Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ilaria Bertini
    • 1
  • Matteo De Felice
    • 1
    • 2
  • Fabio Moretti
    • 2
  • Stefano Pizzuti
    • 1
  1. 1.ENEA (Italian Energy New Technology and Environment Agency) 
  2. 2.Dipartimento di Informatica e AutomazioneUniversità degli Studi “Roma Tre” 

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