Automatic modeling of a gas turbine using genetic programming: An experimental study

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  author =       "Josue Enriquez-Zarate and Leonardo Trujillo and 
                 Salvador {de Lara} and Mauro Castelli and 
                 Emigdio Z-Flores and Luis Munoz and Ales Popovic",
  title =        "Automatic modeling of a gas turbine using genetic
                 programming: An experimental study",
  journal =      "Applied Soft Computing",
  year =         "2017",
  volume =       "50",
  month =        jan,
  pages =        "212--222",
  keywords =     "genetic algorithms, genetic programming, Gas turbine,
                 Data-driven modeling, Local search",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2016.11.019",
  URL =          "",
  sizze =        "11 pages",
  abstract =     "This work deals with the analysis and prediction of
                 the behavior of a gas turbine (GT), the Mitsubishi
                 single shaft Turbo-Generator Model MS6001, which has a
                 30 MW generation capacity. GTs such as this are of
                 great importance in industry, as drivers of gas
                 compressors for power generation. Because of their
                 complexity and their execution environment, the failure
                 rate of GTs can be high with severe consequences. These
                 units are subjected to transient operations due to
                 starts, load changes and sudden stops that degrade the
                 system over time. To better understand the dynamic
                 behavior of the turbine and to mitigate the
                 aforementioned problems, these transient conditions
                 need to be analyzed and predicted. In the absence of a
                 thermodynamic mathematical model, other approaches
                 should be considered to construct representative models
                 that can be used for condition monitoring of the GT, to
                 predict its behavior and detect possible system
                 malfunctions. One way to derive such models is to use
                 data-driven approaches based on machine learning and
                 artificial intelligence. This work studies the use of
                 state-of-the-art genetic programming (GP) methods to
                 model the Mitsubishi single shaft Turbo-Generator. In
                 particular, we evaluate and compare variants of GP and
                 geometric semantic GP (GSGP) to build models that
                 predict the fuel flow of the unit and the exhaust gas
                 temperature. Results show that an algorithm, proposed
                 by the authors, that integrates a local search
                 mechanism with GP (GP-LS) outperforms all other
                 state-of-the-art variants studied here on both
                 problems, using real-world and representative data
                 recorded during normal system operation. Moreover,
                 results show that GP-LS outperforms seven other
                 modeling techniques, including neural networks and
                 isotonic regression, confirming the importance of
                 GP-based algorithms in this domain.",
  notes =        "Cites \cite{Martinez-Arellano:2014:UKSim}",

Genetic Programming entries for Josue Enriquez-Zarate Leonardo Trujillo Salvador de Lara Mauro Castelli Emigdio Z-Flores Luis Munoz Delgado Ales Popovic