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International Joint Conference SOCO’13-CISIS’13-ICEUTE’13 pp 31–40Cite as

Optimization of Solar Integration in Combined Cycle Gas Turbines (ISCC)

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 239))

Abstract

The estimation of the optimum number of loops to operate an integrated solar combined cycle gas turbine (ISCC) represents a complex problem and a very time demanding operation, which must be calculated in near-real time and as a result, it is hardly possible to be solved with regular ISCC production models. This problem is addressed evaluating different soft computing techniques, concluding that the BAG-REPT metamodel fits best generating MAE test of 4.19% and RMSE test of 8.75%. This model presents much lower time than regular ISCC production models and might be used as a decision tool for feasibility assessments and also in pre-design stages of new ISCC projects.

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References

  1. Baghernejad, A., Yaghoubi, M.: Exergy analysis of an integrated solar combined cycle system. Renewable Energy 35, 2157–2164 (2010)

    Article  Google Scholar 

  2. Antonanzas-Torres, F., Sodupe-Ortega, E., Sanz-Garcia, A., Fernandez-Martinez, R., Martinez-de-Pison-Ascacibar, F.J.: Technical feasibility assessment of integrated solar combined cycle power plants in Ciudad Real (Spain) and Las Vegas (USA). In: Proc. 16th International Congress on Project Engineering, Valencia, Spain (2012)

    Google Scholar 

  3. NREL, National Renewable Energy Laboratory (2013), http://www.nrel.gov/csp/solarpaces/

  4. Sanz-Garcia, A., Antonanzas-Torres, F., Fernandez-Ceniceros, J., Martinez-de-Pison, F.J.: Overall models based on ensemble methods for predicting continuous annealing furnace temperature settings. Ironmaking and Steelmaking (2013), doi:10.1179/1743281213Y.0000000104

    Google Scholar 

  5. de Oliveira Penna Tavares, G., Pacheco, M.A.C.: A Genetic algorithm applied to a main sequence stellar model. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011, Part I. LNCS (LNAI), vol. 6678, pp. 32–42. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  6. Pop, P.C., Matei, O., Sitar, C.P., Chira, C.: A genetic algorithm for solving the generalized vehicle routing problem. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds.) HAIS 2010, Part II. LNCS (LNAI), vol. 6077, pp. 119–126. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  7. Martinez-de-Pison, F.J., Pernia, A.V., Blanco, J., Gonzalez, A., Lostado, R.: Control model for an elastomer extrusion process obtained via a comparative analysis of data mining and artificial intelligence techniques. Polymer-Plastics Technology and Engineering 49, 779–790 (2010)

    Article  Google Scholar 

  8. Martinez-de-Pison, F.J., Pernia, A.V., Gonzalez, A., Lopez-Ochoa, L.M., Ordieres, J.B.: Optimum model for predicting temperature settings on hot dip galvanising line. Ironmaking and Steelmaking 37(3), 187–194 (2010)

    Article  Google Scholar 

  9. Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 16, 716–723 (1974)

    Article  MathSciNet  Google Scholar 

  10. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 21–27 (1967)

    Article  MATH  Google Scholar 

  11. Wang, Y., Witten, I.H.: Induction of model trees for predicting continuous classes. In: Proc. 9th European Conference on Machine Learning, Prague, Czech Republic, pp. 128–137 (April 1997)

    Google Scholar 

  12. Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)

    MathSciNet  MATH  Google Scholar 

  13. Stochastic gradient boosting. Stanford University, Standford (1999)

    Google Scholar 

  14. Mckay, M., Beckman, R., Conover, W.: A comparison of three method for selecting values of input variables in the analysis of output from a computer code. Tecnometrics 21, 239–245 (1979)

    MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. EDMANS Research Group, University of La Rioja, Logroño, Spain

    Javier Antoñanzas-Torres, Fernando Antoñanzas-Torres, Enrique Sodupe-Ortega & F. Javier Martínez-de-Pisón

Authors
  1. Javier Antoñanzas-Torres
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  2. Fernando Antoñanzas-Torres
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  3. Enrique Sodupe-Ortega
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  4. F. Javier Martínez-de-Pisón
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Editor information

Editors and Affiliations

  1. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  2. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Bruno Baruque

  3. German Workforce ADL Partnership Laboratory, Waltershausen, Germany

    Fanny Klett

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

  5. Department of Computer Science Faculty of Ele. Eng. & Computer Science, VŠB-TU Ostrava, Ostrava, Czech Republic

    Václav Snášel

  6. Department of Computer Science, University of Sao Paulo at Sao Carlos, Sao Carlos, Brazil

    André C.P.L.F. de Carvalho

  7. DeustoTech Computing, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  8. Department of Computer Science Faculty of Elec. Eng. and Comp. Science, VŠB-TU Ostrava, Ostrava, Czech Republic

    Ivan Zelinka

  9. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  10. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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© 2014 Springer International Publishing Switzerland

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Antoñanzas-Torres, J., Antoñanzas-Torres, F., Sodupe-Ortega, E., Martínez-de-Pisón, F.J. (2014). Optimization of Solar Integration in Combined Cycle Gas Turbines (ISCC). In: Herrero, Á., et al. International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. Advances in Intelligent Systems and Computing, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-319-01854-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-01854-6_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01853-9

  • Online ISBN: 978-3-319-01854-6

  • eBook Packages: EngineeringEngineering (R0)

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