Abstract
The modelling of complex industrial processes is a hard task due to the complexity, uncertainties, high dimensionality, non-linearity and time delays. To model these processes, mathematical models with a large amount of assumptions are necessary, many times this is either almost impossible or it takes too much computational time and effort. Combined Heat and Power (CHP) processes are a proper example of this kind of complex industrial processes. In this work, an optimized model of a steam turbine of a real CHP process using Extreme Learning Machine (ELM) is proposed. Previously, with the aim of reducing the dimensionality of the system without losing prediction capability, a hybrid feature selection method that combines a clustering filter with ELM as wrapper is applied. Experimental results using a reduced set of features are very encouraging. Using a set of only three input variables to predict the power generated by the steam turbine, the optimal number of hidden nodes are only eight, and a model with RMSE less than 1 % is obtained.
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References
Zare, V., Mahmoudi, S.M.S., Yari, M., Amidpour, M.: Thermoeconomic analysis and optimization of an ammonia-water power/cooling cogeneration cycle. Energy 47, 271–283 (2012)
Feidt, M., Costea, M.: Energy and exergy analysis and optimization of combined heat and power systems. Comparison of various systems. Energies 5, 3701–3722 (2012)
Sen, D., Panua, R., Sen, P., Das, D.: Thermodynamic analysis and cogeneration of a cement plant in india-a case study. In: 2013 International Conference on Energy Efficient Technologies for Sustainability (ICEETS 2013), pp. 641–646 (2013)
Rutkowski, L.: Computational Intelligence: Methods and Techniques (2008). Cited By 36
Rossi, F., Velázquez, D., Monedero, I., Biscarri, F.: Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants. Expert Syst. Appl. 41(10), 4658–4669 (2014)
Nikpey, H., Assadi, M., Breuhaus, P.: Development of an optimized artificial neural network model for combined heat and power micro gas turbines. Appl. Energy 108, 137–148 (2013)
Sisworahardjo, N., El-Sharkh, M.Y.: Validation of artificial neural network based model of microturbine power plant. In: Proceedings of 2013 IEEE Industry Applications Society annual Meeting (2013)
Ozel, Y., Guney, I., Arca, E.: Neural network solution to the cogeneration system by using coal. Int. J. Energy 1, 105–112 (2007)
De, S., Kaiadi, M., Fast, M., Assadi, M.: Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden. Energy 32, 2099–2109 (2007)
Huang, G.-B., Wang, D., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern. 2(2), 107–122 (2011). Cited By 323
Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 42(2), 513–529 (2012). Cited By 340
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006). Cited By 1296
Huang, G.-B., Chen, L.: Convex incremental extreme learning machine. Neurocomputing 70, 3056–3062 (2007)
Serre, D.: Matrices: Theory and Applications. Springer, New York
EnergyWorks. http://www.energyworks.com/
de Meteorología, A.-A.E.: http://www.aemet.es/en/portada
Tibshirani, R., Hastie, T., Narasimhan, B., Chu, G.: Class prediction by nearest shrunken centroids, with applications to DNA microarrays. Stat. Sci. 18(1), 104–117 (2003). Cited By 156
Acknowledgments
This work was supported in part by the Basque Country Government under Grants IT733-13, and IG2012/221 (ICOGME), and the Zabalduz Program of the University of the Basque Country (Spain).
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Seijo, S., Martínez, V., del Campo, I., Echanobe, J., García-Sedano, J. (2016). Feature Selection and Modelling of a Steam Turbine from a Combined Heat and Power Plant Using ELM. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-28397-5_34
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DOI: https://doi.org/10.1007/978-3-319-28397-5_34
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