OICC Press International Journal of Energy and Environmental Engineering 2251-6832 5 2-3 (July 2014) 2014 05 22 Computational Intelligence techniques for maximum energy efficiency of an internal combustion engine and a steam turbine of a cogeneration process 10.1007/s40095-014-0121-5 EN Sandra Seijo Fernández Science and Technology Faculty, University of Basque Country UPV/EHU, Leioa-Bizkaia, 48940, ES I. del Campo Science and Technology Faculty, University of Basque Country UPV/EHU, Leioa-Bizkaia, 48940, ES J. Echanobe Science and Technology Faculty, University of Basque Country UPV/EHU, Leioa-Bizkaia, 48940, ES J. García-Sedano OPTIMITIVE S.L., Miñano, Alava, 01510, ES E. Suso OPTIMITIVE S.L., Miñano, Alava, 01510, ES E. Arbizu OPTIMITIVE S.L., Miñano, Alava, 01510, ES Journal Article 2014 05 22 Abstract This paper discusses the development of a model of a real cogeneration plant based on Computational Intelligence (CI) algorithms. In particular, two CI strategies are used: one based on artificial neural network and the other one based on a neuro-fuzzy system. Both systems are trained with a data collection from the cogeneration plant. Data mining techniques are applied to remove erroneous and redundant data, and also to obtain information about the variables and its behaviour. This task allows to select only the relevant information and is also a way to decrease the complexity of the model. In this first approach on the work, two separate subsystems of the cogeneration process are considered: an engine and a steam turbine. The obtained models are used to analyze the role of each involved variable and to derive a set of recommendations (i.e., changes in some of the input variables) to optimize the performance of the system. The recommendations applied to the models improve the behaviour of the plant providing higher energy production with a lower cost.