Dynamic energy and exergy analyses of an industrial cogeneration system

WOS: 000275607000004The study deals with the energetic and exergetic analyses of a cogeneration (combined heat and power, CHP) system installed in a ceramic factory, located in Izmir, Turkey. This system has three gas turbines with a total capacity of 13 MW, six spray dryers and two heat exchangers. In the analysis, actual operational data over one-month period are utilized. The so-called CogeNNexT code is written in C++ and developed to analyze energetic and exergetic data from a database. This code is also used to analyze turbines, spray dryers and heat exchangers in this factory. Specifications of some parts of system components have been collected from the factory. Based on the 720 h data pattern (including 43 200 data), the mean energetic and exergetic efficiency values of the cogeneration system are found to be 82.3 and 34.7%, respectively. Copyright (C) 2009 John Wiley & Sons, Ltd.Eskisehir Osmangazi UniversityEskisehir Osmangazi University; Anadolu UniversityAnadolu University; Ege UniversityEge UniversityContract/grant sponsor: Eskisehir Osmangazi University Contract/grant sponsor: Anadolu University Contract/grant sponsor: Ege Universit

Application of Artificial Neural Network (ANN) method to exergy analysis of thermodynamic systems

8th International Conference on Machine Learning and Applications -- DEC 13-15, 2009 -- Miami Beach, FLWOS: 000291011600112Exergy is a way to sustainable development and may be defined as the maximum theoretical useful work, while exergy analysis identifies the sources, the magnitude and the causes of thermodynamic inefficiencies within each system component. By using the ANN, exergy results can be obtained easily including closer results. The results were solved by CogeNNexT code developed by authors and Fast ANN (FANN) Library is implemented to this C++ code. The main objective of the present study is namely (i) to apply the ANN method to exergy analysis of thermodynamic systems by presenting the performance of the ANN method and (ii) to emphasize the definition of ANN inputs. It may be concluded that most of thermodynamic systems can be trained and analyzed by using the ANN method. It is expected that this study would be very beneficial to those dealing with the intelligent systems of the future.IEEE SMCS, Cal State Univ, Assoc Machine Learning & Appl, Univ Louisvill