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Static and dynamic neural networks for simulation and optimization of cogeneration systems

Zomorodian, R ; Sharif University of Technology | 2006

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  1. Type of Document: Article
  2. DOI: 10.1115/GT2006-90236
  3. Publisher: 2006
  4. Abstract:
  5. In this paper, the application of neural networks for simulation and optimization of the cogeneration systems has been presented. CGAM problem, a benchmark in cogeneration systems, is chosen as a case study. Thermodynamic model includes precise modeling of the whole plant. For simulation of the steady sate behavior, the static neural network is applied. Then using dynamic neural network, plant is optimized thermodynamically. Multi layer feed forward neural networks is chosen as static net and recurrent neural networks as dynamic net. The steady state behavior of CGAM problem is simulated by MFNN. Subsequently, it is optimized by dynamic net. Results of static net have excellence agreement with simulator data. Dynamic net shows that in thermodynamic optimization condition, a and pinch point temperature difference have the lowest value, while CPR reaches a high value. Sensitivity study shows turbomachinery efficiencies have the highest effect on the performance of the system in optimum condition. Copyright © 2006 by ASME
  6. Keywords:
  7. Dynamic net ; Pinch point ; Temperature difference ; Thermodynamic model ; Computer simulation ; Feedforward neural networks ; Mathematical models ; Optimization ; Thermodynamics ; Turbomachinery ; Cogeneration plants
  8. Source: 2006 ASME 51st Turbo Expo, Barcelona, 6 May 2006 through 11 May 2006 ; Volume 4 , 2006 , Pages 615-623 ; 0791842398 (ISBN); 9780791842393 (ISBN)
  9. URL: https://asmedigitalcollection.asme.org/GT/proceedings-abstract/GT2006/615/315455