Energy Systems Project Manager Monenco Iran Company (MAPNA Group), Oil and Gas Division, Tehran, Iran.
Research Engineer Sharif University of Technology, Tehran, Iran
Professor, Sharif University of Technology, Tehran, Iran.
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 steadysate 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 Excellent CGAM problem is simulated by MFNN. Subsequently, it is optimized by dynamic net. Results of static net have excellent agreement with simulator data. Dynamic net shows that in thermodynamic optimization condition, 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.