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

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 casestudy. 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 optimizedthermodynamically. 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.