Static and Dynamic Neural Networks for Simulation and Optimization of Cogeneration Systems

Abstract

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, σ 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.

Computational Intelligence techniques for maximum energy efficiency of an internal combustion engine and a steam turbine of a cogeneration process

“…This work proposes the development of a model of a real cogeneration plant, based on CI algorithms, with the aim of increasing the efficiency of the process [11][12][13] using a database from the cogeneration plant. The database contains values from variables read every minute in a 15-day period.…”