evaluation and comparison of different artificial neural networks and genetic algorithm in analyzing a 60 mw combined heat and power cycle

The constant growth of energy consumption, increased fuel costs, non-renewable fossil fuel sources, and environmental pollution caused by increased emission of greenhouse gases, and global warming highlight the need for the analysis and optimization of main energy generation bases, i.e. power plants. the artificial neural network (ann) is a useful novel method for better processing information and controlling, and optimizing and modeling industrial processes. for the first time in this study, an ann was designed and applied to data extracted from modeling and analyzing a 60 mw combined heat and power generation power plant. to this end, the error backpropagation network was selected as the optimal network, and the generator load or capacity, condenser pressure, and feedwater temperature were considered inputs to the ann. the energy and exergy efficiencies of the power plant and the overall energy and exergy losses of the cycle were considered outputs of the ann. the ann was coded and designed with the help of matlab. the genetic algorithm (ga) was used to obtain the optimal values of input parameters and the minimum losses and maximum efficiencies based on the first and second laws of thermodynamics.