Towards modeling of combined cooling, heating and power system with artificial neural network for exergy destruction and exergy efficiency prognostication of tri-generation components

The current study is an attempt to address the investigation of the CCHP (combined cooling, heating and power) system when 10 input variables were chosen to analyze 10 most important objective output parameters. Moreover, ANN (artificial neural network) was successfully applied on the tri-generation system on account of its capability to predict responses with great confidence. The results of sensitivity analysis were considered as foundation for selecting the most suitable and potent input parameters of the supposed cycle. Furthermore, the best ANN topology was attained based on the least amount of MSE and number of iterations. Consequently, the trainlm (Levenberg-Marquardt) training approach with 10-9-10 configuration has been exploited for ANN modeling in order to give the best output correspondence. The maximum MRE = 1.75% (mean relative error) and minimum R2 = 0.984 represents the reliability and outperformance of the developed ANN over common conventional thermodynamic analysis carried out by EES (engineering equation solver) software.