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

被引:11
|
作者
Taghavifar, Hadi [1 ]
Anvari, Simin [1 ,2 ]
Saray, Rahim Khoshbakhti [2 ]
Khalilarya, Shahram [1 ]
Jafarmadar, Samad [1 ]
Taghavifar, Hamid [3 ]
机构
[1] Urmia Univ, Tech Educ Fac, Dept Mech Engn, Orumiyeh 5756115311, West Azerbaijan, Iran
[2] Sahand Univ Technol, Fac Mech Engn, Sahand New Town, Tabriz, Iran
[3] Urrnia Univ, Dept Mech Engn Agr Machinery, Fac Agr, Orumiyeh, Iran
关键词
ANN; CCHP; Exergy survey; Sensitivity analysis; Tri-generation; TRIGENERATION SYSTEM; OPTIMIZATION; COGENERATION; PLANT; COLD;
D O I
10.1016/j.applthermaleng.2015.06.008
中图分类号
O414.1 [热力学];
学科分类号
摘要
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 trainIm (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 R-2 = 0.984 represents the reliability and outperformance of the developed ANN over common conventional thermodynamic analysis carried out by EES (engineering equation solver) software. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:156 / 168
页数:13
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