Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycle system-34

被引：103

作者：

Arslan, Oguz ^{[1
]}

机构：

[1] Dumlupinar Univ, Fac Engn, Dept Mech Engn, TR-43270 Kutahya, Turkey

来源：

ENERGY | 2011年 / 36卷 / 05期

关键词：

Life cycle cost; Levenberg-Marquardt; Pola-Ribiere conjugate gradient; Scaled conjugate gradient; Multi-layer neural network; Kalina cycle system-34; ARTIFICIAL NEURAL-NETWORKS; ENERGY; PREDICTION;

D O I：

10.1016/j.energy.2011.01.045

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

Recent technical developments have made it possible to generate electricity from geothermal resources of low and medium enthalpy. One of these technologies is the Kalina Cycle System. In this study, electricity generation from Simav geothermal field was investigated using the Kalina cycle system-34 (KCS-34). However, the design of these technologies requires more proficiency and longer times within complex calculations. An artificial neural network (ANN) is a new tool used to make a decision for the optimum working conditions of the processes within the expertise. In this study, the back-propagation learning algorithm with three different variants, namely Levenberg-Marguardt (LM), Pola-Ribiere Conjugate Gradient (CGP), and Scaled Conjugate Gradient (SCG), were used in the network so that the best approach could be found. The most suitable algorithm found was LM with 7 neurons in a single hidden layer. The obtained weights were used in optimization process by coupling the life-cycle-cost concepts. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：2528 / 2534

页数：7

共 37 条

[21] Artificial neural networks for modelling the starting-up of a solar steam-generator [J].

Kalogirou, SA ;

Neocleous, CC ;

Schizas, CN .

APPLIED ENERGY, 1998, 60 (02) :89-100

[22] Artificial neural networks for the prediction of the energy consumption of a passive solar building [J].

Kalogirou, SA ;

Bojic, M .

ENERGY, 2000, 25 (05) :479-491

[23] Applications of artificial neural-networks for energy systems [J].

Kalogirou, SA .

APPLIED ENERGY, 2000, 67 (1-2) :17-35

[24]

Leibowitz H.M., 1999, Global Geothermal Resources, P75

[25]

Lund J.W., 2007, Geo-Heat Center Quarterly Bulletin, V28, P1

[26]

Mlcak H., 2002, Geothermal Resources Council Transactions, V26, P707

[27] Exergy analysis of the Kalina cycle [J].

Nag, PK ;

Gupta, AVSSKS .

APPLIED THERMAL ENGINEERING, 1998, 18 (06) :427-439

[28]

Oztemel E., 2003, Artificial Neural Networks, V1st

[29] Estimation of Diffuse Fraction of Global Solar Radiation Using Artificial Neural Networks [J].

Rehman, S. ;

Mohandes, M. .

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, 2009, 31 (11) :974-984

[30]

RENZ M, 2005, GEOTHERMISCHE ENERGI

← 1 2 3 4 →