ANN-based Determination of Optimum Working Conditions of Residential Combustors with Respect to Optimum Insulation

被引：35

作者：

Arslan, O. ^{[1
]}

机构：

[1] Dumlupinar Univ, Fac Engn, Dept Mech Engn, TR-43100 Kutahya, Turkey

来源：

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS | 2014年 / 36卷 / 23期

关键词：

Levenberg-Marguardt; multilayer neural network; Pola-Ribiere conjugate gradient; residential combustor; scaled conjugate gradient; ARTIFICIAL NEURAL-NETWORKS; THICKNESS;

D O I：

10.1080/15567036.2011.572133

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In this study, optimum working conditions of combustion chambers were determined taking optimum insulation thickness into account. With this aim, different fuels, such as Tuncbilek lignite and natural gas, were selected to be used in the combustion chamber. The combustion process was then evaluated using exergo-economic analysis. The results obtained from this analytic evaluation were used to train a multilayer artificial neural network model with the back-propagation learning algorithm with three different variants, namely, Levenberg-Marguardt, Pola-Ribiere conjugate gradient, and scaled conjugate gradient. The most suitable algorithm was found to be Levenberg-Marguardt with eight neurons in a single hidden layer.

引用

页码：2603 / 2612

页数：10

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