Performance evaluation of open-cycle solar regenerator using artificial neural network technique

被引：14

作者：

Aly, Ayman A. ^{[1
]}

Zeidan, El-Shafei B. ^{[1
]}

Hamed, Ahmed M. ^{[1
]}

机构：

[1] Taif Univ, Dept Mech Engn, Fac Engn, Al Hawyah, Saudi Arabia

来源：

ENERGY AND BUILDINGS | 2011年 / 43卷 / 2-3期

关键词：

Solar energy; Open-cycle; Regeneration; Lithium chloride; Absorption; Liquid desiccant; Neural network; Back-propagation algorithm; COOLING SYSTEMS; DESICCANT; COLLECTOR;

D O I：

10.1016/j.enbuild.2010.09.032

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Theoretical investigation on the performance of lithium chloride (LiCl) absorption cooling system using an artificial neural network (ANN) model is presented. Tabulated data from the literature are used to construct the ANN model. Solar collector desiccant/regenerator is applied to re-concentrate the working solution. Using the proposed model, the effect of system design parameters; namely regenerator length, and air flow rate on the performance of the system is demonstrated. The variation of the thermo-physical parameters along the regenerator length is highlighted. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：454 / 457

页数：4

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