Thermodynamic analysis of an open cycle solid desiccant cooling system using Artificial Neural Network

Koronaki, IP; Rogdakis, E; Kakatsiou, T

dc.contributor.author	Koronaki, IP	en
dc.contributor.author	Rogdakis, E	en
dc.contributor.author	Kakatsiou, T	en
dc.date.accessioned	2014-03-01T02:54:03Z
dc.date.available	2014-03-01T02:54:03Z
dc.date.issued	2012	en
dc.identifier.issn	01968904	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36559
dc.subject	Dehumidification	en
dc.subject	Desiccant wheel	en
dc.subject	Neural network	en
dc.subject	Silica gel	en
dc.subject.other	Air flow	en
dc.subject.other	Airconditioning systems	en
dc.subject.other	Black-box model	en
dc.subject.other	Correlation models	en
dc.subject.other	Dehumidification	en
dc.subject.other	Desiccant cooling systems	en
dc.subject.other	Desiccant wheels	en
dc.subject.other	Experimental data	en
dc.subject.other	Heat pumps	en
dc.subject.other	In-line	en
dc.subject.other	Key parameters	en
dc.subject.other	Model development	en
dc.subject.other	Neural network model	en
dc.subject.other	Open cycles	en
dc.subject.other	Outlet air	en
dc.subject.other	Output values	en
dc.subject.other	Process streams	en
dc.subject.other	Regeneration air	en
dc.subject.other	Sensible heat factor	en
dc.subject.other	Thermo dynamic analysis	en
dc.subject.other	Thermodynamic conditions	en
dc.subject.other	Air conditioning	en
dc.subject.other	Cooling systems	en
dc.subject.other	Humidity control	en
dc.subject.other	Neural networks	en
dc.subject.other	Silica gel	en
dc.subject.other	Thermoanalysis	en
dc.subject.other	Driers (materials)	en
dc.title	Thermodynamic analysis of an open cycle solid desiccant cooling system using Artificial Neural Network	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1016/j.enconman.2012.01.022	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.enconman.2012.01.022	en
heal.publicationDate	2012	en
heal.abstract	This paper examines the performance of an installed open cycle air-conditioning system with a silica gel desiccant wheel which uses a conventional heat pump and heat exchangers for the improvement of the outlet air of the system. A neural network model based on the training of a black box model with experimental data was developed as a method based on experimental results predicting the state conditions of air at the process and regeneration stream. The model development was followed by a Sensitivity Analysis performed on these predicted results. The key parameters were the thermodynamic condition of process and regeneration air streams, the sensible heat factor of the room, and the mass air flow ratio of the regeneration and process streams. The results of this analysis revealed that all investigated parameters influenced the performance of the desiccant unit. Predicted output values of the proposed Neural Network Model for Desiccant Systems are in line with results from other correlation models based on the interpolation of experimental data obtained from industrial air conditioning installations. © 2012 Elsevier Ltd. All rights reserved.	en
heal.journalName	Energy Conversion and Management	en
dc.identifier.doi	10.1016/j.enconman.2012.01.022	en
dc.identifier.volume	60	en
dc.identifier.spage	152	en
dc.identifier.epage	160	en