Modeling and experimental validation of the desiccant wheel in a hybrid desiccant air conditioning system

Abstract Modeling can be strong asset to the operation of air conditioning plants taking into account e.g. the strong dependency of local climate conditions for the operation of HVAC systems. This paper presents a validated physical model and a simplified model based on the results of the physical model for a desiccant wheel, which is the central part of a hybrid air conditioning system. The two models offer different advantages: While the physical model is complex and can be adapted flexibly to different wheel dimensions, desiccant materials or climatic conditions; the simplified model requires no knowledge of underlying equations and modeling language utilized and can be used for a first assessment of the potential of a desiccant cooling system in a certain location or for the use within online control systems. The coexistence of both models ensures that information tailored to the users' needs are made available. The validity of the physical model, and therewith the simplified model, is ensured through comparison with measurement obtained from a hybrid air conditioning system situated in northern Europe. The demonstration plant combines the advantages of a dedicated outdoor air system (DOAS) with the advantages of the common hybrid desiccant system to allow for energy efficient air conditioning in one installation. The availability of primary measurement data is extremely valuable to the process of model validation because knowledge about uncertainties and bias in measurement data unlikely to be known for secondary data can be used to understand and validate model results. A comparison of simulation results from the physical model to measurement data from the demonstration plant shows good compliance for a typical day of wheel operation after adjusting relevant model parameters.

[1]  Gerhard Schmitz,et al.  Enhancement of a Modelica Model of a Desiccant Wheel , 2008 .

[2]  R. Nordman.  IEA Technology Roadmap - Energy-efficient Buildings : Heating and Cooling Equipment , 2011 .

[3]  L. G. Berglund,et al.  Comfort and humidity , 1998 .

[4]  Les Kirkup,et al.  An Introduction to Uncertainty in Measurement: Using the GUM (Guide to the Expression of Uncertainty in Measurement) , 2006 .

[5]  Antonio Carrillo-Andres,et al.  A new approach to the effectiveness method for the simulation of desiccant wheels with variable inlet states and airflows rates , 2013 .

[6]  Arif Hepbasli,et al.  Exergetic modeling and experimental performance assessment of a novel desiccant cooling system , 2011 .

[7]  Carlo Roselli,et al.  Experimental analysis on the dehumidification and thermal performance of a desiccant wheel , 2012 .

[8]  Stanley A. Mumma,et al.  Designing Dedicated Outdoor Air Systems , 2001 .

[9]  Hadi Pasdarshahri,et al.  The effects of operational conditions of the desiccant wheel on the performance of desiccant cooling cycles , 2010 .

[10]  Fred L. Collopy,et al.  Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .

[11]  Jae-Weon Jeong,et al.  Direct Digital Temperature, Humidity, and Condensate Control for a Dedicated Outdoor Air-Ceiling Radiant Cooling Panel System , 2005 .

[12]  Wimolsiri Pridasawas,et al.  Solar-driven refrigeration systems with focus on the ejector cycle , 2006 .

[13]  Dirk Brade.  A generalized process for the verification and validation of models and simulation results , 2004 .

[14]  Marco Beccali,et al.  Energy performance evaluation of a demo solar desiccant cooling system with heat recovery for the regeneration of the adsorption material , 2012 .

[15]  Stanley A. Mumma,et al.  Ceiling Panel Cooling Systems , 2001 .

[16]  William M. Worek,et al.  NUMERICAL SIMULATION OF COMBINED HEAT AND MASS TRANSFER PROCESSES IN A ROTARY DEHUMIDIFIER , 1993 .

[17]  Gerhard Schmitz,et al.  Modeling of Desiccant Assisted Air Conditioning Systems , 2005 .

[18]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[19]  Pradyot Patnaik,et al.  Handbook of Inorganic Chemicals , 1997 .

[20]  A. Chunovkina.  Measurement error, measurement uncertainty, and measurand uncertainty , 2000 .

[21]  P. Ole Fanger,et al.  Human requirements in future air-conditioned environments , 2001 .

[22]  Marcus S. Fisher.  Software verification and validation - an engineering and scientific approach , 2006 .