Enhancement and integration of desiccant evaporative cooling system model calibrated and validated under transient operating conditions

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Applied Thermal Engineering xxx (2014) 1e13

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Applied Thermal Engineering

journal homepage: www.elsevier .com/locate/apthermeng

Research paper

Enhancement and integration of desiccant evaporative cooling systemmodel calibrated and validated under transient operating conditions

Muzaffar Ali a, b, *, Vladimir Vukovic c, Nadeem Ahmed Sheikh d, Hafiz M. Ali b,Mukhtar Hussain Sahir b

a Energy Department, Austrian Institute of Technology, Giefinggasse 2, 1210 Vienna, Austriab Mechanical Engineering Department, University of Engineering and Technology Taxila, Pakistanc Technology Futures Institute, School of Science and Engineering, Teesside University, United Kingdomd Mechanical Engineering Department, Muhammad Ali Jinnah University, Pakistan

h i g h l i g h t s

� Ideal component models of a DEC system are enhanced in Dymola/Modelica program.� Enhanced models are capable of predicting system performance under real conditions.� Component and system models are calibrated and validated using transient measurements.� Developed DEC system model estimated performance of real system with good accuracy.� Minimum system RMSE and PME validation errors are 0.96 kJ/kg and 1.53%, respectively.

a r t i c l e i n f o

Article history:Received 31 May 2014Accepted 20 October 2014Available online xxx

Keywords:Desiccant cooling systemEquation-based object oriented modelingEnhanced system modelReal system operationDymola/Modelica

* Corresponding author. Mechanical Engineering Dgineering and Technology Taxila, Pakistan. Tel.: þ9fax: þ92 51 9047690.

E-mail addresses: [email protected],(M. Ali), [email protected] (V. Vukovic), [email protected] (H.M. Ali), mukhtar.sahir@uett

http://dx.doi.org/10.1016/j.applthermaleng.2014.10.061359-4311/© 2014 Elsevier Ltd. All rights reserved.

Please cite this article in press as: M. Ali, etvalidated under transient operating conditio

a b s t r a c t

Desiccant cooling systems provide new possibilities in air conditioning technology through the use ofeither solid or liquid sorption air dehumidification. Such systems can be more reliable and environmentalfriendly than the conventional systems. The focus of current study is to enhance the existing ideal systemcomponent models of the desiccant evaporative cooling system (DEC) developed in an equation-basedobject-oriented modeling and simulation program, Dymola/Modelica to address the real componentoperation. Initially, the enhanced component models are calibrated and validated through the transientmeasurements obtained from the real DEC system installation. Afterward, using the validated physicalmodels of individual components, complete system model has been experimentally calibrated andvalidated. The obtained results are in good agreement with the actual measurements at the bothcomponent and system levels. At the system level, in terms of specific enthalpy of supply air, theresulting maximum and minimum root mean square error (RMSE) and mean percentage error (MPE)between the simulated and measured transient performance is 0.96 kJ/kg and 1.53%. The results showthat the enhanced system model is able to predict the performance of the real desiccant cooling systemwith good accuracy. As such, the current paper contributes to the efforts of bringing simulations closer toperformance of real systems under building operating conditions.

© 2014 Elsevier Ltd. All rights reserved.

epartment, University of En-2 (0) 3005316356 (mobile);

[email protected]@gmail.com (N.A. Sheikh), h.axila.edu.pk (M.H. Sahir).

4

al., Enhancement and integrns, Applied Thermal Enginee

1. Introduction

Desiccant cooling systems provide a suitable alternative toconventional heating ventilation and air-conditioning (HVAC) sys-tems especially in hot and humid climatic conditions. The latesttechnological enhancements in desiccant dehumidification andevaporative cooling have opened wider areas of application ofthermally active HVAC products. With various innovative configu-rations and integrations, such systems can facilitate temperatureand humidity control for buildings in a variety of climatic

ation of desiccant evaporative cooling system model calibrated andring (2014), http://dx.doi.org/10.1016/j.applthermaleng.2014.10.064

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Nomenclature

CC cooling capacity (kW)C heat capacity rate (kJ/sK)Cp specific heat capacity (kJ/kg K)Dx difference in absolute humidity (kg water/kg air)Dh difference in specific enthalpy (kJ/kg)H relative humidity sensor (%)h specific enthalpy (kJ/kg)_m mass flow rate (kg/s)N wheel rotation speed (rph)u input control signal (e)S pump speed signal (%)T temperature (�C)dt time interval (s)Qoffset offset heat between isenthalpic and actual process

(kW)Qloss heat loss in heat exchanger (kW)

Greek lettersu air humidity ratio (g of moisture/kg of dry air)ε effectiveness (e)

Subscriptsc colddb dry bulbh hotin inletmon monitorednom nominalo outletsim simulateds supply airw waterwb wet bulbw water

M. Ali et al. / Applied Thermal Engineering xxx (2014) 1e132

conditions. For instance, in humid conditions desiccant coolingmethod has shown its worth with the presence of dehumidifierusually in the form of a rotary wheel made up of desiccant material.Due to the versatile attributes, desiccant cooling technologies arethought to be the solution in the current competitive energymarket.

For energy recovery devices, the prediction of heat transferduring HVAC applications is important. Optimal use of these newgeneration devices requires critical performance analysis based ontest conditions such as ARI Standard 1060-2003 [1]. However, eventhe expensive testing does not ensure optimal performance at allpossible operating conditions [2]. It is impossible to test the overallsystem performance at all operating conditions, thus one has to relyon mathematical models and simulations for performance calcu-lation. Through, the modeling of complete systems is also acomplicated task especially with the presence of processesinvolving heat and mass transfer. In general, the analysis involvescomponent level modeling with assumptions of steady state massand energy balances. For steady state standard tests, the uncer-tainty in the latent and sensible effectiveness of different rotarywheels is determined as ±7% and ±5% respectively [3]. Additionally,few studies determined the effectiveness of an energy wheelthrough development of dimensionless groups [4] and correlations[5] for under steady state operating conditions. On the other hand,critical components of the energy recovery systems, such as

Fig. 1. Schematics of desiccant cooling system

Please cite this article in press as: M. Ali, et al., Enhancement and integrvalidated under transient operating conditions, Applied Thermal Enginee

desiccant wheels, experience transient operating conditions. Fewerstudies explore individual desiccant wheel component level oper-ation under transient conditions. A theoretical model is presentedto predict the effectiveness and uncertainty of an energy wheelusing only data obtained under laboratory transient measurements[2]. Afterward, this theoretical model is validated using effective-ness data from several wheels measured [6]. It has been observedthat the transient analysis of the desiccant wheel helps inimproving the coefficient of performance (COP) [7]. A number ofother studies have highlighted the influence of different parame-ters on the performance of desiccant wheels such as inlet airtemperature, moisture contents, face velocities, wheel thicknessand rotation speed [8,9], even at the actual steady [10] and tran-sient [11] operating conditions. However, no similar study is re-ported at the desiccant cooling system level under transientoperating conditions in buildings, which is the topic of this paper.

Generally, in model-based system simulation, to reduce theoverall model complexity ideal component models are used basedon various assumptions that are difficult to achieve in the realsystem operation. Therefore, the actual system performance isdifferent than predicted through simulations. However, in model-based performance prediction, it is important to simulate thecomponents operation near to reality. Despite significant progresson the component level modeling, the actual component models interms of their real operation in buildings are seldom studied.

with instrumentation of ENERGYbase.


Table 1Component design parameters of ENERGYbase system.

Sr. No DEC component design parameter Design value

1 Desiccant wheel Model SECO 1770,Klingenburg

Air volume flow rate (m3/h) 8240Adsorbent LiClWheel diameter (m) 1.77Wheel depth (m) 0.45Pressure drop (Pa) 164Wheel rotation speed (rph) 20Ambient air (�C/%) 32/40Supply air (�C/%) 46.9/12Regeneration air (�C/%) 70/10Exhaust air (�C/%) 55.1/20

2 Heat wheel ModelRRS-P-16-18-1770-400,Klingenburg

Air volume flow rate (m3/h) 8240Wheel diameter (m) 1.77Wheel depth (m) 0.4Wheel rotation speed (rpm) 10Pressure drop (Pa) 119Effectiveness 0.8Outdoor air (�C/%) 45/14Supply air (�C/%) 31.24/29Regeneration air (�C/%) 28/85Exhaust air (�C/%) 41.62/40

3 Supply and returnspray humidifiers

Robatherm

Air volume flow rate (m3/h) 8240Saturation efficiency (%) 90Dehumidification capacity (g/kg) 4.4Water flow rate (m3/h) 4.0Pump power (kW) 0.8Pressure drop (Pa) 235Supply air (�C/%) 32/33Exhaust air (�C/%) 21.26/89

4 Supply and return fans RH 45CAir volume flow rate (m3/h) 8240Pressure drop (Pa) 600Power (kW) 4.3

M. Ali et al. / Applied Thermal Engineering xxx (2014) 1e13 3

Consequently, the current study is a step forward in simulationanalysis as the ideal component models of a DEC system areenhanced through various functions developed using the real sys-temmeasurements. Additionally, the enhanced component modelsare calibrated and validated at both component and system levelsunder the real transient operating conditions instead of steady statelaboratory conditions. The models are implemented usingequation-based object-oriented environment, Dymola/Modelica[12,13] and component calibration and validation results arecompared with the experimental data using actual transientoperating conditions. However, the component models are nottransient themself but are calibrated and validated under the

Table 2System validity range in terms of ambient and operating conditions of selected days.

Day 30th April 1

Ambient conditions T (�C), 4 (%) Tamb(min,max) 15.0,33.1 24(min,max) 14.4,14.5 3

Process side m (kg/s), T (�C), 4 (%) ms(min,max) 0.2, 2.7 0Tin(min,smax) 17.2,33.5 24s(min,max) 17.5,62.3 3Tsup(min,max) 19.6,25.7 24sup(min,max) 19.9,71.7 4

Regeneration Side m(kg/s), T(�C), 4(%) mr(min,max) 0.06,2.8 0Tret(min,smax) 24.9,27.1 24ret(min,max) 28.9,40.7 4Texh(min,max) 25.4,52.6 14exh(min,max) 7.5,54.9 2


specific transient operating conditions. Finally, the presentedmodel-based simulation study using the enhanced componentmodels proved significant to efficiently predict the performance ofreal system under various building operating conditions.

2. Description of desiccant cooling system

The desiccant evaporative cooling system (DEC) of ENERGYbasebuilding [14] consists of a desiccant wheel, a heat wheel, and twodirect humidifiers i.e. supply humidifier and return humidifiers.The system is designed by “Robatherm” air handling company [15].The whole HVAC system is equipped with around 500 sensorscontinuously measuring all significant parameters at the individualcomponent level. Two data monitoring systems, Siemens DESIGOInsight [16,17] and JEVis [18] are used for data recording from allsensors. The system schematic with instrumentation is shown inFig. 1. Key aspects of technical data of main components of theinstalled DEC system are given in Table 1.

3. Physical component models calibration and validation

Initially, the ideal component models are enhanced by inte-grating the empirical equations developed through the real timetransient data obtained by the monitoring systems of ENERGYbasebuilding. The transient measurement data sets are based on the sixselected days in which the DEC system operates in different modes[11]. Table 2 shows the wide range of the DEC system operating andclimate conditions applied in this study. Then, the enhancedcomponent models are calibrated and validated. Finally the vali-dated enhanced component models are used to develop a DECsystem model and then that system model is calibrated and vali-dated using the selected data. This implemented strategy is shownin Fig. 2.

3.1. Desiccant wheel model

The desiccant wheel is a key component of the DEC system. Adetailed model for desiccant wheel is developed and validated inauthors' previous work [11]. The model is capable of accuratelyestimating the performance of any real desiccant wheel under widerange of transient operating conditions in dehumidification andenthalpy modes.

3.2. Humidifier component model (direct evaporative cooler)

The ENERGYbase DEC system consists of two humidifiers, sup-ply humidifier and return humidifier. Both humidifiers have iden-tical physical design but different operating conditions withdifferent control strategies.

9th June 29th June 02nd July 06th July 09th July

3.7,35.3 21.2,36.1 21.6,36.6 23.2,36.7 21,30.13.9,77.7 32.4, 100 27.5,100.0 29.8,85.1 40.4,100.2, 2.1 0.1, 2.2 0.02,2.5 0.9,2.1 0.2,2.13.9,36.1 22.7,36.4 23.8,36.2 25.1,35.8 22.3.30.43.6,61.1 32.8,63.5 30.1,61.7 31.9,58.2 38.5,71.42.2,26.3 21.7,26.9 22.6,29.5 20.5,27.2 20.1,28.46.4,75.8 42.8,74.3 43.0,64.6 35.1,70.3 32.9,81.5.06,2.8 0.02, 2.7 0.1,2.1 0.6,2.4 0.1,2.55.2,27.6 25.1,27.0 25.8,29.1 25.2,28.3 25.1,27.72.5,53.5 39.8,58.3 42.7,51.0 40.9,50.3 44.3,53.53.7,57.8 25.4,52.6 25.4,52.6 25.4,52.6 25.4,52.65.4,52.6 12.5,62.3 9.2,61.8 9.7,60.3 9.7,67.2


Fig. 2. Flow chart of the implemented strategy.

Fig. 3. Humidifier monitoring setup.


The component model for both humidifiers is used from theclass of ‘Mass Exchangers’ available in LBL Buildings library [19].The quantity of water added to the air stream is used as a modelparameter. In addition, air temperature and its absolute humidityare also specified as model inputs. The evaporative cooling processis assumed isenthalpic. The model can act as humidifier or dehu-midifier depending on the value of humidity input control signal, u.A positive value of u indicates humidification. The amount ofmoisture exchanged is determined using Equation (1) [19]. Thevalue to the water temperature port is assigned conditionally eitherby the user or a default value of 20 �C is used.

mw ¼ u*m nom (1)

Each humidifier is equipped with four sensors, two at inlet andtwo at outlet, each pair measuring temperature and relative hu-midity at both points. Consequently, the data set of six selected daysconsists of air temperature and relative humidity along with therotation speed of spray pump. The monitoring setup of both hu-midifiers is shown in Fig. 3.

In the actual monitoring set-up water mass flow rate from thespray pump was not measured. The water mass flow rate dependson the inlet conditions of air in terms of its temperature, absolute


humidity, and mass flow rate along with the rotational speed signalof spray pump. Multiple Linear Regression (MLR) [20] is used todetermine an appropriate correlation between all four input vari-ables (uin, S, Tin, and _m) and monitored humidificationwith respect


Fig. 4. Implementation of MLR to determine the function dependence on: (A) absolute humidity at inlet, (B) temperature at inlet, (C) spray pump position, and (D) mass flow ratewith respect to absolute humidity difference across humidifier.


to the absolute humidity difference across the humidifier. Thefunction is determined as an empirical equation based on thedependence of each input variable through MLR. MLR is appliedthrough Excel in which the significance of each input variable isdecided based on its P-value. The variable having P-value greaterthan 0.05 has less significance thus that variable is not consideredin the function. An example of such implementation based on thetransient data of 6th July is shown in Fig. 4. It can be observed thatthe predicted values of all variables are well in agreement with theactual values having R2 of 0.89. Furthermore, the resulted P-valuesof uin, S, Tin, and _m are 0.000132, 2.4E-49, 2.22E-45, and 1.09E-06,showing the strong influence of all four variables on Dx.

The resulting function is defined in Equation (2).

Dx ¼ 0:335� 0:365uin þ 0:0155Sþ 0:145Tin þ 0:523 _m (2)

However, in few other cases all variables have not shown sta-tistically significant influence on Dx having P-values greater than

Fig. 5. Implementation of MLR to determine the function dependence on: (A) absolute humwith respect to specific enthalpy difference across humidifier.


0.05. For such cases, MLR is applied again with only those variableshaving P-value less than 0.05 to determine the final empiricalequation. Finally, such empirical equations for both supply andreturn humidifiers are determined based on the transient mea-surement data of each respective day. Afterward, all developedempirical equations are incorporated in the model code for thecalibration and validation of humidifier model through Dymola/Modelica. The resulted value of Dx varies from 0.0005 to 0.0035 kgwater/kg of dry air.

The LBL Buildings model of the humidifier is based on isen-thalpic humidification process. However, the humidifier operationin real system deviates from the constant enthalpy process with asignificant difference due to various heat losses. Therefore, in orderto account for deviations, the ideal humidifier model is enhanced.

A variable ‘Qoffset’ is introduced in the model to account for theefficiency loss due to the difference between isenthalpic and realhumidification processes. The Qoffset is defined in terms of acustomized function that relates the enthalpy difference, Dh, to

idity at inlet, (B) temperature at inlet, (C) spray pump position, and (D) mass flow rate


Fig. 6. Graphical representation of calibration and validation setup of humidifier model.

Table 4Calibration errors days of return humidifier model.

Sr.No Calibration day RMSE (kJ/kg) MPE (%)

1 30th April 0.33 0.12 19th June 0.75 0.063 29th June 0.52 0.054 02nd July 0.57 0.025 06th July 0.51 0.056 09th July 0.33 0.04

Table 5


Qoffset based on the monitoring data of a real humidifier underoperation in ENERGYbase. The function's dependence on variousinlet variables is determined by MLR. An example of implementa-tion of MLR for supply humidifier based on the measurement dataof 19th June is shown in Fig. 5. Fig. 5 shows that all four inlet var-iables have influence on Dh. P-values of uin, S, Tin, and _m are 8.39E-17,1.8E-07, 2.28E-05, and 2.05E-13. The resulting empirical functionis defined in Equation (3).

Dh ¼ �1:542� 0:701uin � 0:063Sþ 0:285Tin þ 2:43 _m (3)

The values of Dh vary between 1 and 4 kJ/kg for the consideredmeasurement data.

3.2.1. Calibration and validation of supply and return humidifiersThe supply and return humidifier component models are cali-

brated for determination of water mass flow rate sprayed in thehumidifier. The monitoring data of six selected days with 1 mintime interval are used in terms of air dry bulb temperature, absolutehumidity, and mass flow rate at the humidifier inlet. In order tocalibrate and validate the enhancedmodel, the developed functionsof absolute humidity Dx from Equation (2) and specific enthalpy Dh

from Equation (3) are implemented in the humidifier model ascontrol inputs shown in Fig. 6. For simulation, the input variables ofeach function such as uin, S, Tin, and _mof the considered data areinserted in the program input data file ‘Hum_Data’. Moreover, theinput data file also contains other operating parameters requiredfor model calibration and validation. The simulation setup retrievesall relevant simulation parameters from ‘Hum_Data’ input file tofind the simulated output parameters of the model.

The root mean squared error (RMSE) between the simulated andmonitored specific enthalpy at the humidifier outlet is used as anobjective function for model calibration and validation determinedby Equation (4).

Table 3Calibration errors of supply humidifier model.

Sr.No Calibration day RMSE (kJ/kg) MPE (%)

1 19th June 0.37 �0.022 29th June 0.99 �0.073 02nd July 1.06 �0.024 06th July 0.70 �0.085 09th July 0.93 0.02


fRMSE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðhosim � homonÞ2=dt

q(4)

The time interval, dt is summed up for all time steps i.e.12 h/day.The resulted calibration errors in terms of RMSE of both supply andreturn humidifiers for all days are presented in Tables 3 and 4. Inaddition to RMSE, calibration errors are also defined with respect tomean percentage error (MPE) based on the simulated and moni-tored values of specific enthalpy at the outlet calculated by Equa-tion (5).

fMPE ¼ ðhosim � homonÞ=hosim*100 (5)

The results show that the simulated values are in good agree-ment with the monitored values having minimum RMSE and MPEof 0.367 kJ/kg and 0.018% for the supply humidifier. For the returnhumidifier, minimum errors are 0.332 kJ/kg and 0.02%. Negativevalues in Table 3 show under prediction of simulated valuescompared to the monitored data.

The component models of both humidifiers are validated aftertheir calibration. The design values predicted by the calibratedmodel along with the established function of each calibrated day

Average validations errors of supply and return humidifier models.

Supply humidifier Return humidifier

AverageRMSE (kJ/kg)

Abs. averageMPE (%)

AverageRMSE (kJ/kg)

Abs. averageMPE (%)

30th April e e 0.60 0.4319th June 2.38 2.07 0.80 0.6829th June 1.16 1.00 0.66 0.6702nd July 1.14 0.88 0.60 0.5706th July 2.02 1.70 0.85 1.1509th July 1.36 1.54 0.72 0.75


Fig. 7. Comparison between monitored and simulated results of ideal and enhanced supply humidifier model based on 2nd July (A), specific enthalpy (B), and temperature andabsolute humidity at outlet (C).


are used for model validation. Then, the calibrated model is vali-dated based on the transient measurement data of other remainingdays except the calibration day. Thus, in the current approach,initially the regression functions of each component model areestablished for each day and then the models are validated againsteach other day. The average values of each day of validation errors

Fig. 8. Comparison between monitored and simulated results of ideal and enhanced returnabsolute humidity at outlet (C).


of supply and return humidifier component models with respect toRMSE andMPE are given in Table 5. The resulting averageminimumvalidation errors in terms of RMSE of supply and return humidifiersare 1.14 kJ/kg and 0.6 kJ/kg. Moreover, the resulted minimum errorsof supply and return humidifiers with respect to MPE are 0.88% and0.43%.

humidifier model based on 30th April (A), specific enthalpy (B), and temperature and


Table 6Range of operating parameters for the performance analysis.

Parameters Baselinevalues

Parametricvariations

Temperature of ambient air, Tamb (�C) 30 e

Humidity ratio of ambient air, uamb (g/kg) 10 e

Inlet temperature of air, Tsin (�C) 30 25e40Inlet relative humidity of air, 4a (%) 50 35e65Air mass flow rate, ma (kg/s) 2.1 1.7e3.0Water mass flow rate, mw (kg/s) 2.5 1e2.7Water temperature (�C) 20 18e30

Fig. 10. Monitoring data setup for the heat wheel.


The validation results show that the simulated values are ingood agreement with the monitored values. This comparison forsupply and return humidifiers is shown in Figs. 7 and 8. Actualmodel's simulated and monitored results of selected days withminimum errors in term of specific enthalpy, dry bulb temperature,and absolute humidity are presented in these figures. It can benoted that the trend of actual and simulated specific enthalpy at theoutlet matches quite neatly as shown in Figs. 7(A) and 8(A). Addi-tionally it is observed that the simulated results of the ideal hu-midifier and heat wheel models without the introducedenhancement caused higher deviations leading to greater calibra-tion error compared to monitored values. However, such deviationis only shown for humidifier models in Figs. 7 and 8 as an example.While the enhanced model shows better match with the observeddata in all the cases. Thus in the current study it is proved that theenhanced models developed can adequately represent the realoperation of physical system components.

Fig. 9. Performance analyses of a humidifier in terms of cooling efficiency, temperature drophumidity (B), air mass flow rate (C), water flow rate (D), and water temperature (E).


3.2.2. Parametric analysis of humidifier component modelTypically, performance of direct humidifiers is described by the

cooling efficiency defined by Equation (6). In addition, temperaturedrop and increase in absolute humidity are also an indication ofhumidification capacity. Such performance factors of direct hu-midifiers are influenced by various inlet operating parameters, likeair dry bulb temperature, air relative humidity, air mass flow rate,water temperature, and water mass flow rate.

hc ¼ ðTdbin � TdboutÞ=ðTdbin � TwbinÞ (6)

In the current investigation, parametric performance analysis ofa humidifier is carried out using a validated model. Effects ofvarious parameters on cooling performance are evaluated. Duringanalysis, a specified parameter was varied while keeping the othervariables constant. The variation range of each parameter is definedin Table 6 based on the real system operation installed in ENER-GYbase. Performance analysis of the humidifier model based ondifferent parameters is presented in Fig. 9.

and absolute humidity rise with respect to: air inlet temperature (A), air inlet relative


Fig. 11. Graphical representation of calibration and validation setup of heat wheel model.


Fig. 9(A) confirms the well-established facts about the operationof direct humidifiers [21e23]. High inlet air dry bulb temperatureindicates dry air that favors the evaporation process, thusincreasing the cooling efficiency. However, higher air relative hu-midity decreases temperature drop and thus the cooling efficiencyas shown in Fig. 9(B). In addition, effects of air mass flow rate arealso presented in Fig. 9(C). It is clear that lower air mass flow ratecauses greater temperature drop that increases cooling capacity.High water flow rate also enhances the possibility of more tem-perature and humidity drops as shown in Fig. 9(D). Finally, effectsof varying water temperatures are also analyzed as such variationsoccur in commercial applications. High water temperature de-creases the temperature difference between air and water whichcauses reduction in the evaporation process as shown in Fig. 9(E).Thus cooling capacity decreases. All trends verify that the enhancedhumidifier component model predicts the performance of directhumidifiers accurately.

3.3. Heat wheel component model

Heat wheel is another key component of the desiccant coolingsystems. It helps to reduce the temperature of the process airheated during dehumidification process in the desiccant wheel.

Heat wheel component model is used from the ‘Heat Exchanger’class of LBL Buildings library. It is a model of heat exchanger withconstant effectiveness. However, the existing library model con-siders no heat loss to ambient compared to the real system oper-ation in which heat losses are observed. Therefore the model isenhanced with an additional heat loss variable Qloss.

In the real ENERGYbase installation, heat wheel is equippedwith nine sensors similar to the desiccant wheel, as shown inFig. 10. Out of the eight sensors, four for temperature and four forrelative humidity measurements are installed at the inlet and

Table 7Calibration errors of heat wheel model.

Sr.No Calibration day RMSE (�C) MPE (%)

Pro-side Reg-side Pro-side Reg-side

1 30th April 0.04 0.81 0.07 1.012 19th June 0.03 0.64 0.04 1.033 29th June 0.45 0.59 0.47 0.214 02nd July 0.83 0.66 0.75 �0.105 06th July 0.28 0.49 0.51 �0.016 09th July 0.08 0.48 0.08 0.79


outlet of primary and secondary air streams. One sensor is installedfor determination of the rotation speed of the heat wheel. Transientmeasurement data of six selected days with 1 min time interval areused for the calibration and validation of heat wheel model.

Effectiveness of heat wheel is determined by the monitoringdata through typical Equation (7) according to ARI standard 1060[1].

ε ¼

8>>><>>>:

ChðTho eThi ÞCcðThi eTci Þ

; if Cc <Ch

ChðTho eThi ÞChðThi eTci Þ

; if Ch <Cc

(7)

In Equation (7), C is the heat capacity rate, i.e. C ¼ _mCp.Heat loss, Qloss through the heat wheel is determined by

calculating the heat transfer difference between process andregeneration air streams based on the measurement data ofENERGYbase system installation. Then linear regression is appliedon this difference with respect to DTmax to find the empiricalequations for all six selected days as defined in Equation (8) as anexample. Finally, these equations are implemented in the modelcode to enhance the ideal model.

Qloss ¼ 0:357DTmax � 1:658 (8)

DTmax is maximum available temperature difference across the heatwheel, i.e. Thin e Tcin.

3.3.1. Calibration and validation of heat wheel component modelFor model calibration and validation, the developed function

Qloss from Equation (8) is introduced as an input to the simulationmodel. The values of the function's variable DTmax based on thecomplete simulation data are inserted in the model data file

Table 8Average validations errors of heat wheel model.

Validation day Validation errors

Average RMSE (�C) Abs. Average MPE (%)

30th April 1.27 2.5619th June 0.87 1.4429th June 0.47 0.1002nd July 0.59 1.2106th July 0.63 1.2209th July 0.55 0.41



‘HE_Data’ as shown in Fig. 11. For program simulation, the modelretrieves all data values from ‘HE_Data’ file including Qloss, Tin, and_m. During simulation, the simulated outlet temperatures of bothfluid streams of the heat wheel model are continuously recordedfor model calibration and validation. The objective function isdefined by Equation (9) in terms of RMSE of outlet temperaturedifference between simulated and monitored values of both pro-cess and regeneration air streams. In addition, MPE is also deter-mined by Equation (10).

fRMSE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðhosim � homonÞ2=dt

q(9)

fMPE ¼ ðTosim � TomonÞ=Tosim*100 (10)

Calibration errors in terms of RMSE and MPE of all selected daysare given in Table 7. It is observed that the resulting errors in bothforms are quite small. Afterward, the calibrated model is validatedbased on the transient measurements using the approachdescribed before. The resulted validation errors in form of RMSEand MPE are given in Table 8. The errors show that the simulatedvalues are in good agreement with the monitored values and theresulted minimum average errors are 0.475 �C and 0.1% withrespect to RMSE and MPE on 29th June, 2012. Additionally, com-parison between the simulated and monitored values with respectto outlet temperatures of process and regeneration sides is pre-sented in Fig. 12 based on the validation results of 29th June, 2012.Fig. 12(A) shows that the simulated outlet temperature is wellmodeled using the enhancedmodel using linear fit. While the trendof outlet temperatures of air are also neatly modeled and comparedin Fig. 12(B and C).

4. Calibration and validation of desiccant evaporative coolingsystem

Following the calibration and validation of enhanced individualcomponent models, validation is performed at the system level.

Fig. 12. Comparison between monitored and simulated results of heat wheel model based o(C).


Monitoring setup of the system equipped with twenty sensors isshown Fig. 1.

At the system level, a model is developed with validatedcomponent models as shown in Fig. 13. In the system model,functions of different individual components are also implementedbased on their resultedminimumvalidation error. The regenerationtemperature is maintained at 70 �C in view of maximum allowabletemperature for LiCl adsorbent. For calibration and validation of thesystem model, transient measurement data of four days are useddue to few operational problems at the system level on 30th Apriland 06th July, 2012. Two objective functions are used for the systemmodel calibration and validation. The first objective function is theroot mean squared error (RMSE) between the monitored andsimulated outlet specific enthalpy of supply air. In addition, cali-bration errors based on specific enthalpy are also presented interms of MPE. Calibration errors in both forms of specific enthalpyare given in Table 9. Secondly, RMSE between the monitored andsimulated system cooling capacity is considered as an objectivefunction through Equation (11). Calibration errors with respect tosecond objective function are given in Table 10.

fob ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðCCsim � CCmonÞ2=dt

q(11)

Here CC is cooling capacity of the system determined by Equation(12) [24].

CC ¼ ms

�hamb � hsupply

�(12)

The obtained system model calibration errors presented inTables 9 and 10 are slightly higher than those for the individualcomponent models presented in Tables 2e4, 6 and 7. This is due tothe fact that the calibration errors at individual component modellevels are compiled at the system level. The calibrated values ofMPE in Table 9 showed that the simulated system performance isunder estimated compared to the actual with minimum absolutevalue of 7.79%.

n 29th June (A), specific enthalpy (B), and temperature and absolute humidity at outlet


Fig. 13. Graphical representation of calibration and validation setup of desiccant evaporative cooling system model.

Table 9Calibration errors based on outlet specific enthalpy.

Sr.No Calibration day Calibration error,RMSE (kJ/kg)

MPE (%)

1 19th June 1.01 1.492 29th June 1.39 1.693 02nd July 2.63 3.204 09th July 0.88 1.15


For model validation, three days transient measurement data isused excluding the calibration day. Again, two objective functions,outlet specific enthalpy and cooling capacity are used in terms ofRMSE and MPE. Table 11 presents validation errors of the systemmodel considering specific outlet enthalpy and cooling capacity asobjective functions. For specific outlet enthalpy, resulting averageminimum RMSE and MPE are 0.965 kJ/kg and 1.53%. Therefore, it isconcluded that simulated values are in good agreement with themonitored results. Although validation errors for cooling capacityare slightly high with average minimum values of 2.872 kW and6.76%, but are within the acceptable limit.

Table 10Calibration errors based on system cooling capacity.

Sr.No Calibration day Calibration error,RMSE (kW)

MPE (%)

1 19th June 2.63 �14.362 29th June 3.18 �14.463 02nd July 3.79 �18.564 09th July 1.95 �7.79


Finally, the system model validation results of few exemplarydays are presented in Figs. 14 and 15. Fig. 14 shows the simulatedand monitored values in terms of outlet specific enthalpy, tem-perature and absolute humidity. In addition, simulated and moni-tored results based on cooling capacity are also presented in Fig. 15.Simulated and monitored cooling energy delivered by the wholesystem is also shown. All validation results confirm that thedeveloped model of desiccant cooling system is capable of pre-dicting the real system performance with good accuracy. Howeverslightly higher values for absolute humidity (Fig. 14(C)) can beobserved compared to monitored values with a correspondingslightly lower predicted outlet air temperature compared tomonitored data. Similar slight discrepancies can be observed inFig. 15(B and C) for system cooling energy delivered. On the otherhand the qualitative trends are remarkably similar. The observeddifference between the model and monitored data is fairly smalland can be due to variations in sensor data and associated externalfluctuations.

Table 11Validation errors in terms of specific outlet enthalpy and cooling capacity.

Specific output enthalpy Cooling capacity

AverageRMSE (kJ/kg)

Abs. averageMPE (%)

AverageRMSE (kJ/kg)

Abs. averageMPE (%)

19th June 1.83 1.53 3.17 6.7629th June 0.96 1.61 2.87 11.7702nd July 1.07 2.97 3.95 23.1309th July 1.11 1.89 3.30 17.59


Fig. 14. Comparison between monitored and simulated results of system model based on 19th June (A), specific enthalpy (B), and temperature and absolute humidity at outlet (C).


5. Conclusions

In the current work, calibration and validation of a desiccantcooling system model based on the real building system design isperformed at both component and system levels. The real transientoperating conditions are used. The existing state-of-the-art likehumidifiers and heat wheel component models can only be usedfor the ideal component operation. However, the actual perfor-mance of such components often significantly differs from the ideal

Fig. 15. Comparison between monitored and simulated cooling capacity of syste


operation in reality. Therefore, humidifier and heat wheel compo-nent models are enhanced to match the performance in realoperation. Thus, additional variables are added in the models thatcould be determined based on the real transient measurementsobtained from the building monitoring system.

The enhanced component models are calibrated and validatedwith six different days transient operating conditions. Calibrationand validation errors are presented in two categories, RMSE andMPE. The resulting errors of all componentmodels are around 1% or

m model (A), system cooling capacity (B), and cooling energy delivered (C).



less, showing that simulated values are well in agreement with themonitored results. Following the calibration and validation at thecomponent level, overall desiccant cooling system model isdeveloped using the validated component models. Then systemmodel is validated with respect to outlet specific enthalpy andsystem cooling capacity as objective functions. The resulting RMSEand MPE are slightly higher than at component level but wellwithin the acceptable range, allowing more accurate simulations ofreal building desiccant cooling system performance.

Acknowledgements

The authors express profound gratitude to the Energy Depart-ment of Austrian Institute of Technology (AIT), Vienna, Austria forits support to perform such study on their ENERGYbase desiccantevaporative cooling system.

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Enhancement and integration of desiccant evaporative cooling system model calibrated and validated under transient operating conditions