Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved International Review of Mechanical Engineering, Vol. 5, N. 2 Special Issue on Heat Transfer
301
TABLE II
M
EAN
R
ELATIVE
A
ND
A
BSOLUTE
E
RRORS
O
F
N
EURAL
N
ETWORK
M
ODEL
Variables Feedforward Neural
Network model Radial Basis Neural
Network Model Mean
Relative Error
Mean Absolute
Error Mean
Relative Error
Mean Absolute
Error T
2
0.0184 °C 25.35
0.0108 °C 9.68
RH
2
0.3743 1.152 0.3935 1.227 T
9
0.0194 °C 3.39
0.0178 °C 3.124
RH
9
0.2834 0.763
0.366 0.966
II. Design Conditions
The design of the desiccant cooling system is controlled by many operating conditions.
Referring to Figures 1 and 2, the following parameters may be used as a basis for designing the system: ambient
conditions, inside room conditions, regeneration air temperature before the dehumidifier, supply and return
air flow rates, and design sensible and latent cooling loads:
Ambient conditions: Ambient conditions are based on ARI Standard 1060 2005. These values are T
1
DB=35 °C, T
1
WB=24 °C.
Inside room conditions: Recommended standard design conditions for a residential air conditioner are
based on ARI Standard 1060 2005 and the values are T
5
DB=26.7 °C, RH
5
= 50. Regeneration air temperature: Hence, fixing the final
state of air for the regeneration process would give a better picture of the influence of this constraint on the
cycle. In this study, this was fixed at various temperatures 75, 90, 100
°C. These temperatures are also proven acceptable
according to the studies performed on a rotary dehumidifier.
Supply and regeneration air flow rates: Modelling of the dehumidifier was performed taking into account that
the system runs with volume air flow ratio between regeneration and supply side r, equal or less than 1
one.
Sensible and latent cooling loads: Based on the potential application of the system on different building
uses the Sensible Heat Factor, hence the sensible to total heat ratio was varied.
III. Ventilation Cycle Analysis
Referring to Figure 1, warm and wet ambient air is introduced in the process air at State 1.
This process air is dried, while it passes over the desiccant, resulting in hot, dry air as it exits the
dehumidifier of State 2. This increase in temperature is due to the release of
heat of condensation of the water vapour when moisture is removed by the desiccant material.
The air of State 2 is then heat exchanged with room air that is adiabatically humidified to State 6 to create air
at State 3. This air is then humidified to State 4.
For the regeneration air stream, indoor air at State 5 is humidified to State 6.
This air is then heat exchanged with the process air stream at State 2 to produce air at State 7.
This air is then heated to the regeneration State 8. After regenerating the desiccant, air at State 9 is then
exhausted back to the outdoors. Following, the ventilation cycle for the three
aforementioned models of the desiccant wheel is presented in the psychrometric chart bellow, developed
for the Lab of Applied Thermodynamics of 40 kW cooling capacity and 30 of latent loads.
Moreover, all the components are considered as ideal. From the psychrometric chart bellow it could be
noticed that there is an increase in dry bulb temperature of State 2 due to the release of heat of condensation of
the water vapour when moisture is removed by the desiccant material.
This release of heat after dehumidification is greater in Beccali’s and NNMDCS’ models interpolation of
experimental results, resulting also in greater Coefficient of Performance for the whole cycle.
The non-linear analogy method of Bank’s overestimates the dehumidification capability of the
desiccant wheel.
Fig. 4. Psychrometric chart of the ideal ventilation cycle using neural network’s, Beccali’s and Banks’ models
IV. Effects of Components Characteristics
on System Performance
Due to the fact that Ventilation Cycle is an open cycle working fluids are open to the atmosphere, its
performance will be dependent on the thermodynamic state of the air being processed as well as on the
regeneration temperature of the desiccant wheel.
Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved International Review of Mechanical Engineering, Vol. 5, N. 2 Special Issue on Heat Transfer
302 IV.1.
Effect of Regeneration Temperature and Sensible Heat Factor Fluctuation on System Performance
Fig. 5a and Fig. 5b show the fluctuation of the Coefficient of Performance with the Sensible Heat Factor
for 75, 90, 100 °C regeneration temperatures and five
values of ratios ratio of regeneration to process mass flows.
For Beccali’s model which is based on manufacturers data, the COP values drop as the ratio increases.
This drop is similar to the neural network model as both are based on actual measurements of desiccant
wheels. COP decrease between the two models is from 1-1.6.
Also small fluctuations in the COP values have been identified with changing SHF Sensible Heat Factor
values. The fluctuations are from 10.3 to 47.3,
approximately the same for the two models. From the Figures 5a,b it could be derived that COP
always decreases with regeneration temperature increase in all examined models.
a Beccali model b Neural Network Model for a Desiccant Cooling System NNMDCS
Figs. 5. a, b Coefficient of Performance fluctuations with the Sensible Heat Factor for 75, 90, 100 °C regeneration temperatures
and five values of ratios
IV.2. Effects of Heat Exchanger and Evaporative
Cooler Effectiveness on System Performance The performance of heat exchanger is critical to the
successful operation of the desiccant cooling system. The heat exchanger not only cools the process air
stream, providing cooling capacity but also preheats the regeneration air stream, increasing COP.
Fig. 6 shows the effect of heat exchanger’s effectiveness. As the rotary heat exchanger outlet on the
process stream moves to the right 3, 3’, 3’’, 3’’’ with decreasing heat exchanger effectiveness, the enthalpy
difference decreases, thus decreasing the cooling capacity.
On the regeneration stream, the rotary heat exchanger’s outlet moves to the left decreasing its
effectiveness and hence decreasing COP. Fig.7 shows the effect of the evaporative cooler
effectiveness on desiccant cooling system performance.
Fig. 6. Psychrometric chart of the ventilation cycle using NNMDCS model for three values of heat exchanger’s effectiveness
Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved International Review of Mechanical Engineering, Vol. 5, N. 2 Special Issue on Heat Transfer
303
Fig. 7. Psychrometric chart of the ventilation cycle using the NNMDW model for four values of evaporator cooler’s effectiveness
Lowering evaporative cooler effectiveness reduces cooling capacity and thus COP. As the evaporative
cooler effectiveness decreases, the regeneration air is drier with slightly lower relative humidity. The reduced
evaporative cooler effectiveness which results in higher enthalpy difference 3’’-4’’ has as a consequence lower
cooling capacity.
Another conclusion that is derived from the Figures above is that system performance at higher Regeneration
Temperatures is more adversely affected especially in NNMDW model.
V. Conclusion
