Design algorithm - WIDE University cooling (nocturnal) Tw Twti Twt1 Tr Tc qL= ε σAw ( Tw4 –Ts4)...
Transcript of Design algorithm - WIDE University cooling (nocturnal) Tw Twti Twt1 Tr Tc qL= ε σAw ( Tw4 –Ts4)...
Lecture‐4December 17‐2007
Kamaruddin AbdullahLaboratory of Solar Conversion TechnologyFaculty of EngineeringDarma Persada [email protected]
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Design algorithm
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Decide product quality specification
Thermo‐physical properties of products
Mathematical modeling, simulation and optimization
Performance test
Lab and field test
ε<Ε−04?
Print results
no
yes
ε<Ε−04?
no
yes
Measurement of thermal diffusivity of spherical product
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T1
T2
T3
T4
∂T/∂θ= ∇2 αT……………………[1]
∂T/∂θ = α (1/r2) ∂/∂r(r2∂T/ ∂r)….[2]
IC: θ=0, T=To for 0<r< R
BC: θ≥ 0, at r=R, T= C or T=f (θ)
Numerical Solution:
Let Tr=Θ then
α = Θj+1i /{ ∆t/∆x2 {Qj
i‐1 ‐2Qji +Qj
i+1 }.[3]
)6....(........../}/{/ WdtDdMdzdM ρπ−= )6....(........../}/{/ WdtDdMdzdM ρπ−=
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Apparent thermal diffusivity, α of Indonesian fruits
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No.Products α(cm2/h) References
1. Guava 1.745 Setiawan, 19802. Sapodilla 0.49 Setiawan, 19803. Avocado 1.29 Setiawan, 19804. Malang Apples 1.0 Setiawan, 19805. Siyem Orange 4.33 Gde Handi, 19826. Water melons:
•Sengkaling•Quality•Jumbo
0.0850.0750.12
Harsitorukmi, 1987
7. Melon 0.102 Andry, 19928. Mangoes 1.2-6 Arnida, 19919 Pure water 4.97
Solar cooling (nocturnal)
Tw
Twti
Twt1
Tr
Tc
qL= ε σ Aw ( Tw4 – Ts4)
Tp
Main componentShallow water pond 4 m x 6 m x 0.05 mRoom dimension: 4 m x 3 m x 2.58 mRecycling water pump 100 W (45 liter/min.)Cooling tower: blower 1/3 Hp,Q=0.72 kg/s
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Simulation studyCase 1. The energy balance at niget time cooling
Pond water temperature TwMpw Cpw dTpw/dt = hwa Apw (Ta‐Tpw)
+ mi Cpw (Tpwi ‐ Tpw) −ε σ Apw (Tpw4 ‐ Ts4) . (1)
Sky temperature, Ts Ts4 = ε Ta4 (1 ‐ 0.261 exp(‐0.000777) (Ts ‐ 273)2 (2)
Cooling coil temperature, Tcw
Mcw Cpw dTcw/dt = hca Acw (Tr‐Tcw) ‐mi Cpw (Tcw ‐ Tpw) . .(3)
Storage room temperature, TrMr Cpa dTr/dt = U Ad (Ta ‐ Tr) + Qresp ‐ hca Acw (Tr‐Tcw) ‐ hpa Ap N (Tr‐Tp) ‐ Qc ..(4)
Product temperature TpMp Cpp dTp/dt = hpa Ap N (Tr‐Tp) . .(5)
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Simulation (contd.)Case 2. Cooling during the day, Cooling coil temperature Tcw
Mcw Cpw dTcw/dt = hca Acw (Tr‐Tcw) . (6)Room air temperature, Tr
Mr Cpa dTr/dt = U Ad (Ta ‐ Tr) + Qresp ‐ hca Acw (Tr‐Tcw) ‐ hpa Ap N (Tr‐Tp) –Qc (7)
Product temperatute Tp:
Mp Cpp dTp/dt = hpa Ap N (Tr‐Tp) .. (8)
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Simulation results
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05
1015202530
0 12 24 36 48
Elapsed time
Tem
pera
tre
(C)
TaTr2Tr3Tr4
Simulation results when water pond area Aw, was increased 1.2 times and twice, respectively, when the room was loaded with 1000 kgs of potatoes and auxiliary cooling was applied at a rate of 746 W (Tr2 at Aw =25.35 m2, Tr3 at 1.2 x Aw and Tr4 with 2 x Aw)
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11:00 08:00‐22:00 Nokturnal
23:30‐02:30
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18:00
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Cooling tower
system
Temporary storage
Water pond
Vegetable field
Nocturnal cooling unit at Candikuning, Bali
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load: 67,65 kg bananasTest date:16-18 August 2002
1012141618202224262830
05.0011.00
17.0023.00
05.0011.00
17.0023.0
005.00
11.0017.00
Local time
T (o C
)
TrT aTcw
Test results for banana storage, Malang
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Cooling of vegetables using hybrid nocturnal cooling
0.0
5.0
10.0
15.0
20.0
25.0
30.0
0.0
1.0
4.0
7.3
12.2
18.2
24.2
30.5
37.5
44.5
50.5
Time (hrs)
T(o C
)
TevapTcTu-tTu-bTdblingTkentang
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0,05,0
10,015,020,025,030,0
Tem
pera
ture
(C )
Time (hr.)
Hybrid Nocturnal at Candikuning viilage, Bali, with 746 W auxiliary cooling
Tevap
TwbCS
Tc
Tu-t
Tu-b
Tdbling
Twbling
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Temperature distribution within nocturnal cooling system for vegetable strage at Candikuning, Bali
0.0010.0020.0030.00
11:0
011
:45
14:0
017
:00
20:0
023
:00
2:00
5:00
8:00
Local time
Tem
pera
ture
(C)
Tlingbk (C )Tkoilout (C )Tcoilin (C )TRbk (C )
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Table 1. Comparison of test results of nocturnal cooling system
Nocturnal system Storage room
without cooling facility
Load (kg)
Day 1 (%)
Day 2 (kg)
Day 2 (%)
Load (kg)
Day 1 (%)
Day 2 (kg)
Day 2 (%)
Carrot 23 100.0 22.5 100.0 12.5 100.0 11 88.0
Lettuce 14 93.3 13.5 90.0 2 66.7 1.5 50.0
Shallot 6.75 96.4 6.5 92.9 1.5 60.0 0 0.0
Pack choy 3 100.0 2 66.7 0.5 50.0 0 0.0
Cabbage 5 100.0 4 80.0 2.5 71.4 1 28.6
Potatoes 13 100.0 13 100.0 9.5 100.0 9.5 100.0
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cummulative weight lost of stored bananas
0
5
10
15
20
0 4 8 12 16Storage period (days)
Wei
ght l
oss
(%)
PendinginGudang
Colour change from green to red (a*) of stored bananas
-5
0
5
10
15
20
25
1 2 3 4 5
Storage period (days)
colo
ur in
dex(
a*)
PendinginGudang
Hradness index of bananas in mm/g sec)of stored bananas
00.0020.0040.0060.008
0.010.0120.014
0 4 8 12 16
Stored period (days)
Pen
etra
tion(
mm
/g s
ec.)
PendinginGudang
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2nd generation cooling system
mdota mdota1 mdota2 mdota3
mdotw
HX1 HX2 HX3
Cool water storage Tt Tr¥
Tp
Tr Tr1 Tr2 Tr3
Tp
fan
Ducting
Room
Product Product
Shallow water pond
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Mathematical modelingEnergy balance within cool water tank, Tt
(8)Cool water temperature change in pre‐cooler, Tc
(9)
The temperature drop of cooling air after passing through the pre‐cooler is given by
(10)
( ) ( )tcwwtawtwtt
wtwt TTcpmTTAUdtdTcpm −+−= &
( ) ( )tcwwcrccc
wcwc TTcpmTTAhdt
dTcpm −−−= &
( ) ( )crccrawdwdraa TTAhTTAUTcpm −−−=∆ 11&
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Mathematical modelingRoom temperature change, Tr is given by
(13)
If the amount of heat of respiration is neglected, product temperature change Tp, can have the following form.
(14)
( ) ( ) ( )33 rraarppprawrwrr
aar TTcpmTTAhTTAUdt
dTcpm −−−+−= &
( )rpppp
pp TTAhdt
dTcpm −−=
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Mathematical modeling (Contind)After passing through the 1st chilled water heat exchanger, the air temperature will drop such that
(11)
After passing the 2nd chilled water heat exchanger, the amount of air temperature drop will be as given by
(12)
( ) ( )2122122 hxrhxhxrawdwdraa TTAhTTAUTcpm −−−=∆&
( ) ( )3233233 hxrhxhxrawdwdraa TTAhTTAUTcpm −−−=∆&
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Typical simulation result in a hybrid nocturnal cooling system for temporary storage
-10
0
10
20
30
0 5 10 15
Elapsed time (hr)
Tem
pera
ture
(oC
)
Tt
Tr1
Tr2
Tr3
Tr
Tp
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Change in final storage room temperature under different amount of loading
Operating conditions
Cooling load (kg)
Final storage room temperature
(oC)
Contribution of nocturnal cooling effect for pre-
cooler (min.)
1. 3000 1.97 n.a.2
2000 0.25 4.83
2500 0.27 154
3000 0.31 25.25
2500 0.36 34.8
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Optimization using Lagrange Multiplier
C = Σ ki Xi‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ minimum
φi ( x1,x2,..xn) =0
∇C – Σλi∇ φi =0
X1= m=recirculation flow rate (kg/s)X2= A=Shallow water pond area (m2)X3= W=load (kg)X4=Vt=volume of cool water storage tank (m3)X5=Ac=Heat transfer area of chilled water heat exchanger (m2)X6=Qcl=capacity of cooling tower (kW th)X7=P=power of water re‐circulating pump (Watt)
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Optimization resultsk m
(kg/s)Tr(oC)
W(kg)
Ac(m2)
Vt(m3)
Qct(kWth)
P(Watt)
Total*)(x million Rp)
1 0.5 15 450 1.00 9.76 5.42 100.00 4.44
1 0.5 15 450 1.00 10.00 5.42 110.72 4.52
1 0.5 15 450 1.00 10.00 5.88 115.02 4.54
1 0.5 15 4500 2.00 9.52 5.42 100.00 6.08
1 0.5 16 4500 0.75 8.84 3.25 100.00 3.89
1 0.5 17 4500 0.50 7.90 3.25 100.00 3.35
2 0.5 17 4500 0.23 4.97 0.10 100.00 2.52
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Hybrid nocturnal cooling for fruits and vegetable storage
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ConclusionsNocturnal cooling could be used for temporary storage of vegetables and fruits in IndonesiaStored fruits and vegetables in the nocturnal powered cool storage could extend shelf life of the productsThe cooling effect due to night sky radiation and modified cooling tower could reduce the cooling load.Of auxiliary cooling machineFrom the simulation studies it was understood that the existing storage facility could accept maximum load up to one ton of potatoes, requiring only one kW cooling capacity of an auxiliary unit.
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References
CREATA‐LPPM‐IPB, (2000) Utilization of Environmentally Friendly Natural Energy to Promote Agro‐based Industry‐a Japanese Government ODA Grassroots Project, Final Report. Center for Research on Engineering Applications in Tropical Agriculture, the Research Institute of IPB, Bogor. Kamaruddin A.(2007). Editor:Teknologi Berbasis Sumber Energi Terbarukan untuk Pertanian.CREATA‐LPPM,IPB (Renewable Energy Sources Based Technology for Agriculture),IPB Press.
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