Design algorithm - WIDE University cooling (nocturnal) Tw Twti Twt1 Tr Tc qL= ε σAw ( Tw4 –Ts4)...

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Lecture4 December 172007 Kamaruddin Abdullah Laboratory of Solar Conversion Technology Faculty of Engineering Darma Persada University [email protected] 03/06/2008 1 Kamaruddin A./2007 Design algorithm 03/06/2008 Kamaruddin A./2007 Decide product quality specification Thermophysical 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

Transcript of Design algorithm - WIDE University cooling (nocturnal) Tw Twti Twt1 Tr Tc qL= ε σAw ( Tw4 –Ts4)...

Page 1: Design algorithm - WIDE University cooling (nocturnal) Tw Twti Twt1 Tr Tc qL= ε σAw ( Tw4 –Ts4) Tp yMain component yShallow water pond 4 m x 6 m x 0.05 m yRoom dimension: 4 m x

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

Page 2: Design algorithm - WIDE University cooling (nocturnal) Tw Twti Twt1 Tr Tc qL= ε σAw ( Tw4 –Ts4) Tp yMain component yShallow water pond 4 m x 6 m x 0.05 m yRoom dimension: 4 m x

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

Page 4: Design algorithm - WIDE University cooling (nocturnal) Tw Twti Twt1 Tr Tc qL= ε σAw ( Tw4 –Ts4) Tp yMain component yShallow water pond 4 m x 6 m x 0.05 m yRoom dimension: 4 m x

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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Page 8: Design algorithm - WIDE University cooling (nocturnal) Tw Twti Twt1 Tr Tc qL= ε σAw ( Tw4 –Ts4) Tp yMain component yShallow water pond 4 m x 6 m x 0.05 m yRoom dimension: 4 m x

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&

Page 12: Design algorithm - WIDE University cooling (nocturnal) Tw Twti Twt1 Tr Tc qL= ε σAw ( Tw4 –Ts4) Tp yMain component yShallow water pond 4 m x 6 m x 0.05 m yRoom dimension: 4 m x

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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 −−−=∆&

Page 13: Design algorithm - WIDE University cooling (nocturnal) Tw Twti Twt1 Tr Tc qL= ε σAw ( Tw4 –Ts4) Tp yMain component yShallow water pond 4 m x 6 m x 0.05 m yRoom dimension: 4 m x

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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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