advances in food refrigeration tuan pham school of chemical engineering and industrial chemistry...
TRANSCRIPT
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ADVANCES IN FOOD REFRIGERATION
Tuan Pham
School of Chemical Engineering and Industrial ChemistryUniversity of New South Wales
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History of Food Refrigeration• Harrison - ice making (1860), frozen meat export (1873)• China 1000BC - ice harvesting• Ancient Egypt - (evaporative cooling, ice making)• Prehistory - use of caves and ice
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Food refrigeration is BIG
• Annual investment in refrigerating equipment: US$170
• Annual refrigerated foodstuffs: US$1200 billion
(3.5 times USA military budget)
• 700-1000 million household refrigerators
• 300 000 000 m3 of cold-storage facilities
and causes big problems!
• Ozone-depleting effects - Montreal protocol
• Global-warming effects - Kyoto agreement
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Plan of talk
Part I: Common industrial problems- Chillers and freezers- Cold stores- Refrigerated transport- Retail display
Part II: Simulation of food refrigeration- Temperature and moisture changes- Quality and microbial growth
Part III: Optimisation of food refrigeration
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PART ONE:
COMMON PROBLEMS IN FOOD REFRIGERATION
EQUIPMENT
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Typical refrigeration system
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Chillers and Freezers
Chillers and freezers can be classified into
• air-cooled
• immersion
• spray
• cryogenic
• surface contact chillers.
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Air Chillers/Freezers
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Immersion and Spray Chillers/Freezers
• faster than air chilling, especially for small products
• absorption of liquid or solutes by the product, leading to undesirable appearance or other quality losses
• cross-contamination between products • leaching of food components such as fat• effluent disposal problem
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Surface contact chillers/freezers
• Include plate chillers/freezers, mould freezers, belt chillers, scraped surface freezers
• High heat transfer rate (similar to immersion freezers) - only metal bw refrigerant & product
• No absorption of liquid• No liquid effluent. • Need products with flat surfaces, such as cartons
Preferably thin or small products such as fish and peas.
• Labor intensive or need sophisticated automation.
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How to have efficient cooling/freezing
k
R
hTT
Rt
aff
1
)(
For faster cooling/freezing and higher throughput:
• Reduce temperature Ta
• Increase h (high air velocity, use spray/ immersion/ contact, less packaging)
• Decrease product size R
Biot Number hR/k (= external/internal resistance) should be not too far from 1
Surface resistance Internal resistance
Freezing time
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Cold store
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Cooling coil
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Air Infiltration through Doors
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Effectiveness of door protective devices
• Vertical air curtain: 79%
• Horizontal air curtain: 76%
• Plastic strip curtain: 93%
• Air + plastic strip: 91%
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Vapour barrier breach
•Heat bridge•Delamination•Collapse
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Frost heave
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Problems with transport vehicles & containers are same as in cold rooms, but multiplied several-fold (because of high A/V ratio and fluctuating ambient conditions)
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Retail display
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Retail display
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Selection and Operation of Refrigeration Components
• ReliabilityFood remains safe and wholesome according to specifications.
• Flexibility Ability to handle different products or production rates
• Capital and Operating costs
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Selection and Operation of Refrigeration Components
Freezers and chillers:• Extract heat within a certain time from product
and other sources• Cool product uniformly • Avoid surface drying, contamination, microbial
growth and other quality problems• Avoid condensation
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Selection and Operation of Refrigeration Components
• System must be well balanced to give optimal performance for given price.
An undersized cooling coil or freezer will require oversized compressors, condensers etc.
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PART TWO:
SIMULATION OF FOOD REFRIGERATION
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What happens in the productHeat & mass transfer
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Mass transfer in wrapped food
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Heat & mass transfer in Cartoned food
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Heat & mass transfer in irregular food
Re-circulation causes
• High temperature
• Moist surface
• Microbial growth
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Mathematical Simulation
Objectives: to predict changes in
• temperature at surface and centre• moisture, especially surface moisture• heat load • quality changes• microbial risks
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Simulation: Overview of models
• Lumped capacitance (uniform temperature) model• Tank network model• Product discretization models:
- finite differences
- finite elements
- finite volumes• Computational fluid dynamics (CFD) model
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Simulation: Tank models
• Uniform temperature model
• Network of tank
)( TThAdt
dTmc Ap
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Accuracy of two-tank model for lamb freezing
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Simulation: (2-D) finite difference model
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Accuracy of F.D. model for beef chilling weight loss (70 tests)
0
1
2
3
0 1 2 3
Experimental Weight Loss (kg)After 20 hours in Chiller
FD
Mod
el W
eigh
t L
oss
(kg)
Aft
er 2
0 ho
urs
in C
hille
r
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Simulation: (2-D) finite element model
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Accuracy of F.D. & F.E. model for beef chilling heat load (70 tests)
0
1
2
3
4
5
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0 1 2 3 4 5 6 7 8
Experimental Heat Removed (MJ) During first 2 hours in Chiller
Pre
dic
ted
Hea
t R
emov
ed (
MJ)
D
urin
g fi
rst 2
hou
rs in
Chi
ller
Davey and Pham (1997) FE Model
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2-TankModel
FD Model FE Model
Average % error in heatremoved during first 2 hours
-1.5 % -12.6 % -5.6 %
Average % error in weightloss
N/A -1.2% 2.3 %
Running Time(Pentium 166 Mhz)
< 1 sec < 1 min 5 hours
Accuracy of predictions by various models(based on 70 beef chilling tests)
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CFD Models
• Can simulate the flow field outside the product (air, water, cryogen...) as well as inside
• Computationally expensive (fast computers, lots of memory, days of runtime)
• Software expensive (especially for non-U)• Need lots of expertise to use properly• Need lots of time for data preparation• Accuracy NOT guaranteed even when all the
above are satisfied!
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Why is CFD so difficult?• Solve several interacting partial differential
equations simultaneously (density, v, T, c, turbulence parameters)
• Must discretize the object and its surrounding into tens of thousands to millions of volume elements
Why is CFD not quite accurate?• Calculation of turbulence only approximate• Turbulence affects boundary layer and hence heat
and mass transfer rates
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CFD example: Beef chilling - model
100,000 nodes
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CFD example: Beef chilling - results
0
5
10
15
20
25
30
35
40
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0 5 10 15 20
Time in chiller, hTe
mpe
ratu
re, d
eg.C
0
200
400
600
800
1000
0 5 10 15 20
Time in chiller, h
Hea
t loa
d, W
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CFD model of display case: Predicted (color) vs measured (number) temperatures
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Other CFD Applications
• Chillers and freezers
• Cold stores
• Transport containers
• Pasteurisation/cooling of liquid foods
• Design of cooling coils, air curtains
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Quality: Physical changes
• Weight loss, dry appearance• Water absorption, bloated appearance• Drip• Crystal growth (ice cream)• Water penetration (bakery products)
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Quality: Biochemical changes
• Tenderness (beef, lamb)• Fat rancidity flavour• PSE (pale soft exudative) (pork)• DFD (meat)• Flavour (fish)• Colour (meat)• Browning, spots, freezing injury (fruit)• Tissue breakdown (fruit)
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Quality: Fungal & microbial changes
• Mildew, rot (fruit)• Spoilage organisms• Pathogenic organisms
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Modelling microbial growth
Growth Rate = Optimum rate × Temperature Inhibition Factor× Water Activity Inhibition Factor× pH Inhibition Factor× Other Inhibition Factors
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Growth rate: dependence on Temperature
Ratskowsky’s square root model:
Zwietering model:
)( minTTbm
)(exp1)( max2
min TTcTTbm
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Growth rate: dependence on Humidity & pH
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Predictive microbiological modelling
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Predictive microbiological modelling
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Predictive microbiological modelling
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Microbial death
• Death rate influenced by – High temperature– Low pH– Low water activity– Combination
• Death during freezing– high solute concentration (low aw)– membrane shrinkage and damage– intracellular ice (?)
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Microbial death during freezing
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PART THREE:
OPTIMIZATION OF FOOD REFRIGERATION
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The ultimate objective of simulation is to control and optimize
Optimizer
Process inputs:Air temperatureWashing, cleaningProduct shape, wrap...etc.
Process model
Results:Product qualityCostReliabilityetc...
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Search (optimisation) methods
Gradient (classical) methods
- fast & methodical
- ends up at nearest local optimum
Stochastic methods (SA, GA...)
- methods with madness
- can be time consuming - 100,000 trials?
- better at obtaining global optimum
- better at dealing with errors
- can perform multi-objective optimisation
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Optimising air temperature in beef chilling
Objectives:• Chill centre to 7C in 24 hours• Tenderness score is minimized• E. Coli grows less than 8-fold at surface
However• Fast chilling (low air T) causes
toughness (high tenderness score) in loin• Slow chilling encourages microbial
growth on leg surface
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Optimising air temperature in beef chilling
A variable temperature regime is the answer:
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Controlling air temperature in lamb freezingObjective:To freeze all product in exactly 16 hoursProblems:
• Product weight varies (10-24 kg)• 16 hour lag time!
FREEZER(16-h lag)
Air T, vCss weight
Frozen csses
Controller
OptimizerProcessModel
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• Attention to details needed in design and operation of refrigeration facilities.
• Growing computer power allows more precise simulation of processes and prediction of product quality.
• CFD is not yet the answer to the maiden’s prayers.
• In near, computer control and optimisation of refrigeration processes will become more widespread.
CONCLUSIONS