ADVANCES IN
FOOD REFRIGERATION
Tuan Pham
School of Chemical Engineering and Industrial Chemistry
University of New South Wales
tuan.pham@unsw.edu.au
History of Food Refrigeration
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•
•
•
Harrison - ice making (1860), frozen meat export (1873)
China 1000BC - ice harvesting
Ancient Egypt - (evaporative cooling, ice making)
Prehistory - use of caves and ice
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
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
PART ONE:
COMMON PROBLEMS
IN FOOD REFRIGERATION
EQUIPMENT
Typical refrigeration system
Chillers and Freezers
Chillers and freezers can be classified into
• air-cooled
• immersion
• spray
• cryogenic
• surface contact chillers.
Air Chillers/Freezers
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
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.
How to have efficient cooling/freezing
R
1 R
tf 
  
(T f  Ta )  h k 
Freezing time
Surface resistance
Internal resistance
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
Cold store
Cooling coil
Air Infiltration through Doors
Effectiveness of door protective devices
• Vertical air curtain:
79%
• Horizontal air curtain: 76%
• Plastic strip curtain:
93%
• Air + plastic strip:
91%
Vapour barrier breach
•Heat bridge
•Delamination
•Collapse
Frost heave
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)
Retail display
Retail display
Selection and Operation of
Refrigeration Components
• Reliability
Food remains safe and wholesome according to
specifications.
• Flexibility
Ability to handle different products or production
rates
• Capital and Operating costs
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
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.
PART TWO:
SIMULATION OF FOOD
REFRIGERATION
What happens in the product
Heat & mass transfer
Mass transfer in wrapped food
Heat & mass transfer in Cartoned food
Heat & mass transfer in irregular food
Re-circulation causes
• High temperature
• Moist surface
• Microbial growth
Mathematical Simulation
Objectives: to predict changes in
•
•
•
•
•
temperature at surface and centre
moisture, especially surface moisture
heat load
quality changes
microbial risks
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
Simulation: Tank models
• Uniform temperature model
dT
mc p
 hA(TA  T )
dt
• Network of tank
Accuracy of two-tank model for lamb freezing
Simulation: (2-D) finite difference model
Accuracy of F.D. model for beef chilling
weight loss (70 tests)
After 20 hours in Chiller
FD Model Weight Loss (kg)
3
2
1
0
0
1
2
Experimental Weight Loss (kg)
After 20 hours in Chiller
3
Simulation: (2-D) finite element model
Accuracy of F.D. & F.E. model for beef
chilling heat load (70 tests)
8
Davey and Pham (1997)
FE Model
During first 2 hours in Chiller
Predicted Heat Removed (MJ)
7
6
5
4
3
2
1
0
0
1
2
3
4
5
6
Experimental Heat Removed (MJ)
During first 2 hours in Chiller
7
8
Accuracy of predictions by various models
(based on 70 beef chilling tests)
2-Tank
Model
Average % error in heat
removed during first 2 hours
Average % error in weight
loss
Running Time
(Pentium 166 Mhz)
FD Model FE Model
-1.5 %
-12.6 %
-5.6 %
N/A
-1.2%
2.3 %
< 1 sec
< 1 min
5 hours
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!
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
CFD example: Beef chilling - model
100,000
nodes
CFD example: Beef chilling - results
Temperature, deg.C
Heat load, W
1000
800
600
400
200
0
0
5
10
Time in chiller, h
15
20
45
40
35
30
25
20
15
10
5
0
0
5
10
Time in chiller, h
15
20
CFD model of display case: Predicted (color)
vs measured (number) temperatures
Other CFD Applications
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•
•
•
•
Chillers and freezers
Cold stores
Transport containers
Pasteurisation/cooling of liquid foods
Design of cooling coils, air curtains
Quality: Physical changes
• Weight loss, dry appearance
• Water absorption, bloated appearance
• Drip
• Crystal growth (ice cream)
• Water penetration (bakery products)
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)
Quality: Fungal & microbial changes
• Mildew, rot (fruit)
• Spoilage organisms
• Pathogenic organisms
Modelling microbial growth
Growth Rate = Optimum rate
× Temperature Inhibition Factor
× Water Activity Inhibition Factor
× pH Inhibition Factor
× Other Inhibition Factors
Growth rate: dependence on Temperature
Ratskowsky’s square root model:
Zwietering model:
 m  b(T  Tmin )
m  b(T  Tmin )2 1  expc(T  Tmax )
Growth rate: dependence on Humidity & pH
Predictive microbiological modelling
Predictive microbiological modelling
Predictive microbiological modelling
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 (?)
Microbial death during freezing
PART THREE:
OPTIMIZATION OF FOOD
REFRIGERATION
The ultimate objective of simulation
is to control and optimize
Optimizer
Results:
Product quality
Cost
Reliability
etc...
Process inputs:
Air temperature
Washing, cleaning
Product shape, wrap...
etc.
Process
model
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
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
Optimising air temperature in beef chilling
A variable temperature regime is the answer:
Controlling air temperature in lamb freezing
Objective:To freeze all product in exactly 16 hours
Problems:
• Product weight varies (10-24 kg)
• 16 hour lag time!
Css weight
Air T, v
Controller
FREEZER
(16-h lag)
Process
Model
Optimizer
Frozen csses
CONCLUSIONS
• 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.