ADVANCES IN FOOD REFRIGERATION

Tuan Pham

School of Chemical Engineering and Industrial ChemistryUniversity of New South Wales

tuan.pham@unsw.edu.au

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

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

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

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

• ReliabilityFood 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 productHeat & 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

• Network of tank

)( TThAdt

dTmc Ap

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)

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

Simulation: (2-D) finite element model

Accuracy of F.D. & F.E. model for beef chilling heat load (70 tests)

0

1

2

3

4

5

6

7

8

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

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)

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

0

5

10

15

20

25

30

35

40

45

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

CFD model of display case: Predicted (color) vs measured (number) temperatures

Other CFD Applications

• 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:

)( minTTbm

)(exp1)( max2

min TTcTTbm

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

Process inputs:Air temperatureWashing, cleaningProduct shape, wrap...etc.

Process model

Results:Product qualityCostReliabilityetc...

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 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

• 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