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ADVANCES IN FOOD REFRIGERATION Tuan Pham School of Chemical Engineering and Industrial Chemistry University of New South Wales

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Presentation on theme: "ADVANCES IN FOOD REFRIGERATION Tuan Pham School of Chemical Engineering and Industrial Chemistry University of New South Wales"— Presentation transcript:

1 ADVANCES IN FOOD REFRIGERATION Tuan Pham School of Chemical Engineering and Industrial Chemistry University of New South Wales

2 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

3 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 m3 of cold-storage facilities and causes big problems! Ozone-depleting effects - Montreal protocol Global-warming effects - Kyoto agreement

4 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


6 Typical refrigeration system

7 Chillers and Freezers Chillers and freezers can be classified into air-cooled immersion spray cryogenic surface contact chillers.

8 Air Chillers/Freezers

9 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

10 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.

11 How to have efficient cooling/freezing For faster cooling/freezing and higher throughput: Reduce temperature TaTa 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

12 Cold store

13 Cooling coil

14 Air Infiltration through Doors

15 Effectiveness of door protective devices Vertical air curtain: 79% Horizontal air curtain:76% Plastic strip curtain: 93% Air + plastic strip: 91%


17 Vapour barrier breach Heat bridge Delamination Collapse

18 Frost heave

19 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)

20 Retail display


22 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

23 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

24 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.


26 What happens in the product Heat & mass transfer

27 Mass transfer in wrapped food

28 Heat & mass transfer in Cartoned food

29 Heat & mass transfer in irregular food Re-circulation causes High temperature Moist surface Microbial growth

30 Mathematical Simulation Objectives: to predict changes in temperature at surface and centre moisture, especially surface moisture heat load quality changes microbial risks

31 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

32 Simulation: Tank models Uniform temperature model Network of tank

33 Accuracy of two-tank model for lamb freezing

34 Simulation: (2-D) finite difference model

35 Accuracy of F.D. model for beef chilling weight loss (70 tests)

36 Simulation: (2-D) finite element model

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

38 Accuracy of predictions by various models (based on 70 beef chilling tests)

39 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!

40 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


42 CFD example: Beef chilling - model 100,000 nodes

43 CFD example: Beef chilling - results

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

45 Other CFD Applications Chillers and freezers Cold stores Transport containers Pasteurisation/cooling of liquid foods Design of cooling coils, air curtains

46 Quality: Physical changes Weight loss, dry appearance Water absorption, bloated appearance Drip Crystal growth (ice cream) Water penetration (bakery products)

47 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)

48 Quality: Fungal & microbial changes Mildew, rot (fruit) Spoilage organisms Pathogenic organisms

49 Modelling microbial growth Growth Rate = Optimum rate × Temperature Inhibition Factor × Water Activity Inhibition Factor × pH Inhibition Factor × Other Inhibition Factors

50 Growth rate: dependence on Temperature Ratskowsky’s square root model: Zwietering model:

51 Growth rate: dependence on Humidity & pH

52 Predictive microbiological modelling



55 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 (?)

56 Microbial death during freezing


58 The ultimate objective of simulation is to control and optimize Optimizer Process inputs: Air temperature Washing, cleaning Product shape, wrap... etc. Process model Results: Product quality Cost Reliability etc...

59 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


61 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

62 Optimising air temperature in beef chilling A variable temperature regime is the answer:

63 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! FREEZER (16-h lag) Air T, vCss weight Frozen csses Controller Optimizer Process Model

64 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

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