2. Outline
Contribution to FSA’s Key Success Indicators
Main projects
Core research areas
Modelling High Pressure Thermal Sterilisation (HPTS)
Compression heating propertiesCompression heating properties
Development of temperature loggers for HPTS processes
Other projects
Future Work
3. Competitive and sustainable business
EU Food Company (HPTS2 project)
Potential new project with EU Food Company
FFF (Drying of natural plant material)
CSIRO TFT (U/S wool wax recovery)
Aus and Int Veg Processor (U/S chips)
Aus Fruit Processor (U/S pears, HPP peach)
BD (HPTS; HP-T logger)
Building partnerships
Collaboration with FSA North Ryde
Niche Manufacturing Flagship/CSIRO
Minerals
Thermochron manufacturer
Sonosys GmbH
Others (e.g. institutes, divisions etc)
IFT/NPD symposium proposal
Book “Multiphysics Modelling of Emerging Technologies”
Contributions to FSA’s KSIs
Relevant and excellent science
Peer-reviewed papers: 5
Industry papers: 3
Book chapters: 3
Abstracts/proceedings: 15
Posters: 7
Oral presentations: 10
Internal: 15+
Symposium and Book
IFT International Division website editor
People and culture
FSA-WB modelling team
HP-T process logger team (WB/NR)
HPTS team (WB/NR)
FFF processing team (NR/WB)
Capability Development team (WB/NR)
Supervision of students (STI3 PhD, work
experience)
4. Outline
Contribution to FSA’s Key Success Indicators
Main projects
Core research areas
Modelling High Pressure Thermal Sterilisation (HPTS)
Compression heating propertiesCompression heating properties
Development of temperature loggers for HPTS processes
Other projects
Future Work
5. Main projects
STI3 project (# 110653)
Initial HPTS modelling
Temperature mapping (35 L vessel)
Preliminary studies of HP-T process logger
EU Food Company HPTS2 project (# 112812)EU Food Company HPTS2 project (# 112812)
HPTS modelling
Temperature mapping (3 L vessel)
Carrier design
Compression heating of sauces
ITD value development
6. Main projects
Capability development of innovative processes (# 112740)
HPTS optimisation algorithm (including ITD)
HP-T process logger
3 L Stansted HPP unit commissioning
Compression heating properties of water and water/glycol mixtures
Modelling, Validation and Equipment (Re)Design of
Innovative ProcessesInnovative Processes
HPTS modelling (Validation)
Compression heating properties (insulating materials)
Inactivation model development
PEF modelling
U/S modelling
Cost modelling of HPP
Cool Plasma characterisation
7. Outline
Contribution to FSA’s Key Success Indicators
Main projects
Core research areas
Modelling High Pressure Thermal Sterilisation (HPTS)
Compression heating propertiesCompression heating properties
Development of temperature loggers for HPTS processes
Other projects
Future Work
8. Outline – Modelling HPTS
CFD modelling of HPTS in the Avure 35L vessel
Metal and PTFE carrier
Temperature mapping Validation of model
Inactivation modelling of C. botulinum spores (based on thermal only linear
kinetics)
Enhancement of previous model
CFD modelling of improved 35 L HPTS model
Coupling CFD model to C. botulinum inactivation models
Optimisation routine for PTFE carrier (temperature uniformity and heat retention)
CFD modelling of HPTS in Stansted 3L HPP unit
Various carrier designs
Inactivation modelling of C. botulinum spores (various models)
Temperature mapping
9. Physical principles of high pressure thermal
sterilisation
Process conditions:
Pressures up to 600-800 MPa
Moderate initial temperature 60-90ºC
Holding times up to 5 minutes
Heat source:
p
p
C
T
dP
dT
ρ
α
=
t
P
T
t
T
C pp
∂
∂
=
∂
∂
αρ
Heat source
Location
independent
Heat source:
Compression heating: up to sterilisation
temperatures
10. Physical principles of high pressure thermal
sterilisation – “Typical” pressure/temperature curve
Assuming no heat
losses during holding
11. High Pressure Thermal Processing
– Pressure and Temperature Distribution
Uniform pressure distribution
But: temperature variation
through the vesselthrough the vessel
Characterisation of vessel
re: temperature distribution essential
Measurement
Simulation
12. Numerical modelling of high pressure sterilisation
Motivation
Process Assessment
Process and equipment modification
Develop new processing strategies while
maintaining high standards
Equipment development and optimisation
e.g. scale-up studies
Industrial design solutions at reduced cost
e.g. scale-up studies
Industrial benefits
Reduced costs and time of experimentation and
equipment use
Improved efficacy
Compared to analytical models
With:
Utilisation of advantages and minimisation of
disadvantages
14. Modelling a 35 L HPTS vessel
- The system and a computational model
Flow Pressure Systems
35L-600 sterilization machine
(Avure Technologies, USA)
top water
entrance
z
r
carrier
water inlet
water
preheater
vessel
carriers:
•Metal
•PTFE
15. Simulation of Flow and
Temperature Distributions
using COMSOL Multiphysics™
Materials:
Compression medium:
Water
Carrier: Structural steel
carrier
water
Carrier: Structural steel
Parameters and variables:
Pressure: 0-600 MPa
Tinit = 90 °C
tpressurize = 130 s
thold = 285 s
tdecompression = 15 s
16. Materials:
Compression medium:
Water
Carrier: PTFE
Parameters and variables:
Simulation of Flow and
Temperature Distributions
using COMSOL Multiphysics™
carrier
water
Parameters and variables:
Pressure: 0-600 MPa
Tinit = 90 °C
tpressurize = 130 s
thold = 285 s
tdecompression = 15 s
17. Distribution of spore reduction
- Log-linear kinetics approach
Transformation of temperature distribution as function of
time into spore inactivation distribution
MATLAB routine
@
),,(
log10),(
N
N
DdtyxF ref
T
ref
T
t
z
TyxtT
⋅== ∫
−
3 scenarios:
Empty vessel
Vessel including metal carrier
Vessel including PTFE carrier
0
@
0
NrefT∫
18. Distribution of spore reduction
- linear kinetics approach
Effect of carrier presence
a) Empty vessel
b) Steel carrierb) Steel carrier
c) PTFE carrier
logS
20. Mapping of Temperature Distributions using TC-
Arrays and Image Processing - The system
35LHPPvessel
Carrier
Materials:
Compression medium:
Water
Carrier: Structural steel
Parameters and variables:
35LHPPvessel
Carrier
Parameters and variables:
Pressure: 0-600 MPa
Tinit = 90 °C
tpressurize = 130 s
thold = 285 s
tdecompression = 15 s
22. Modeling a 35 L HPTS vessel
- Validation of the simulated temperature distribution
Simulation:
Only inside carrier (water)
115.3 °C 115.8 °C
Vessel
Water
Carrier
Measurement:
2-D cross section
single time comparison
116.5 °C 116.1 °C
108.6 °C108.0 °C
3
6
12
45
789
TC array in an
axis-symmetric
cross-section
23. Validation of the simulated temperature distribution
- 3x3 matrix, 14 time steps
Good agreement was found between
simulation and measured values
24. Enhanced Model:
Spore inactivation distributions and
equipment optimisation
CFD ODE
T and flow
distribution
Inactivation
distribution
Computational
model
25. Modeling a 35 L HPTS vessel
- Inclusion of vessel lid, packages and carrier bottom valve
Flow Pressure Systems
35L-600 sterilization machine
(Avure Technologies, USA)
1
2
3
air layer
stainless steel
metal lid
top water
entrance
z
r
7
6
5
4
3stainless steel
carrier
metal valve
water inlet
water
preheater
vessel
PTFE
carrier
26. Modeling the 35 L HPTS vessel
Including: - vessel lid
- cylindrical packs
- carrier bottom valve
Materials:
Compression medium: Water
Carrier: PTFE
Vessel: Structural steel
packs
vessel
air
COMSOL MultyphisicsTM
carrier
water
Vessel: Structural steel
Packs: Model food
Parameters and variables:
Pressure: 0-600 MPa
Tinit = 90 °C
tpressurize = 130 s
thold = 220 s
tdecompression = 15 s
air
End of holding time; t = 350 s Initial conditions
t = 0
27. - Spore inactivation models
Log-linear kinetics:
Weibull distribution:
( ) ( )( ) ( )( )tTn
ttTb
tN
⋅−=log
))((
)(
log
0 tTD
t
N
tN
−=(a)
(b)
Modeling inactivation distribution of C. botulinum
nth order kinetics:
Combined log-linear-nth order kinetics:
( )( ) ( )( )tTn
ttTb
N
⋅−=
0
log
( ) ( ) ( )( )[ ])1(,'1log
1
1
log
0
nttTtPk
nN
tN
−⋅⋅−⋅
−
=
(b)
(c)
(d) If T<373 K, (a), else, (c)
28. -10
-8
-6
-4
-2
0
(a) Log-linear kinetics
(b) nth order kinetics
(c) Combined log-linear-nth
Modeling inactivation distribution of
C. botulinum
- Distribution of spore reduction
-16
-14
-12
-10
(a) (b) (c) (d)
(c) Combined log-linear-n
order kinetics
(d) Weibull distribution
29. 80
100
120
140
Temperature[ºC]
conventional retort process
high pressure process
Comparison HPTS and Retort
F value 3.60 min; can volume 384 mL
F value 3.56 min; pack volume 346 mL
0 10 20 30 40 50 60 70 80
20
40
60
80
Time [min]
Temperature[ºC]
Retort process time
Preheating time
High pressure process time
Juliano et al., 2007
30. Comparison HPTS and Retort
-8
-6
-4
-2
0log(N/N0
)
A: linear kinetics
B: Weibull
C: nth
order
D: linear-nth
order
0 10 20 30 40 50 60 70 80
-18
-16
-14
-12
-10
Time [min]
log(N/N
Retort process time
Preheating time
High pressure process time
HPTS
HPTS
HPTS
HPTS
Retort
Retort
Retort
Retort
31. CFD models for optimisation - Motivation
Problem
Insulating carrier can occupy a large portion of the vessel
volume
Thickness of insulating material is often overdesigned
Solution
Reduce thickness while maintaining temperature uniformityReduce thickness while maintaining temperature uniformity
and magnitude
Trial and error is hard to accomplish and too expensive
CFD approach allows for reduced costs and time of
experimentation and equipment use
32. Finding the optimum
- Parameters to consider
Required:
Maximal usable volume, i.e. wall thickness
Temperature uniformity
Temperature magnitude
during holding time
Temperature magnitude
Measure for temperature performance:
ITD value: Evaluating temperature distribution and
heat retention
33. Modelling high pressure thermal sterilisation
- The system and the model
Flow Pressure Systems
35L-600 sterilization machine
(Avure Technologies, USA)
Steel
wall
Variable carrier
wall thickness
Carrier
HP
chamber
preheater
vessel
carrier
Water
34. Determination of optimum PTFE carrier thickness
- CFD simulation and analysis
CFD runs through a number of wall thicknesses:
0 mm ≤ d ≤ 70 mm
Carrier top and bottom size is fixed
Assumption: PTFE shows no compression heating
Solve models and export temperature output
Carrier performance analysis
MATLAB® routine
Select temperature distr. output at thickness d1
Define region of interest (inside carrier)
Calculate ITD at d1
Calculate usable carrier volume at d1
Repeat for other thickness values di
Plot ITD and usable volume vs wall thickness
Plot normalised values vs wall thickness
d = 0 mm d = 5 mm d = 70 mm
End of holding time; t = 280 s
35. 0.35
0.4
0.45
0.5
20
25
usablevolume/L
0.6
0.7
0.8
0.9
1
normalisedvolumeandITD
Determination of optimum
- CFD simulation and analysis
Optimum?
Intersection between ITD and usable volume curves
Optimum Optimum
PTFE heat sink effect PTFE heat sink effect
0 10 20 30 40 50 60 70
0
0.05
0.1
0.15
0.2
0.25
0.3
carrier wall thickness / mm
ITD/-
ITD parameter
0 10 20 30 40 50 60 70
0
5
10
15
usablevolume/Lusable vessel volume
0 10 20 30 40 50 60 70
0
0.1
0.2
0.3
0.4
0.5
0.6
carrier wall thickness / mm
normalisedvolumeandITD
normalised usable volume
normalised ITD
For perfect temperature performance 15 % of maximum usable volume has to be sacrificed.
Optimum of both ITD and usable volume: dWall = 4 mm.
36. Summary – CFD models of Avure 35L HPTS unit
Model developed describing flow and temperature
distributions in a HPHT process
Validated for metal carrier
Coupled to log-linear thermal only inactivation kinetics (C. botulinum)
Model improved
Vessel walls, cool lid, carrier valve, packages
Platform for assessing models predicting C. botulinum spore reduction
Optimisation algorithm developed
ITD value introduced assessing uniformity of treatment
Optimum carrier wall thickness found, increasing usable volume by
more than 100%
37. Summary – CFD models of Avure 35L HPTS unit
Scientific impact
Paper in AIChE Journal (October 2007)
Paper in Biotechnology Progress (September 2008)
Paper on Optimisation in preparation (possible JFE, Sep/Oct 2008)
2 book chapters in “Engineering Aspects of Thermal Processing”
Article in Food&Drink MagazineArticle in Food&Drink Magazine
3 posters at IFT 2007 (USA), ICEF 2008 (Chile)
4 oral presentations at GC Hahn Award Ceremony 2007 (Germany), ICEF
2008 (Chile), IFT 2008 (USA)
Commercial impact
HPTS2 project (key project area: modelling HPTS in 3L vessel)
Potential new project, modelling horizontal systems in 3D
38. Outline
Contribution to FSA’s Key Success Indicators
Main projects
Core research areas
Modelling High Pressure Thermal Sterilisation (HPTS)
Compression heating propertiesCompression heating properties
Development of temperature loggers for HPTS processes
Other projects
Future Work
39. Determination of pressure and temperature
dependent compression heating factors
from adiabatic heating curves
1
1.5
x 10
-10
/Pa-1
Why determination of adiabatic heating coefficients necessary:
Coefficients can be used to predict maximal achievable temperature upon pressurisation
of any material in HP process
Furthermore to predict the initial temperature from any maximum target temperature
Functions can be used as input source in CFD simulations of high pressure processes
280
300
320
340
360
380
0
200
400
600
0
0.5
Temperature / K
Pressure / MPa
kC
/Pa
40. Protocols for determination of thermophysical
properties
( )PTfp ,=α
dP
C
TdT
p
p
ρ
α
⋅= For components:
• Liquids
• Insulating polymers
• …thermal expansion coefficient (K-1)
Ordinary differential equation
(ODE) describing T-change
upon P-change
(adiabatic conditions)
),( PTfkC =
),( PTfCp =
( )PTf ,=ρ
MATLAB routine
4. Fit integrated ODE to pT-sub-
range
5. Extract compression heating
factors at specific T, P
6. Fit values f = f(T,P)
Experimental Part
1. Equilibrate to initial T
2. Apply pressure
3. Record pressure and
temperature curve
Verification
7. Compare with water values
from NIST database
isobaric heat capacity (J · kg-1 · K-1)
density (kg / m3)
41. Experimental procedure:
Essentials
“Perfect” insulation required to
avoid heat loss or gain during
come-up time
High data acquisition rate
Thermocouple
Medium to be
investigated
Plastic bottle
Centrifuge
tube
(plastic)
High data acquisition rate
Temperature
Pressure
Set to 200 ms log rate for
both P and T
investigated
43. Determination of kC(P,T)
Fit to integrated ODE in all P intervals
dPkTdT C⋅= ( ) ( )0
0
PPkC
eTPT −⋅
⋅=
Integration
Assuming constant kC in each subP
Fit yields kC(subP,subT)
Repeat for all subintervals and a wide
range of Tinit
44. 1
1.5
x 10
-10
/Pa
-1
1
1.5
x 10
-10
kC
/Pa
-1
Determination of kC(P,T); example: pure water
Surface fit (4th order) for P,T,kC
280
300
320
340
360
380
0
200
400
600
0
0.5
Temperature / K
Pressure / MPa
kC
/Pa
280
300
320
340
360
380
0
200
400
600
0
0.5
Temperature / K
Pressure / MPa
k
surface shapes are similar for both the “measured” kC values and the ones from NIST
for the range of Tinit = 5ºC to 90ºC investigated, both curves give almost identical values
R2 = 0.9374
“measured” NIST database
R2 = 0.9961R2 = 0.9827
45. 280
300
320
340
360
380
0
200
400
600
0
0.5
1
1.5
x 10
-10
Temperature / K
Pressure / MPa
kC
/Pa
-1
Results at discrete concentrations
- varying cGlycol, yielding kC=f(P,T)
280
300
320
340
360
380
0
200
400
600
0
0.5
1
1.5
2
2.5
x 10
-10
Pressure / MPaTemperature / KkC
/Pa
-1
300
350
400
0
200
400
600
0.5
1
1.5
2
2.5
x 10
-10
Temperature / K
Pressure / MPa
kC
/Pa
-1
c = 0%
400Pressure / MPa
300
350
400
0
200
400
600
0.5
1
1.5
2
2.5
x 10
-10
Temperature / K
Pressure / MPa
k
C
/Pa-1
300
350
400
0
200 400
600
0.5
1
1.5
2
2.5
x 10
-10
Temperature / KPressure / MPa
kC
/Pa-1
cGlycol = 0%
cGlycol = 25% cGlycol = 50%
cGlycol = 75% cGlycol = 100%
46. Applying procedure to water and water/glycol mixtures
- Adiabatic heating as function of p and T0
dPkTdT C⋅=
47. Summary
Adiabatic heating coefficients (kC) were determined as function
of P and T for:
Pure water (proof of concept), i.e. cGlycol = 0%
cGlycol = 25%, 50%, 75%, 100%
Different concentrations show significant differences inDifferent concentrations show significant differences in
adiabatic heating
cGlycol is close to 36% in 3L unit and depending on fluid in carrier
changing from run to run
I.e. there is a necessity to determine adiabatic heating of
processing fluid with kC = f(P,T,cGlycol)
48. Supplemental for the determination of
compression heating of water/glycol
mixtures
– from discrete to arbitrary concentrations
kC(P,T,cG) =
250
300
350
400
0
200
400
600
800
-5
0
5
10
x 10
-10
Temperature / K
Pressure / MPa
a
250
300
350
400
0
200
400
600
800
-1
-0.5
0
0.5
1
x 10
-9
Temperature / K
Pressure / MPa
b
250
300
350
400
0
200
400
600
800
-4
-2
0
2
4
x 10
-10
Temperature / K
Pressure / MPa
c
· cG
3 + · cG
2 + · cG +
+ kC,NIST(P,T)
49. Approach for getting cGlycol-dependence
- allocate kC-values to P,T combinations
Response surface equations (for all cGlycol) are used to calculate kC-
values at all p,T combinations (0-700 MPa, 5-125°C)
Yielding five 2D matrices (one for each cGlycol)
T
kC,0%
kC,25%
kC,50%
kC,75%
kC,100%
P
50. Approach for getting cGlycol-dependence
- allocate kC-values to P,T combinations
2D matrices are “squeezed” into one array
Yielding 2D array containing kC-vectors for all cGlycol
(0MPa,125°C)
kC,50%
kC,75%
kC,100%
P
T
cG,100
cG,75
cG,50
cG,25
cG,0
cG,100
cG,75
cG,50
cG,25
cG,0
cG,100
cG,75
cG,50
cG,25
cG,0
cG,100
cG,75
cG,50
cG,25
cG,0
cG,100
cG,75
cG,50
cG,25
cG,0cG,100
cG,75
cG,50
cG,25
cG,0
kC(0MPa,125
kC(700MPa,125°C)
kC(700MPa,5°C)
kC(0MPa,5°C)
kC,0%
kC,25%
51. Approach for getting cGlycol-dependence
- perform fit at each P,T combination
3rd order polynomial is fitted to kC-vector at each P,T
combination
kC(cGlycol) = a·cGlycol
3 + b·cGlycol
2 + c·cGlycol + kC,NIST
With kC,NIST being the the adiabatic heatingWith kC,NIST being the the adiabatic heating
coefficient of pure water, i.e. cGlycol = 0%
Yielding values for a,b,c, as well as R2 at each P,T
combination
52. Approach for getting cGlycol-dependence
- step4: perform surface fit for 3rd order polynomial coefficients
300
350
400
0
500
-4
-2
0
2
4
6
x 10
-10
a
250
300
350
0
200
400
600
-1
-0.5
0
0.5
1
x 10
-9
b
250
300
350
0
200
400
600
-4
-2
0
2
4
x 10
-10
c
250
300
1000 Pressure / MPa
Temperature / K
350
400
600
800 Temperature / K
Pressure / MPa
350
400
600
800 Temperature / KPressure / MPa
Surface fits (4th order) of the previously determined 3rd order polynomial fit
coefficients yields a,b,c = f(P,T)
i.e. kC(P,T,cGlycol) = a(P,T)·cGlycol
3+b(P,T)·cGlycol
2+c(P,T)·cGlycol+kC,NIST(P,T)
53. Validation of approach
- comparison of predicted and measured T curves
360
380
400
420
Temperature/K
360
380
400
Temperaturepredicted/K
R2
= 0.99948
Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement,
yielding an R2 of 0.99948.
cGlycol = 30%:
0 1 2 3 4 5 6 7
x 10
8
260
280
300
320
340
360
Pressure / Pa
Temperature/K
280 300 320 340 360 380 400
280
300
320
340
360
Temperature measured / K
Temperaturepredicted/K
bisecting line
Tinit
= 4ºC
Tinit
= 43ºC
Tinit
= 91ºC
.
from c-dependent kC
P,T measurements
54. Summary – Compression heating properties
Methodology for measuring compression heating properties of
liquids and semi-solids developed
Validated for deionised water
Water/Glycol mixtures: 0, 25, 50, 75, 100%
From discrete to arbitrary concentrations
Next steps
Paper well in progress
Modify methodology for insulating carrier materials
Measure compression heating properties of solids
And a range of food products and/or model food substances
55. Outline
Contribution to FSA’s Key Success Indicators
Main projects
Core research areas
Modelling High Pressure Thermal Sterilisation (HPTS)
Compression heating propertiesCompression heating properties
Development of temperature loggers for HPTS processes
Other projects
Future Work
56. HP-T process logger
- A novel approach for measuring- A novel approach for measuring
temperatures at HPHT conditions
57. In HPHT processing accurate temperature control is
indispensable
Heat retention aids (e.g. insulated carriers) are not always reliable
Thermocouple Issue
Fail after several runs
Readings may be disturbed by internal heaters
Motivation
Readings may be disturbed by internal heaters
Wireless systems needed
Temperature mapping of empty carriers/vessels …
… also filled carriers
Tracing of process temperature
Assistance in regulatory approval
58. The shell
Highly stress resistant
Low specific heat capacity
heat sink effect minimal
High thermal conductivity
The data logger
Wireless temperature logger
The system = pressure resistant shell + data logger
Prototype
stable for more
than 70 runs
P = 600- 800 MPa
and
T ≤ 130°C
Temperature range 0ºC ≤ T ≤ 130ºC
Measurement intervals ≥ 1 s
Memory: 4,000 logs per run New
design
No clamps
required
59. Delayed readings due to:
Temperature logger’s
inherent delay
Heat transfer
through
The system = pressure resistant shell + data logger
- The problem
55
60
65
thermocouple
thermochron
500
600
pressure
HP-T logger
through
aluminium shell
Multi-step HP process
0 500 1000 1500 2000
35
40
45
50
55
time / s
temperature/ºC
0 500 1000 1500 2000
0
100
200
300
400
pressure/MPa
60. The solution – Multistep algorithm
Reverse logic algorithm
Step 1 calculates temperatures inside the
shell accounting for delayed readings of
temperature logger
Step 2 performs “self-calibration” of deviceStep 2 performs “self-calibration” of device
Step 3 predicts temperature outside the shell
based on energy balance
dt
dT
mcQ P=& ThAQ ∆=&
dt
dT
hA
mc
tTtT
shellinP
shellinreal
_
_ )()( +=
Energy required to heat the shell Energy flow due to temperature difference
61. Validation of measurements
- Retort trials T = 121ºC, p = 2 bar
105
110
115
120
125
temperature/ºC
TC measurement
recalculated temperature in shell
recalculated temperature outside
Magnified view of
end of holding stage
Magnified view of end of temperature
come-up stage
400 500 600 700 800 900 1000 1100
100
time / s
recalculated temperature outside
thermocouple in retort
2600 2700 2800 2900 3000 3100 3200
100
105
110
115
120
time / s
temperature/ºC
TC measurement
recalculated temperature in shell
recalculated temperature outside
thermocouple in retort
62. Validation of algorithm
- with thermocouple in HP trials, 3L system
45
50
55
60
Temperature/ºC
thermocouple data
HP-T logger data
60
65
R2
= 0.9906
m*Cp
/h*A = 69
Parity plot shows very good agreement
Initial T = 45ºC
P = 0.1-300-450-600-400-150 MPa
0 500 1000 1500 2000
35
40
Time / s
35 40 45 50 55 60 65
35
40
45
50
55
Tthermocouple
/ ºC
THP-Tlogger
/ºC
p
63. Summary – HP-T process logger
Aluminium shell
Prototypes have proven to be stable at high pressure and high temperature
conditions
Due to low thermal mass, heat sink effect minimal
Latest design does not require clamps; easier to handle
Software
Reverse logic algorithm accounts for both the delay caused by the logger
and the shell
Instantaneous readings without delay
Self-calibration possible
Business Development
Industry highly interested in logger
Presentation and Brochure at IFT 2008
64. Socket for chip,
connected to
battery
Chip
Future:
microHP-T Process Logger
- A potential miniature version of the HP-T process logger
+
-
Chip
Battery
Plug for USB reader
Heat shrink
65. Outline
Contribution to FSA’s Key Success Indicators
Main projects
Core research areas
Modelling High Pressure Thermal Sterilisation (HPTS)
Compression heating propertiesCompression heating properties
Development of temperature loggers for HPTS processes
Other projects
Future Work
66. Further project involvement
Ultrasound projects
Starch modification by high and low frequency ultrasound (co-supervision of PhD student)
Ultrasound assisted tomato break (commercial)
Wool wax recovery (CSIRO TFT)
Chips modification (commercial)
Calcium infusion in pears (commercial)
Food Futures Flagship
NMR methodology for diffusion coefficient measurementNMR methodology for diffusion coefficient measurement
Rheology model fitting
Temperature mapping of drying oven
High Pressure Processing
Several internal projects (HP-T logger, Tinit-determination to reach Ttarget, …)
Operating HP unit (commercial)
HPTS concept product development (co-supervision of work experience student)
PEF modelling (thermal-only)
And more …
67. Outline
Contribution to FSA’s Key Success Indicators
Main projects
Core research areas
Modelling High Pressure Thermal Sterilisation (HPTS)
Compression heating propertiesCompression heating properties
Development of temperature loggers for HPTS processes
Other projects
Future Work
68. Future Work
Modelling, Validation and Equipment (Re)Design of Innovative Processes
HPTS modelling (Validation)
Compression heating properties (insulating materials)
Inactivation model development
PEF modelling
U/S modelling
Cost modelling of HPPCost modelling of HPP
Cool Plasma characterisation
Publications and Conferences
2-3 papers on compression heating properties as function of P and T
2 papers as outcome of HPTS2 project
Papers as outcome of students projects (PEF and Cool Plasma)
Book on “Multiphysics Modelling of Emerging Food Processing Technologies”
Symposium at IFT 2009 on “Advanced Modelling of Innovative Processes”
