The document describes simulations and experiments on microwave heating of food. It discusses using coupled software to simulate electromagnetic fields and heat transfer to model microwave heating. Temperature distributions from simulations are validated against measurements using magnetic resonance imaging. Feedback controlled simulations are proposed to optimize temperature distributions for processes like pasteurization by regulating microwave power over time.

1. August 11,
1Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
40th Annual Microwave Symposium
Boston, MA
Controlled Simulations of
Microwave Thermal Processing of
Arbitrarily Shaped Foods
K. Knoerzer, M. Regier, H. Schubert, H.P. Schuchmann
Institute of Process Engineering in Life Sciences
Dept. I: Food Process Engineering
Universität Karlsruhe (TH)
Research University · founded 1825

2. August 11,
2Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
outline
• simulation of microwave heating
 motivation
 our approach of simulation
• validation of the simulated data
 conventional methods
 temperature mapping using magnetic resonance imaging
• optimization of temperature distributions by
feedback-controlled simulations
• conclusions

3. August 11,
3Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
outline
• simulation of microwave heating
 motivation
 our approach of simulation
• validation of the simulated data
 conventional methods
 temperature mapping using magnetic resonance imaging
• optimization of temperature distributions by
feedback-controlled simulations
• conclusions

4. August 11,
4Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
simulation of microwave processes avoids ‘trial-and-error’
motivation:
simulation of complete process of microwave treatment
design and optimization of microwave processes
without elaborate ‘trial-and-error’

5. August 11,
5Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
Linking two commercial software packages:
• QuickWave-3D™: simulation of electromagnetic fields
 FDTD (finite difference time domain)
• COMSOL™: simulation of heat transfer
 FEM (finite element method)
 graphical user interface created in MATLAB™ controls both
software packages
Our method for simulation of MW processes:
Based on an interface coupling commercial software packages

6. August 11,
6Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
simulated geometry – part of a developed microwave device
rectangular waveguide
microwave generator circular
waveguide magnet
sample
waterload
appr.2.5m
birdcage
simulated
area

7. August 11,
7Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
QuickWave-3D™: simulates E-fields and local power dissipation
graphical user interface:
waveguide
(d = 84 mm)
cylindrical
sample
(d = 33 mm,
h = 34 mm)
water load
ε‘,ε‘‘ = const.

8. August 11,
8Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
COMSOL™: simulates heat transport
z/m
x / m
y / m
cylindrical
sample
(d = 33 mm,
h = 34 mm)

9. August 11,
9Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
QuickWave-3D™: calculation of 3D distribution of dissipated
power
pdiss/
W*mm-3
y / mm
x / mm y / mm
x / mm
z/mm
MW power

10. August 11,
10Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
z/mm
x / mm y / mm
T/K
theating = 300 s, PMW = 19 W, d = 33 mm, h = 34 mm
surrounding
conditions:
- Text = 295 K
- free convection
COMSOL™: coupled simulation calculates temperatures
locally and time specified

11. August 11,
11Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
COMSOL™: coupled simulation calculates temperatures
locally and time specified
MW treatment: ttotal = 1000 s, tpower,on = 50 s, tpower,off = 500 s, PMW = 19 W
surrounding
conditions:
- Text = 295 K
- free convection

12. August 11,
12Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
outline
• simulation of microwave heating
 motivation
 our approach of simulation
• validation of the simulated data
 conventional methods
 temperature mapping using magnetic resonance imaging
• optimization of temperature distributions by
feedback-controlled simulations
• conclusions

13. August 11,
13Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
 to avoid the problems: Magnetic Resonance Imaging (MRI)
advantage: non-invasive determination of temperature specified locally
and in time
previous methods:
• thermocouples
• fibre optic probes
• model substances
• thermo paper
• liquid crystal foils
• infrared thermography
measuring temperatures in electromagnetic fields:
a challenging task

14. August 11,
14Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
Bruker Biospin/
Oxford Instruments Super-
Wide-Bore Cryomagnet
max. diameter 64 mm
magnet. flux: 4.70 T
 ωL = γ·B0  fprecession = 200 MHz
• magnet
• birdcage (RF coil)
the deployed MRI tomograph:
a new tool for measuring temperature distributions
64 mm

15. August 11,
15Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
design of the microwave device for inline observation of
temperature and/or humidity changes
rectangular waveguide
microwave generator circular
waveguide magnet
sample
waterload
appr.2.5m
birdcage

16. August 11,
16Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
measurement data – short times, high resolution
• pulse sequence: GEFI (gradient echo fast imaging)
• varying number of 2D - slices:
- perpendicular to z-axis
- slice thickness: 1 mm
- 64 x 64 pixels (birdcage diameter: 64 mm)
 3D - resolution: 1 mm³
• measurement time: < 13 s
• accuracy: ± 2 K

17. August 11,
17Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
output of MRI measurement:
3D temperature distribution as function of time
heating of a model food cylinder:
discrete time: theating = 300 s, PMW = 19 W, d = 33 mm, h = 34 mm
slices
x - dim / mm y – dim. / mm
T/K

18. August 11,
18Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
output of MRI measurement:
3D temperature distribution as function of time
T/K
MW treatment: ttotal = 883 s, tpower,on = 50 s, tpower,off = 500 s, PMW = 19 W

19. August 11,
19Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
simulation
measurement
x / mm y / mm
z/mm
x / mm y / mm
slices
T / K
MW heating of a model food cylinder: PMW = 19 W, t = 170 s
comparison: simulation vs. measurement
good qualitative agreement

20. August 11,
20Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
comparison: simulation vs. measurement in selected spots
good quantitative agreement (physical properties = f(T))
heating curve in a hot and a cold spot (λ,cP,ε“ = f(T))
MW heating:
PMW = 19 W,
tMW on = 50 s
tMW off = 500 s
hot spot measured
cold spot measured
hot spot simulated
cold spot simulated

21. August 11,
21Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
comparison: simulation vs. measurement in a selected slice
good quantitative agreement (physical properties = f(T))
comparison of temperatures: identical locations of an intersecting plane
(λ,cP,ε“ = f(T))
MW heating:
PMW = 19 W,
tMW on = 50 s
tMW off = 500 s
temperature (measured) / °C
temperature(simulated)/°C
bisecting line

22. August 11,
22Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
• simulation of microwave heating
 motivation
 our approach of simulation
• validation of the simulated data
 conventional methods
 temperature mapping using magnetic resonance imaging
• optimization of temperature distributions by
feedback-controlled simulations
• conclusions
outline
challenge:
simulation of real inhomogeneous foods

23. August 11,
23Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
MRI: measurement of 3D structures of real foods and
determination of different materials
example: chicken wing

24. August 11,
24Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
MRI: measurement of 3D structures of real foods and
determination of different materials
example: chicken wing

25. August 11,
25Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
QuickWave-3D™: calculation of 3D distribution of dissipated
power
MW power

26. August 11,
26Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
MW treatment: ttotal = 1000 s, tpower,on = 200 s, tpower,off = 800 s, PMW = 25 W
T / K
COMSOL™: coupled simulation calculates temperatures
locally and time specified

27. August 11,
27Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
outline
• simulation of microwave heating
 motivation
 our approach of simulation
• validation of the simulated data
 conventional methods
 temperature mapping using magnetic resonance imaging
• optimization of temperature distributions by
feedback-controlled simulations
• conclusions

28. August 11,
28Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
levels of controlling microwave applications
Pconst
MWA
T(t,x,y,z)
Pconst
MWA
T(t,x,y,z)
Pconst
control PR(t)
MWA
T(t,x,y,z)
measuring
element
Tm(t,x,y,z)
(i) not controlled
(ii) controlled
(iii) feedback-controlled
control
PC(t)

29. August 11,
29Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
simulation allows for „feedback-controlling“ microwave
applications
feedback-control of microwave processes is difficult, because
temperature measurements (mostly) are:
• not inline
• not locally specified
• too expensive for daily use (MRI)
feedback-control is possible in simulations
new process controls can be developed by feedback-
controlled simulations
but:
in a simulation temperatures are well known at every time, in every
spot, i.e.:

30. August 11,
30Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
example: pasteurization
definition:
pasteurization is the process of short-time heating of foods (appr. 60 to 90°C)
for the purpose of killing all pathogenic viable organisms.
problem in conventional pasteurization processes (in case of solid foods):
limiting factor regarding process time and product quality is the thermal conductivity
advantage of microwave processes:
volumetric heating across the complete product,
but: uneven temperature distributions
www.wadsworth.org/databank/ecoli.htm
E. coli
Penicillium
(molds)
avian influenza H5N1
source: dpa
Mycobacterium tuberculosis

31. August 11,
31Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
implementing an ON/OFF-feedback-control in the simulation
(example of a model food cylinder)
T0 = 295 K
Tmax = 343 K
Ttarget = 333 K
Tsurrounding = 295 K
pasteurization requires regulation of the simulations regarding
Tmax and Tmin (pure MW heating  Ttarget could not be reached)
target temperature
maximum temperature
time / s
temperature/K

32. August 11,
32Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
target temperature
maximum temperature
time / s
temperature/K
implementing an ON/OFF-feedback-control in the simulation
(example of a model food cylinder)
T0 = 295 K
Tmax = 343 K
Ttarget = 333 K
Tsurrounding = 333 K
pasteurization requires regulation of the simulations regarding
Tmax and Tmin (combined process  Ttarget could be reached)

33. August 11,
33Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
controlled simulation allows for optimization of the temperature
distribution during microwave heating
T / K
implementing an ON/OFF-feedback-control in the simulation
(example of a model food cylinder)
T0 = 295 K
Tmax = 343 K
Ttarget = 333 K
Tsurrounding = 333 K

34. August 11,
34Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
output: microwave power pulse program for a secure
pasteurization procedure
time / s
microwavepower/W

35. August 11,
35Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
outline
• simulation of microwave heating
 motivation
 our approach of simulation
• validation of the simulated data
 conventional methods
 temperature mapping using magnetic resonance imaging
• optimization of temperature distributions by
feedback-controlled simulations
• conclusions

36. August 11,
36Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
conclusion
• microwave applications offer advantages compared to conventional
processes of thermal food treatment
• but serious disadvantages as well  mainly inhomogeneous heating
patterns
• new approach for simulating microwave heating allows for a complete
calculation of the heating patterns in arbitrarily shaped foods and thus:
 „manual“ optimization of geometries (oven, product)
 regulation of the MW power on the basis of arising temperatures

37. August 11,
37Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
Acknowledgement
German Research Foundation (DFG)
for financial support in research group
Dr. Edme H. Hardy
Emilio Oliver Gonzalez

38. August 11,
38Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering

39. August 11,
39Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
backup-slides:
basics of MRI
measuring water contents and temperatures

40. August 11,
40Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
• protons (1
H) have a nucleus spin:
• and thus a magnetic moment:
• an external magnetic field B0
causes an alignment/orientation
of the magnetic moments
 magnetisation M
+ magnetic moment
nucleus spin / angular
momentum
I
M B0
µ
I

⋅= γµ
what is magnetic resonance (MR)?

41. August 11,
41Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
• the magnetic field B0 causes a
precession of the magnetisation M
Lamor frequency:
• HF-pulses switches the magnetisation M
in the XY-plane (90°-pulse) Uind
0BL ⋅= γω
• MR-signal ~ spin density  H-density  water content
B0
M
• this precession of the magnetisation M
generates an AC voltage in an RF coil
 MR-signal
B0
M(t0)
M(t>tp)
ψ
φ
M(tp)
magnetic resonance allows to measure 3D water distribution ...

42. August 11,
42Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
measuring temperature by MRI
• based on the temperature dependence of the water proton
chemical shift  precession frequency and thus spin angle (phase)
decreases (0.01 ppm / °C ≙ 2 Hz / °C in our tomograph)
• calculation of the temperature from a measured phase difference
between the sample with known initial temperature and the heated
sample
B0
ϕ
B0
ψ
known temperature increased temperature
… and also 3D temperature distribution

43. August 11,
43Institute of Engineering in Life Sciences
Dept. I: Food Process Engineering
phase image at
known temperature
phase image at
increased temperature
difference image temperature distribution
∆f/H
z
T/K
from phase image to temperature image