Transcript Slide 1

Dynamics in Logistics
IMSAS
Shelf life prediction by intelligent RFID Technical limits of model accuracy
Jean-Pierre Emond, Ph.D.
Associate Professor,
Co-Director
UF/IFAS Center for Food Distribution and Retailing
University of Florida
MCB
Reiner Jedermann
Walter Lang
IMSAS Institute for Microsensors, -actuators and systems
MCB Microsystems Center Bremen
SFB 637 Autonomous Logistic Processes
University of Bremen
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Outline
IMSAS
 CFDR / University of Florida
 Evaluation of quality
 Case Study “Strawberries”
 IMSAS / University Bremen
 Integration of quality models into embedded hardware
 Intelligent RFID
 Feasibility / required hardware resources
2
Center for Food Distribution and
Retailing
IMSAS
3
IMSAS
Laboratory evaluation of shelf life
models
 Several
attributes have
to be tested




color
firmness
aroma / taste
vitamin C
content
(Nunes, 2003)
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Strawberries – Case Study
IMSAS
Joint project between IngersollRand Climate Control and UF
Truck 1 - Front Pallet - Bottom
5.0
4.5
Air
Product
Temperature sensors were
placed inside and outside the
load at all locations in the trailers
4.0
Temperature (ºC)
3.5
3.0
2.5
Quality was assessed from
beginning to end
2.0
1.5
1.0
How retailers evaluate the
quality of a shipment?
0.5
0.0
-0.5
-1.0
Wed 07/13
Thu 07/14
Fri 07/15
Sat 07/16
Sun 07/17
Mon 07/18
Tue 07/19
Economic impact of monitoring
temperature and quality prediction
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Strawberries – Case Study
IMSAS
P3 - Temperature - Transport
10
= 3 full days
9
= 2 full days
= 1 full day
= 0 day
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Temperature (ºC)
7
6
5
4
3
2
1
0
09/29/05
17:45
09/30/05
05:45
09/30/05
17:45
10/01/05
05:45
10/01/05
10/02/05
17:45 Time 05:45
10/02/05
17:45
10/03/05
05:45
10/03/05
17:45
Air Temperature (ºC) - B
Pulp Temperature (ºC) - B
Air Temperature (ºC) - C
Pulp Temperature (ºC) - C
Air Temperature (ºC) - T
Pulp Temperature (ºC) - T
RFID Temperature Tag + Prediction Models
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IMSAS
Strawberries – Case Study
FEFO = First expires first out
= 3 full days
RFID + Models decision:
= 2 full days
= 1 full day
= 0 day
2 pallets never left origin
2 pallets rejected at arrival
5 pallets sent immediately for stores
8 pallets sent to nearby stores
7 pallets with no special instructions (remote stores)
RFID Temperature Tag + Prediction Models
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Strawberries – Case Study
Days Number
left of pallets
IMSAS
Waste
random
retail
Waste
(RFID +
Model)
(Recommendation)
0
2
91.7%
(rejected)
(don’t transport)
1
5
53 %
(25%)
(sell immediately)
2
8
36.7%
(13.3%)
(nearby stores)
3
7
10%
(10%)
(remote stores)
Results at the store level (22 pallets sent)
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IMSAS
Strawberries – Case Study
Actual
RFID + Model
REVENUE
COST
$47,573
$49,876
$58,556
$45,480
PROFIT
($2,303)
$13,076
Revenue and Profit
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IMSAS
The idea of intelligent RFID
 Avoid communication bottleneck by preprocessing temperature data inside RFID
Function to access
effects of temperature
onto quality
Only state flag transmitted at
read out
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Shelf Life (days)
Temperature
curve
12
6
T (°C)
0
0
10
20
30
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Chain supervision by intelligent RFID
Step 1:
Configuration
Step 2:
Transport
Manufacturer
Step 3:
Arrival
Reader gate
Measures and
stores
temperature
List
Calculates
shelf life
• Shelf life
IMSAS
Step 4:
Post control
Handheld
Reader
Full protocol
• Temperature
• Transport Info
Sets flag on
low quality
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IMSAS
Modeling Approaches
Reaction kinetic model (Arrhenius)
Shelf life / loss in days .
 Different model types
10
8
Tables for different temperatures
6
5
Taste
4
4
Loss per Day
4.8 days
shelf life at
6 °C
Tripple speed of
quality decay at
14 °C
Reference
temperature
6 °C
2
1
Temperature °C
0
0
3
0 °C
5 °C
2
10 °C
15 °C
20 °C
1
0
Shelf life(T)
Activation
energy for
Lettuce
2
4
6
Days 8
5
10
15
20
Differential equation for bio-chemical
processes
d[P] / dt
= −kPPO*[P]
d[PPO] / dt = kPPO[P] − kbrown*[PPO]
d[Ch] / dt = kbrown*[PPO]
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IMSAS
Example Table Shift Approach
Color index for Mushrooms
Color index(Scaled to 20 as initial value)
 Only curves for
constant
temperature are
known
 How to calculate
reaction towards
dynamic
temperature?
 Interpolate over
temperature and
current quality to
get speed of
parameter change
20
4 °C
8 °C
12 °C
18 °C
Temperature
Change from
12 °C to 4 °C
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16
14
12
10
8
6
4
2
0
0
5
10
15
20
25
Time in Days
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Model accuracy
IMSAS
 Measurement tolerances
 Parameters like firmness or taste have high
measurement tolerances
 Question: Is this table shift approach allowed?
 Yes, if all entailed chemical processes have the
similar activation energies (similar dependency to
temperature)
 Otherwise testing for the specific product required
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IMSAS
Simulation
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 Parameter
tolerances 1 %
and 5%
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Temperature °C and color index
 Comparison of
reference model
(Mushroom
DGL) with table
shift approach
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14
12
10
8
6
Temperature °C
Diff. equation model
Table interpolation R=1%
Table interpolation R=5%
4
2
0
0
2
4
6
8
10
12
14
16
18
20
Time in Days
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Hardware Platforms
IMSAS
Wireless sensor nodes
 Tmode Sky from Moteiv
 Own development (ITEM)
 Goal
 Integration into
RFID-Tag
 Comparable to RFID
data loggers
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IMSAS
Required Hardware Resources
Type of
Resource
Calculation of
Arrhenius
equations
Look up table
for Arrhenius
model
Table-Shift
Approach
1.02 ms
0.14 ms
1.2 ms
Program
memory
868 bytes
408 bytes
1098 bytes
RAM memory
58 bytes
122 bytes
428 bytes
Energy
6 µJoule
0.8 µJoule
7 µJoule
Processing time
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IMSAS
Available Energy
 Power
consumption of
model is not the
issue
 Multi parameter
models are
feasible on low
power
microcontroller
 Reduce stand by
current
Power consumption per month
Update every 15 minutes
(Table shift / 1 Parameter)
20 mJ / month
Stand by current of MSP430
(1µA at 2.2V)
5700 mJ / month
Typical battery capacities
Button cell
300 … 3000 J
Turbo Tag (Zink oxide battery)
80 J
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Summary and Outlook
IMSAS
 Case study (strawberries) showed the potential
to reduce waste and increase profits
 Quality evaluation of the level of RFID tags is
feasible
 Testing on existing hardware of sensor nodes
 Development of new UHF hardware required
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The End
IMSAS
Thanks for your attention
www.intelligentcontainer.com
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