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High throughput experimentsand predictive modelling
Micro-dispensing of liquid food droplets on
a solid substrate
Jimmy Perdana
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Outline
High throughput experiments
Predictive modelling
Case study: thesis
Conclusions
Q&A
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Project
High throughput experimentation on spraydrying
Current sub-project (thesis):Investigate dispensing of liquid micro-droplets (d = 80-150 μm) on a solidsubstrate
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Why: High Throughput?
Large experiments =
• Large amount of sample• Long time experimentation
• Difficult to control• Slow respond
• More expensive
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How: High Throughput?
• Small scale experiments• Can be completely different• Should be representative
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Modelling
• Simplification of reality• A tool to get a grip on reality without knowing the
reality in complete detail
• Empirical model: no underlying physical theory• Mechanistic model: based on (an) underlying
physical theory
• Prediction : e.g.: shelf life• Design : e.g.: experiments• Control : e.g.: product quality
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Droplet dispensing
Processparameters:
•
Valve opening time• Applied pressure• Needle tip diameter
Fluid properties:• Viscosity• Density• Rheological behavior
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Droplet imaging
Imaging
( )( ) ( )( )
−−−−= α α π cos1
2
1cos1
6
1
3
4 33r V
θ α −°=180 θ ≈ 145O
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Experimental result
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ΔP (Pa)
V
( n L )
80 ms (e)
60 ms (e)
50 ms (e)
40 ms (e)
Fluid : MEGDispenser tip : 0.1 mm ID
Valveopeningtime (t)
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Droplet dispensing - model
Model assumption:• Dispensing resistance is only in the
dispenser needle tip
• Steady laminar flow• No slip between fluid and needle-tip
wall• Newtonian behavior
P1
P2
x
r
L
D=2R
umax
t g L P RQt V
+∆== ρ
µ π 8
4
Poiseuille's equation
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0 0.1 0.2 0.3 0.4 0.5
ΔP (Pa)
V
( n L )
80 ms (m)
60 ms (m)
50 ms (m)
40 ms (m)
80 ms (e)
60 ms (e)
50 ms (e)
40 ms (e)
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0 0.1 0.2 0.3 0.4 0.5
ΔP (Pa)
V
( n L )
80 ms (m)
60 ms (m)
50 ms (m)
40 ms (m)
80 ms (e)
60 ms (e)
50 ms (e)
40 ms (e)
Model vs. Experiment
Model: underestimation at low ΔP needle-pistonmovement
overestimation at high ΔP higher resistance
Valveopeningtime (t)
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Model optimization
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4
8
V t g L
P RV
+
+
∆
=ρ
µ
π
Optimized parameters: L and V0
• L represents the total resistance of dispenser
• V0 represents the minimum volume
dispensed, caused by the piston movement
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Model optimization
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ΔP (Pa)
V
( n L )
80 ms (m)
60 ms (m)
50 ms (m)
40 ms (m)
80 ms (e)
60 ms (e)
50 ms (e)
40 ms (e)
L = 2.09 ± 0.027 cm (P = 0.95)V0 = 16.25 ± 1.08 nL (P = 0.95)
Valveopeningtime (t)
rLV0 = 0.71
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Model optimization
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ΔP (Pa)
V r e s i d u a l [
V m o d e
l - V e x p . ]
( n L )
80 ms
60 ms
50 ms
40 ms
Residual is scattered, confidence interval P =0.95
Valveopeningtime (t)
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Effect of fluid viscosity
Model “confirms” the effect of fluidviscosity
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ΔP (Pa)
V
( n L )
DEG
PEG
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Conclusion
•
High throughput experiments andpredictive modelling can be used as thetools to improve the research
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Thank you
Questions?