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Particle ScienceTheory and Practice
Ian Haley
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Outline of Modules 1
The Basics Part 1 - What is Particle Size
Part 2 - Presentation Method and Weighting
Part 3 – The Importance of Shape
Part 4 – Count-based Measurement
Instrumentation for Particle Characterisation Part 5 – FBRM and The Chord Length Distribution
Part 6 – Chord Length and Particle Shape
Part 7 - An Outline of Different Particle Characterisation Methods and the
Effect of Particle Shape
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Outline of Modules
Statistics and Data Handling Part 8 - Understanding the Mean, Median and Mode of a Particle System
Part 9 – Precision and Accuracy
Part 10 – Correlating FBRM to Other Data
Part 11 - Channel Grouping and Statistics
Part 12 – Signal Aliasing
Practical Aspects of Using FBRM Part 13 - Probe Location and Orientation
Part 14 – Standard Procedures
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Part 1: What is Particle Size?
Ian Haley
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Particle Size
Particle “size” = 326 µmBut how was it calculated?And what does this tell us about the particle and others like it?
Many particles are complexThree-dimensional objects.Yet we want to represent their‘size’ by just one number.
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Size
What is the size of this particle?
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10m
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Size
But what is the size of this particle?
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10m
50m
50 microns?
10 microns?
30 microns?
Some other number?
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What is particle size?
“There is no single definition of particle size… In this (document), particle size is defined as the diameter of a sphere having the same physical properties; this is known as the spherical equivalent diameter.” (Source: International Standards Organization (ISO 9276-1:1998))
?
By volume By surface area
By settling
velocityBy sieve analysis
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Part 2: Presentation Method and Weighting
Ian Haley
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Particle Size and Physical Property
“There is no single definition of particle size… In this (document), particle size is defined as the diameter of a sphere having the same physical property; this is known as the spherical equivalent diameter.” (Source: International Standards Organization (ISO 9276-1:1998))
?
By volume By surface area
By settling
velocityBy sieve analysis
The physical property we use has a major effect on the way in which ”particle size” is calculated.
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Using a Physical Property to Calculate Mean Size
What is the average size of this two-particle system?
In other words, “What does my PSA lab tell me?”
Diameter: 10µm Diameter: 100µm
Surface area: 314µm2
Volume: 524µm3
Surface area: 31,400µm2
Volume: 524,000µm3
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Mean size: calculated statistics for a two-particle system
These numbers are all the “correct” average size.
There are a large number of other methods of calculation that would be correct as well.
Diameter: 10µm Diameter: 100µm
Mean diameter
Number-based dist. = 55.0 µm
Area-based dist. = 99.1 µm
Volume-based dist. = 99.9 µm
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Mean size for equal volumes of 100 µm and 10 µm particles
Equal Volumes
Number-based dist. = 10.1 µm
Area-based dist. = 18.2 µm
Volume-based dist. = 55.0 µm
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Comparing different physical properties
One 100 µm particle (A) contains the same quantity of material as one-thousand 10 µm particles (B)
A B
Total number 1 particle 1000 particles
Diameter 100 µm 10 µm
Surface area 31,400 µm2 314,000 µm2
Total volume 524,000 µm3 524,000 µm3
A B
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©2006 Mettler-Toledo AutoChem, Inc.
10µm Diameter: 100µm
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Number and Volume Distributions
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©2006 Mettler-Toledo AutoChem, Inc.
10µm Diameter: 100µm
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Sensitivity of Each Distribution to Fines
Insensitive to change fines!
Sensitive to change in fines!
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©2006 Mettler-Toledo AutoChem, Inc.17
Coarse in the presence of fines
1000
10µm
100µm
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©2006 Mettler-Toledo AutoChem, Inc.18
Adding one coarse particle
1000
10µm
100µm 100µm
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©2006 Mettler-Toledo AutoChem, Inc.19
Detecting breakage in a particle system
Breakage of 1 large particle into 1000 small particles
Initial particle system
After breakage of one particle
Relative change
Total number 27 particles 1026 particles 3700% increase
Total surface area 847,000 µm2 1,161,800 µm3 37% increase
Total volume 14,140,000 µm3 14,140,000 µm3 0%
Number mean diameter 100.0 µm 12.3 µm 87.7% decrease
Volume moment mean diameter 100.0 µm 96.7 µm 3.3% decrease
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Why is the particle distribution range important?
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All three distributions have the same mean but significantly different distributions!
Particle dimension (µm)
Pro
babi
lity
(%)
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Defining Number and Square Weighting
What is the mean of this distribution?
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Weighted vs. Unweighted Distributions, Part I
Example: A Population of Cubes
The size distribution is presented as: a) Total number
b) Total length (based on projected area)c) Total surface area d) Total volume of particles within each size classification
Mean = 7.1 µm Mean = 9.2 µm
Mean = 12.2 µm Mean = 15.2 µm
Which distribution is the most sensitive in your application?
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Number, Length, Area, Volume Distributions
Presentation method determines sensitivity to different regions of the population. (Example: Population of Cubes)
Number Distribution
Area Distribution
Length Distribution
Volume Distribution
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Data presentation of cube distribution
The statistic choice/presentation method determines the sensitivity to different regions of the population.
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Sq Weighted vs. Unweighted Distributions
Example: A population of cubes
Mean = 12.2 µm Mean = 15.2 µm
FBRM®
No Weight
FBRM®
Square Weight
Emphasizes changes to the fine (small) end of the distribution
Emphasizes changes to the coarse (large)
end of the distribution
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Square weighted Distribution
Weighted vs. Unweighted Distributions (1)
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On Left: No Weighted FBRM distributions at 3 time points show a decrease in count and an increase in dimension
Unweighted distribution is sensitive to fine particles population
No Weighted Distribution
Time=0 min
Time=90 min
Time=180 min
On Right: Square Weighted FBRM distribution for same 3 time points show enhanced resolution to growth in coarse particle dimension
Square weighted distributions is sensitive to coarse particle dimension
Time=0 min
Time=90 min
Time=180 min
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FBRM Distributions and Trended Statistics
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Unweighted Distribution
#/s <50 µm
#/s 50-1000 µm
Square Weighted Distribution
Time=0 min
Time=90 min
Time=180 min
Time=0 min
Time=90 min
Time=180 min
Mean0 = 75µm
Mean180 = 141µm
Mean90 = 82µm
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Weighted vs. Unweighted Distributions, (1)
Distribution weighting (by number, length, area, or volume) can significantly enhance or reduce the resolution to change.
Selecting the appropriate weighting function will enhance the changes that directly relate to the application goal.
In this example, the square-weighted distribution does not detect small changes in the concentration of fine material.
However, the unweighted distribution is very sensitive to the amount of fine material present.
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Weighted vs. Unweighted Distributions, (2)
Distribution weighting (by number, length, area, or volume) can significantly enhance or reduce the resolution to change.
Selecting the appropriate weighting function will enhance the changes that directly relate to the application goal.
However, the square-weighted distribution is very sensitive to the amount of coarse material.
In this example, the unweighted distribution does not detect small changes in the concentration of coarse material.
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Definitions: Fines vs. Coarse
Our descriptions of “fine” and “coarse” material are relative
Terms are used to describe the smallest and largest particles in a given system
Definitions are system specific
Fines Coarse
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When talking particles, “size” is a generic term
Reporting of particle size must include definitions of:
The physical property selected to characterize size of the particles, for example:- Diameter- Chord length- Projected area- Surface area- Volume- Settling rate- Response of electrical, optical, or acoustical field
Statistical calculations and display, for example:- Number-based distribution- Length-based distribution- Area-based distribution- Volume-based distribution- Scale (log vs. linear)- Channel grouping- Count vs. normalized
Assumptions, for example:- Shape- Refractive index- Coincidence effects
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Sensitivity to a given particle system trait depends on the chosen statistic
Distribution mean depends on both the property measured and the calculation used to characterize the particle distribution.
- Property Sphere having the same settling rate Sphere fitting through the same-size sieve aperture Sphere producing a similar diffraction pattern No shape assumption - Chord length distribution
- Calculation Number Length Area Volume
Different techniques use different properties to calculate size. None are fundamentally wrong, they just measure different properties of the particles.
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Part 3: The Importance of Shape
Ian Haley
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The Volume Spherical Equivalent Diameter (VSED)
The surface area of the needle is 60% greater than that of the sphere.
100 µm
10 µm
Calculate the diameter of a sphere with the same volume as a ‘needle’
24.7 µm
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Why do we assume all particles are spherical?
Simplicity: A sphere is the only shape that can be described by one unique number (the diameter), regardless of the particle’s orientation.
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How can particle size be calculated for non-spheres?
18 µm
Calculate the diameter of a sphere with the same volume as the cylinder
50 µm
5 µm
How does the VSED relate to the length and width of this particle?
The surface area of the cylinder is 73% greater than the sphere!
A needle will handle and flow differently to a sphere!
A spherical equivalent diameter based on volume
(VSED)
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How can particle size be calculated for non-spheres?
Calculate the diameter of a sphere with the same volume as the cylinder
100 µm
5 µm
24 µm
The cylinder has doubled in length; but the diameter of the equivalent sphere has only increased by 33%
A spherical equivalent diameter based on volume
(VSED)
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Is the Spherical Equivalent Diameter practical?
YES!
NO!
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Can the SED help improve a process?
Run 5
Run 3
Run 5
Run 6
Very different morphologies for three batches of the same process
Would a SED provide meaningful information about the process?
Could it help improve the process?
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Even if a true spherical equivalent diameter was measured…
In a chemical process, do spherical and non-spherical particles behave the same way?
Can spheres be used to model the behavior of non-spherical particles?
- They do not have the same surface area or flow properties.
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Shape: If particles are not spherical, what happens?
Most instruments will assume that the signal is derived from a spherical particle, and in turn derive a spherical equivalent diameter based on this incorrect assumption.
At this point, there are no successful correction factors in commercial software (excluding some image analysis packages) that account for non-spherical shapes. Most will generally track growth or reduction of shaped particles, but not in an absolute sense.
The further particle shape moves away from a sphere, the less accurate instruments based on a spherical equivalent model become.
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Part 4: Count-based Measurement
Ian Haley
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Count-Based Particle Size Measurement
Instruments and techniques used to measure particle size fall into
two key groups:
A measurement is made on a ‘cloud’ or ‘ensemble’ of particles simultaneously.
Particles are not measured individually
The distribution is expressed as size vs percentage or ‘distribution density’
These are normalised techniques
Conversely….
Some instruments derive their data by measuring particles individually
The data is sensitive to changes in population
The distribution is expressed as size vs number
These are count-based techniques
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Normalized vs. Count-Based Distributions, Part 1
Count-based distributions display changes in particle dimension and/or changes in the number-based particle concentration.
Normalization removes particle population information from the data.
In this example, particle dimension is held constant as the concentration of the dispersed phase increases.
While the number of measured chords increases, the normalized distribution shows that the size and shape of the particles remain relatively unchanged.
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Normalized vs. Count-Based Distributions, Part 2
Each channel in a count-based distribution is independent of change occurring in other ranges of the distribution.
Each channel in a normalized distribution is dependent on changes occurring in other regions of the distribution.
The normalized, unweighted distribution indicates dramatic relative change in this size region.
The count-based distribution shows that the actual number of particles measured in this range did not change.
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Count-Based Particle Size Measurement
Allows you to measure particle population
You can track changes in particle concentration
Track absolute changes in isolated size regions, independent of other
size regions
But, normalized techniques:
Hide the effect of concentration
Only relative changes in concentration can be tracked
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Part 5: FBRM and The Chord Length Distribution
Ian Haley
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How does FBRM work?
FBRM® Probe TubeFBRM® Probe Tube
SapphireWindowSapphireWindow
Beam splitterBeam splitter
Rotating opticsRotating optics
FBRM® Probe TubeFBRM® Probe Tube
SapphireWindowSapphireWindow
Laser source fiberLaser source fiber
Beam splitterBeam splitter
Rotating opticsRotating optics
Focused beamFocused beamFBRM® Probe TubeFBRM® Probe Tube
SapphireWindowSapphireWindow
Detection fiberDetection fiber
Laser source fiberLaser source fiber
Beam splitterBeam splitter
Rotating opticsRotating optics
Focused beamFocused beamFBRM Probe TubeFBRM Probe Tube
SapphireWindowSapphireWindow
Cutaway view of FBRM In-process Probe
PVM® image illustrating the view from the FBRM Probe Window
Probe installed in process stream
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What is FBRM® Technology?PVM® image illustrating the view from the FBRM® Probe Window
Enlarged view
Probe detects pulses of Backscattered light
And records measured Chord Lengths
Path of Focused Beam
This core patented technology is called Focused Beam Reflectance Measurement [FBRM®]
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FBRM® Method of Measurement
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What is FBRM® Technology ?
Path of Focused Beam
Enlarged view
Thousands of Chord Lengths are measured each second to produce the FBRM® Chord Length Distribution :
Typical FBRM applications include:-Crystallization-Formulations-Precipitation-Polymerization -Emulsification-Microencapsulation -Dissolution and disintegration-Flocculation-Fermentation
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FBRM Instrument Configuration – Standard Focal Position
Standard Focal Position--0.02 mm (20µm inside the window) measured from outside surface of probe window.
Advantages-In majority of cases, provides excellent sensitivity to real-time change in count and dimensions of particle population.
-Minimizes noise from properties of the system that are not under investigation. Process flow
direction
Sapphire Window
Focused Beam
Rotating Lens
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What Happens if the Focal Point is Outside the Window?
Outbound:
Intensity of focused beam is degraded by
- Absorption by the carrying fluid.
- Attenuation due to particles in front of the measuring zone.
Return:
Return signal is also degraded by- Absorption by the carrying fluid.
- Attenuation due to particles between the window and measuring zone.
Note: Particles between the window and the measuring zone will reflect light that will be detected as background signal. This will significantly degrade the signal-to-noise ratio.
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Chords are Measured from every AspectChords are Measured from every Aspect
Chord Lengths from a Sphere
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Chord length Probability: A Graphical Approach
1234567891 0
1 2 3 4 5 6 7 8 9 10C
ount
s
C hordlength [a.u.]
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Part 6: Chord Length and Particle Shape
Ian Haley
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Chords are Measured from every AspectChords are Measured from every Aspect
Chord Lengths Measurements, FBRM
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FBRM Particle Shape
A chord length distribution is a function of average shape and dimension of particles and particle structures as they actually exist in process.
- No shape is assumed.
- Affect of shape on FBRM measurement is known.
- In most cases the affect of shape on measurement can be filtered out or enhanced to track the change.
Sphere
Needle
Platelet
Sphere
Platelet
Needle
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Monodispersed Distribution of Spheres, SED 500 µm
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Normal Distribution of Spheres, Mean SED 200 µm, Std Dev 25 µm
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Normal Distribution of Needles AR 2:1, Mean SED 200 µm, Std Dev 25 µm
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Normal Distribution of Needles AR 4:1, Mean SED 200 µm, Std Dev 25 µm
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growth of a needle
Modelling Chord Length Distributions 1
Ruf, A., Worlitschek, J, Mazzotti, M. Modeling and Experimental Analysis of PSD Measurements through FBRM. Part & Part Syst Characterization. 17 (4), 167-179, 2001.
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growth of a needle
Modelling Chord Length Distributions 2
Ruf, A., Worlitschek, J, Mazzotti, M. Modeling and Experimental Analysis of PSD Measurements through FBRM. Part & Part Syst Characterization. 17 (4), 167-179, 2001.
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growth of a needle
Modelling Chord Length Distributions 3
Ruf, A., Worlitschek, J, Mazzotti, M. Modeling and Experimental Analysis of PSD Measurements through FBRM. Part & Part Syst Characterization. 17 (4), 167-179, 2001.
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Distributions
How do we define a collection of particles of differing size and/or shape?
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Number, Length, Area, Volume Distributions
Presentation method determines sensitivity to different regions of the population. (Example: Population of Cubes)
Which statistics are sensitive to length or width?
Square Wt
0
100000000
200000000
300000000
400000000
500000000
600000000
700000000
1 2 3 4 5 6 7 8
Dimension
0
100
200
300
400
500
600
1 3 5 7 15 25 40 65
Co
un
t/sec
Dimension
No Wt
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Part 7: An Outline of Different Particle Characterisation Methods
and the Effect of Particle Shape
Ian Haley
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PVM® TechnologyParticle Video Microscope
Microscope quality images, in-process and in real time
Characterize particle systems from 2μm to 1mm
FBRM® TechnologyFocused Beam Reflectance Measurement
Track real-time changes in particles and droplets as they naturally exist in the process
Characterize particle systems from 0.5μm to 3mm
In-Situ Particle Characterization Tools
10 µm droplets
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Imaging and Image Analysis METTLER TOLEDO PVM enables qualitative and quantitative particle
system characterization
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9 chords
1 long chord
8 fine chords
The following schematic represents the change in morphology.
Long Needles
Short cubic/diamond crystals
Tracking the shape change with FBRM
5 chords
5 medium chords
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PVM® Shows Seed Morphology
t=10mins
PVM® images show metastable seeds are long needle shaped crystals
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PVM ® and FBRM ® Identify Habit Shift (Form Conversion)
t=25mins
At 25mins - polymorphic transformation occurs
The habit shifts from needles to blocks
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FBRM ® and PVM ® Identify When Conversion is Complete
t=45mins
After 45mins the transformation is complete
The FBRM® distribution is narrower and tighter
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How Laser Diffraction Works
Dilute sample (0.01% wt or lower) of particles added to Laser Diffraction bench top system
They are circulated to the measurement zone (particles can break, dissolve, etc during this step)
Particles are illuminated by a laser beam in transmission.
The particles scatter this light in all direction, the light scatter on the detector is collected (diffraction pattern)
A mathematical model (Mie and/or Fraunhofer theory) is used to fit a diffraction pattern of spheres with the measured diffraction pattern
Dilute Particles DetectorLaser
For Internal Use Only
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What does the Laser Diffraction size data look like?
Volume Based Distribution
Normalized distribution
Distribution Assuming all particles are spheres
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Sample 1: Bimodal distribution of Glass Spheres
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Laser Diffraction
Laser Diffraction
Image Analysis
Electro Sensing Zone
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Sample 3: Needle like crystals
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Laser Diffraction
Image Analysis
Electro Sensing Zone
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Sieving will ‘size’ based on the 2nd largest particle dimension
Impact of Particle Shape on Sieving
Particles of the same ‘width’ will be ‘sized’ the same
Particles of different shapes but the same width will be ‘sized’ the same
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Determining the appropriate method of measurement
What is the process or product parameter of concern? Critical parameters may include:
- Downstream processing efficiency Filtration Milling Drying Flow properties
- Product yield and purity
- Bulk Product quality properties Dissolution Bulk density Formulation properties
What region of the particle population directly affects the critical parameters?
What instrument permits us to monitor this critical parameter?
Is sampling or safety an issue?
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Part 8:Understanding the Mean, Median and Mode
of a Particle System
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Objective for this Module
Understand how the mean, median and mode are calculated
Study how particle system changes impact the mean and median
Understand the best statistic to choose for a given objective
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9µm
25µm
32µm
50µm
Mean = 28µm (9+25+25+32+50)/5
Median = 25µm (50% greater than this size; 50% smaller than this size)
Mode = 25µm (most common occurrence)
25µm
A sample distribution of particles…
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What happens when we add two coarse particles?
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9µm
25µm
32µm
50µm25µm
Mean = 49µm (9+25+25+34+50+80+120)/7
Median = 32µm (50% greater than this size;50% smaller than this size)
Mode = 25µm (most common occurrence)
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What happens when we add 4 fine particles?
88
25µm
32µm
50µm25µm
Mean = 18µm (5+5+5+5+9+25+25+34+50)/9
Median = 9µm (50% greater this size;50% smaller than this size)
Mode = 5µm (most common occurrence)
5µm
5µm
5µm5µm
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The best stat depends on your region of interest
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Mean: +75%Median: +22%
Mean: -35%
Median: -64%
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Tracking Real Particle Attrition
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Mean: -40%
Median: -67%
PVM
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Tracking Real Particle Attrition
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Mean: -40%
Median: -67%
Particle Count >20µm: + 185%
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Conclusions
The mean, median and mode are all averages used to characterize particle systems
The mean is sensitive to outliers – a small number of very large (or very small) particles; for example large boulders during milling
The median is sensitive to changes in particle number; especially at the fine end of the distribution; for example secondary nucleation
Particle count in certain size classes is also a powerful statistic to study
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Part 9: Precision and Accuracy
Ian Haley
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Precision & Accuracy Defined
Precision- The ability of the instrument to yield the same
response to repeated measurements of the same unchanging sample.
Accuracy- The ability of an instrument to yield results that are as
close as possible to the absolute properties possessed by a sample.
- The ability of an instrument to yield results that are as close as possible to a recognized Reference or Standard Method
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Accuracy
When discussing accuracy it is important to specify:
- The absolute property in question. (e.g. absolute chord length, true diameter, etc)
- The ‘reference’ technique by which that absolute property is independently determined.
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Precision and Accuracy Explained
A. Both Precise and Accurate.
B. Measurement capable of monitoring and control: Precise measurement with a consistent offset (bias). Good sensitivity to change.
C. Poor measurement for process monitoring and control. Poor sensitivity to change. Average (x) of all measurements will approach the true value.
D. Measurement with both random error and offset.
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Precision and Accuracy Explained (2)
Two goals of FBRM instrument design:
1)To ensure high instrument to instrument repeatability, so instruments are repeatable and can be validated across sites and during scale up (lab to plant).
2)To ensure high repeatability instrument to itself, so measurements of the same system will always measure the same distribution and provide opportunity for control, quality by design, and process optimization.
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High Precision and High Accuracy
Case A (Ideal):
Accurate and Precise
True Mean = 4.0
Measured Mean = 4.0
95% Confidence Interval = +/-5.0%
0.0
2.0
4.0
6.0
8.0
0.0 1.0 2.0 3.0 4.0 5.0
Me
as
ure
d V
alu
e
0%
20%
40%
60%
80%
100%
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0
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Poor Precision and High Accuracy
Case C: Accurate Measurement, poor precision
True Mean = 4.0
Measured Mean = 4.0
95% Confidence Interval = +/-50%
0.0
2.0
4.0
6.0
8.0
0.0 1.0 2.0 3.0 4.0 5.0
Time (minutes)
Me
as
ure
d V
alu
e
0%
5%
10%
15%
20%
25%
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0
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High Precision and Poor Accuracy
Case B: Precise Measurement with an Offset
True Mean = 4.0
Measured Mean = 6.0
95% Confidence Interval = +/-5.0%
0.0
2.0
4.0
6.0
8.0
0.0 1.0 2.0 3.0 4.0 5.0
Me
as
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d V
alu
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0%
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40%
60%
80%
100%
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0
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Precision Depends on the Selected Statistic
Why is this true?
Any given statistic is primarily the function some specific region of the chord length distribution.
Some regions may have more or less counts depending on the particle system.
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Sensitivity Defined
The ability to of the instrument to respond to a real change in the process parameter of interest.
The higher the sensitivity, the smaller the real change in process the instrument is able to detect.
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Precision and Sensitivity to Change
Is this noise?
Or is there insufficient information to provide a signal of sufficient stability (precision)?
0.0
2.0
4.0
6.0
8.0
0.0 1.0 2.0 3.0 4.0 5.0
Me
as
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Precision and Sensitivity to Change
A 0.1% increase in the solids concentration resulted in a corresponding 0.1% increase in the output signal.
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Precision and Measurement Duration
Increasing single measurement duration improves measurement precision.
FB
RM
Cou
nt,
88-2
98µ
m
(cho
rds/
sec)
Elapsed Time (hr:min)
5 min.
1 min.
10 sec.
5 sec.
2 sec.
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Precision and the Effect of Averaging
Increasing number of measurements to average (navg) improves measurement precision.
FB
RM
Cou
nt,
88-2
98µ
m
(cho
rds/
sec)
Elapsed Time (hr:min)
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Precision vs Response Time
0.1
1
10
100
1000
0.1 1 10 100 1000
Single Measurement Duration (tm) [sec]
Min
imu
m R
es
po
ns
e T
ime
(=
t m
) [s
ec
]
0.1%
1.0%
10.0%
Pre
cis
ion
(9
5%
co
nfi
de
nc
e lim
its
) [%
]
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Measurement Duration and Sensitivity to Change
Increasing the Measurement Duration (with no averaging) provides more stable data, but will increase the minimum response time.
1200
1250
1300
1350
1400
1450
1500
-1.0 0.0 1.0 2.0 3.0 4.0
Elapsed Time (min)
FB
RM
Co
un
t (1
8.6
-14
9 µ
m)
+ 1.0 % by weight, SMD = 1 secStep Change in Concentration
1200
1250
1300
1350
1400
1450
1500
-1.0 0.0 1.0 2.0 3.0 4.0
Elapsed Time (min)
FB
RM
Co
un
t (1
8.6
-14
9 µ
m)
+ 1.0 % by weight, SMD = 10 secStep Change in Concentration
1200
1250
1300
1350
1400
1450
1500
-1.0 0.0 1.0 2.0 3.0 4.0
Elapsed Time (min)
FB
RM
Co
un
t (1
8.6
-14
9 µ
m)
+ 1.0 % by weight, SMD = 60 secStep Change in Concentration
1200
1250
1300
1350
1400
1450
1500
-1.0 0.0 1.0 2.0 3.0 4.0Elapsed Time (min)
FB
RM
Co
un
t (1
8.6
-14
9 µ
m)
+ 1.0 % by weight, SMD = 120 secStep Change in Concentration
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Effect of Averaging on Response Time
Increasing the number of measurements to average (navg) improves precision. However, response is dampened with increased averaging.
1200
1250
1300
1350
1400
1450
1500
-1.0 0.0 1.0 2.0 3.0 4.0
Elapsed Time (min)
FB
RM
Co
un
t (1
8.6
-14
9 µ
m)
+ 1.0 % by weight, Average = 10Step Change in Concentration
1200
1250
1300
1350
1400
1450
1500
-1.0 0.0 1.0 2.0 3.0 4.0
Elapsed Time (min)
FB
RM
Co
un
t (1
8.6
-14
9 µ
m)
+ 1.0 % by weight, Average = 30Step Change in Concentration
1200
1250
1300
1350
1400
1450
1500
-1.0 0.0 1.0 2.0 3.0 4.0
Elapsed Time (min)
FB
RM
Co
un
t (1
8.6
-14
9 µ
m)
+ 1.0 % by weight, Average = 60Step Change in Concentration
1200
1250
1300
1350
1400
1450
1500
-1.0 0.0 1.0 2.0 3.0 4.0
Elapsed Time (min)
FB
RM
Co
un
t (1
8.6
-14
9 µ
m)
+ 1.0 % by weight, Average = 120Step Change in Concentration
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Goal of Successful Instrument Implementation
Provide a precise measurement that reflects the smallest change of interest to the process or product parameter of concern.
Precision is of greater concern than Accuracy
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Part 10: Correlating FBRM to Other Data
Ian Haley
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Nucleation & Growth Kinetics:
A Comparison of FBRM and Laser Diffraction
Paul Barrett & Brian Glennon
Department of Chemical Engineering,
University College Dublin,
Ireland
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FBRM vs. Laser Diffraction
100
120
140
160
180
200
220
240
50 70 90 110 130 150
FBRM Mean Chord Sqr. Wt. (Microns)
LD
Vo
l M
ea
n (
mic
ron
s)
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Isothermal Batch: FBRM,PVM,LD & FBRM Prediction
0
50
100
150
200
250
300
350
0 200 400 600 800 1000 1200 1400 1600 1800
Time (s)
Mea
n (
D 4
,3)
LD Mean
FBRM Mean Sqr. Wt.
LD Extrapolation
PVM Dimension
Extrapolating LD data
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Sieve Correlation with FBRM®
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Sieve Correlation with FBRM®
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Sieve and Coulter Counter Correlation with FBRM®
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Correlation to downstream product quality or process efficiency
Bruce A. Keiser, Ph.D.Nalco Chemical Company
How close does the FBRM instrument response come to the measurement of product quality or process efficiency
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©2009 METTLER TOLEDO
1) A correlation is made between specific cake resistance (filterability) and both the dimension and number of particles
2) One can measuring the in-situ particle dimension and count with FBRM® and predict downstream filtration rates.
3) FBRM® is highly successful in predicting filtration because of its high sensitivity to changes in the number of fine particles
Optimization of Pharmaceutical Batch Crystallization for Filtration and scale-upBrian K. Johnson, Carol Szeto, Omar Davidson and Art AndrewsAIChE Annual Meeting, Los Angeles, CA, November 1997
Optimizing Filtration and Scale-up
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AJ Parker CRC forHydrometallurgy
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Relating settling rate to chord length
0
5
10
15
20
25
0 100 200 300 400 500
Mean square-weighted chord length (µm)
200 rpm
Hin
de
red
se
t tlin
g r
a te
(m h
-1)
100 rpm
The Use of FBRM in the Study of Flocculation ProcessesPhil Fawell, CSIRO
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Correlating biomass & ethanol production with FBRM
SPSC 01 (Ge et al 2004)
Correlation between first FBRM peak (flocs) biomass
Correlation between second FBRM peak (bubbles) and ethanol production
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Part 11: Channel Grouping and Statistics
Ian Haley
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Channel Grouping*
How to Choose the Right Channel Grouping for Your Work & the Affect of the Chosen Grouping on Statistics
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Channel – A Definition
A bin with a specific upper and lower limit in microns. Counts with a chord length measured between those limits are put in that specific channel.
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Hardware & Channel Grouping
The FBRM hardware is based on 4096 linear 0.25 micron channels, so the primary x-axis is this linear scale.
Software display provides user with options to group the distribution channels.
FBRM logarithmic scales are calculated from the linear scale channel data.
The choice between linear and log scales will change your statistics
Many other particle size instruments use hardware based on a log scale. They do not provide statistics based on a linear scale.
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Logarithmic Grouping
Each channel width is progressively wider than the preceding channel width.
The distance between channel midpoints is proportionate to their logarithms.
High resolution is provided on the small-particle side of the distribution.
Significantly lower resolution (progressively wider channels) is provided on the large-particle side of the distribution.
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Linear Grouping
All channels have equal width.
The distance between the channel midpoints is also equal.
Equal resolution is provided throughout the distribution.
Each channel has an equal probability of a count being placed in it.
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Logarithmic Grouping
100-Channel Log Grouping (same data set as linear):
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Linear Grouping
100-Channel Linear Grouping (same data set as log):
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Grouping Effect on Statistics
Comparison of statistics (linear vs. logarithmic channel grouping for the same data set):
Statistic 100 Linear, 0-1000 µm
100 Log, 1-1000 µm
% Difference
#/sec 501,000 500,998 0.0004%
#/meas 1,002,000 1,001,996 0.0004%
Median 500.00 µm 499.96 µm 0.008%
Mean 500.00 µm 500.12 µm 0.024%
Mode 495.00 µm 520.79 µm 5.21%
10th Percentile 223.28 µm 223.17 µm 0.05%
50th Percentile 500.00 µm 499.96 µm 0.008%
90th Percentile 776.72 µm 778.21 µm 0.2%
12.525th Percentile 250.00 µm 249.96 µm 0.016%
%<250 µm 12.525% 12.53% 0.04%
%>=250 µm 87.475% 87.47% 0.006%
StdDev 204.35 µm 204.72 µm 0.18%
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Choosing Channel Grouping
The more counts per channel, the better the statistical stability. The fewer channels chosen, the more counts there will be per channel.
The more channels chosen, the higher the potential resolution of change and the more counts required for statistical stability.
The fewer channels chosen, the lower the potential resolution of change and the less counts required for statistical stability.
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Channel Grouping
Rules of Thumb:
Select the smallest channel range possible that encompasses all the data.
Use/explore linear and log channel groupings.
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Internal usage only
Log v. Linear – Mostly fine particles
In this example most of the particle counts are less than 100µm – with an increase in the number of particles in this range over time
Using a linear scale – one-tenth of the channels are for particles less than 100µm – not very sensitive to change in this region
Using a logarithmic scale two-thirds of the channels are for particles less than 100µm – much more sensitive to change in this region*
Alternatively, zoom in on the linear channel
134*NOTE: The caveat is for channels narrower than the actual data (0.25 um), the data is interpolated
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Internal usage only
Log v. Linear – Mostly coarse particles
In this example most of the particle counts are greater than 100µm – with an increase in the number of particles in this range over time
Using a linear scale – nine-tenths of the channels are for particles greater than 100µm –very sensitive to change in this region
Using a logarithmic scale one-third of the channels are for particles greater than 100µm – much less sensitive to change in this region
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Internal usage only
Log scale
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Internal usage only
Linear scale
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Isolate region of change easily when looking at a linear scale
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Internal usage only
Linear scale
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Isolate region of change easily when looking at a linear scale
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Part 12: Signal Aliasing
Ian Haley
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Signal Aliasing
If a process shows periodic oscillations, the issue of ‘aliasing’ can be important.
Under certain conditions a combination of the process oscillation time, instrument response time, data lag and averaging can conspire to present a misleading result
The following slides explain….
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Considering Signal Aliasing
Case A (measurement interval = 30 sec)
Ideal.
The output signal closely approximates the process variable.
a) Measurement Interval = 30 sec
Pro
ce
ss
Va
ria
ble
Process Variable Measured Data Points Instrument Output Reconstructed Signal
Time (minutes)
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Measurement Precision without Aliasing
Case B (30sec MD 5 measurement average= 150 sec)
Process dynamics are maintained, but the time lag is increased and the amplitude of the oscillations is dampened.
Time (minutes)
b) Averaging = 5 measurements
Pro
ce
ss
Va
ria
ble
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Considering Signal Aliasing
Case C (measurement interval = 120 sec)
Process dynamics are maintained, but the time lag is increased and the amplitude of the oscillations is dampened.
Time (minutes)
b) Measurement Duration = 120 sec
Pro
ce
ss
Va
ria
ble
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Considering Signal Aliasing
Case D (measurement interval = 240 sec)
Aliasing.
Process dynamics are misrepresented for a measurement interval greater than 175 seconds (half the period of the process oscillations).
c) Measurement Interval = 240 sec
0.0 5.0 10.0 15.0 20.0 25.0 30.0
Time (minutes)
Pro
ce
ss
Va
ria
ble
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Measurement Precision without Aliasing
Case C (30sec MD 10 measurement average = 300 sec)
Aliasing does not occur, even as the total measurement duration approaches the period of the process.
Note: At TMD = 350 seconds, the measured signal shows no dynamics.
c) Averaging = 10 measurements
10.0 15.0 20.0 25.0 30.0 35.0 40.0
Time (minutes)
Pro
ce
ss
Va
ria
ble
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Part 13: Practical Aspects of Using FBRM: Probe Location and Orientation
Ian Haley
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The “Typical” FBRM® System
LENGTH
375MM14.75IN
FLANGE WELDEDTO DIP PIPE
BY CUSTOMER
EXHAUSTTUBE
RETAININGFLANGES
REACTOR TOP
ADAPTER WELDED TODIP PIPEBY CUSTOMER
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Why is Probe Location and Orientation Important?
FBRM is a ‘point’ measurement
Particles passing that ‘point’ must be sufficiently representative of the process for process changes to be tracked.
The instrument can only measure what it can see.
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Choosing a probe location
Probe insertion: 30-60° angle to the flow
- Presents probe tip with fresh slurry
- Maintains a clean probe window
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Probe Orientation
Probe orientation becomes more important with:
- Extremes in individual particle density (very low or very high in relation to the carrying solution).
- Lower solids concentration.
- Lower carrying solution viscosity.
- A larger median particle size.
- A wider particle size distribution.
- Greater particle shape deviation from a sphere.
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Probe Orientation
More flexibility in probe location is allowed by:- Smaller differences between particle density
and carrying solution density.
- Higher solids concentration (dispersed-phase liquid).
- A smaller median particle size.
- A narrower particle size distribution.
- Smaller differences between average particle shape and a sphere.
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FLOW
7 1
5
4
3
6
2
Ideal Probe Location in a Pipeline
Probe installed in a vertical, up-flow pipe, three to five pipe diameters from the top of the last elbow:
- Provides an ideal length of obstruction-free pipe upstream of the probe
- Offers the most uniformly random and representative presentation of the dispersed phase to the measurement zone
- Keeps the probe window residue-free
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Typical Pipeline Installation: FBRM® D600S
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Mounting in Stirrer Vessels
The goal is to provide a well-mixed, representative sample to the probe
Choose a mounting location that will present the material of interest to the probe tip
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Mounting in Stirred Vessels
Avoid areas that are not completely homogeneous
If the probe is inserted from the top of the reactor, locate it near the leading side of the baffle
Avoid the trailing side of the baffle, as this is where there are dead areas and eddies where particles may settle or segregate
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Down- vs. up-flow impeller
Location of the probe within the vessel must take into account the vertical direction of the flow. (Is the flow upward or downward at the vessel wall?)
For example, if the probe is inserted from the top of the vessel, the probe must be installed in a location where the flow is in a generally upward direction.
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Part 14: Standard Procedures
Ian Haley
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D600L Performance Verification
Instrument Repeatability Assurance Assessment of instrument measurement performance
Initial instrument OQ Uses PVC Reference Standard & Fixed Beaker Stand
Unique PVC Standard prepared and measured on new instrument in
Lasentec factory
Standard delivered with instrument to customer
Standard measured and compared with factory reference data
Continued instrument PQ Uses PVC Reference Standard & Fixed Beaker Stand
Measure Standard at regular intervals and compare with factory reference
data
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Calibration Verification through Measurement of the PVC Reference Standard
PVC Measurement:
at the Factory
at startup (IQ/OQ)
after 3 months (PQ)
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D600 Window Reference Procedure
Correct focus position of the laser is important Reproducibility
Best quality data
Window Reference Position is on the window surface Minimises effect of light scattering or refractive difference changes on quality of data
Over time, window position may drift Procedure for locating correct Window Reference Position
Precision micrometer used to adjust focus position