A Comparison of the Particle Sizing

Techniques of Sieve Analysis and

Eyecon™

Key results

This white paper demonstrates a method to successfully model the output of Eyecon against that of

sieve analysis. This paper also highlights the advantages of Eyecon as a valid means of particle sizing

and characterisation.

Sieve Analysis

Sieve analysis is a method of determining the particle size distribution of a material. The process

separates fine particles from more course particles by passing the material through a number of sieves

of different mesh sizes. This allows the mass fraction of particles within each size range to be

measured and cumulative distribution constructed. Sieve analysis is the oldest and most widely known

method used to characterise particle size distributions and is employed throughout a number of

industries. There are 2 types of sieve analysis that can be carried out; wet sieving and dry sieving. Wet

sieving is suitable for particle sizes from 20μm up to 3mm while dry sieving is suitable for particles

from 30μm up to 125mm.

Figure 1: Sieves of different mesh sizes and diameters.

Method

The material to be analysed is vibrated through a series of sequentially decreasing sieves using a single,

or combination of horizontal, vertical or rotational motion. Particles under motion will eventually

orientate to present their 2 smallest dimensions to the sieve mesh opening and pass to the next sieve

of smaller nominal opening. Upon completion of the sieving process the weight of the sieves are

measured and compared to the weight of the sieves before addition of the sample. This gives the mass

of material on each sieve. Through addition of the mass fraction on each sieve, from the smallest to

the largest sieve size, a cumulative mass distribution of test sample is obtainable.

Challenges of Sieve Analysis

Many methods exist for the determination of particle size distributions within a sample material. It

may therefore be required at times to compare results from two different measurement methods.

Unfortunately, some of these methods operate on fundamentally different principals resulting in

difficulties of results comparison. Often, the compatibility of sieve analysis particle sizing results to

other methods of particle size measurement is not good and manipulation of data may be necessary

in order to compare each methods results. Presented below are some of the limiting factors of sieve

analysis that may result in poor correlation between sieve analysis and a different measurement

system.

• Sieve analysis does not account for particle shape effects of different particles. The 2 minor

dimensions of a 3 dimensional particle dictate whether a particle passes through a mesh

opening. The major dimension does not affect the particle size calculation or the particle size

distribution.

• Sieve analysis may not have the ability to provide high resolution for a sample with a narrow

distribution owing to the limited range of mesh sizes available.

• No further particle shape information is available from sieve analysis without additional

examination of the particles using other measurement methods.

• Sieve analysis does not have the ability to analyse single particles.

• Sieve analysis has longer measurement times and a lower measurement speed than that of

laser diffraction and Eyecon direct imaging.

• Sieves are prone to blinding. This is the obstruction of the sieve openings by the material that

is being analyses. It occurs as a result of particles of the same or similar diameter to the sieve

openings becoming lodged within the apertures. This reduces the available openings for other

particles to pass through and can result in particles that are smaller than the sieve apertures

being trapped on that sieve. This will greatly affect the mass distribution and thus accuracy of

the PSD.

• Sieve analysis is an offline method of particle size distribution and is lacking in any real-time

ability for the monitoring of processes. No images of particles obtainable.

Eyecon

The Eyecon from InnopharmaLabs is a direct imaging device that overcomes some or all of the

associated issues with sieve analysis. The Eyecon achieves this by calculating particle size distributions

based on the measurement of individual particles within a sample. It captures every particles

parameters and uses these to create a volumetric distribution of the sample in order to determine the

D-values.

This method removes the grouping of particle sizes which is characteristic of sieve analyses by

determining the full range of particle sizes in the sample and hence drawing the true volumetric

distribution. The Eyecon can be used offline or inline to provide real-time particle size information and

is therefore suitable as a process analytical technology device. The Eyecon also removes problems

associated with direct sample contact such as blinding and sample material loss.

Determination of the D-values is achieved by first fitting individual particles with an ellipse in order to

determine the minimum and maximum diameter of the particle, dmin and dmax.

The 3rd unknown dimension is predicted by taking the average of dmin and dmax to get davrg.

Using these 3 dimensions the volume of the ellipse which surrounds any particle is calculated using

the equation:

The D-values themselves are then computed by notionally arranging all particles measured in order of

ascending volume. The total volume is computed first. An iterative algorithm then adds the volumes

starting with the smallest and working up to the largest. The D10, D25, D50 etc. are the particles which

corresponded to reaching 10%, 25% and 50% of the cumulative volume respectively. All recorded data

can be analysed offline once testing is completed to determine the overall process D-values based on

the data captured throughout the whole test. The process is explained in further detail in “Eyecon –

Explanation of D-Value Determination Method”.

Figure 2: A Representation of the ellipsoid that is fitted to particles using the Eyecon software. Using the 2 visible known

dimensions and predicting the 3rd unknown dimension it is possible to calculate the volume of each individual particle.

Comparing Sieve Analysis and Eyecon

As sieve analyses results in a mass distribution of particles while the Eyecon results in a volume

distribution, to compare both, a conversion of mass to volume or vice versa is required. No volume

information is achievable from sieve analyses and so it would be required to convert the Eyecon

volumetric distribution to a mass distribution by density.

The density (ρ) cannot be measured for the particles being analysed using the Eyecon hardware

without directly sampling the material and performing density testing methods such as pycnometry

and so another method of comparison must be used.

It should be noted however that if such a test was carried out it would result in a density for particles

of a range of particle sizes. In order to have a direct comparison of sieve analysis and Eyecon results it

would be necessary to assume a uniform density across the range of particle sizes in order to calculate

a mass distribution using the volume distribution that is acquired from the Eyecon software reducing

the accuracy of the comparison as the density is not constant.

Fundamental Differences in Measurement Methods

Particle Shape Effect

The ellipsoid as in figure 3 has radii a, b and c and diameters of A, B and C respectively. During sieve

analysis this particle will present its 2 minor axis parallel to the sieve mesh opening. Once both of

these axes are smaller than the opening of the sieve the particle will pass through the sieve regardless

of the length of the third major axis. As a result only 2 dimensions are ever challenged, resulting in an

erroneous PSD.

Figure 3: Tri-axial Ellipsoid with radii of length a, b and c on its 3 semi-axes. For particles of this shape, during sieve analysis

the 2 minor dimensions will be orientated as to pass through a sieve mesh of nominal opening diameter (Source:

en.wikipedia.org)

Interpolation

Sieve analysis provides a discreet set of known data points. In order to determine any values not at

these points it is necessary to interpolate. The calculation of frequently used D-Values such as D10, D50

and D90 is often done by interpolation. Interpolation can lead to inaccuracy as the distribution of

particles between 2 meshes may, for example, be assumed to be linear when in reality it could be an

exponential or polynomial relationship. Therefore it may result in the D-Values being skewed.

Modelling Sieve Analysis Using the Eyecon

While both measurement systems give the same output, particle diameter, the method used to

calculate this differs significantly for sieve analysis versus Eyecon direct imaging measurement.

As the Eyecon presents its particle size distribution (PSD) data based on the calculated volume of

each individual particle it is necessary to use the recorded diameters of particles and apply sieve

control parameters to this data. This requires a number of data manipulation steps described as

follows.

Step 1: The first process is to tabulate the dimensional data for every individual particle measured. The

collective particle data is presented in a combined worksheet. The particle data is columnated under

the following column headings; Particle No., dmin, dmax, davrg, Aspect ratio, Volume and dfactor.

Step 2: The sieve nominal openings are selected to reflect the sieve aperture sizes that were used in

the physical testing. Table 1 shows the selected sieves with the zero sieve size representing the pan

which is at the base of the sieve stack.

Step 3: Next is to establish parameters and equations which will be used to model the sieve analysis

process under the selected sieve sizes. The dfactor is used as the particle parameter that will determine

if a particle will pass through the sieve nominal opening. If dfactor ≥ Sieve_mesh(n) it will measured

against Sieve_mesh(n+1). When dfactor < Sieve_mesh(n+i) it will be counted on the Sieve_mesh(n+(i-

1)).

Step 4: When a particle size is between two sieves the volume of that particle is calculated. This volume

is added to volume of all particles within that size range. This process is iterated for every particle that

has been recorded. This allows for a volume distribution across the number of sieves to be established.

The cumulative volume distribution of the test is used to identify the D-values of importance. By simple

substitution it is possible to determine the volume on each sieve. The volume on each sieve over the

total volume of all particles gives the volume fraction per sieve, similar to the mass fraction that is

calculated for sieve analysis.

The cumulative % is calculated by summing the sieve fraction for all sieves of smaller mesh size than

the current sieve. When displayed graphically this gives a cumulative function of the distribution of all

particles below a sieve mesh size. This can be seen in figure 4 which is the cumulative volume

distribution of mixed cellets ranging in size from 100-1000μm.

Table 1: Data as presented when performing Eyecon sieve analysis modelling

Sieve size

(μm)

0 36 125 355 710 1000 2000 2800

Cumulative

Vol 0 510692528 4665100168 7934135530 10131516264 10957231885 10957231885 10957231885

Volume 0 510692528 4154407640 3269035362 2197380734 825715620 0 0

% Vol 0 5 38 30 20 8 0 0

%

Cumulative 0 0 5 43 72 92 100 100

Figure 4: A cumulative volume distribution as presented by Eyecon sieve analysis modelling.

A Note on Eyecon Sieve Analysis Model

A number of assumptions are made which allow for this analysis to be representative and comparable

to physical sieve analysis:

All particles are modelled as having the same density independent of particle size or morphology. This

allows for the cancelling of the assumed constant density for every particle which allows the

cumulative volume percentage of particles from Eyecon model to be compared to the cumulative mass

percentage of particles from sieve analysis.

The Eyecon sieve analysis model uses the calculated dfactor to determine if a particle will pass through

a sieve mesh or not. The dfactor can be calculated using a number of different attributes of a particle

and factors that will model the likelihood of a particle to orientate itself as to pass through the sieve

opening. For simplicity the dfactor was set to equal daverage for the above tests. The reason for using the

daverage was to avoid passing particles that may have a disproportionally small dmin compared to the

dmax. Using the calculated 3rd dimension better reflects the need for a particle to be smaller in 2

dimensions for it to pass through a sieve mesh opening.

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Cumulative Volume Distribution of Mixed Cellets

The Eyecon sieve analysis model populates the sieves from the pan and smallest mesh opening up to

the largest. While the direction of filling of the sieves will not affect the model. How it differs from

mechanical sieve analysis is that there is no opportunity for small particles to be trapped on a large

sieve as a result of blinding, agglomeration, excessive motion, electrostatic charge or the breakage of

granules as a result of vibration.

Occasionally it will be necessary to include an imaginary oversized sieve that is not included in the

physical sieve analysis testing. This is necessary as the model only counts particles below a specific

sieve size so should there be any oversized particles, in order for them to be included in the total

volume calculation, a sieve with a nominal gap size greater than the largest particle is required.

Feature Comparison of Eyecon and Sieve Analysis

Table 2: A comparative table of the functions that are capable of measuring using the Eyecon and Sieve Analysis.

Feature Eyecon Sieve Analysis

Inline Measurement Offline Measurement Volume Distribution Mass Distribution *

Number Distribution Particle Shape Analysis Individual Particle Analysis Direct Measurement Real-Time Information Limited Sample Size Repeatability High Speed (<1 min) Determine Aspect Ratio Sample Contact Sample Pre-Treatment **

* Mass distribution is only possible when the particle density is assumed to be constant.

**It may be necessary to include lubricants and glidants to samples with a low particle diameters to avoid

agglomeration.

Comparison of Sieve Analysis and Eyecon Particle Size

Distributions

In order to examine the range of results from sieve analysis, Eyecon direct inline imaging and the

Eyecon Sieve analysis modelling a number of tests were carried out at the Institute of Technology

Tallaght (ITT) in Dublin, Ireland. The tests carried out included performing sieve analysis and laser

diffraction on both a granulated sample with a wide particle size distribution and a sample which

included highly spheronised cellets of mixed diameters.

The first test that was carried out was with 100g of granulated sample. The sieve mesh sizes that were

used were 0.036mm, 0.125mm, 0.355mm, 0.710mm, 1.000mm, 2.000mm and 2.800mm. The sieve

stack was vibrated at an amplitude of 2.5 for a duration of 300 seconds.

The cumulative distribution curve for the same test sample can be seen in figure 5. It can be seen that

the Eyecon sieve analysis model closely matches the recorded sieve analysis data. The deviation at the

lower particle sizes assumed to be as a result of blinding, agglomeration or electrostatic charging

which reduces the mass of particles which are below 0.125mm for sieve analysis.

Figure 5: Cumulative distributions for Eyecon and Sieve analysis.

Table 3 shows the calculated D-Values for the sieve analysis test, Eyecon inline direct imaging and also

the Eyecon Sieve Analysis model. These values are also displayed in figure 6 and from this we can see

that the Eyecon sieve analysis model has the lowest D10, D50 and D90. The close correlation between

the results of sieve analysis and Eyecon sieve analysis model show by selecting different parameters

of the same data as taken inline, the measurement results of sieve analysis can be closely replicated.

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Cumulative % Distribution

Sieve Analysis

Eyecon SA

Table 3: Eyecon and sieve analysis D-values. The direct imaging refers to data recorded inline by the Eyecon.

D10 D50 D90

Sieve Analysis 350 590 980

Eyecon Direct Imaging 348.83 665.40 1071.46

Eyecon Sieve Analysis 285 580 920

A second test was carried out under the same test conditions and sieve mesh size. The sample used

for this test was cellets of mixed diameters. A 60g sample consisting of 10g of cellets 100, 200, 350,

500, 700 and 1000μm was made. Of this sample 50g was run under sieve analysis conditions as

described previously. These particles are highly spherical and should lead to a closer correlation

between Eyecon and sieve analysis particle size distribution results as the shape factor will not lead to

an erroneous PSD.

From figure 7 it can be seen that there is a variation in the cumulative curves for sieve analysis and

Eyecon modelled sieve analysis. This is as a result of the cellets 350 blinding the sieve openings on the

355μm sieve and blocking the passage of smaller cellets to sieves of smaller mesh sizes. This data

identifies one of the flaws of sieve analysis and highlights why it should not be used as the bench mark

method of particle sizing.

Figure 6 : Graphical representation of D - values as table 3

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D - Value Comparison

Sieve Analysis Eyecon DI Eyecon SA

Figure 7: The cumulative distribution of mixed cellets is shown. The variation between the 2 curves is as a result of blinding in

the sieve with 355μm sized mesh

Figure 8: D-values of the 3 methods of analysis. The high D-values of sieve analysis is as a result of blinding of the 355μm sieve

mesh.

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Cumulative % by Measurement Mode

Sieve Analysis

Eyecon SA

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D - Value Comparison

Sieve Analysis Eyecon DI Eyecon SA

Summary

A number of different methods of particle size analysis are in use (laser diffraction, sieve analysis and

direct inspection). Each method is based on a different principal of measurement and have their own

strengths and limitations. Comparison between these methods is not always valid because of the

differences in their measurement principals.

This paper has outlined that sieve analysis is not always ideal for the particle sizing of every type of

process. Despite this it remains the industry standard of choice. Frequently alternative methods are

compared and judged against that of sieve analysis even though the principals of measurement means

that a direct comparison between methods is not valid.

Despite the limitations outlined above, this paper describes a means of successfully modelling the

output of sieve analysis against that of Eyecon. Where differences between the modelling of Eyecon

data against that of sieve analysis occurred highlights the limitations of sieve analysis (Blinding,

agglomeration, electrostatic charge, excessive motion and breakage of granules).

This white paper shows that Eyecon is a valid alternative to the industry standard and overcomes

several of the limitations associated with sieve analysis.

For More Information on EyeconTM Please Contact

Mike Mulcahy, Sales Director

Innopharma Labs

mulcahym@innopharmalabs.com

+353 1 485 3346