Powders are produced or handled in a variety of
industries. Particle size distribution (psd) is the single most
important property of any powder. This P affects the other two P's of a
powder, namely the power consumed to produce the powder and the performance
characteristics of the powder. Even if all the particles are spherical,
there is always a distribution of sizes present. To complicate matters,
all industrial powders are nonspherical in shape. Have you ever
wondered, what is the particle size of the paperweight present on your
table; or, what is the particle size distribution of dust inhaled by you and
what portion of this distribution enters your lungs? If you do not have
the answers to these simple questions, just imagine how complex would powders
be that contain particles of a wide distribution of shapes and sizes.
Knowing the particle size distribution is of vital
importance to industries like cement, minerals, ceramics, coal and ash (thermal
power), fertilizer, polymers, toners, catalysts, dyes and pigments, cosmetics,
pharmaceuticals, pesticides, detergents, chemicals, refineries, dairy products, beverages
etc., manufacturers of equipments like grinding mills, classifiers, cyclones, bag filters,
ESP's, boilers, kilns and reactors, spray dryers, powder mixers, pollution control
equipments etc., and test houses, R&D labs, consultants, designers etc.
Several expensive instruments are available to
measure the
psd of a powder like Coulter counter, Photosedimentometer, Xray
Sedigraph, Laserbased instruments, Microscopy etc. Even if you do
posses any of these instruments,
do you know that you
can use one instrument's data to predict the information given by all the other
instruments, and more? Each instrument measures one particular psd
of a powder (from among a variety of different psd's possible!) and
therefore the measurements of one instrument do not tally with those of
another. We have identified all these problems and done fundamental
research establishing correlations among different instruments and further, to
predict some powder properties not measured by any of these
instruments. All these features are incorporated into the software SIZEANAL.
These have also received international recognition by way of research papers
published, and fetched Outstanding Consultant Award of Rs 20,000 in the year
1989.
The following powder parameters are predicted
by SIZEANAL :
 Volume,
surface, surfacevolume, Stokes diameters
 Number,
area and volume (or weight) distributions
 Specific
surface area and steepness quality factor
 RosinRammler
(RR) equation fit and prediction of RR exponent, preexponential factor,
d50 and d63.2 (the 63.2% passing size)
 Number
per unit weight, number per unit surface area and a host of other such
useful powder properties
 Psd
predictions for particle sizes for which measurements are not directly
available are made using the RRparameters estimated above
 Other distribution equations like Normal,
Lognormal, GaudinSchumann, Monotonic etc. are also available for
extrapolation and prediction of powder parameters
 Graphical
representation of number, area and weight distributions
 Graphical
representation of area distribution vs. weight distribution (which
shows, for instance, 3% by weight of powder having 50% of surface area
and the graph shows the entire distribution of the same). This
curve will always be a convex curve above the diagonal.
 Graphical
representation of specific surface area distribution by weight
 All
graphs can be generated on Linear, Loglog, Semilog and Inverse
Semilog scales
The various methods and calculational
procedures employed in SIZEANAL are presented in our technical
report No. PTC:021.
The effect of particle shape is also accounted
for in SIZEANAL. The fact that finer particles are more spherical
than coarser ones is also considered. This can have important
ramifications where one instrument is used for measurements (like a Coulter
counter which measures the volume diameter of particles) whereas the
application area may require a different particle diameter (like cyclone
operation where the important diameter would be the Stokes diameter).
The measurements of Coulter counter in such a case would be directly useful only
if all the particles are exactly spherical. For any practical powder
containing nonspherical particles, the use of SIZEANAL would be
necessary to first convert the measured volume diameters into their
corresponding Stokes diameters before applying the results for cyclone design
and optimisation. We are competent to identify the particle diameter and
distribution measured by your instrument as well as the same required for your
specific application on hand, and use SIZEANAL appropriately.
We have used SIZEANAL to give
consultancy and advice to hundreds of industries to solve their powderrelated
problems, optimise grinding mills, spray dryers,
cyclones/hydrocyclones/classifiers etc. The software can be used with
the raw data of any one instrument to obtain the results of all the other
instruments.
Typical graphical outputs of SIZEANAL are
shown below for the "raw data" of two instruments, namely
Sieving set and Microscope. Two powders (cement and rawmix) were
measured with a sieving set and the raw measurements of residues on
individual sieves versus the particle size (sieve aperture size) are
shown in Fig. 1.0. These "raw data" are used to
obtain the complete particle size distribution (psd) information,
and these are shown in Figs. 1.1 1.7 below. Another
powder (Single Super Phosphate) was measured using a microscope and the
number of particles in individual particle size ranges (Martin's
diameter) are shown in Fig. 2.0. These "raw data"
are again used to obtain the complete psd information as shown in Figs.
2.1 2.3. It may be noted
from the graphs that the software can be used to instantaneously show
any set of data in Linear, Loglog, Semilog and Inverse Semilog
scales. Further, it may be seen from Figs. 1.1 and 2.1 that
SIZEANAL permits calculation of yvalue for any specified xvalue
as well as backcalculation of xvalue for any desired yvalue.
This is especially useful to instantly estimate the median values (d50)
of the psd's.
The differential distributions by WAN for
cement and SSP are shown in Figs. 1.81.10 and Figs. 2.42.6
in three different formats respectively. For cement, RosinRammler
equation with an exponent close to 1, and for SSP, the monotonic
equation with an exponent close to 2 are obtained. From the bar
graphs shown in Figs. 1.8 and 1.9, it can be seen that
for cement, while the number distribution is all concentrated
practically in the finest size interval, the area distribution falls
exponentially, the weight distribution follows the wellknown
"bellshaped" curve. However, for SSP in Figs. 2.4
and 2.5, all three of the WANcurves are "bellshaped"
with the steepness being the greatest for number and lowest (or most
gradual) for weight distribution. These conclusions are specific
only to the two powders analysed here and should by no means be treated
as generally applicable to all cement and SSP powders. The
representation of psd in the form of piegraphs as shown in Figs. 1.10
and 2.6
is also unique and is an integrated feature in SIZEANAL.
The various graphs amply demonstrate the power of SIZEANAL and
without them, the knowledge of your powders is certainly incomplete.
