Last Updated on: May 2021

PROJECT DESCRIPTION

BACKGROUND

Powders

and

granular

materials

can

be

found

in

many

processing

steps

in

powder-based

manufacturing;

they

exhibit

a

variety

of

flow

patterns,

and

their

state

and

behavior

differs

from

application

to

application.

Since

there

is

a

lack

of

fundamental

understanding

of

powder

behavior,

multiple

problems

can

be

encountered

during

production,

such

as

jamming

of

hoppers,

sub-standard

blending

performance,

and

weight

variability

of

final

products

due

to

segregation

and/or

agglomeration.

Scale-up

can

also

be

a

challenge,

since

the

lack

of

constitutive

equations

for

granular

materials

forces

most

scale-up

efforts

to

follow

the

trial-and-error

route.

There

are

numerous

methods

to

characterize

the

flow

properties

of

granular

materials,

such

as

avalanching

testers,

fluidizers,

shear

cells,

indicizers,

density

methods,

angle

of

repose,

etc.;

however,

most

of

them

are

application-specific,

and

it

is

not

clear

how

they

correlate

with

each

other

or

with

process

performance.

For

this

reason,

the

use

of

most

of

these

testers

is

restricted

to

a

specific

application,

for

which

they

were

designed,

and

any

attempts

to

apply

the

results

of

such

experiments

to

a

different

application

frequently

result

in

process

problems.

PROJECT GOALS

The

goal

of

this

project

is

to

develop

a

fundamental

understanding

of

granular

and

powder

flow

and

shear

properties,

so

that

the

behavior

of

powder

products

during

manufacturing

and

processing

can

be

predicted

and

controlled.

The

techniques

and

methods

investigated

in

this

project

could

provide

our

partners

with

valuable

tools

and

ideas

to

efficiently

design

and

scale

powder manufacturing processes.

SUMMARY OF STUDIES

In this work, we have created a family of materials, spanning a wide range of flow properties from very cohesive to free- flowing. Figure 1 shows the appearance of alumina powders after adding deionized water at various weight percentages. We then used the characterization equipment to investigate the flow properties of these materials and to determine the correlations between the techniques. Then multivariate analysis, principle component analysis (PCA) was applied to the material properties library and partial least square regression (PLS) was used to correlate material’s flow properties to the process performance. A cubic score plot was used to visualize how each material is projected into the reduced dimension space (shown in Figure 2). The study has found that loss-in-weight feeder’s feeding performance is highly related to material flow properties, its relative standard deviation (RSD), and the relative deviation between the mean (RDM). The target feed rate is predictable by material flow properties library set up with PCA. We have also confirmed that the feed rate deviation caused by hopper refill is predictable based on material flow properties. We are currently working on improving the model’s prediction, testing for scale-up and also applying our model to other unit operations. Figure 1. Observation of alumina powder with different weight percentage of water added using SEM analysis: (a) 0%, (b) 10% and (c) 25% Figure 2: A cubic score plot was used to visualize how different materials are distributed in the projected spaces. The coordinates of each material are shown as the scores of each principal component. The similarity score based on weighted Euclidean distance can be calculated to further quantify similarity or dissimilarity between different materials.

(a)
(b)
(c)
Last Updated on: May 2021

PROJECT DESCRIPTION

BACKGROUND

Powders

and

granular

materials

can

be

found

in

many

processing

steps

in

powder-based

manufacturing;

they

exhibit

a

variety

of

flow

patterns,

and

their

state

and

behavior

differs

from

application

to

application.

Since

there

is

a

lack

of

fundamental

understanding

of

powder

behavior,

multiple

problems

can

be

encountered

during

production,

such

as

jamming

of

hoppers,

sub-standard

blending

performance,

and

weight

variability

of

final

products

due

to

segregation

and/or

agglomeration.

Scale-up

can

also

be

a

challenge,

since

the

lack

of

constitutive

equations

for

granular

materials

forces

most

scale-up

efforts

to

follow

the

trial-and-error

route.

There

are

numerous

methods

to

characterize

the

flow

properties

of

granular

materials,

such

as

avalanching

testers,

fluidizers,

shear

cells,

indicizers,

density

methods,

angle

of

repose,

etc.;

however,

most

of

them

are

application-specific,

and

it

is

not

clear

how

they

correlate

with

each

other

or

with

process

performance.

For

this

reason,

the

use

of

most

of

these

testers

is

restricted

to

a

specific

application,

for

which

they

were

designed,

and

any

attempts

to

apply

the

results

of

such

experiments

to

a

different application frequently result in process problems.

PROJECT GOALS

The

goal

of

this

project

is

to

develop

a

fundamental

understanding

of

granular

and

powder

flow

and

shear

properties,

so

that

the

behavior

of

powder

products

during

manufacturing

and

processing

can

be

predicted

and

controlled.

The

techniques

and

methods

investigated

in

this

project

could

provide

our

partners

with

valuable

tools

and

ideas

to

efficiently

design

and

scale

powder

manufacturing processes.

SUMMARY OF STUDIES

In this work, we have created a family of materials, spanning a wide range of flow properties from very cohesive to free-flowing. Figure 1 shows the appearance of alumina powders after adding deionized water at various weight percentages. We then used the characterization equipment to investigate the flow properties of these materials and to determine the correlations between the techniques. Then multivariate analysis, principle component analysis (PCA) was applied to the material properties library and partial least square regression (PLS) was used to correlate material’s flow properties to the process performance. A cubic score plot was used to visualize how each material is projected into the reduced dimension space (shown in Figure 2). The study has found that loss-in-weight feeder’s feeding performance is highly related to material flow properties, its relative standard deviation (RSD), and the relative deviation between the mean (RDM). The target feed rate is predictable by material flow properties library set up with PCA. We have also confirmed that the feed rate deviation caused by hopper refill is predictable based on material flow properties. We are currently working on improving the model’s prediction, testing for scale-up and also applying our model to other unit operations. Figure 1. Observation of alumina powder with different weight percentage of water added using SEM analysis: (a) 0%, (b) 10% and (c) 25% Figure 2: A cubic score plot was used to visualize how different materials are distributed in the projected spaces. The coordinates of each material are shown as the scores of each principal component. The similarity score based on weighted Euclidean distance can be calculated to further quantify similarity or dissimilarity between different materials.

(a)
(b)
(c)