Optimization of granular flows for industrial …pasi2014.njit.edu/Lectures/mort2.pdfpaddle type forward cross cross forward type paddle ... ... Variable Sensors:

1

Optimization of granular flows for

industrial applications

Paul Mort

Procter & Gamble Company

Member of International Fine Particle

Research Institute (IFPRI)

P. Mort 1 PASI on Frontiers in Particulate Media,

La Plata, 2014

My Background: • Ceramic Science and Engineering

• Started at P&G in 1993, focus on detergent.

– Involved w/ IFPRI† while at P&G.

Ceramics… • Aluminosilicate powder

• Add a little surfactant/

polymer as a process aid,

• Forming process…

Detergent… • Surfactant active

• Add a little

aluminosilicate as a

process aid,

• Granulation process…

Saint Gobain Ceramics

Procter & Gamble

Industrial issue… Products with high

organic compositions

may be softer, stickier

and more susceptible to

fouling in processes that

require granular flow:

– Mixing,

– Milling,

– Agglomeration,

– Drying,

– Conveying…


La Plata, 2014

Motivation – optimizing processes that handle

particulates either as intermediates or end products

• We desire efficient processes:

– Maximize production throughput

– Minimize the spent energy.

• Two aspects in this optimization:

1. Process equipment and its integration within a system; and

2. Properties of the particulate material that are relevant to the processing conditions, specifically the material’s response to flow and stress fields that are imposed by the process equipment.

• Industrially-relevant materials have a complex coupling among:

– imposed flow field and boundary conditions,

– constitutive properties of the material function of shear rate, packing density…

– resultant stress & energy consumption.

• Optimization requires understanding of flow and stress fields.

Dense flows are of interest

Optimize the use of energy

to achieve desired product

transformation

energyspecific mass

flowstress

time


La Plata, 2014

2

Multi-scale Approach to Particulate Flow

Scale

m

icro

m

eso

bu

lk

Granular Temp.

(gas continuum)

Binary collisions,

infinitesimal contact

time

Transient

clusters?

Fluid-like

continuum

(N.S. analogy)

Transient

contacts –

friction, rolling

Spatial and

temporal

distribution of

coherence,

stress chains

Particle

packing

contacts

Domain

interactions

(coherence

length scale)

Quasi-static or

Frictional

continuum

f1n;,~ n high;~ 2

Dimensionless shear rate, 2

1

g

dp*

0 0.2 3 >>

(Tardos, Savage)

Stiffness/shear 3d

k

high low

Contact time tc / tbc high ~2 1 (Campbell)

Stokes # 0 ~10 high d6

m

(Brady)

0

p

P

d

Inertia /

confinement, (GDR Midi)

Is there an optimal

particle flow

operating regime?

• Fluid-like;

• Dense flow;

• Efficient particle

interactions:

• Boundaries,

• Particles,

• added Liquid,

• Gas.

• Low E/M.

Coupling of particulate flow and stress fields –

a multi-scale challenge

• Fundamental understanding of particulate

flow and stress fields and their material coupling.

• Particle-particle and particle-boundary interaction:

– particle characteristics such as size, size distribution,

shape, surface characteristics, and properties such

as complex modulus.

• Flow and stress in critical sections of the process,

e.g., the gap between an impeller and a process

boundary.

• Unit operation, its flow fields, residence time

distributions and scale-up.

• System scale of ancillary process interactions:

– feeders, intermediate storage, product and

intermediate handling, recycle integration, etc.

1) Survey experiments

and models of flow

2) Apply to mixing

3) Process optimization

6) Implication of

local stress

fluctuations

5) What can we infer

about stress based

on power and/or

torque measures?

Granular rheology

4) Process control

mic

ro…

…m

acro

…

5

1

Survey of experiments and models of flow

– Optimize process using fluid-like flow;

link regime map to operating strategy.

– Examples:

• Centrifugal flow granulators.

• Dual-axis mixers.


La Plata, 2014

3

Flow Fields in Mixer-Granulators

• Stress-bounded flow, at least one free surface

• Dominant driving force:

– Gravity / Centripetal force / Air fluidization

Fr = w2r/g > 1


La Plata, 2014

Vertical axis mixer/granulators – a stable fluid-like operating regime?

Scale

m

icro

m

es

o

b

ulk

Granular Temp.

(gas continuum)

Binary collisions,

infinitesimal

contact time

Transient

clusters?

Fluid-like

continuum

(N.S. analogy)

Transient

contacts –

friction, rolling

Spatial and

temporal

distribution of

coherence,

stress chains

Particle

packing

contacts

Domain

interactions

(coherence

length scale)

Quasi-static or

Frictional

continuum

f

n~ 1n 2~

Dimensionless shear

rate for centripetal flow, R2*

w

0 0.2 3 >>

A B C

Flow spiral

(~ )

R

Impeller

A B

C?

Pre-binder

addition:

Fine powder,

aerate-able.

Shear stress is

not effectively

transmitted

above impeller

Post-binder

addition:

Shear stress is

transmitted

throughout granular

mass.

Centripetal “rope-

flow” pattern, all

material is in a

helical swirl;

Uniform stress and

flow fields

uniform granulate.

Centripetal flow-field analysis for scale-up

• At high Fr#, substitute w2r for g.

• Let K1 be the ratio of tangential particle velocity vs. impeller velocity, Up/Ui

• Let K2 be the ratio of axial shear transmission / mixer radius, /R

• Then the rotational speed drops out of the equation…

• …providing a fairly broad “intermediate regime” flow field.

w

RK4UK2

2/

U1i1p

1%

10%

100%

1% 10% 100%

i

pU

U1K

20.*

K2 =

/R

3*

A

B

C

2

12

*

K

K2

R

w

R2*

w

What about scale-up of stress field?

P. Mort 9 PASI on Frontiers in Particulate

Media, La Plata, 2014

4

Examples: Modeling and Characterization of Flow

Considerable work has been reported on characterization and modeling of powder flow behavior during mixing under various process conditions.

A partial list:

– M. Moakher, T. Shinbrot, F. Muzzio, “Experimentally validated computations of flow, mixing and segregation of non-cohesive grains in 3D tumbling blenders,” (2000).

– Y. Muguruma, T. Tanaka, Y. Tsuji, “Numerical simulation of particulate flow with liquid bridge between particles simulation of centrifugal tumbling granulator,” (2000).

– Motion in a Particle Bed Agitated by a Single Blade, AIChE Journal, Volume 46, 2000, B. F. C. Laurent, J. Bridgwater and D. J. Parker

– R. Yang, R. Zou, A.B. Yu, “Micro-dynamic analysis of particle flow in a horizontal rotating drum,” (2003).

– S. Forrest et al, “Flow patterns in granulating systems,” (2003).

– A. Hassanpour, HS Tan, et al, “Analysis of Particle Motion in a Paddle Mixer,” (2009).

Experimental validation of flow fields vs. Discrete Element Method simulations provide reasonable agreement:

– DEM uses “ideal particles” and manipulated DEM model parameters (stiffness, damping, restitution, friction, simplified boundary conditions…)

– Experimental measures (PEPT, NMR, optical tracers…) include realistic particles in small-scale industrial mixers.


La Plata, 2014

*Y. Muguruma, T. Tanaka and Y. Tsuji, “Numerical

simulation of particulate flow with liquid bridge between

particles,” Powder Technology, 109 (2000).

Granulator

Schematic

Snapshot of Flow

dry +12%

binder

Analysis of shear field in flow

Example: Model and validation of a

centrifugal-flow granulator

11

Position of particle

generation

Impellers

Plan view, red = up, blue = down

Section view, red = out of page, blue = into page

PEPT Measurements D50 ~ 500 um, sgv ~ 1.6

EDEM Simulations Granular packets ~ 5 mm +/- 1 mm

Example: Comparison of Time-Averaged Particle Flow

Position of particle

generation

Impellers

Impeller shaft

locations

Plan View

Section

View

A Hassanpour et al, “Analysis of Particle Motion in a Paddle Mixer,” Sheffield Granulation Conference (2009).

5

2

Apply flow model to mixing process

– Markov Chain model • adapted from Freireich & Wassgren, Purdue Univ.


La Plata, 2014

1%

10%

100%

10 100

elapsed mixing time, from start of tracer addition (s)

RS

D (

mix

ture

)

V=4X, U=80%,

E/M*=170%

V=4X, U=100%,

E/M*=160%

V=2X, U=100%,

E/M*=130%

V=1X, U=100%,

E/M*=100%

V=1X, U=130%,

E/M*=95%

Batch mixing analysis –

Markov Chain approach

• Add “pulse” tracer over a paddle position.

• Model dispersion over time.

• Asymptote at about 13-14 revolutions. – Blend # (B) scales with

revolutions.

paddle type forward cross cross forward type paddle

1 fwd 70% 30% 100% 0% rev 14

2 fwd 70% 30% 30% 70% fwd 13

3 fwd 70% 30% 30% 70% fwd 12

4 fwd 70% 30% 30% 70% fwd 11

5 fwd 70% 30% 30% 70% fwd 10

6 fwd 70% 30% 30% 70% fwd 9

7 rev 0% 100% 30% 70% fwd 8

Channel A Channel B

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

1 4 7 10 13

paddle position

puls

e c

oncentr

ation

mixture @ 12 s, RSD = 66.9%

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

1 4 7 10 13

paddle position

puls

e c

oncentr

ation

mixture @ 32 s, RSD = 2%

• Mixer volume (V)

• Tip speed (U)

• E/M* is specific

energy (kJ/kg)

required to mix to

<2% RSD



3

Process optimization

– Mixer example (using Markov model)



6

Process optimization

Empirical model for free-flowing

granular materials:

– Power (P) scales with mass (M) and

dimensionless impeller speed (u):

P / M = k • um

– Specific Energy (E/M) calculated over

residence time ():

E / M = (P/M) • = k • B • 2R • (u)m-1

Production efficiency model:

M’ / V = ( • ) / [(2R • B / u) + ]

Mass throughput / mixer volume

Mixer radius

Paddle tip speed

0.01

0.1

1

10

100

0.01 0.1 1 10 100

net power predicted

net

pow

er

measure

d

R^2 = 99.53%

Scaling of Net Power

Consumption for Mixing of

Free-Flowing Granules

ln(x) = -4.73 + 1*ln(mass) + 0.82*ln(U)

Data collected over:

Mixer volumes,

• Fill levels,

• Product densities,

• Impeller tip speed.

Blend #

Batch transition idle time, 0 for continuous

Fill level

Product density



0.0

0.5

1.0

1.5

2.0

2.5

3.0

1 10 100

mass rate

Sp

ecific

en

erg

y

0

10

20

30

40

50

60

Pro

du

ctio

n e

ffic

ien

cy

E/M

M'/V

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1 10 100

mass rate

Sp

ecific

en

erg

y

0

10

20

30

40

50

60

Pro

du

ctio

n e

ffic

ien

cy

E/M

M'/V

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1 10 100

mass rate

Sp

ecific

en

erg

y

0

10

20

30

40

50

60

Pro

du

ctio

n e

ffic

ien

cy

E/M

M'/V

Process optimization…

Integrate rate and energy models.

Consider:

– achievable production efficiency (M’/V)

– specific energy input (E/M)

as a function of mixer size and

operating strategy:

– batch or continuous,

– batch transition time;

all other parameters held equal (tip

speed, Blend #, density, fill level).

Batch,

5 minute transition

Batch,

1 minute transition

Continuous

Continuous operation can

improve efficiency & simplify

scale-up.

Continuous operation requires

achievable steady-state

operation.

V = 8X

V = 2X

V = X

4

Process control

– System flowsheets

– Agglomerator example

• Doyle group, UCSB, IFPRI project


La Plata, 2014

7

Particulate process control – achievable steady state?

• Continuous processes typically have recycle combined with raw material feeds.

– Ratio of recycle / raw material affects the process;

• An important parameter in a multivariate control problem.

• At steady state, recycle production must be balanced with its consumption.

– Do we need a recycle surge to get to steady state?

• Flowsheet models integrate process models:

– Useful to optimize system performance:

• Achievable steady states

• Dynamics, startup, shutdown, changeovers, upsets…

Bioreactor example, www.psenterprise.com/gproms/

.../biotreatment_white_paper.pdf

Urea granulator example,

http://www.solidsim.com/www/examples-of-use

Multivariate process control

For 1:1 relations (sensor:actuator), simple PI or PID controllers can be used.

– Note, a sensor may be inferred (soft) based on a model.

For multivariate relations, consider:

– Integrated mode control – may be empirical, e.g., based on a multivariate data regression.

– Model predictive control – predicts forward based on process simulation, compares predicted and measured outputs.

Inputs:

Controlled,

Variable

Sensors:

Measured,

Inferred

Process:

• Target specs,

• Product quality

Particulate processes have

many:many relations among sensors,

actuators, and product attributes…

Control

Model

Process

Manipulated

Inputs:

Sensors:

Measured,

Inferred

targets

…use a control model to convert

many:many relations into more

tractable one:many relations.


La Plata, 2014

Example: Continuous Detergent Agglomeration

• High throughput production of Detergent Granules in continuous granulation.

• Integrated recycle systems.

• Variety of in-process sensors and actuators are used to meet product specifications, a multivariate problem.

• Agglomeration processes tend to be under-actuated – not easy to reverse coalescence…

…is there a steady state in a binder-

granulation process?

Normal production…

Material properties in coupling of flow &

stress fields are critical to the answer!

8

Process control modeling

and validation using

“Persistent Excitation”

• Multiple handles are

adjusted within relevant

operational window (as

per model prediction).

• Validate by statistical

comparison of

measured results and

model prediction. Is

there a bias?

• How do model biases

compare to known

operational biases in

plants?

-2

-1

0

1

2

3

Time

scal

ed u

nits

var 1 var 2 var 3 var 4

-4

-2

0

2

4

6

8

Time

scal

ed u

nits

var 5 var 6 var 7 var 8 var 9

0 5 10 15 20 25 30 35 40

0.6

0.8

1

1.2

1.4

1.6

Time []

d50 (

norm

aliz

ed)

measurements

infinite horizon prediction

model cross validation

Frank Doyle group

(UCSB), IFPRI project


La Plata, 2014

5

What can we infer about stress based on

power and/or torque measures?

Granular rheology

– Instrumented axial Couette

• Tardos & Kheiripour, CUNY

– Constitutive characterization of dense flows in

the intermediate regime (IFPRI)


La Plata, 2014

Granular Rheology –

stress analysis in

Couette flow

• Tardos and Kheiripour, CCNY, IFPRI

• Interrogate stress field in a drained

vertical couette flow

Experimental options:

closed-bed (Batch, close packed);

open-bed (Continuous, allow dilation)

Powder Feed

Powder

Discharge

Torque

meter

Stress

Sensors

Rotating

Cylinder

Shearing

Gap

Over-

burden

h

R w

H

L

y

Normal

Stress

Sensors

Rotating

Cylinder

Stationary

Outer

Wall

9

Multi-scale Approach to Particulate Flow

Scale

m

icro

m

eso

bu

lk

Granular Temp.

(gas continuum)

Binary collisions,

infinitesimal contact

time

Transient

clusters?

Fluid-like

continuum

(N.S. analogy)

Transient

contacts –

friction, rolling

Spatial and

temporal

distribution of

coherence,

stress chains

Particle

packing

contacts

Domain

interactions

(coherence

length scale)

Quasi-static or

Frictional

continuum

f1n;,~ n high;~ 2

Dimensionless shear rate, 2

1

g

dp*

0 0.2 3 >>

(Tardos, Savage)

Stiffness/shear 3d

k

high low

Contact time tc / tbc high ~2 1 (Campbell)

Stokes # 0 ~10 high d6

m

(Brady)

0

p

P

d

Inertia /

confinement, (GDR Midi)

Is there an optimal

particle flow

operating regime?

• Fluid-like;

• Dense flow;

• Efficient particle

interactions:

• Boundaries,

• Particles,

• added Liquid,

• Gas.

• Low E/M.

)/1( s

rr

ra

s

8.02.038.0 a

66.044.054.0 a

Shear / normal stress ratio as a function of

boundary conditions

(G. Tardos, M. Kheiripour, IFPRI project)

4 mm polyethylene beads:

Smooth wall

Rough wall


La Plata, 2014

Liquid-Like

Solid-Like

34.0n

500 m Glass

yy

xy

s

2/1* )/( gd p

Axial Flow rate: 2 g/sec

n=0.34

Liquid-Like

Solid-Like

34.0n

Liquid-Like

Solid-Like

34.0n

500 m Glass

yy

xy

s

2/1* )/( gd p

Axial Flow rate: 2 g/sec

n=0.34tan

n

ij

ij

ij

ij

ij

ijijapppT

)cos(2)sin(2

Schaeffer Equation

(solid- like behavior)

Liquid-like behavior

n

ij

ij

ij

ij

ij

ijijapppT

)cos(2)sin(2

Schaeffer Equation

(solid- like behavior)

Liquid-like behavior


Determine Constants in Constitutive Equation from

Rheometer Experiments in Couette Device

Growing interest in applying intermediate regime rheology to continuum flow solvers


La Plata, 2014

10

Effect of particle shape


Glass beads Crushed

glass, sieved


La Plata, 2014

Coupling of stress

and dilation

Capacitance sensor measures

solids fraction in Couette gap.

• Various elastomeric materials (A,B,C) show markedly different stress and packing profiles

• Power law rheology is stronger for materials that retain packing state with increasing shear.

– Analogy w/ incompressible fluids?

• Materials that dilate with large fluctuations in packing can maintain relatively stable stress ratios at higher strain rates.

– Average can be steady, local highly fluctuating.

A

B

C

C

A

B

C

A

Str

ess r

atio

Solid

s f

raction

29

What about compressible powders?

Free-flowing granules tend to be stiff and elastic

– If particles are stiff and elastic, springback dilation.

• Note, “elastic-inertial” regime, C.S. Campbell / Powder Technol 162 (2006)

– If particles are soft (e.g., viscoplastic) and the local stress

exceeds the particle’s deformation yield stress (sy)…

– While average stress may be < sy, stress fluctuations may

initiate fouling; once started, fouling may propagate.

… smearing

and build-up

on boundaries

can happen

(fouling).

Granular

free flow

Soft

granular†

Granular

w/ liquid

bridge

Fine

powder

cohesive

Tensile strength

Com

pre

ssib

ility

com

pre

ssib

le

stiff

† May become cohesive under higher consolidation stress,

i.e., an upward curving flow fuction

11

6

Local stress chains and stress fluctuations

– Stress chain visualization and measurement

• Behringer group, Duke University

– Dynamics and Rheology of Cohesive and

Deformable Granular materials (IFPRI)


La Plata, 2014

Stress fluctuations in

volume-constrained

flow

s / smean

4 mm beads, 2 cm fill height,

quasi static flow

In a confined flow…

Local stress fluctuations

may be much higher than

the average stress.

Behringer et al 32

Spatial distribution of stress chains

Iconic image of stress chains

illuminated by photo-elastic particles

in vertical axis 2-D Couette flow

Behringer group, Duke University

Horizontal axis, modified impeller

Behringer, Clark, IFPRI project


La Plata, 2014

12

Spatial distribution of stress chains

Iconic image of stress chains

illuminated by photo-elastic particles

in vertical axis 2-D Couette flow

Behringer group, Duke University

Horizontal axis, modified impeller

“paddle” edge highlighted in red.

Stress chains extend from paddle tip

to wall of mixer – implies opportunity

for jamming.


La Plata, 2014

Summary 1: Constitutive relations in

process optimization

• Take advantage of fluid-like granular rheology

– “Intermediate” or “elastic-inertial” regime.

– Innovation by analogy?

• Energy can be analyzed in terms of stress and flow

fields. Stress and flow are coupled via granular

constitutive properties.

– Success or failure of particulate process scale-up and

optimization depends on understanding of material-process

interactions.

– Much progress in DEM and continuum modeling; many

challenges remain!


La Plata, 2014

Summary 2: Apply granular rheology in

process models

• Take advantage of continuum models in broader process

context.

• Modeling can be used to drive process efficiency:

– System scale, dynamic flowsheets

– Macro scale (unit op), throughput and energy efficiency as a function

of scale up and operating mode.

– Model-based process control.

– Multi-scale modeling continues to be an important opportunity.

13

Summary: Granular Flow Topics

• Flow fields in mixers – examples show growth of basic understanding and modeling capability.

– DEM models show good predictive capability of macroscopic flow, even with “packet” particles up to 10X actual.

– Growing interest and capability in continuum modeling using granular rheology.

• Stress fields (and energy dissipation) are coupled with flow via granular constitutive properties.

– Granular flows can compress and dilate, rheology, s = f ( ’, )

– Important considerations:

• Boundary conditions,

• Particle’s material properties (e.g., elastic vs. visco-plastic),

• Particle’s shape, size distribution, frictional characteristics

• Understanding of fluctuations and jamming may be critical, especially in confined volume flows.

– A statistical mechanics approach using stress chain characteristics?

Thank-you for your kind attention


La Plata, 2014

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Optimization of granular flows for industrial …pasi2014.njit.edu/Lectures/mort2.pdfpaddle type forward cross cross forward type paddle ... ... Variable Sensors: