s ross thesis

Simon Ross / Individual Inquiry October 27, 2000

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TABLE OF CONTENTS

1.0 INTRODUCTION 1

1.1 Background 3

1.2 Granulation of Fertilizer 3

1.3 Objectives 5

2.0 LITERATURE REVIEW 6

2.1 Factors Affecting the Growth of Fertilizer Granules 62.1.1 Growth Mechanisms 62.1.2 Liquid Phase Content 102.1.3 Initial Particle Size Distribution 122.1.4 Circuit Performance 13

2.2 Dynamic Modelling of Fertilizer Granulation Circuits 142.2.1 Population Balance Modelling 152.2.2 The Coalescence Kernel 162.2.3 Crusher, Dryer and Screen Modelling 18

2.3 Scope of Inquiry 19

3.0 EXPERIMENTAL PROCEDURE 21

3.1 Data Collection 21

3.2 Sampling 223.2.1 Sampling Regime 253.2.2 Sampling Equipment 263.2.3 Reduction of the Bulk Sample 27

3.3 Size Distribution Analysis 27

3.4 Moisture Content Analysis 29

3.5 Model Validation 29

4.0 RESULTS & DISCUSSION 31

4.1 Data Collection 314.1.1 Sampling Errors 314.1.2 Analysis Errors 33

4.2 Plant Audit Results 35

4.3 Data Consistency 38


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4.4 Circuit Analysis 394.4.1 Analysis of the Drum Granulator 394.4.2 Analysis of Dryer 434.4.3 Analysis of Samples Obtained from the Screens 454.4.4 Analysis of the Crusher 474.4.5 Analysis of the Recycle Stream 48

4.5 Model Validation 494.5.1 Granulator 504.5.2 Dryer 544.5.3 Screens 554.5.4 Crusher 574.5.5 Mixer 574.5.6 Complete Circuit 57

5.0 CONCLUSION 59

6.0 RECOMMENDATIONS 58

7.0 NOMENCLATURE 61

8.0 REFERENCES 63

APPENDIX A - RAW DATA AND CALCULATIONS 63

APPENDIX B - OPERATING DATA 124

APPENDIX C - DAESIM CODE 125


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1.0 INTRODUCTION

1.1 Background

This individual inquiry investigates the use of population balance to model

granulation drums, as a part of a general model for an entire granulation circuit. It

aims to show how modelling can be beneficial to industrial granulation processes such

as fertilizer production. To support this idea, a plant audit was carried out on Incitec’s

ammonium sulphate granulation plant located at Gibson Island in Brisbane during

July 2000. The analysis and subsequent modelling of this data formed the basis for

this thesis.

1.2 Granulation of Fertilizer

Granulation is a broad term referring to many size enlargement processes where small

particles are combined into larger more permanent masses. Granulation is widely used

to improve the storage, handling and transportation qualities of materials such as food,

pharmaceuticals, ceramics and chemicals such as fertilizers by reducing dust losses

and obtaining free flowing material that is resistant to caking. Other benefits are that

the appearance of these materials is improved and greater control over the physical

and chemical properties is achieved.

Fertilizers such as ammonium sulphate are processed in a rotary drum or mixer

granulator, which forms part of a granulation circuit as shown in Figure 1. In this flow

diagram, a liquid binder feed is dispersed throughout a solids feed in the granulation

drum. Encouraged by collisions due to the rotation of the drum, these particles

agglomerate. The material is then dried in a rotary dryer and screened to remove

oversize and undersize material from the product. The oversize material is then

crushed and recombined with the undersize material to form the recycled feed to the

granulator.


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Figure 1: Typical Granulation Circuit (Zhang et al, 2000)

Adetayo et al (1993) suggests that the successful operation of granulation circuits is

often hindered by three major problems. These are:

1. Product size specifications are very strict and as a result only a small

proportion of the total solids flow actually leaves the process. Recycle ratios

of up to 6:1 are common.

2. Control of granulation processes is quite difficult due to surging and drifting

phenomena coupled with large dead times.

3. The product properties are sensitive to process variables such as moisture

content, binder properties and the product size distribution of the feed.

In addition to these problems, fertilizers (such as ammonium sulphate) generally have

their own very unique properties that makes granulation challenging.

• The material is often soluble so the chemical composition and moisture

content of the granule significantly affects granulation.

• The size distribution of the feed is generally very broad, overlapping the

product specifications.

• The recycled granules that form the feed to the granulator are hard and not

easily deformable which makes agglomeration difficult in some cases.


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A validated model for an operational granulation circuit that accurately predicted

circuit dynamics would vastly improve the control and operability of the plant,

decrease the energy consumption, aid significantly in maintaining a stable operation

and allow a better understanding of the granulation process.

1.3 Objectives

The aim of this inquiry is to show that a dynamic simulation or model can be used to

great effect in improving the efficiency at industrial granulation plants. This will

include:

• Demonstrating how population balance modelling of rotary granulation drums

can be used to simualte actual processing scenarios of industrial fertilizer

granulation circuits, particularly for ammonium sulphate.

• Illustrating how these simulations can be used as a powerful tool in identifying

the actual cause of major processing problems in these circuits as opposed to

identifying and treating the symptoms.

• Displaying how population balance simulations can be used to assess the

impacts of possible improvements to a granulation circuit, without using the

costly plant trial and error approach.

• Identifying possible scale up improvements to current granulation circuit

models derived from experimental studies to make the implementation of plant

data into the model for simulation more accurate.

• Recommending improvements that may be made to Incitec’s ammonium

sulphate granulation circuit to improve the control and operability of the

planbt and make the process more efficient.

The completion of a plant audit of the Incitec plant and subsequent validation of a

recently developed granulation circuit model (Cameron and Balliu, 2000) will help to

achieve these inquiry objectives.


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2.0 LITERATURE REVIEW

Granulation is an important process in the fertilizer industry. It achieves a product that

is easier to handle, store and disperse and apply to soil. Two major problems exist

with the operation of fertilizer granulation plants (Adetayo et al, 1993).

• Due to the wide size distribution of the recycle seed granules only a small

proportion of the granules exiting the granulator are actually of product size.

Large recycle ratios of up to 7:1 are common.

• Surging and drifting problems coupled with slow response makes controlling

granulation circuits quite difficult.

The understanding of the granulation process is still quite limited. The commercial

interest in decreasing capital and operating costs and maintaining the stability and

reliability of the process has ensured that much literature has been published in the

last thirty years to improve the understanding of the granulation process. Generally

this literature has focused on two major areas. These areas are:

1. Factors affecting the effectiveness of the granulation process

2. The dynamic simulation and modelling of fertilizer granulation circuits.

2.1 Factors Affecting the Growth of Fertilizer Granules

2.1.1 Growth Mechanisms

Within a granulator many mechanisms of size enlargement (and reduction) of

granules may occur such as those outlined in Figure 2. These mechanisms have been

used widely in conjunction with experimental observations to attempt to explain

growth behaviour in many different size enlargement processes. This presents

problems because usually these mechanisms have been applied to fit experimental

data and in most cases the results are unique to specific classes of materials and

processes (Adetayo and Ennis, 1997).


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Figure 2: Growth Mechanisms (Adetayo et al, 1993)

Much work has been completed to explain the particle growth experienced during the

drum granulation of fertilizer. This process is unique largely due to three reasons:

• The initial size distribution fed into the granulator is generally very broad,

overlapping the product size distribution (Adetayo et al, 1993).

• The recycled particles, which form majority of the feed to the granulator, are hard

and not easily deformable. This means that any given two colliding particles are

less likely to agglomerate and lower growth rates are experienced (Adetayo et al,

1993).

• The total liquid phase is greater than the moisture content alone because fertilizer

is soluble meaning that the processing conditions and chemistry of the fertilizer

has a significant affect on granulation (Bathala et al, 1998).


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Using experimental data based on three fertilizers including ammonium sulphate

(AS), mono-ammonium phosphate (MAP) and di-ammonium phosphate (DAP),

Adetayo et al (1993) proposed a two-stage granulation mechanism that was explained

using the granulation regimes described by Ennis et al (1991).

In the first of these stages, majority of all collisions between particles cause successful

agglomeration and the growth of particles occurs by random coalescence. The initial

size distribution narrows due to collisions between fine and large particles present in

the recycle causing rapid removal of majority of the fines. This means that the first

stage is completed quite rapidly as it continues until equilibrium is reached at a point

where the smallest granules in the size distribution reach a critical size. At low

moisture contents this is the only stage of granulation.

The second stage of this granulation mechanism only occurs for deformable granules

with high moisture contents. The size distribution of the particles widens due to a

preferential growth mechanism. In this stage not all collisions between particles

result in successful coalescence. This is dependent on collisions causing compression

and forcing liquid to the surface of the particle and deformation occurring at the

collision surface of each particle. As a result, the second stage of the granulation

mechanism proposed by Adetayo et al (1993) progresses quite slowly and is

dependent on the kinetics of granulation.

The two-stage granulation mechanism was validated, for all three fertilizers, using

experimental results from a laboratory scale batch drum in a further study by

Adetayo et al (1995). It was found that DAP and MAP demonstrated both stages of

the two stage granulation mechanism, whilst ammonium sulphate (the fertilizer used

for this study) only exhibited the first stage of the mechanism.

The extent of granulation that can occur by each of the stages in this mechanism is

governed by the balance between the kinetic energy of the colliding particles and the

binding effect of the viscous and capillary forces of the binder and particle

respectively. Ennis et al (1991) investigated these forces and defined the viscous

Stokes number, Stv (1), and the critical viscous Stokes number, Stv* (2), to quantify

the relationship between them.


v

µρ9

8 VrSt g

v = (1)

+=

av h

he

St ln1

1* (2)

Where,

Stv = Viscous Stokes Number (dimensionless)

Stv* = Critical Viscous Stokes Number (dimensionless)

ρg = Granule Density (g/cm3)

µ = Binder Viscosity (Pa.s)

r = Effective Granule Size (mm)

V = Velocity of granule collision (m/s)

e = Coefficient of Restitution

h = Binder Layer Thickness

ha = Asperity Height

During the first stage of granulation, the non-inertial regime is prevalent or when

Stv << Stv*. In this regime all collisions cause successful coalescence. This growth

however increases the value of Stv as the effective granule size increases. Equilibrium

is soon reached as Stv approaches Stv*.

The inertial granulation regime exists when Stv ≈ Stv* and can be compared to the

second stage of the granulation mechanism. In this regime only some collisions are

successful. Ennis et al (1991) also suggested that a third regime exists when the

viscous Stokes number is higher than the critical Stokes number. This is a coating

regime that has little effect on fertilizer granulation due to its non-deformable nature.


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The importance of using an the micro-level based approach of Ennis et al (1991), ain

conjunction with experimental validation of a proposed growth mechanism with a

model was stressed by Adetayo and Ennis (1997). The choice of which growth

mechanism is prevalent is usually based upon observed trends in experimental size

distributions such as ‘gelling’ or ‘non-gelling’ behaviour or in this case narrowing or

widening of the size distribution.

Using this approach as the sole basis for the selection of a growth mechanism for a

specific process can lead to misleading conclusions as shown by the wide array of

granule growth mechanisms experimentally observed (Figure 2). Explaining the

experimental observations of granule growth, with a concept such as the Stokes

regime) that can be applied in all situations is a good method of analysis to ensure

misleading conclusions about the type of growth occurring are not developed.

2.1.2 Liquid Phase Content

There is a very strong relationship between relationship between the liquid phase

content of a granule and the average granule size. The contact between the agitated

bed of solids and liquid components in a granulator causes mobile liquid binding. This

produces agglomerates or clusters, held together by surface tension or capillary

forces. The relative amount of liquid components present determines which state of

capillary force exists between the liquid and solid phases. These states are outlined in

Figure 3.

Figure 3: States of Mobile Liquid Bonding (Brooker, 1999)


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The pendular state refers to when small amounts of liquid are held discretely at the

point of contact between the solid and liquid. The capillary state occurs when all of

the air space between each of the solid particles in the cluster is replaced with the

liquid phase. The funicular state refers to the stage where air and liquid are both

dispersed among the pores of the cluster.

As the particles are dried the surface tension or capillary forces are replaced with solid

crystal bridges. This consolidates the granule. The strength of this crystal bridge is

dependant on the state of the capillary force between the solid and liquid phases and

the viscosity of the binder between each phase. The extent to which granulation

occurs is then not only dependent on the amount of binder present but also on:

• How well the liquid phase is dispersed amongst the solid phase

• How quickly the liquid phase (binder) can penetrate into the pores of the clusters

If the mixing of the phases is not sufficient caking or pooling phenomena can occur.

Caking occurs when a limited amount liquid phase is present in an area and very large

weakly bonded clumps of material are formed. Pooling occurs when the liquid phase

is not dispersed well or a particular area in the drum becomes supersaturated. This

will cause bonds to redisperse.

Sherrington (1968) was the first to propose that volume of the liquid phase present

during granulation determined the extent of granulation. From this the concept of a

solution phase ratio (y) was developed to relate the volume of solution to the volume

of solid in a granule. This is defined in Equation 3.

( )( ) l

f

gs

sgy

ρρ

−

+=

1

1(3)

Where,

y = Solution Phase Ratio

g = Mass Fraction of Water in the Granule

s = Solubility of the Fertilizer Salt in Water (gsalt/gwater)

ρf = Density of Fertilizer Salt (g/cm3)

ρl = Density of Fertilizer Solution (g/cm3)


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The importance of this definition is highlighted by a simulation completed by

Adetayo et al (1994) that considered the effect of the solution phase ratio on various

processing parameters. This simulation used a combination of plant and experimental

data for di-ammonium phosphate fertilizer (DAP) to find that the extent and rate of

granulation increased with greater binder content and as a result the recycle ratio of a

granulation circuit can be optimised with the binder content. The results found by

Adetayo et al (1994) are summarised in Figure 4.

Figure 4: Effects of Binder Content and Crusher Efficiency on Granulation

(Adetayo et al, 1995)

2.1.3 Initial Particle Size Distribution

The effects of the initial particle size distribution were studied by Adetayo et al (1993

& 1995). It was found that the initial size distribution (that is of the recycle / feed

stream) has a strong and complex effect on granulation. The removal of all the

oversize was found to cause significant reduction in the effects of the second stage of

the two-stage granulation mechanism. The addition of fines (< 1mm) material was

also examined (Figure 5). As not many data points were taken it is difficult to fully

assess the effects, however it would appear that an optimal level of fines might be

used to increase the growth rate. For DAP it was found to be at 30%.


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Figure 5: Effect of fines content of the feed on Granulation (Adetayo et al, 1993)

2.1.4 Circuit Performance

A granulation circuit is also heavily influenced by the performance of the other unit

operations within it. This is an important consideration because in most circumstances

the efficiency of these processes is difficult to manipulate quickly. The performance

of the dryer in relation to an entire granulation circuit has not been widely discussed

in literature, however it will have a direct affect on the consolidation of bonds

between particles during the formation of the crystalline bridges as discussed in

Section 2.1.2.

The performance of the screen and crusher and the effects on the entire granulation

circuit were examined using a dynamic simulation by Adetayo et al (1995). This

simulation was based on both experimental and plant data. The use of a more efficient

crusher was found to vastly improve the recycle ratio allowing higher growth rates

and moisture contents as shown in Figure 4. In this diagram, Case 1 (the solid line)

represented a circuit with a low efficiency crusher. Case 2 (the dotted line)

represented a high efficiency crusher. As can be seen a higher efficiency crusher

allows a greater moisture content causing higher volumes of rapid growth of fines into

the product size range, whilst reducing the recycle ratio.


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Figure 6: Effect of Screen Efficiency on Circuit Performance

(Adetayo et al, 1995)

The analysis of the screens found that the efficiency of the screens has a defining

effect on the performance of the circuit especially for the recycle ratios, however it

did not affect the stability of the circuit. It merely determines the recycle ratio without

affecting the operating range or circuit stability. For example Figure 6 outlined the use

both a low efficiency (dotted line) and high efficiency screen (solid line). Whilst the

use of a lower efficiency screen did affect the recycle ratio, it did not affect the

optimal moisture content of the granulator.

2.2 Dynamic Modelling of Fertilizer Granulation Circuits

Bathala et al (1998) developed a model for rotary drum granulation of fertilizers using

a mass balance as the basis for the constitutive equations used. It was developed

mainly for use in the control of fertilizer granulation circuits. It considered only the

growth of particles by layering and the death of particles by breakage due to

collisions. The model was validated using a nitrogen-potassium fertilizer.

This approach was used mainly because a control model is required to relate the

behaviour of output variables to specified input variables. At the time the work of

Bathala et al (1998) were completing their work, very little literature was readily

available on suitable population balance models for control purposes. With the

exception of this study all other attempts have used population balance equations as a

basis for the modelling and simulation of fertilizer granulation circuits.


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2.2.1 Population Balance Modelling

A population balance is a powerful statement of continuity used to describe how a

particle size distribution (PSD) changes with time and space. It follows the change of

a PSD throughout a process due to birth, death and growth mechanisms. It was

introduced as a general equation for particulate systems by Hubert and Katz (1964)

and Randolph and Larson (1962) and has been used to model the formation in a

number of processes including crystallisation, pelletization and granulation. The

general form of the population balance for granulation is shown below.

( )

∫

∫∞

−−−

−−+

+∂

−∂−+=

∂∂

0

0

),(),(),,(1

),(),(),,(2

1

)(),(*)*(

)()(,

dutuvntuntuvuN

dutuvntuntuvuN

vBv

tvnAGvn

V

Qvn

V

Q

ttvn

t

y

t

nucexex

inin

β

β (4)

Where,

V = Volume of the Granulator

Q = Inlet and Outlet Flowrate of the Material

G* = Layering Rate

A* = Attrition Rate

N(v,t) = Number of particles in each size fraction

Bnuc(v) = Nucleation Rate

B(u,v,t) = Coalescence Kernel

Nt = Total Number of Particles per unit volume

This balance can be simplified by making assumptions to suit particular situations.

For example Adetayo et al (1994,1995) defined the population balance for a well

mixed batch system with growth occurring by coalescence only as

( )

∫

∫∞

−−−

−−=∂

∂

0

0

),(),(),,(1

),(),(),,(2

1,

dutuvntuntuvuN

dutuvntuntuvuNt

tvn

t

y

t

β

β(5)


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In most cases the population balance for particulate systems cannot be solved

analytically. Therefore to solve models derived from population balances numerical

methods are required. A numerical method was developed by Hounslow et al (1990).

This method is used widely in solving population balance models for fertilizer

granulation simulations. This method, using a volume basis, discretizes or divides the

particle size range into geometric sections (vi=2vi-1) giving the following result for the

population balance above.

−−

+

=

∑ ∑

∑−

=

∞

=−

+−

−

=−−−−

+−−

j

i

j ijjiijji

iji

i

jiiijji

iji

t

i

NNNN

NNN

Ndt

dN1

1,,1

1

2

1

211,1,1

11

2

2

12

1

ββ

ββ(6)

2.2.2 The Coalescence Kernel

The most significant parameter in the population balance models and simulations, as

discussed previously, is the coalescence kernel Bi,j. This term is combines the birth

and death terms of the population balance into a variable explaining the growth due

coalescence in time and space. It is generally assumed that the coalescence kernel can

be divided into two sections as shown below.

),(0, jiji υυβββ = (7)

The first section β0 is the coalescence rate constant and is a function of the operating conditions

such as moisture content, binder viscosity and drum speed. The second section β(υi,υj)

determines the shape of the particle size distribution. As two stages of granulation were identified

by Adetayo et al (1993), it follows that the coalescence kernel should have two stages to

adequately describe the granulation behaviour. Adetayo et al (1995) validated the following two

stage granulation kernel for a well-mixed batch system.

>>+=≤

≤=

=

122

111

,

);(

;0

ttSSvvAk

SS

ttSAk

critsatji

critsat

sat

jiβ (8)


v

)1(

)1)(1(

swl

sfwsat SXp

SpXS

−

+−=

ρρ

(9)

Where,

K1 = The first stage granulation rate constant

K2 = The second satge granualtion rate constant

Ssat = Fractional saturation of granules

Scrit = Critical saturation of granules

A1 = Parameter for the random size-dependant coalescence kernel

A2 = Parameter for the preferential size-independent coalescence kernel

ρf = Density of Fertilizer Salt

ρl = Density of Fertilizer Solution

p = Porosity of Granule

Xw = Moisture Content

Ss = Solubility of Fertilizer Salt in Water

The first part of this kernel (up until t1) represents the non-inertial growth regime

discussed in Section 2.1.1. In this stage the chance of coalescence is not dependent on

the collision velocity or binder viscosity rather the likelihood that binder is present at

the collision site. The kernel assumes that the binder is well distributed and the

probability of collision is size independent. As a result the growth rate in the first

stage is constant.

After t1 (the point at which the viscous Stokes number and the critical viscous Stokes

number are equal), growth is size dependent, as deformation is required to release

binder. Therefore the second stage growth rate constant generally only applies in

collisions involving large particles of high inertia, which increase the value of Ssat

above the critical value for coalescence.

Adetayo et al (1995) published results of an experiment completed using a laboratory

scale batch granulator examining the granulation rate constants for three different

fertilizers including ammonium sulphate, mono-ammonium phosphate and di-


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ammonium phosphate. The result of these experiments (at 4% moisture content) are

summarised in Table 1.

Table One: Granulation Rate Constants for some Fertilizer Types (4% Binder)

Variable AmmoniumSulphate

Mono-ammoniumPhosphate

Di-ammoniumPhosphate

A 27.7 25.8 39.9y 0.106 0.090 0.092Ssat 0.150Scrit 0.36 0.20 0.13k1 2.85 1.70 3.39k2 0.0002 -0.016 0.0043

It was found that at low moisture contents that the value of k2 was zero for all

fertilizers. Due to the relatively high critical saturation of ammonium sulphate even at

higher moisture content values (up to 8%) the fractional saturation of the granules

remained low and the value of k2 remained at zero. The outcome of this is that

modelling of ammonium sulphate granulation should only require the use of the first

size independent coalescence kernel.

2.2.3 Crusher, Dryer and Screen Modelling

There modelling of other units in the granulation circuit are much simpler as results

from other applications can be applied, unlike the granulator. A brief description of

models generally used for fertilizer granulation circuits is included below:

• Screen models used have generally been based on the screen model of Whiten

(1974). This model is based on the probability that a particle will pass through a

specific aperture or size fraction.


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• Crusher models including that of Adetayo (1993) are generally based on one

equation describing the flow of material into the crusher and three other functions.

A selection function is used to describe the probability of breakage in each stage;

a breakage function gives the relative distribution of the broken particle and a

classification function describes the differential movement of the particles. In the

case of fertilizer granulation circuits a perfect mixing assumption is used, as

hammer mills are quite common.

• Dryer models have generally been based on the assumption that they only act to

reduce the moisture content of the granules.

2.3 Scope of Inquiry

The scope of this thesis can best be defined as the continuation of work completed by

David Brooker and Jang Zhang in the past year at The University of Queensland.

Zhang et al (2000) developed a model of a typical granulation circuit based on a

sound understanding of granulation mechanisms as developed by Adetayo et al (1993,

1995) and others developed recently. The model used a multi-phase heat and mass

transfer model for the rotary dryer. In addition pseudo steady state models were used

for the screens and crushers as the kinetics of these processes were fast and did not

have a large effect on the system dynamics.

The work of Zhang et al (2000) used the NIMBUS simulator to evaluate control

strategies for industrial granulation of di-ammonium phosphate fertilizer and found

that using a crude measurement of the recycle size distribution defined by the fraction

of oversize and undersize material in the stream. It was found by using three case

studies such as a disturbance in the binder flow rate that recycle ratio control

strategies could be proposed and developed using the granulation circuit model.

Brooker (1999) attempted a model validation of the Zhang model using data obtained

by way of a plant audit of the granulation circuit at Incitec producing ‘N-Gold’

fertilizer. Using the plant audit Brooker analysed the granulation circuit and proposed


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strategies of how the operation of the granulation circuit with respect to ‘N-Gold’

production could be improved. His work also focused on attempting to validate the

Zhang model using the NIMBUS simulation. It was found that there were many

limitations in validating this model due to the unstable operation of the Incitec plant

during sampling, sampling issues and limitations with the model.

This inquiry is similar in nature to that of Brooker (1999). Like the Brooker thesis this

inquiry is aimed at analysing achieving a detailed analysis of a granulation circuit,

although in this case the granulation of ammonium sulphate will be examined. Also

like the Brooker thesis this inquiry will attempt a model validation.

The Zhang model produced in the NIMBUS simulator will not be used. The NIMBUS

simulation package has been super ceded by Daesim an improved version of the

NIMBUS simulator. Cameron and Balliu (2000) are currently finalising a new

granulation circuit model based on the same principles as Zhang, in the Daesim

simulator and this investigation help to finalise some of the details.

This inquiries scope has also gone beyond the Brooker thesis in that it attempts to

validate each unit of the granulation circuit with the plant data rather than solely

dealing with the validation of the granulator drum.


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3.0 EXPERIMENTAL PROCEDURE

This section outlines the procedure used to sample and analyse the material obtained

from the four plant audits completed at the Incitec Plant. It qualitatively assesses each

of the procedures taken during the plant audit to determine the major areas where

experimental error may have significantly affected the results.

3.1 Data Collection

To best assess the data obtained from the plant audit as much information as possible

was required. Information was to be obtained from three sources around the plant.

The first is through sampling of material (s), which is discussed in Section 3.2. The

other two are the information obtained from the operators from the control panel and

daily plant measurements in the plant (o) and a mass and energy balance package used

by Incitec to determine flow information around the plant (m). Table 2 presents a

summary of the data available from the plant audit and the source it was obtained

from.

Table 2: Data Obtained During the Plant Audit

Stream Particle SizeDistribution

MoistureContent

Flow Temperature

Recycled AS Feed toGranulator

X (s) X (m) X (c) X (c)

Binder Feed toGranulator

X (c)

AS Flow to Dryer X (s) X (s) X (m) X (c)Air Flow into Dryer X (c)AS Flow to Oversize X (s) X (s) X (m)

Air Flow out ofDryer

X (c)

Oversize X (s)Product Size X (s)

Undersize X (s)Polishing Screen

UndersizeX (s)

Crushed Material X (s)Product X (s) X (c)

(s) – data obtained from sampling(o) – data obtained from operators (control panel & daily measurements)(m) – data obtained from mass and energy balance package


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Due to problems with the mass and energy balance program, the information obtained

from this source was not available for the plant audit. The major implication of

missing this data is that the consistency of the data could not be fully assessed. This is

discussed in Section 5.2. In most cases the information obtained for the flowrate and

temperature from the operators was obtained using on-line measurements, however in

some cases the temerature was measured by taking a measurement with a

thermometer of a sample in a bucket. Infra-red sensors were not used due to their

innaccuracy for this application

3.2 Sampling

Sampling was completed at 9 major sample points around the plant with 15 separate

samples taken during each run to take into account the sample points where the stream

had been divided into two separate flows. The sampling points have been identified in

Figure 7.

Crusher

Rotary Dryer

Screens Polishing Screen

Rotary Granulation Drum

1

2

3

7

4

6

8

5

9

Figure 7: Sampling Points at the Ammonium Sulphate Granulation Circuit


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One of the limitations in validating the model is the quality of the samples obtained

from the plant audit. A number of measures were taken to ensure that the sampling

error was minimized, however in some circumstances, high error was inevitable.

Table 3 outlines the features of each sampling point that were not conducive to

accurate sampling.

When sampling particulate matter there are two major sources of error that must be

prevented. These are delimitation errors and extraction errors (Gy, 1983).

Delimitation errors occur when not all the particles in a stream have an equal

probability of being sampled. Extraction errors occur when the chance of particles on

the edge of the cutter being included is size dependant. Both of these errors must be

avoided to ensure that a representative sample is obtained.

Table 3: Sample Point Characteristics

Ref # Name Description

1 Recycle / Feed Constant, high velocity stream of many fine particles.

Difficult to sample across the entire length of the stream

due to the small chamber and high speed.

2 Granulator Exit Material falls at an intermittent rate due to the motion of

the drum in a 2 metre wide curtain. The sample point was

a door approximately 500 mm from the start of the curtain.

Difficult to sample to entire cross-section (causing

delimitation errors) and length of the stream. A shovel was

used to sample causing extraction errors due to larger

particles falling from the edge.

3 Dryer Exit

(2 Samples)

Stream divided into two causing the need for two samples

to be taken. Relatively easy sample point to take the entire

cross section and width of the flow. The major cause of

sampling error in this stream is the segregation cause by

the intermittent flow due to the screw feed.


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4 Oversize

(2 Samples)

The sampling from all the screens could not be taken from

a free-falling flow due to their arrangement. However a

reasonably accurate sample was taken using a cutter on the

surface of the screen across its entire length. Very minimal

extraction would have been experienced due to this

sampling method. Large delimitation and extraction errors

would be present. It is assumed that the sample would

have a distinct under-representation of fines due to this.

5 Crusher Exit

(2 Samples)

This sample point obtained quite possibly the most

erroneous sample. The point was a small opening in a

large chamber directly underneath the crusher. A small

cutter had to be used. In addition, high volumes of dust

escaped from the sample opening indicating that the

movement of particles within the chamber were random

and unpredictable.

6 Undersize

(2 Samples)

The undersize sample was taken from a constant, high

capacity free-falling stream of narrow width and length.

Relatively easy to take a representative sample from.

7 Product Size

(2 Samples)

See description for the oversize stream.

8 Polishing Fines Constant, free falling, low velocity stream of particles with

a very small spread. Would be ideal for sampling except

the pipe containing the flow is cylindrical with a small

diameter, which makes sampling of the entire stream

difficult. Sampling was completed with the smaller of the

two cutters shown in Figure X.

9 Product Sample A mechanical sampler was present at the end of the

product conveyor belt across a constant flow falling freely

from the conveyor belt. This resulted in relatively small

errors.


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The underlying reason that particulate material is difficult to sample is that unlike a

liquid homogeneous samples cannot be produced via mixing due to the nature of

solids. Vibrations occurring in processing equipment and on conveyor belts as well as

the storage of particulate materials on stockpiles or in hoppers leads to segregation

effects where particles of similar size and density converge in the same region of the

sample. In this case, segregation is not attributable to the storage in stockpiles or

hoppers, however extensive use of conveyor belts means this must be considered.

3.1.1 Sampling Regime

The best technique of avoiding errors due to segregation is to move the sample cutter

at constant speeds at right angles to the stream completely through the entire cross

section of the flow while the stream is in a state of free fall (Goldberger et al, 1984).

In addition it is desirable to sample the stream over many short time intervals to form

a larger sample rather than taking a single sample over a longer time period. This is to

allow for variation in the flow.

There are no set guidelines to determine the size of sample that should be taken to get

a representative sample of the total flow of particulate materials. Some standard

industry guidelines and Gy’s theory can be used to determine the sampling regime

required to meet these requirements, however in this case they were not considered.

To compensate for this an error analysis for sampling using Gy’s Theory was

completed, so the effect of sampling error can be quantified.

The actual methodology used to sample the fertilizer from the granulation circuit

considered the total flows and variability of the flows. It involved taking bulk samples

of 2.5 +/- 0.5 kg samples. These samples were collected in two increments

approximately 90 minutes apart. Where possible the samples were collected manually,

directly cutting across the entire length of the stream. The cases where this was not

possible are highlighted in Table 3.


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3.1.2 Sampling Equipment

The design of the sampling equipment is also another major consideration of when

attempting to reduce the sampling error. With the exception of the product sample,

where an automatic mechanical sampler was present, manual sampling was used for

all streams. Snow et al (1997) presents the following criteria for the design of a

traversing sampler for gravity flow:

• The sampling device moves at a constant speed as it traverses across the entire

flow of the material. The cutter should also move far enough past the flow of

material so nothing is collected whilst the cutter is stationary.

• The cutter length should be at least 10mm or three times the diameter of the

largest particle in the stream.

• The cutter width should be three times the diameter of the falling stream.

• After the sample is taken, the sampler shouldn’t be more than half full.

These criteria were fulfilled in most circumstances throughout the sampling process.

In some cases the sample point was not wide enough to extend past the edge of the

flow. This same problem was met in ensuring the cutter width was three times the

diameter of the falling stream. The configuration of the two sampling devices used is

shown in Figure 8 below.

Figure 8: Configuration of the Sampling Devices


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3.1.3 Reduction of the Bulk Sample

To obtain an acceptable sampling error level the sample from the plant taken is quite

large (2.5 kg), however for sieving purposes this sample needs to be reduced to about

100 to 300 grams. To ensure the representative nature of the sample is not

compromised a Jones riffle was used to split the sample several times. The material

was distributed into the feed hopper of the riffler to ensure each division had an equal

amount of material flow to reduce error. Figure 9 shows an example of a Jones riffle

sampler.

Figure 9: Jones riffle sampler

3.3 Size Distribution Analysis

The particle size distribution of the samples was analysed using standard dry sieving

methods. A ü2 sieve series was used ranging from 63 microns to 8 mm aperture was

used. In addition extra sievc eizes were used to obtain more data points for the

frequency distribution plots. These extra sizes were 106 micron, 1.18mm, 2.36mm,

3.35mm, 4.75mm and 6.7mm apertures. The resulted in having much more detailed

size distribution data for analysis.


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An analysis of the accuracy of the sieving procedure is included in Section 5.1. The

factors considered in designing an appropriate sieving regime are listed below:

• Sieving Time

• Particle Shape

• Sieve Load

• Agitation Method

• Blinding

• Breakage of Granules

The overall method of attaining minimal variance in the results was to maintain a

constant sieving conditions such as sieving time, agitation method and rate. Taking all

these factors into consideration the following sieving methodology was used for the

anaysis. A mechanical sieve shaker as shown in Figure 6 was used for the analysis.

1. A sample of approximately 150 to 250 grams was taken from the bulk sample

from the plant using a sieve shaker. This introduces an error of approximately

3.4 % (Brooker, 1999).

2. The fertilizer was sieved in three separate stages due to the size of the sieve

shaker and the total number of sieves. In each of these stages the fertilizer

weas sieved for a total of 5 minutes. The first stage included to sieves from

8mm to 2.36mm. The second was from 2mm to 0.5mm and the third form 355

microns to the collection tray.

3. Each size fraction was weighed and the results were plotted on a frequency

distribution graph.


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Figure 10: Sieve Shaker

3.4 Moisture Content Analysis

The critical issue in moisture content analysis is avoiding changes in the moisture

content between the point in time at which the sample is taken and the time the

analysis is completed. Goldberger et al (1984) recommended closed chute work and

sealed sample containers. The analysis of the moisture content for fertilizer must also

consider the solubility. In this case the moisture content was only recorded for the

granulator exit sample. The procedure was as follows:

1. Immediately after taking a separate sample than the size analysis sample recording

the weight.

2. Placing the sample in a sealed bag.

3. Drying the sample in an oven at 60°C for 24 hours.

4. The final mass is recorded

The moisture content is then calculated using the following equation:

weightwet

weightdryweightwetmoisture

−=% (8)

3.5 Model Validation

A model validation was attempted on a model developed by Cameron and Balliu

(2000). This model was constructed in Daesim, a simulation package developed by


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Cameron and Newell. The approach to the model validation was to first validate each

of the individual units of the circuit and then to combine them into a validation for the

completed circuit

This model (shown in Appendix C) was fitted to the plant data using the following

procedure:

1. Defining the size distribution of the feed to the process.

2. Defining the physical constants such as density and solubility appropriate to

ammonium sulphate granulation circuits.

3. Adjusting the empirical model fitting parameter (only for the granulator)


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4.0 RESULTS & DISCUSSION

4.1 Data Collection

A successful model validation can only be achieved when it is ensured that the

experimental error in sampling and analysis is of sufficiently small magnitude and it

is found that the data gives consistent results. To ensure that the data obtained during

the plant audit was reliable an error analysis was performed on the experimental

procedure including sampling, sieving and moisture content analysis. In addition data

reconciliation was used to compare the results obtained from each data point to ensure

they were consistent with the next. This section presents the results of both the error

analysis and data reconciliation techniques used to ensure that this was the case.

4.1.1 Sampling ErrorsThe sampling of particulate material will always be erroneous to a certain degree

despite measures being taken to reduce it. Gy (1983) devised an equation that could

be used to size of sample required to obtain a certain error level. In this case, this

equation has been used to determine the amount of error, a3s the sample size was

arbitrarily chosen for the plant audit.

395

2 11)100( hdl

MLxx vαρσ

−−= (10)

Where,

ρ = granule density (units)

x = mass fraction being sampled

M = sample weight (g)

L = mass of total sample (g)

l = liberation factor

αv = shape factor

h = spread of size distribution (mm)

D95 = 95% passing size of the distribution (mm)

Table 4 shows the maximum and average maximum error value for the sampling at

each point using Gy’s theorem.


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Table 4: Sampling Error using Gy’s Theorem

Sample Maximum CalculatedError (%)

Average MaximumCalculated Error (%)

Granulator Exit 63.80 20.48Dryer Exit 85.42 39.85Oversize - -Product Size 15.57 5.07Undersize 0.40 0.14Crusher Exit 71.05 41.50Polishing Screen Feed 3.47 1.32Polishing Screen Exit 0.19 0.07Product 3.62 1.04Recycle 7.88 2.59

The theory proposed by Gy (Equation 8) assumes that unbiased samples are taken at

random. Therefore the results obtained from this error analysis will not give the actual

error experienced in the sampling, but the minimum attainable error with the sampling

regime used.

Many of the samples had a large bias due to the characteristics of the sample points

they were taken from. The error introduced by the biased sampling techniques cannot

be determined without much more extensive and accurate sampling techniques to

assess what the true values for the size distribution and moisture content are.

A qualitative assessment of the expected error can be determined by looking at Table

3. This table gives the details of the major features of each sampling point and the

problems associated with taking samples. From this table it can be said that the

granulator exit and crusher exit streams have a high bias error, whilst the dryer exit,

polishing screen fines and recycle streams have a moderate bias error. For the

remaining samples, a low error could be assumed meaning that Gy’s theorem gives an

accurate measurement of the sampling error.

Using this information and the error value calculated by Gy’s Theorem, an assessment

could be made about the sampling regimes and how they may effect the results of the

model validation. The granulator exit, crusher exit and dryer exit samples cause the

most concern. Any analysis over the crusher, dryer or granulator is severely limited

by the high sampling errors experienced during the plant audit.


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There are improvements that need to be made to the sampling process for any future

work aimed at validating a model based on plant data obtained from Incitec. These

include:

• Taking smaller, more frequent bulk samples, especially of streams containing

larger material. For example changing the sampling regime from 2 x 1.25 kg

samples to a regime of 8 x 300 gram samples would reduce the maximum

error of the crusher outlet sample to 13.15%. The only consideration is that the

size of the samples may be too small to sample the entire width of the stream.

• Modifying some of the equipment at Incitec to incorporate some more

accurate sampling points. This is particularly with reference to the crusher,

which not only has a poor sampling point, but also is also hazardous and

messy to take samples from due to the large amounts of hot fertilizer dust

escaping from the opening.

4.1.2 Analysis ErrorsThere are three areas, where errors may have occurred in the analysis of the samples.

These are in the riffling, sieving and drying of the material. Several tests were taken

to quantify the error associated with the analysis in these cases.

Figure 10 assesses the performance of the Jones riffle used to reduce the samples

appropriate for sieving . The recycle sample from the first plant audit was split into

two samples of approximately 200 grams. The recycle sample was used as it contains

the broadest size distribution and is most likely to show up the any error involved in

riffling the samples. Each sample was then sieved at a frequency of 50 Hz for 5

minutes to obtain the results in Figure 10.


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Figure 10: Analysis of Riffling Error

As can be seen from the results the error associated with poor representation from

riffling the material is quite insignificant and is not a consideration in the successful

validation of the model.

The precision of the sieve was also tested to ensure that it was giving consistent

results and to analyse the amount of error associated with choosing the parameters for

sieving such as the sieving time and vibration frequency was minimal. Brooker (1999)

completed a similar test for N-Gold Fertilizer and found that the effects of the sieving

time were insignificant in the correct analysis of size distribution and that minimal if

any breakage occurred on the sieve trays.

The error analysis completed aimed to verify this result for ammonium sulphate. One

of the samples used for the riffling analysis was sieved for two further periods of five

minutes again at 50 Hz to ensure that:

1. Five minutes was sufficient time for all the granules to distribute to the

appropriate sieve trays.

2. The vibration frequency was small enough to ensure that no breakage of

material occurred on the sieve trays.


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Figure 11 shows that both of these scenarios are also applicable to ammonium

sulphate granules as well as N-Gold. There is little variation at all between the three

plots.

Figure 11: Analysis of Sieving Error

The remaining consideration in the analysis errors is the accuracy of the moisture

content data obtained. Section 2 illustrates how moisture content is an important

factor in the operation of granulation circuits. The major errors in determining the

moisture content occur with the drying of the sample before it can be analysed and

weighing errors. It is expected that these results will be quite accurate and not have a

significant effect on the results of the model validation.

4.2 Plant Audit Results

A large amount of data was obtained from the plant audit and subsequent analysis

produced results useful for many applications. Some of these applications extend well

beyond the scope of this inquiry and have not been discussed as a part of this report

but have been appended (Appendix A). The most relevant and useful results have

been summarised in Table 5 and Table 6.


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Table 5: Summary of the Size Distribution Results

Run 14/07/00

Run 27/07/00

Run 315/07/00

Run 416/07/00

Sample

d50

SpreadD90-d10 d50

Spreadd90-d10 d50

Spreadd90-d10 d50

Spreadd90-d10

Recycle/Feed 1.67 3.40 1.45 3.58 1.40 1.75 1.47 2.32GranulatorOutlet

1.87 2.72 2.20 9.55 2.10 5.95 1.87 4.80

Dryer Outlet 2.03 7.75 2.52 7.70 1.86 6.62 2.08 7.02Oversize 8.60 - 8.70 - 7.85 - 8.95 -Product Size 2.43 1.53 2.85 1.89 2.75 1.70 2.55 1.76Undersize 1.45 1.31 1.27 1.42 1.21 1.42 1.23 1.42Crusher Outlet 2.60 7.12 2.40 6.78 2.05 6.43 2.10 5.80PolishingScreen Feed

2.30 1.52 2.63 1.75 2.60 1.85 2.40 1.70

PolishingScreen Fines

1.52 0.82 1.60 0.78 1.57 0.75 1.52 0.75

Product 2.40 1.65 2.70 1.97 2.55 1.83 2.45 1.53

Table 5 summarises the changes in the size distribution over the plant four each of the

four sampling runs. For majority of these results a consistent mean size of the

distribution and the spread of the distribution are obtained with quite reasonable

results. The major deviation from this is the results for the granulator outlet. The

spread of the size distribution for this sample is erratic and in three of the four cases

the distribution widens over the granulator. This is inconsistent with the two-stage

growth mechanism suggested by Adetayo et al (1993).

Two ideas can be suggested for this anomaly. The first is that the accuracy of the

analysis for the larger sizes is quite poor due to the maximum sieve size used being

8mm. This means that for the larger sized distributions regression had to be used to

calculate the D90 size and even the D50 size for the oversize distribution in some cases.

This means that the accuracy of this result is reduced. It is also why the spread of the

size distribution for the oversize sample could not be calculated.


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Other possibilities for these results can be attributed to the large particles present in

the recycle to the granulator. These grow quite quickly in the first stage of the two

stage growth mechanism. In addition to this they are quite a large proportion of the

total mass and small changes in this size range can cause large changes in the spread

of the size distribution.

The other feature of the results presented in Table 5 is the general trend for the size

distribution to widen over the dryer. In three of the four sampleing runs this has

occurred. This could be due to inconsistent sampling of the material or a widening of

the size distribution to to granule breakage in the dryer. This has been discussed in

more detail in Section 4.3.

Table 6 gives a summary of the operating conditions of each of the plant audit

sampling runs. As can be seen reasonably constant operating conditions were

maintained throughout each of the runs. Despite this as the plant is dynamic in its

operation the consistency of the data obtained during the plant audit must be assessed.

This is considered in the next Section.

Table 6: Summary of the Operating Conditions

Moisture Content (%)Run BinderFeed (L/s) Gran.

ExitGran.Entrance

GranulatorTemp. (C)

RecycleRatio

Weather

1 6.0 2.14 0.20 92 3.8 21 C(Cold andovercast)

2 4.8 2.42 0.22 89 4.1 18 C(Showers)

3 6.3 2.30 0.27 79 4.6 27 C(Hot andHumid)

4 6.4 2.50 0.13 82 4.0 26 C(Hot andHumid)


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The two major applications of these results that have been considered in this inquiry

include:

1. Analysing the performance of the ammonium sulphate granulation circuit and

suggesting areas where the operation of this plant may be improved.

2. Performing a model validation on the current model developed by Cameron

and Balliu (2000) and verification of the expected effects of these

improvements.

These applications involve the comparison between various samples. Before these

comparisons can be made, it must be ensured that the relationship between the

samples is consistent.

4.2 Data Consistency

To ensure the relationship between the samples is accurate the following factors must

be considered:

• The error involved in sampling and analysis.

• The precision of the sampling methods (reproducibility).

• The surging, drifting and dead time phenomena of the granulation circuit.

The errors involved in sampling and analysis have already been discussed and

quantified; however the other two considerations have not. A qualitative method of

determining the sampling precision would have been to take a series of samples in

quick succession from the same sampling point. The samples could then be compared

and the variance calculated. This was not completed during the plant audits.

Due to this, the analysis of the precision of the sampling methods was very limited.

Using a similar method as described above Brooker (1999). The reproducibility of the

sampling of the exit from the granulator introduced an maximum error of

approximately 4.4%. As the sampling methods for the exit to the granulator and the

crusher exit are the least accurate by assuming that the precision of sampling ‘N-

Gold’ fertilizer is the same as the precision of sampling ammonium sulphate , it can

be assumed that the error due to the non-reproducibilty of the results is quite small.


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The third consideration in the reliability of the data is the characteristic surging and

drifting phenomena and the large dead times associated with fertilizer granulation. To

ensure that these phenomena are not the cause of trends experienced within the size

distribution data. The best method of quantifying the effect of this error is to use data

reconciliation techniques to compare the flows of material. This involves using

overall and component mass balances to ensure that the data obtained is physically

acheivable.

Unfortunately due to problems with the mass and energy balance program at Incitec,

insufficient flowrate data was obtained to complete the data reconciliation techniques

adequately. As seen in the next section however each sample point gave consisitent

results over each of the four sampling runs. As the effects of drifting and surging

would cause random variances between the data, qualitatively it could be assessed

that the data is consistent, however this limitation must be considered when

completing further analysis of this data.

4.3 Circuit Analysis

4.3.1 Analysis of the Drum Granulator

Figures 12 to 15 show the change in the size distribution of particles over the

granulator for each of the four sampling runs. Each of these plots show a large

decrease in the fine particles in the granulator and a general shift of the size

distribution to the right.

It was expected that as the moisture contents obtained for each of the four runs were

quite small that the results obtained from the granulator would display the first stage

of the two-stage granulation mechanism proposed by Adetayo et al (1993) without

illustrating any of the effects of the second stage mechanism. In each of the four plots

the size distributions show a very large decrease in fines causing a distinct narrowing

of the size distribution. The right hand side of each plot shows a constant shift to the

right across the size distribution indicating the size dependent growth typical of the

outlined mechanism


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Analysis of the larger granules also shows that the effects of the second stage or size

dependant mechanism are quite small. In Figures 12, 13 and 15 there is a negligible

increase in the frequency of the larger particles in the distribution. The second stage

growth mechanism would see these particles increasing in size proportionally faster

due to a layering mechanism of growth. As this does not occur it can be assessed that

by modelling the first stage growth mechanism a simulation should be able to fit the

data presented.

Figure 12: Analysis of the Drum Granulator (Run1)

Figure 13: Analysis of the Drum Granulator (Run 2)


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Out of the other three effects mentioned in literature (initial particle size distribution,

moisture content and circuit performance) only the initial particle size distribution

varied for a significant analysis to be performed on how the growth of granules is

affected by this factor. Figure 16 compares the difference in the spread of the size

distribution between the samples and the growth of the particle size distribution

(defined by the change in the mean size or d50).

As expected due to the lack of information on the data points, no solid conclusions

can be made about the effect of the initial size distribution in this case. This is mainly

due to the fact that unlike experimental situations, this factor cannot be carefullly

controlled. In future work in may be beneficial to attempt to examine the effect of

these factors by controlling plant data.


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Figure 16: Analysis of the Initial Size Distribution Effects on Granulation

Although in each of the samples the moisture content in the granulator did not vary

much at all a comparison has been the moisture content and amount of growth in the

granulator. This has been shown in Figure 17.

Figure 17: Analysis of the Moisture Content Effect on Granulation

Again due to a lack of data points from not being able to vary the operating conditions

of the granulation circuit it is difficult to verify any past literature with the results

obtained from the plant audit. Even if many data points had been taken many

erroneous results would be expected due to the large dead time effects experienced in

granulation circuits. This effect would be better examined by validating the model and

then simulating the effect of a changing moisture content.


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4.3.2 Analysis of DryerThe analysis of the dryer over each of the four runs (Figure 18 to 21) has shown that

the removal of moisture is not the only mechanism occuring in the unit. If this was the

case virtually no change would be experienced in the size distribution from the exit of

the granulator to the exit of the dryer. As can be seen there are large variations

between each of these size distributions.

There are a number of possible explanantions for this. The first is that there is no

variation at all and the high sampling errors experienced can explain the variation.

This idea obtains more merit when it is assessed from the plots that each of the

sampling points provides different distributions. This may be accurate, however if the

splitting of the stream from the dryer has a high bias error.

In Figure 11, the riffling and sieving errors are shown to be quite small and it is

assumed that this factor plays no major role in the results experienced. The other

consideration is that breakage of granules and/or removal of dusty fines in the air

stream maybe occuring in the dryer. The analysis of whether this is actually occuring

is difficult due to the two samples points giving different size distributions. The most

likely scenario is that these mechanisms are both occuring in the dryer and the

magnitude to which they occur is the unknown.

One additional factor that must be considered is the sampling of the damp granulator

exit stream. Although some measures were taken to ensure that this stream was not

affected by the moisture content of the sample such as spreading the sample out for

the period of sampling to dry before transporting the sample, this quite obviously may

have had a large affect on the comparison of the data between the dryer and

granulator exit samples.

To fully determine the magnitude of the effect of breakage in the dryer would require

more sampling and analysis however, the variation in the size distributions are quite

siginificant which would suggest that some level of breakage is occuring in the dryer.

Any model attempting to simulate the granulation circuit must therefore consider this

mechanism.


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Figure 18: Analysis of the Dryer (Run 1)




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4.3.3 Analysis of Samples Obtained from the ScreensThe analysis of the material sampled from the screens provided more insights into

some of the problems experienced in the granulation circuit. Figure 21 and 22 show

the size distribution of the oversize stream. Like the dryer this flow was split and two

samples had to be taken to compare the overall oversize results. The results are quite

consistent between samples and sample points.

The major consideration in the analysis of the oversize is the large amount of

‘nuggets’ present. Particles were present in the size distribution that are 2 to 3 times

greater than the upper limit of the product size distribution.

Figure 22: Analysis of the Oversize Samples (Sample Point One)The possibilities for this can be narrowed down to two options:


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1. The crusher is operating inefficiently and a large proportion of the recycle to

the granulator is oversize material.

2. Poor binder distribution is occuring in the granulator causing clumps of

material to form.

The likelihood of both of these problems could be tested in a fully validated model.

Figure 23: Analysis of Oversize Samples (Sample Point Two)

Figure 24: Analysis of the Undersize Samples (SamplePoint One)Figures 22 and 23 show the undersize distribution obtained from both of the sampling

points at the Incitec Plant. The most prominent feature of these two plots is the


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inconsistency between the two samples. Figure 22 shows a trend of a slightly bimodal

size distribution, whilst Figure 23 showing the size distribution of the second stream

of the shows very little indication of this phenomena. The two most likely causes for

this are:

1. Breakage of granules on the screens of the leading to Crusher A.

2. A bias split of granules in the exit stream from the dryer.

To assess what is actually occuring in the granulation circuit more plant sampling will

need to be completed. The outcome however is that a method of modelling breakage

on both the screens and in the dryer now need to developed for an effective model

validation to be completed.

Figure 25: Analysis of the Undersize Samples (Sample Point Two)

4.3.4 Analysis of the CrusherAs discussed in Section 3, the analysis of the crusher is severely limited by the

sampling points used to obatain the data. For this reason the best method of analysing

the results is to look at them together so that individual sample bias does not affect the

analysis of the results. Figure 24 shows the combined analysis of all the samples taken

for both crushers at the Incitec plant.


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Figure 26: Analysis of the Crusher

The results of the analysis for the oversize stream have been reinforced in this graph.

In some cases up to 30% of the oversize material entering the crusher also left as

oversize material. Adetayo (1995) has shown that crusher efficiency has a significant

effect on the recycle ratio due to the narrowing of the feed size distribution

experienced with high efficiency crushers. It is recommended that the modification of

the crushers to would significantly improve the efficiency of the process.

4.3.5 Analysis of the Recycle Stream

Figure 27: Analysis of the Recycle Stream


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Figure 25 shows the composition of the streams forming the recycle or feed stream to

the granulator. The recycle stream consists of three types of flows including:

• The crushed oversize stream

• The undersize stream

• The undersize stream from the polishing screens

Both the undersize streams can be seen to form quite a narrow distribution relative to

the crushed oversize stream. The only feed larger than the product size distribution is

from this stream. In addition to this the feed from the polishing screens only adds a

narrow distribution with very little fine material less than 1mm. This is the material

that is quickly removed in the granulator (Figure 12) facilitating fast growth rates.

Based on these observations there are two simulations that should be completed using

a validated model:

1. Using a high efficiency crusher to remove all the material above 4mm in size

before the granulator.

2. The possible use of a grinder to be used on the polishing screen fines to

increase the amount of (<1mm) fines in the feed to the granulator

4.3 Model Validation

The parameter estimation for the granulation circuit model in Daesim was first

attempted on a unit-by-unit basis. This is the most systematic and analytical method

of approaching a validation of a large and complex model. A successful validation of

each unit in the process will enable the complete granulation circuit to be built quite

easily with the addition of the recycle stream to replace the current granulator feed.

Daesim provided a user-friendly interface for completing this task. The model

developed by Cameron and Balliu (2000) was developed using the current solid-liquid

source, solid-liquid sink, solid-liquid stream/link, mixer and screen models previously

developed by Bob Newell and Ian Cameron, (2000) during the development of the

Daesim simulation package. These models were combined with the additional dryer

and drum-granulator models developed by Cameron and Balliu (2000) into a

complete granulation circuit as shown in Figure 26.


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Figure 28: Granulation Circuit Model (Cameron and Balliu, 2000)

Once the modifications were made to the model, the parameters were selected on unit-

by-unit basis with the aim of validating the entire circuit. The results of this parameter

selection and model validation are included in the following five subsections.

4.3.1 Granulator

The granulator is the critical unit in the process and as a result was the first unit a

model validation was attempted on. The major assumptions made in the development

of this model are:

• The model is based on the two-stage coalescence kernel developed by

Adetayo et al (1993).

• Based on past work by Adetayo et al (1995) it has been found for ammonium

sulphate does not demonstrate the second stage of growth in this kernel, hence

the growth constant k2, for the preferential coalescence stage is zero, and the

model is based solely on the size dependant kernel.

• The model uses a complete mixing assumption


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A complete copy of the code for the granulation drum model can be found in

Appendix C. There were three types of data that needed to be placed into the

granulation model to adjust the model to fit the ammonium sulphate granulation

process. These are:

• The initial particle size distribution

• The physical properties of the fertilizer (ammonium sulphate)

• The empirical growth constants.

The data for the initial particle size distribution had to altered slightly to be made

suitable. First the model is based on a geometric series of size fractions starting at an

initial diameter that was greater than zero. This meant that the data had to be

transformed to a geometric series, which was not used in the data analysis and the

material passing through the 63 micron sieve had to be neglected. This would have

introduced a small error into the validation.

The next step was to fit the physical properties of ammonium sulphate to the model

equations to determine a rate constant for the growth of the granules in the circuit.

The rate constant is defined by the physical properties of ammonium sulphate using

the size dependent kernel as follows:

satji SA1, =β (10)

)1(

)1)(1(

swl

sfwsat SXp

SpXS

−

+−=

ρρ

(11)

Where ,


Ssat = Fractional Saturation of the Granules

ρf = Density of Fertilizer Salt

ρl = Density of Fertilizer Solution





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These physical properties as outlined in Table 7 were entered into the model to define

Ssat in the growth constant in the model. The moisture content was added indirectly by

making the moisture content of the feed 2%.

Table 7: Physical Parameters for Ammonium Sulphate

Physical Parameter Value UsedDensity of Salt 1700 kg/m3

Density of Solution 1300 kg/m3

Solubility of Salt in Water 0.58Porosity 0.38Granule Density 1680 kg/m3

Moisture Content 0.02

The model now can finally be fitted to the data with the use of the empirical constant,

A1. Initial trial and error fitting of this parameter found 0.05 to give the best fit of the

model to the data. This constant has been defined by Adetayo et al (1995) to be 27.3

+/- 3.0. These results appear to contradict each other a lot, however this data by

Adetayo et al (1995) was discovered too late to test on the model during this thesis.

The effect of increasing A1, however was investigated by Brooker (1999) and

Adetayo et al (1993). It was found that increasing the empirical constant has the effect

of shifting the size distribution to the right. Looking at Figures 27 to 30, showing the

results of the model validation using an empirical constant of 0.05, it can be seen that

shifting the fines side of each of these distribution right would provide a much better

fit to the experimental data. This is one improvement that needs to be made to the

granulator model to provide a better fit.

Apart from the use of incorrect data for the model validation one other improvement

can also be suggested using the work of Adetayo et al (1995) as a basis. Using

experimental data based on a batch granulator, a very good fit was validation was

obtained for ammonium sulphate.


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Looking at the results for the model validation in can be seen that at the base of the

right hand side of each distribution, a slightly higher production of oversize material

is produced consistently. As the continuous equivalent of a batch process is a plug

flow process, it would be useful to examine the effects of changing the model from a

completely mixed system to a plug flow system.

Figure 29: Model Validation Results – Granulator (Run 1)



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4.3.2 DryerAs the model validation did not progress to the stage where the flow could be

recycled back to the granulator in a stable circuit, the function of the dryer became

quite redundant. The dryer model is developed using the following base assumptions:

• The drying process can be described by a plug flow assumption.

• The only to mechanisms considered in the dryer model are the removal of

moisture content from the granules and removal of fine dust particles in the air

stream. This is achieved on a mass balance basis.


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The process of changing the drying rate was examined showing that based on this rate

the moisture content of the stream leaving the dryer could be altered very easily. This

would become important when the model validation reaches a stage where the recycle

stream could be connected to the granulator giving stable results.

This was not possible within this inquiry as due to major problems in validating the

crusher model due to the sampling accuracy of the data from the plant. The only

improvements that can be recommended thus far for the dryer model are:

• The incorporation of an energy balance into the drying mechanism of the

model to enable the effect of processing temperature to be examined by

industrial granulation circuits.

• A consideration of breakage or further coalescence that occurs in the dryer, In

this case, the breakage mechanism is probably the most important feature to be

considered as there are some indicators suggesting that extra fines may be

produced here or on the screens.

4.3.3 ScreensFigure 31, 32 & 33 show the model validation results for the oversize, undersize and

product size sample respectively. The screen model is based on the screen model by

Whiten (1974) and is basically a dynamic mass balance model based on the

probablility that a particle of a certain size fraction will pass through the aperture.

By examining the graphs it can be seen that there are two areas where the fit is not

ideal. The first is the oversize model fit. This can easily be explained by the high error

in the size distribution analysis due to the limited large sieve sizes available. The

second area is shown on Figure 32 where there is a large dip experienced by the

experimental results that can’t be predicted by the model. Section 4.3 shows that this

result is more than likely attributable to granule breakge of particles on the screens. It

is recommended that in the future development of the mdoel, that this is considered.

Apart from these two anomalies the screen model could quite easily be fitted to the

experimental data and with the addition of a mechanism describing the breakage of

particles on the screen the model describes the situation well.


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Figure 33: Model Validation Results – Oversize (Run 1)

Figure 34: Model Validation Results – Undersize (Run 1)

Figure 35: Model Validation Results – Product Size (Run 1)


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4.3.4 Crusher

Figure 36: Model Validation Results – Crusher Outlet (Run 2)

In Section 2, the crusher model was described as being based on three basic functions

to descibe the attrition of particles in the crusher. These parameters were adjusted to

obtain the fit as shown in Figure 34. This model has been validated for previously by

Adetayo (1993) and others and in this case the validation is expected to only be

limited by the sampling problems experienced when collecting the data.

To properly validate the crusher model using plant data from Incitec, the sampling

method for the exit to the crusher has to be reassessed with the possibility of

modifying the crusher to obtain a better sampling point or modifying the old one so a

representative sample can be taken safely.

4.3.5 MixerThe mixer model consists of simple mass balance to combine each of the streams

entering the granulator. Although a validation could not be attempted on this unit due

to problems in modelling the crusher and granulator, no problems are foreseen in the

successful validation of the mixer model.

4.3.6 Complete CircuitDue to problems with the validation of the crusher and granulator models, it was not

possible to obtain a stable model of the complete granulation circuit including a

recycle feed to the granulator. To get to this stage the following changes should be

made to the model:


v

• Breakage mechanisms need to be considered in dryer and screen models.

• A full heat and mass transfer model needs to be considered for the dryer to

determine the effects of temperature on the granulation process.

• The distribution of the binder in the granulator needs to be modelled to

account for variations to the two-stage granulation model due to poor binder

distribution.

• A plug-flow assumption should be used to model the granulation regime.


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5.0 Conclusion

This investigation into the industrial granulation of ammonium sulphate has found

that the growth of the granules during the process occurs by random coalescence as

described by the proposed two-stage growth mechanism by Adetayo et al (1993). The

growth is promoted by the rapid coalescence of fines predominantly less than 1mm in

size causing size independent growth.of the rest of the dsitribution. As a result a

narrowing of the size distribution occurs.

Although the mositure content and initial particle size distribution were investigated

no concrete conclusions could be made to support or refute the claims made in

literature about the effect of these variables upon granulation.

The analysis of the performance of the granulation circuit found that there were three

major improvements that could be made to granulation circuit to improve the

efficiency of the process. These were optimising the binder addition and distribution

in the granulator, increasing the efficiency of the crusher and grinding the fines from

the polishing screens before they are recycled to the granulator.

Despite coming up with these proposals a simulation could not be tested due to the

fact that that the model could not be validated using the palnt data obtained in this

inquiry. There weere three major reasons for this including:

• Sampling inaccuracy due to the inherent nature of the sampling points

avaliable at Incitec.

• Limited analysis due to number osf sieves available for the size analysis and

the problems experienced with the mass and energy balance program at

Incitec.

• Wrong assumptions used in the model for the ammonium sulphate granulation

circuit.

The problems experienced can all be overcome with more research into the

mechanisms required by the model and more appropriate sampling and analysis

techniques. Some of these are discussed in the next section.


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6.0 Recommendations

More work is recommended on this project. It is expected that once the improvements

suggested are made that the model will be validated and simulations can be performed

on the model to test the improvements suggested for the granulation circuit. These

recommendations include:

• Revising the sampling regime used at the Incitec plant. This will involve

obtaining more flowrate data, conducting further precision analyses on the

sampling points to better assess their accuracy and taking more frequent but

smaller samples around the plant. In addition some of the sampling points will

need to be modified to obtain more representative samples.

• Improving the use of the correct equipment in the analysis of particles including

obtaining larger sieve sizes for better analysis of the oversize particles.

• Making the following improvements to the model

• Consider breakage mechanisms need in the dryer and screen models.

• Upgrade the dryer model to include a full heat and energy balance so that the

temperature of the process can be examined.

• Modelling of the liquid distribution in the granulator to account for variations

to the two-stage granulation model due to poor binder distribution.

• Use a plug flow model to model the granulation regime.


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7.0 Nomenclature

αv = shape factor


A2 = Parameter for the preferential size-independent coalescence kernel

A* = Attrition Rate

Bnuc(v) = Nucleation Rate

B(u,v,t) = Coalescence Kernel

D95 = 95% passing size of the distribution (mm)

e = Coefficient of Restitution

g = Mass Fraction of Water in the Granule

G* = Layering Rate

h = spread of size distribution (mm)

h = Binder Layer Thickness

ha = Asperity Height

K1 = The first stage granulation rate constant

K2 = The second satge granualtion rate constant

L = mass of total sample (g)

l = liberation factor

µ = Binder Viscosity (Pa.s)

M = sample weight (g)

N(v,t) = Number of particles in each size fraction

Nt = Total Number of Particles per unit volume


Q = Inlet and Outlet Flowrate of the Material

ρg = Granule Density (g/cm3)

ρf = Density of Fertilizer Salt (g/cm3)

ρl = Density of Fertilizer Solution (g/cm3)

r = Effective Granule Size (mm)

Stv* = Critical Viscous Stokes Number (dimensionless)

s = Solubility of the Fertilizer Salt in Water (gsalt/gwater)

Ssat = Fractional saturation of granules

Scrit = Critical saturation of granules

Stv = Viscous Stokes Number (dimensionless)


v


V = Velocity of granule collision (m/s)

V = Volume of the Granulator


x = mass fraction being sampled

y = Solution Phase Ratio


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8.0 References

1. Adetayo, A.A. & Ennis, B.J., ‘Unifying Approach to Modelling Granule

Coalescence Mechanisms’, AIChE Journal, 43(4), pp 927-934, (1997).

2. Adetayo, A.A., Litster, J.D. & Cameron, I.T., ‘Steady State Modelling and

Simulation of a Fertilizer Granulation Circuit’, Computers Chem. Engng, 19(4),

pp 383-393, (1995).

3. Adetayo, A.A., Litster, J.D. & Desai, M., ‘The Effect of Process Parameters on

Drum Granulation of Fertilizers with Broad Size Distributions’, Chemical

Engineering Science, 48(23), pp 3951-3961 (1993).

4. Adetayo, A.A., Litster, J.D., Pratsinis, S.E. & Ennis, B.J., ‘Population Balance

Modelling of Drum Granulation of Materials with Wide Size Distribution’,

Powder Technology, 82, pp 37-49, (1995).

5. Bathala, C.J., Dodlaty, V.S., Madaboosi, S.A. & Chamarati, D.P.R., ‘Modelling of

Continuous Fertilizer Granulation Process for Control, Part. Part. Syst. Charact.,

15, pp 156-160, (1998).

6. Brooker, D., ‘Improving Granulation Techniques of ‘N-Gold’ Fertilizer’,

Undergraduate Thesis, Department of Chemical Engineering, The University of

Queensland, (1999).

7. Davis, G., ‘Collection of Granulation Circuit Data for Plant Evaluation and

Simulation’, Undergraduate Thesis, Department of Chemical Engineering, The

University of Queensland, (1996).

8. Ennis, B.J., Agglomeration and Size Enlargement - Session Summary Paper,

Powder Technology, 88, pp 203-225, (1996)

9. Ennis, B.J., Tardos, G. & Pfeffer, R., ‘A Microbial-Based Characterization of

Granulation Phenomena’, Powder Technology, 65, pp 257-272, (1991).


v

10. Iveson, S.M., Litster, J.D. & Ennis, B.J., ‘Fundamental Studies of Granule

Consolidation – Part One: Effects of Binder Content and Binder Viscosity’,

Powder Technology, 88, pp 15-20, (1996).

11. Litster, J.D. & Sarwono, R., ‘Fluidised Drum Granulation: Studies of

Agglomerate Formation’, Powder Technology, 88, pp 165-172, (1996).

12. Litster, J.D., Smit, D.J. & Hounslow, M.J., ‘Adjustable Discretized Population

Balance for Growth and Agglomeration’, AIChE Journal, 41(3), pp 591-603,

(1995).

13. Sherrington, P.J., ‘The Granulation of Sand as an Aid to Understand Fertilizer

Granulation: The Relationship Between Liquid-Phase Content and Average

Granule Size’, The Chemical Engineer, 220, pp CE201-CE215, (1968).

14. Zhang, J., ‘Dynamics and Control of a Fertilizer Granulation Unit’, Postgraduate

Thesis, Department of Chemical Engineering, The University of Queensland,

(1996).

15. Zhang, J., Litster, J.D., Wang, F.Y. & Cameron, I.T., ‘Evaluation of Control

Strategies for Fertilizer Granulation Circuits using Dynamic Simulation’, Powder

Technology, 108, pp 122-129, (2000).


v

APPENDIX A – RAW DATA AND SIZE DISTRIBUTION

CALCULATIONS

4/07/00

Sieve Size Mean Size Tare Weight (1) Tare Weight (2)(mm) (mm) (g) (g)

11.2008.000 9.600 443.13 441.806.700 7.350 432.04 432.005.600 6.150 424.38 423.604.750 5.175 420.24 420.204.000 4.375 416.29 415.303.350 3.675 402.75 401.902.800 3.075 396.02 394.802.360 2.580 399.46 398.202.000 2.180 386.61 385.701.700 1.850 374.41 373.401.400 1.550 366.39 365.301.180 1.290 351.12 349.901.000 1.090 355.43 354.200.710 0.855 318.92 317.800.500 0.605 304.77 303.800.355 0.428 293.66 292.700.250 0.303 277.65 276.800.180 0.215 275.41 274.500.125 0.153 265.05 263.900.106 0.116 270.74 269.600.090 0.098 262.90 261.800.063 0.077 259.47 258.300.000 0.032 242.97 242.50

Total


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Exit Granulator

Total Mass AS Mass Mass Fraction Mass Frequency(g) (g) (mm-1)

451.50 9.70 0.0543 0.0170435.80 3.80 0.0213 0.0164427.50 3.90 0.0218 0.0198424.20 4.00 0.0224 0.0263418.80 3.50 0.0196 0.0261406.40 4.50 0.0252 0.0387401.20 6.40 0.0358 0.0651412.10 13.90 0.0778 0.1768414.20 28.50 0.1595 0.4430404.20 30.80 0.1724 0.5745394.20 28.90 0.1617 0.5391363.90 14.00 0.0783 0.3561364.10 9.90 0.0554 0.3078328.10 10.30 0.0576 0.1988307.80 4.00 0.0224 0.1066294.30 1.60 0.0090 0.0617277.30 0.50 0.0028 0.0266274.70 0.20 0.0011 0.0160264.20 0.30 0.0017 0.0305269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

178.70 1.00 3.05


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Exit Dryer#1Total Mass AS Mass Mass Fraction Mass Frequency

(g) (g) (mm-1)

448.70 6.90 0.0637 0.0199434.90 2.90 0.0268 0.0206427.10 3.50 0.0323 0.0294422.40 2.20 0.0203 0.0239417.90 2.60 0.0240 0.0320405.20 3.30 0.0305 0.0469398.00 3.20 0.0295 0.0537406.00 7.80 0.0720 0.1637402.10 16.40 0.1514 0.4206388.50 15.10 0.1394 0.4648381.60 16.30 0.1505 0.5017357.10 7.20 0.0665 0.3022360.60 6.40 0.0591 0.3283325.20 7.40 0.0683 0.2356307.40 3.60 0.0332 0.1583294.50 1.80 0.0166 0.1146277.60 0.80 0.0074 0.0704275.20 0.70 0.0065 0.0923264.10 0.20 0.0018 0.0336269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

108.30 1.00 3.11


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Exit Dryer #2


486.10 42.97 0.1326 0.0414444.71 12.67 0.0391 0.0301437.35 12.97 0.0400 0.0364431.37 11.13 0.0343 0.0404429.47 13.18 0.0407 0.0542415.35 12.60 0.0389 0.0598408.21 12.19 0.0376 0.0684424.81 25.35 0.0782 0.1777438.04 51.43 0.1587 0.4407415.16 40.75 0.1257 0.4191405.21 38.82 0.1198 0.3992368.68 17.56 0.0542 0.2462367.96 12.53 0.0387 0.2148331.01 12.09 0.0373 0.1286309.44 4.67 0.0144 0.0686295.48 1.82 0.0056 0.0387278.47 0.82 0.0025 0.0241275.80 0.39 0.0012 0.0172265.25 0.20 0.0006 0.0112270.74 0.00 0.0000 0.0000262.90 0.00 0.0000 0.0000259.47 0.00 0.0000 0.0000242.97 0.00 0.0000 0.0000

324.14 1.00 2.52


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Oversize #1


733.00 291.20 0.5553 0.1735485.70 53.70 0.1024 0.0788484.20 60.60 0.1156 0.1051471.90 51.70 0.0986 0.1160458.90 43.60 0.0831 0.1109415.00 13.10 0.0250 0.0384398.30 3.50 0.0067 0.0121399.60 1.40 0.0027 0.0061386.60 0.90 0.0017 0.0048374.70 1.30 0.0025 0.0083366.20 0.90 0.0017 0.0057351.20 1.30 0.0025 0.0113355.40 1.20 0.0023 0.0127317.80 0.00 0.0000 0.0000303.80 0.00 0.0000 0.0000292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

524.40 1.00 0.68


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Oversize #2


649.80 208.00 0.4425 0.1383485.90 53.90 0.1147 0.0882476.80 53.20 0.1132 0.1029476.60 56.40 0.1200 0.1411473.60 58.30 0.1240 0.1654424.60 22.70 0.0483 0.0743403.30 8.50 0.0181 0.0329400.90 2.70 0.0057 0.0131388.60 2.90 0.0062 0.0171374.40 1.00 0.0021 0.0071366.10 0.80 0.0017 0.0057350.40 0.50 0.0011 0.0048354.70 0.50 0.0011 0.0059318.30 0.50 0.0011 0.0037304.00 0.20 0.0004 0.0020292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

470.10 1.00 0.80


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Product Size #1


441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.20 0.00 0.0000 0.0000425.10 9.80 0.0539 0.0719423.00 21.10 0.1161 0.1787426.40 31.60 0.1739 0.3162454.50 56.30 0.3099 0.7042430.70 45.00 0.2477 0.6879384.60 11.20 0.0616 0.2055369.50 4.20 0.0231 0.0771350.90 1.00 0.0055 0.0250355.20 1.00 0.0055 0.0306318.20 0.40 0.0022 0.0076303.90 0.10 0.0006 0.0026292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

181.70 1.00 2.31


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Product Size #2


441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.20 0.00 0.0000 0.0000417.40 2.10 0.0129 0.0172416.10 14.20 0.0871 0.1339428.30 33.50 0.2054 0.3734450.40 52.20 0.3200 0.7274430.40 44.70 0.2741 0.7613384.40 11.00 0.0674 0.2248368.40 3.10 0.0190 0.0634350.90 1.00 0.0061 0.0279354.90 0.70 0.0043 0.0238318.30 0.50 0.0031 0.0106303.90 0.10 0.0006 0.0029292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

163.10 1.00 2.37


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Undersize#1Total Mass AS Mass Mass Fraction Mass Frequency

(g) (g) (mm-1)

443.13 0.00 0.0000 0.0000432.04 0.00 0.0000 0.0000424.38 0.00 0.0000 0.0000420.24 0.00 0.0000 0.0000416.29 0.00 0.0000 0.0000402.75 0.00 0.0000 0.0000396.21 0.19 0.0006 0.0012405.86 6.40 0.0214 0.0486433.21 46.60 0.1557 0.4325448.16 73.75 0.2464 0.8214443.16 76.77 0.2565 0.8551359.75 8.63 0.0288 0.1311384.89 29.46 0.0984 0.5469349.81 30.89 0.1032 0.3559319.56 14.79 0.0494 0.2353300.42 6.76 0.0226 0.1558280.70 3.05 0.0102 0.0971276.73 1.32 0.0044 0.0630265.60 0.55 0.0018 0.0334270.85 0.11 0.0004 0.0193262.90 0.00 0.0000 0.0000259.47 0.00 0.0000 0.0000242.97 0.00 0.0000 0.0000

299.27 1.00 3.80


v


(g) (g) (mm-1)

443.13 0.00 0.0000 0.0000432.04 0.00 0.0000 0.0000424.38 0.00 0.0000 0.0000420.24 0.00 0.0000 0.0000416.29 0.00 0.0000 0.0000402.79 0.04 0.0001 0.0002396.20 0.18 0.0007 0.0012401.56 2.10 0.0078 0.0178403.37 16.76 0.0624 0.1732413.20 38.79 0.1443 0.4812426.98 60.59 0.2255 0.7516389.42 38.30 0.1425 0.6478388.19 32.76 0.1219 0.6773358.58 39.66 0.1476 0.5089325.32 20.55 0.0765 0.3641304.24 10.58 0.0394 0.2715282.47 4.82 0.0179 0.1708277.66 2.25 0.0084 0.1196266.06 1.01 0.0038 0.0683270.95 0.21 0.0008 0.0411262.98 0.08 0.0003 0.0186259.52 0.05 0.0002 0.0069242.97 0.00 0.0000 0.0000

268.73 1.00 4.32


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Exit Crusher #1


473.40 30.27 0.1026 0.0321446.61 14.57 0.0494 0.0380444.48 20.10 0.0681 0.0619442.12 21.88 0.0741 0.0872438.85 22.56 0.0764 0.1019422.33 19.58 0.0663 0.1021415.86 19.84 0.0672 0.1222416.49 17.03 0.0577 0.1312403.90 17.29 0.0586 0.1627390.25 15.84 0.0537 0.1789385.07 18.68 0.0633 0.2110363.67 12.55 0.0425 0.1933367.17 11.74 0.0398 0.2210336.53 17.61 0.0597 0.2058317.87 13.10 0.0444 0.2114302.12 8.46 0.0287 0.1977282.46 4.81 0.0163 0.1552278.32 2.91 0.0099 0.1409267.12 2.07 0.0070 0.1275271.46 0.72 0.0024 0.1284263.61 0.71 0.0024 0.1504260.49 1.02 0.0035 0.1280244.74 1.77 0.0060 0.0952

295.11 1.00 3.18


v

Exit Crusher #2


468.28 25.15 0.0816 0.0255446.53 14.49 0.0470 0.0362442.48 18.10 0.0587 0.0534438.61 18.37 0.0596 0.0701436.52 20.23 0.0656 0.0875423.06 20.31 0.0659 0.1014416.09 20.07 0.0651 0.1184418.69 19.23 0.0624 0.1418405.85 19.24 0.0624 0.1734391.83 17.42 0.0565 0.1884387.52 21.13 0.0686 0.2286365.40 14.28 0.0463 0.2106370.25 14.82 0.0481 0.2672342.55 23.63 0.0767 0.2644320.65 15.88 0.0515 0.2454302.89 9.23 0.0300 0.2066282.86 5.21 0.0169 0.1610278.48 3.07 0.0100 0.1423267.26 2.21 0.0072 0.1304271.55 0.81 0.0026 0.1383263.74 0.84 0.0027 0.1704260.67 1.20 0.0039 0.1442246.20 3.23 0.0105 0.1664

308.15 1.00 3.47


v

Polishing Screen Feed


441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.20 0.00 0.0000 0.0000417.10 1.80 0.0159 0.0212409.00 7.10 0.0628 0.0967410.10 15.30 0.1354 0.2462425.00 26.80 0.2372 0.5390419.90 34.20 0.3027 0.8407389.90 16.50 0.1460 0.4867372.90 7.60 0.0673 0.2242352.10 2.20 0.0195 0.0885355.20 1.00 0.0088 0.0492318.20 0.40 0.0035 0.0122303.90 0.10 0.0009 0.0042292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

113.00 1.00 2.61


v

Exit Polishing Screen


443.13 0.00 0.0000 0.0000432.04 0.00 0.0000 0.0000424.38 0.00 0.0000 0.0000420.24 0.00 0.0000 0.0000416.29 0.00 0.0000 0.0000402.84 0.09 0.0005 0.0008396.11 0.09 0.0005 0.0009399.72 0.26 0.0014 0.0032387.14 0.53 0.0029 0.0081427.32 52.91 0.2905 0.9684423.33 56.94 0.3127 1.0422389.46 38.34 0.2105 0.9569375.23 19.80 0.1087 0.6040329.94 11.02 0.0605 0.2087306.49 1.72 0.0094 0.0450294.08 0.42 0.0023 0.0159277.65 0.00 0.0000 0.0000275.41 0.00 0.0000 0.0000265.05 0.00 0.0000 0.0000270.74 0.00 0.0000 0.0000262.90 0.00 0.0000 0.0000259.47 0.00 0.0000 0.0000242.97 0.00 0.0000 0.0000

182.12 1.00 3.85


v

Product


443.13 0.00 0.0000 0.0000432.04 0.00 0.0000 0.0000424.38 0.00 0.0000 0.0000421.66 1.42 0.0056 0.0066424.88 8.59 0.0340 0.0454426.47 23.72 0.0940 0.1446434.29 38.27 0.1517 0.2758463.06 63.60 0.2521 0.5728465.12 78.51 0.3111 0.8643405.77 31.36 0.1243 0.4143372.19 5.80 0.0230 0.0766351.72 0.60 0.0024 0.0108355.81 0.38 0.0015 0.0084319.00 0.08 0.0003 0.0011304.77 0.00 0.0000 0.0000293.66 0.00 0.0000 0.0000277.65 0.00 0.0000 0.0000275.41 0.00 0.0000 0.0000265.05 0.00 0.0000 0.0000270.74 0.00 0.0000 0.0000262.90 0.00 0.0000 0.0000259.47 0.00 0.0000 0.0000242.97 0.00 0.0000 0.0000

252.33 1.00 2.42


v

Recycle


450.59 7.46 0.0256 0.0080435.10 3.06 0.0105 0.0081430.94 6.56 0.0225 0.0205425.13 4.89 0.0168 0.0198423.92 7.63 0.0262 0.0349410.15 7.40 0.0254 0.0391405.62 9.60 0.0330 0.0599414.09 14.63 0.0502 0.1142420.73 34.12 0.1172 0.3255419.50 45.09 0.1548 0.5161414.00 47.61 0.1635 0.5450376.90 25.78 0.0885 0.4024375.17 19.74 0.0678 0.3766342.00 23.08 0.0793 0.2733318.00 13.23 0.0454 0.2163301.29 7.63 0.0262 0.1807282.04 4.39 0.0151 0.1436278.34 2.93 0.0101 0.1437267.16 2.11 0.0072 0.1317271.45 0.71 0.0024 0.1283264.54 1.64 0.0056 0.3520261.08 1.61 0.0055 0.2048243.28 0.31 0.0011 0.0169

291.21 1.00 4.26


v

7/07/00

Exit Granulator

Total Mass AS Mass Mass Fraction MassFrequency

(g) (g) (mm-1)

469.80 28.00 0.1617 0.0505436.90 4.90 0.0283 0.0218430.30 6.70 0.0387 0.0352425.30 5.10 0.0294 0.0346422.30 7.00 0.0404 0.0539406.60 4.70 0.0271 0.0417403.30 8.50 0.0491 0.0892411.60 13.40 0.0774 0.1758406.90 21.20 0.1224 0.3400391.90 18.50 0.1068 0.3560383.20 17.90 0.1033 0.3445360.20 10.30 0.0595 0.2703362.10 7.90 0.0456 0.2534326.40 8.60 0.0497 0.1712308.50 4.70 0.0271 0.1292294.90 2.20 0.0127 0.0876278.20 1.40 0.0081 0.0770275.50 1.00 0.0058 0.0825264.70 0.80 0.0046 0.0840270.00 0.40 0.0023 0.1216261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

173.20


v

Exit Dryer#1Total Mass AS Mass Mass Fraction Mass

Frequency(g) (g) (mm-1)

462.70 20.90 0.1276 0.0399440.70 8.70 0.0531 0.0409430.70 7.10 0.0433 0.0394425.50 5.30 0.0324 0.0381421.80 6.50 0.0397 0.0529409.40 7.50 0.0458 0.0704403.10 8.30 0.0507 0.0921407.40 9.20 0.0562 0.1277401.10 15.40 0.0940 0.2612387.70 14.30 0.0873 0.2910381.00 15.70 0.0958 0.3195359.20 9.30 0.0568 0.2581362.70 8.50 0.0519 0.2883328.60 10.80 0.0659 0.2274310.80 7.00 0.0427 0.2035296.80 4.10 0.0250 0.1726278.60 1.80 0.0110 0.1047275.60 1.10 0.0067 0.0959264.70 0.80 0.0049 0.0888270.30 0.70 0.0043 0.2249262.20 0.40 0.0024 0.1526258.70 0.40 0.0024 0.0904242.50 0.00 0.0000 0.0000

163.80


v

Exit Dryer #2


(g) (g) (mm-1)

494.10 52.30 0.3071 0.0960438.20 6.20 0.0364 0.0280430.20 6.60 0.0388 0.0352424.40 4.20 0.0247 0.0290420.50 5.20 0.0305 0.0407407.50 5.60 0.0329 0.0506401.50 6.70 0.0393 0.0715406.50 8.30 0.0487 0.1108397.60 11.90 0.0699 0.1941386.30 12.90 0.0757 0.2525378.70 13.40 0.0787 0.2623357.50 7.60 0.0446 0.2029361.20 7.00 0.0411 0.2284327.50 9.70 0.0570 0.1964309.20 5.40 0.0317 0.1510295.90 3.20 0.0188 0.1296278.50 1.70 0.0100 0.0951275.60 1.10 0.0065 0.0923264.70 0.80 0.0047 0.0854270.00 0.40 0.0023 0.1236261.90 0.10 0.0006 0.0367258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

170.30


v

Oversize #1


(g) (g) (mm-1)

754.70 312.90 0.6378 0.1993494.00 62.00 0.1264 0.0972476.00 52.40 0.1068 0.0971462.00 41.80 0.0852 0.1002433.00 17.70 0.0361 0.0481404.60 2.70 0.0055 0.0085395.60 0.80 0.0016 0.0030398.40 0.20 0.0004 0.0009385.80 0.10 0.0002 0.0006373.40 0.00 0.0000 0.0000365.30 0.00 0.0000 0.0000349.90 0.00 0.0000 0.0000354.20 0.00 0.0000 0.0000317.80 0.00 0.0000 0.0000303.80 0.00 0.0000 0.0000292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

490.60


v

Oversize #2


(g) (g) (mm-1)

721.40 279.60 0.5324 0.1664500.90 68.90 0.1312 0.1009488.20 64.60 0.1230 0.1118474.60 54.40 0.1036 0.1219455.00 39.70 0.0756 0.1008413.10 11.20 0.0213 0.0328398.10 3.30 0.0063 0.0114399.40 1.20 0.0023 0.0052386.30 0.60 0.0011 0.0032373.60 0.20 0.0004 0.0013365.80 0.50 0.0010 0.0032350.30 0.40 0.0008 0.0035354.50 0.30 0.0006 0.0032318.10 0.30 0.0006 0.0020303.80 0.00 0.0000 0.0000292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

525.20


v

Product Size #1


(g) (g) (mm-1)

441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000424.30 0.70 0.0034 0.0031423.70 3.50 0.0170 0.0200436.70 21.40 0.1041 0.1388432.00 30.10 0.1465 0.2253438.60 43.80 0.2131 0.3875468.60 70.40 0.3426 0.7786409.60 23.90 0.1163 0.3231380.40 7.00 0.0341 0.1135367.40 2.10 0.0102 0.0341350.80 0.90 0.0044 0.0199354.90 0.70 0.0034 0.0189318.40 0.60 0.0029 0.0101304.20 0.40 0.0019 0.0093292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

205.50


v

Product Size #2


(g) (g) (mm-1)

441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.40 0.20 0.0011 0.0013428.50 13.20 0.0702 0.0936439.20 37.30 0.1984 0.3052445.50 50.70 0.2697 0.4903454.60 56.40 0.3000 0.6818409.30 23.60 0.1255 0.3487377.90 4.50 0.0239 0.0798366.70 1.40 0.0074 0.0248350.20 0.30 0.0016 0.0073354.30 0.10 0.0005 0.0030317.90 0.10 0.0005 0.0018304.00 0.20 0.0011 0.0051292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

188.00


v

Undersize#1Total Mass AS Mass Mass Fraction Mass


441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.20 0.00 0.0000 0.0000415.30 0.00 0.0000 0.0000401.90 0.00 0.0000 0.0000395.10 0.30 0.0016 0.0030401.60 3.40 0.0187 0.0424407.40 21.70 0.1192 0.3310409.00 35.60 0.1955 0.6517406.30 41.00 0.2252 0.7505371.50 21.60 0.1186 0.5392372.10 17.90 0.0983 0.5461337.40 19.60 0.1076 0.3711314.00 10.20 0.0560 0.2667297.80 5.10 0.0280 0.1931279.60 2.80 0.0154 0.1464276.40 1.90 0.0104 0.1491264.80 0.90 0.0049 0.0899269.70 0.10 0.0005 0.0289261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

182.10


v

Undersize#2Total Mass AS Mass Mass Fraction Mass


441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.20 0.00 0.0000 0.0000415.30 0.00 0.0000 0.0000401.90 0.00 0.0000 0.0000395.10 0.30 0.0037 0.0068398.80 0.60 0.0075 0.0170389.00 3.30 0.0411 0.1142380.60 7.20 0.0897 0.2989378.30 13.00 0.1619 0.5396360.10 10.20 0.1270 0.5774364.20 10.00 0.1245 0.6919331.40 13.60 0.1694 0.5840312.70 8.90 0.1108 0.5278298.10 5.40 0.0672 0.4638279.60 2.80 0.0349 0.3321276.10 1.60 0.0199 0.2846265.20 1.30 0.0162 0.2944270.30 0.70 0.0087 0.4588262.50 0.70 0.0087 0.5448259.00 0.70 0.0087 0.3229242.50 0.00 0.0000 0.0000

80.30


v

Exit Crusher #1


(g) (g) (mm-1)

457.60 15.80 0.1051 0.0328437.90 5.90 0.0392 0.0302436.50 12.90 0.0858 0.0780429.30 9.10 0.0605 0.0712427.60 12.30 0.0818 0.1090410.40 8.50 0.0565 0.0869405.00 10.20 0.0678 0.1233405.80 7.60 0.0505 0.1148392.90 7.20 0.0479 0.1330380.30 6.90 0.0459 0.1529374.40 9.10 0.0605 0.2017355.60 5.70 0.0379 0.1723360.20 6.00 0.0399 0.2216326.30 8.50 0.0565 0.1949310.90 7.10 0.0472 0.2248298.10 5.40 0.0359 0.2476280.40 3.60 0.0239 0.2280277.10 2.60 0.0173 0.2470266.00 2.10 0.0140 0.2539270.50 0.90 0.0060 0.3149263.00 1.20 0.0080 0.4987259.50 1.20 0.0080 0.2955243.10 0.60 0.0040 0.0633

150.40


v

Exit Crusher #2


(g) (g) (mm-1)

449.50 7.70 0.0578 0.0181436.50 4.50 0.0338 0.0260429.10 5.50 0.0413 0.0375427.20 7.00 0.0525 0.0618425.40 10.10 0.0758 0.1010409.50 7.60 0.0570 0.0877402.60 7.80 0.0585 0.1064406.10 7.90 0.0593 0.1347394.00 8.30 0.0623 0.1730380.30 6.90 0.0518 0.1725375.30 10.00 0.0750 0.2501357.10 7.20 0.0540 0.2455362.70 8.50 0.0638 0.3543330.20 12.40 0.0930 0.3208311.80 8.00 0.0600 0.2858297.30 4.60 0.0345 0.2380279.30 2.50 0.0188 0.1786276.20 1.70 0.0128 0.1822265.70 1.80 0.0135 0.2455270.50 0.90 0.0068 0.3554262.70 0.90 0.0068 0.4220259.30 1.00 0.0075 0.2778243.00 0.50 0.0038 0.0595

133.30


v



(g) (g) (mm-1)

441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.60 0.40 0.0017 0.0020425.80 10.50 0.0438 0.0583438.30 36.40 0.1517 0.2333445.90 51.10 0.2129 0.3871463.90 65.70 0.2738 0.6222436.40 50.70 0.2113 0.5868389.50 16.10 0.0671 0.2236371.50 6.20 0.0258 0.0861351.40 1.50 0.0063 0.0284355.10 0.90 0.0038 0.0208318.30 0.50 0.0021 0.0072303.80 0.00 0.0000 0.0000292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

240.00


v



(g) (g) (mm-1)

441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.20 0.00 0.0000 0.0000415.30 0.00 0.0000 0.0000402.00 0.10 0.0008 0.0013395.70 0.90 0.0075 0.0136399.00 0.80 0.0066 0.0151386.30 0.60 0.0050 0.0138417.00 43.60 0.3615 1.2051412.70 47.40 0.3930 1.3101362.60 12.70 0.1053 0.4787361.40 7.20 0.0597 0.3317322.10 4.30 0.0357 0.1229304.90 1.10 0.0091 0.0434293.30 0.60 0.0050 0.0343277.10 0.30 0.0025 0.0237274.80 0.30 0.0025 0.0355264.60 0.70 0.0058 0.1055269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

120.60


v

Product


(g) (g) (mm-1)

441.80 0.00 0.0000 0.0000431.40 0.00 0.0000 0.0000423.70 0.10 0.0006 0.0006420.90 0.70 0.0044 0.0051427.00 11.70 0.0731 0.0975427.40 25.50 0.1594 0.2452427.10 32.30 0.2019 0.3670436.60 38.40 0.2400 0.5455421.80 36.10 0.2256 0.6267384.10 10.70 0.0669 0.2229366.90 1.60 0.0100 0.0333350.60 0.70 0.0044 0.0199354.70 0.50 0.0031 0.0174318.30 0.50 0.0031 0.0108304.20 0.40 0.0025 0.0119293.10 0.40 0.0025 0.0172277.10 0.30 0.0019 0.0179274.60 0.10 0.0006 0.0089263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

160.00


v

Recycle


(g) (g) (mm-1)

443.50 1.70 0.0104 0.0033433.10 1.10 0.0067 0.0052427.40 3.80 0.0233 0.0212423.60 3.40 0.0208 0.0245420.60 5.30 0.0325 0.0433406.00 4.10 0.0251 0.0387398.40 3.60 0.0221 0.0401403.10 4.90 0.0300 0.0682400.30 14.60 0.0895 0.2485393.30 19.90 0.1219 0.4065388.50 23.20 0.1422 0.4739363.30 13.40 0.0821 0.3732366.40 12.20 0.0748 0.4153332.70 14.90 0.0913 0.3148314.00 10.20 0.0625 0.2976299.00 6.30 0.0386 0.2662281.10 4.30 0.0263 0.2509277.50 3.00 0.0184 0.2626266.70 2.80 0.0172 0.3119271.10 1.50 0.0092 0.4837263.60 1.80 0.0110 0.6893260.80 2.50 0.0153 0.5674247.20 4.70 0.0288 0.4571

163.20


v

15/07/00

Exit Granulator


461.00 19.20 0.0717 0.0224443.10 11.10 0.0415 0.0319438.30 14.70 0.0549 0.0499431.30 11.10 0.0415 0.0488425.90 10.60 0.0396 0.0528411.00 9.10 0.0340 0.0523407.30 12.50 0.0467 0.0849418.60 20.40 0.0762 0.1733424.50 38.80 0.1450 0.4028408.40 35.00 0.1308 0.4360399.30 34.00 0.1271 0.4235366.20 16.30 0.0609 0.2769366.50 12.30 0.0460 0.2554330.00 12.20 0.0456 0.1572309.50 5.70 0.0213 0.1014294.80 2.10 0.0078 0.0541277.60 0.80 0.0030 0.0285275.10 0.60 0.0022 0.0320264.50 0.60 0.0022 0.0408270.10 0.50 0.0019 0.0983261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

267.60


v

Exit Dryer#1Total Mass AS Mass Mass Fraction Mass Frequency

(g) (g) (mm-1)

457.90 16.10 0.1020 0.0319435.90 3.90 0.0247 0.0190428.50 4.90 0.0311 0.0282423.50 3.30 0.0209 0.0246420.50 5.20 0.0330 0.0439406.30 4.40 0.0279 0.0429401.20 6.40 0.0406 0.0737408.30 10.10 0.0640 0.1455403.70 18.00 0.1141 0.3169391.90 18.50 0.1172 0.3908384.70 19.40 0.1229 0.4098360.40 10.50 0.0665 0.3025363.80 9.60 0.0608 0.3380329.50 11.70 0.0741 0.2557310.10 6.30 0.0399 0.1901296.80 4.10 0.0260 0.1792278.60 1.80 0.0114 0.1086275.50 1.00 0.0063 0.0905264.70 0.80 0.0051 0.0922270.00 0.40 0.0025 0.1334262.00 0.20 0.0013 0.0792259.50 1.20 0.0076 0.2817242.50 0.00 0.0000 0.0000

157.80


v

Exit Dryer #2


455.40 13.60 0.0770 0.0241435.90 3.90 0.0221 0.0170428.30 4.70 0.0266 0.0242423.10 2.90 0.0164 0.0193420.00 4.70 0.0266 0.0355406.80 4.90 0.0277 0.0427401.20 6.40 0.0362 0.0659409.50 11.30 0.0640 0.1454408.80 23.10 0.1308 0.3633397.30 23.90 0.1353 0.4511390.00 24.70 0.1399 0.4662362.80 12.90 0.0730 0.3320365.20 11.00 0.0623 0.3460331.30 13.50 0.0764 0.2636310.80 7.00 0.0396 0.1888296.00 3.30 0.0187 0.1289278.50 1.70 0.0096 0.0917275.60 1.10 0.0062 0.0890264.70 0.80 0.0045 0.0824270.10 0.50 0.0028 0.1490262.10 0.30 0.0017 0.1062258.70 0.40 0.0023 0.0839242.50 0.00 0.0000 0.0000

176.60


v

Oversize #1


654.00 212.20 0.5304 0.1657485.90 53.90 0.1347 0.1036483.00 59.40 0.1485 0.1350465.20 45.00 0.1125 0.1323439.50 24.20 0.0605 0.0806405.70 3.80 0.0095 0.0146395.90 1.10 0.0027 0.0050398.70 0.50 0.0012 0.0028385.70 0.00 0.0000 0.0000373.40 0.00 0.0000 0.0000365.30 0.00 0.0000 0.0000349.90 0.00 0.0000 0.0000354.20 0.00 0.0000 0.0000317.80 0.00 0.0000 0.0000303.80 0.00 0.0000 0.0000292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

400.10


v

Oversize #2


649.00 207.20 0.4423 0.1382490.00 58.00 0.1238 0.0952493.70 70.10 0.1496 0.1360477.30 57.10 0.1219 0.1434467.50 52.20 0.1114 0.1486420.90 19.00 0.0406 0.0624398.50 3.70 0.0079 0.0144399.20 1.00 0.0021 0.0049385.90 0.20 0.0004 0.0012373.40 0.00 0.0000 0.0000365.30 0.00 0.0000 0.0000349.90 0.00 0.0000 0.0000354.20 0.00 0.0000 0.0000317.80 0.00 0.0000 0.0000303.80 0.00 0.0000 0.0000292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

468.50


v

Product Size #1


441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.60 0.40 0.0027 0.0032425.20 9.90 0.0664 0.0885424.50 22.60 0.1515 0.2330431.50 36.70 0.2460 0.4472439.00 40.80 0.2735 0.6215415.90 30.20 0.2024 0.5623378.90 5.50 0.0369 0.1229366.00 0.70 0.0047 0.0156350.60 0.70 0.0047 0.0213355.50 1.30 0.0087 0.0484318.10 0.30 0.0020 0.0069303.90 0.10 0.0007 0.0032292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

149.20


v

Product Size #2


441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.60 0.40 0.0027 0.0032425.20 9.90 0.0664 0.0885424.50 22.60 0.1515 0.2330431.50 36.70 0.2460 0.4472439.00 40.80 0.2735 0.6215415.90 30.20 0.2024 0.5623378.90 5.50 0.0369 0.1229366.00 0.70 0.0047 0.0156350.60 0.70 0.0047 0.0213355.50 1.30 0.0087 0.0484318.10 0.30 0.0020 0.0069303.90 0.10 0.0007 0.0032292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

149.20


v


(g) (g) (mm-1)

441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.20 0.00 0.0000 0.0000415.30 0.00 0.0000 0.0000401.90 0.00 0.0000 0.0000395.10 0.30 0.0024 0.0043400.30 2.10 0.0167 0.0380398.80 13.10 0.1042 0.2895396.40 23.00 0.1830 0.6099392.40 27.10 0.2156 0.7186364.40 14.50 0.1154 0.5243367.30 13.10 0.1042 0.5790332.00 14.20 0.1130 0.3895311.70 7.90 0.0628 0.2993297.40 4.70 0.0374 0.2579279.30 2.50 0.0199 0.1894276.20 1.70 0.0135 0.1932264.60 0.70 0.0056 0.1013270.10 0.50 0.0040 0.2094262.10 0.30 0.0024 0.1492258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

125.70


v


(g) (g) (mm-1)

441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.20 0.00 0.0000 0.0000415.30 0.00 0.0000 0.0000401.90 0.00 0.0000 0.0000394.80 0.00 0.0000 0.0000399.20 1.00 0.0091 0.0207388.40 2.70 0.0246 0.0684381.20 7.80 0.0712 0.2372382.20 16.90 0.1542 0.5140363.50 13.60 0.1241 0.5640368.70 14.50 0.1323 0.7350338.20 20.40 0.1861 0.6418317.30 13.50 0.1232 0.5865301.10 8.40 0.0766 0.5286281.50 4.70 0.0429 0.4084277.20 2.70 0.0246 0.3519265.70 1.80 0.0164 0.2986270.20 0.60 0.0055 0.2881262.30 0.50 0.0046 0.2851258.80 0.50 0.0046 0.1690242.50 0.00 0.0000 0.0000

109.60


v

Exit Crusher #2


450.10 8.30 0.0477 0.0149440.80 8.80 0.0506 0.0389434.60 11.00 0.0632 0.0575430.20 10.00 0.0575 0.0676427.70 12.40 0.0713 0.0950409.40 7.50 0.0431 0.0663405.70 10.90 0.0626 0.1139408.50 10.30 0.0592 0.1345396.20 10.50 0.0603 0.1676382.10 8.70 0.0500 0.1667378.10 12.80 0.0736 0.2452358.60 8.70 0.0500 0.2273363.10 8.90 0.0511 0.2842332.50 14.70 0.0845 0.2913314.20 10.40 0.0598 0.2846299.50 6.80 0.0391 0.2695281.10 4.30 0.0247 0.2354277.40 2.90 0.0167 0.2381266.40 2.50 0.0144 0.2612270.60 1.00 0.0057 0.3025263.20 1.40 0.0080 0.5029259.30 1.00 0.0057 0.2129242.70 0.20 0.0011 0.0182

174.00


v



441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000422.00 1.80 0.0104 0.0122427.70 12.40 0.0716 0.0955422.10 20.20 0.1166 0.1794430.50 35.70 0.2061 0.3748441.00 42.80 0.2471 0.5616427.00 41.30 0.2385 0.6624386.30 12.90 0.0745 0.2483369.50 4.20 0.0242 0.0808350.90 1.00 0.0058 0.0262354.50 0.30 0.0017 0.0096318.40 0.60 0.0035 0.0119303.80 0.00 0.0000 0.0000292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

173.20


v



441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.20 0.00 0.0000 0.0000415.30 0.00 0.0000 0.0000401.90 0.00 0.0000 0.0000395.00 0.20 0.0017 0.0031398.20 0.00 0.0000 0.0000386.80 1.10 0.0094 0.0260405.10 31.70 0.2702 0.9008419.80 54.50 0.4646 1.5487364.70 14.80 0.1262 0.5735362.30 8.10 0.0691 0.3836322.50 4.70 0.0401 0.1382304.70 0.90 0.0077 0.0365293.10 0.40 0.0034 0.0235276.90 0.10 0.0009 0.0081274.80 0.30 0.0026 0.0365264.40 0.50 0.0043 0.0775269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

117.30


v

Product


441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.30 0.10 0.0006 0.0008425.50 10.20 0.0662 0.0883421.90 20.00 0.1298 0.1997420.90 26.10 0.1694 0.3079437.40 39.20 0.2544 0.5781426.40 40.70 0.2641 0.7337387.40 14.00 0.0909 0.3028366.90 1.60 0.0104 0.0346350.40 0.50 0.0032 0.0147355.50 1.30 0.0084 0.0469318.20 0.40 0.0026 0.0090303.80 0.00 0.0000 0.0000292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

154.10


v

Recycle


443.40 1.60 0.0119 0.0037432.10 0.10 0.0007 0.0006425.00 1.40 0.0104 0.0095422.20 2.00 0.0149 0.0176418.00 2.70 0.0201 0.0269404.30 2.40 0.0179 0.0276397.80 3.00 0.0224 0.0407401.20 3.00 0.0224 0.0509396.30 10.60 0.0791 0.2197390.80 17.40 0.1299 0.4328389.60 24.30 0.1813 0.6045362.80 12.90 0.0963 0.4376365.90 11.70 0.0873 0.4851333.70 15.90 0.1187 0.4092313.50 9.70 0.0724 0.3447298.30 5.60 0.0418 0.2882279.90 3.10 0.0231 0.2203276.60 2.10 0.0157 0.2239265.60 1.70 0.0127 0.2307270.40 0.80 0.0060 0.3142262.50 0.70 0.0052 0.3265259.50 1.20 0.0090 0.3317242.60 0.10 0.0007 0.0118

134.00


v

16/07/00

Exit Granulator


458.10 16.30 0.0805 0.0251432.50 0.50 0.0025 0.0019427.30 3.70 0.0183 0.0166425.10 4.90 0.0242 0.0285421.40 6.10 0.0301 0.0401407.10 5.20 0.0257 0.0395402.50 7.70 0.0380 0.0691413.10 14.90 0.0735 0.1671415.00 29.30 0.1446 0.4017403.60 30.20 0.1491 0.4969396.10 30.80 0.1520 0.5067365.70 15.80 0.0780 0.3545366.70 12.50 0.0617 0.3428331.20 13.40 0.0661 0.2281310.30 6.50 0.0321 0.1528295.40 2.70 0.0133 0.0919277.80 1.00 0.0049 0.0470275.10 0.60 0.0030 0.0423264.40 0.50 0.0025 0.0449269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

202.60


v

Exit Dryer #1


448.50 6.70 0.0373 0.0117436.40 4.40 0.0245 0.0189429.60 6.00 0.0334 0.0304427.70 7.50 0.0418 0.0492423.20 7.90 0.0440 0.0587407.10 5.20 0.0290 0.0446403.50 8.70 0.0485 0.0881412.20 14.00 0.0780 0.1773407.00 21.30 0.1187 0.3296393.30 19.90 0.1109 0.3695387.10 21.80 0.1214 0.4048362.10 12.20 0.0680 0.3089364.50 10.30 0.0574 0.3188331.10 13.30 0.0741 0.2555311.80 8.00 0.0446 0.2122298.20 5.50 0.0306 0.2113279.20 2.40 0.0134 0.1273276.20 1.70 0.0095 0.1353265.30 1.40 0.0078 0.1418270.20 0.60 0.0033 0.1759262.20 0.40 0.0022 0.1393258.60 0.30 0.0017 0.0619242.50 0.00 0.0000 0.0000

179.50


v

Exit Dryer #2


467.80 26.00 0.1619 0.0506436.70 4.70 0.0293 0.0225428.00 4.40 0.0274 0.0249423.40 3.20 0.0199 0.0234421.30 6.00 0.0374 0.0498406.10 4.20 0.0262 0.0402401.10 6.30 0.0392 0.0713409.40 11.20 0.0697 0.1585406.50 20.80 0.1295 0.3598391.50 18.10 0.1127 0.3757384.30 19.00 0.1183 0.3944359.00 9.10 0.0567 0.2576362.50 8.30 0.0517 0.2871326.60 8.80 0.0548 0.1889308.50 4.70 0.0293 0.1394295.00 2.30 0.0143 0.0988277.90 1.10 0.0068 0.0652275.10 0.60 0.0037 0.0534264.50 0.60 0.0037 0.0679269.90 0.30 0.0019 0.0983262.20 0.40 0.0025 0.1557258.80 0.50 0.0031 0.1153242.50 0.00 0.0000 0.0000

160.60


v

Oversize #1


674.60 232.80 0.6479 0.2025466.10 34.10 0.0949 0.0730466.00 42.40 0.1180 0.1073446.00 25.80 0.0718 0.0845432.00 16.70 0.0465 0.0620405.00 3.10 0.0086 0.0133395.90 1.10 0.0031 0.0056398.70 0.50 0.0014 0.0032386.00 0.30 0.0008 0.0023374.40 1.00 0.0028 0.0093365.60 0.30 0.0008 0.0028350.30 0.40 0.0011 0.0051354.50 0.30 0.0008 0.0046318.30 0.50 0.0014 0.0048303.80 0.00 0.0000 0.0000292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

359.30


v

Oversize #2


567.70 125.90 0.4547 0.1421460.50 28.50 0.1029 0.0792459.70 36.10 0.1304 0.1185454.50 34.30 0.1239 0.1457449.70 34.40 0.1242 0.1656410.70 8.80 0.0318 0.0489398.20 3.40 0.0123 0.0223399.70 1.50 0.0054 0.0123386.30 0.60 0.0022 0.0060374.40 1.00 0.0036 0.0120365.80 0.50 0.0018 0.0060350.60 0.70 0.0025 0.0115354.80 0.60 0.0022 0.0120318.40 0.60 0.0022 0.0075303.80 0.00 0.0000 0.0000292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

276.90


v

Product Size #1


441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000421.90 1.70 0.0076 0.0090430.30 15.00 0.0674 0.0899425.50 23.60 0.1061 0.1633426.90 32.10 0.1443 0.2624456.60 58.40 0.2626 0.5968443.00 57.30 0.2576 0.7157392.70 19.30 0.0868 0.2893374.20 8.90 0.0400 0.1334352.60 2.70 0.0121 0.0552356.10 1.90 0.0085 0.0475318.60 0.80 0.0036 0.0124304.50 0.70 0.0031 0.0150292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

222.40


v

Product Size #2


441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.20 0.00 0.0000 0.0000420.90 5.60 0.0277 0.0369427.00 25.10 0.1240 0.1908438.50 43.70 0.2159 0.3926463.40 65.20 0.3221 0.7321432.30 46.60 0.2302 0.6395384.80 11.40 0.0563 0.1877368.50 3.20 0.0158 0.0527350.80 0.90 0.0044 0.0202354.60 0.40 0.0020 0.0110318.00 0.20 0.0010 0.0034303.90 0.10 0.0005 0.0024292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

202.40


v


(g) (g) (mm-1)

441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.20 0.00 0.0000 0.0000415.30 0.00 0.0000 0.0000401.90 0.00 0.0000 0.0000395.20 0.40 0.0028 0.0051400.50 2.30 0.0163 0.0370400.00 14.30 0.1012 0.2811398.30 24.90 0.1762 0.5874394.60 29.30 0.2074 0.6912367.00 17.10 0.1210 0.5501368.80 14.60 0.1033 0.5740334.80 17.00 0.1203 0.4149313.40 9.60 0.0679 0.3235297.80 5.10 0.0361 0.2489279.60 2.80 0.0198 0.1887276.00 1.50 0.0106 0.1517265.00 1.10 0.0078 0.1415270.00 0.40 0.0028 0.1490262.10 0.30 0.0021 0.1327258.90 0.60 0.0042 0.1573242.50 0.00 0.0000 0.0000

141.30


v


(g) (g) (mm-1)

441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.20 0.00 0.0000 0.0000415.30 0.00 0.0000 0.0000401.90 0.00 0.0000 0.0000395.10 0.30 0.0019 0.0034399.10 0.90 0.0057 0.0129391.10 5.40 0.0340 0.0945389.40 16.00 0.1008 0.3359394.20 28.90 0.1820 0.6066372.80 22.90 0.1442 0.6555375.80 21.60 0.1360 0.7557346.20 28.40 0.1788 0.6167320.10 16.30 0.1026 0.4888301.40 8.70 0.0548 0.3778281.10 4.30 0.0271 0.2579276.70 2.20 0.0139 0.1979265.30 1.40 0.0088 0.1603270.20 0.60 0.0038 0.1989262.20 0.40 0.0025 0.1574258.80 0.50 0.0031 0.1166242.50 0.00 0.0000 0.0000

158.80


v

Exit Crusher #1


452.00 10.20 0.0591 0.0185435.90 3.90 0.0226 0.0174430.30 6.70 0.0388 0.0353427.70 7.50 0.0434 0.0511427.70 12.40 0.0718 0.0957411.10 9.20 0.0533 0.0820406.60 11.80 0.0683 0.1242408.50 10.30 0.0596 0.1355396.90 11.20 0.0649 0.1801382.90 9.50 0.0550 0.1834377.30 12.00 0.0695 0.2316358.80 8.90 0.0515 0.2342362.90 8.70 0.0504 0.2799332.10 14.30 0.0828 0.2855316.60 12.80 0.0741 0.3529300.20 7.50 0.0434 0.2995281.80 5.00 0.0290 0.2757278.50 4.00 0.0232 0.3309267.20 3.30 0.0191 0.3474271.00 1.40 0.0081 0.4267262.60 0.80 0.0046 0.2895259.10 0.80 0.0046 0.1716243.00 0.50 0.0029 0.0460

172.70


v

Exit Crusher #2


450.60 8.80 0.0559 0.0175436.40 4.40 0.0279 0.0215435.00 11.40 0.0724 0.0658428.90 8.70 0.0552 0.0650427.80 12.50 0.0794 0.1058414.20 12.30 0.0781 0.1201405.40 10.60 0.0673 0.1224407.20 9.00 0.0571 0.1299396.30 10.60 0.0673 0.1869382.40 9.00 0.0571 0.1905377.30 12.00 0.0762 0.2540357.60 7.70 0.0489 0.2222362.80 8.60 0.0546 0.3034330.50 12.70 0.0806 0.2781311.80 8.00 0.0508 0.2419297.00 4.30 0.0273 0.1883278.80 2.00 0.0127 0.1209275.90 1.40 0.0089 0.1270265.20 1.30 0.0083 0.1501270.40 0.80 0.0051 0.2673262.70 0.90 0.0057 0.3571258.80 0.50 0.0032 0.1176242.50 0.00 0.0000 0.0000

157.50


v



441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.20 0.00 0.0000 0.0000418.70 3.40 0.0254 0.0338414.00 12.10 0.0903 0.1389413.30 18.50 0.1381 0.2510436.40 38.20 0.2851 0.6479418.70 33.00 0.2463 0.6841390.00 16.60 0.1239 0.4129373.40 8.10 0.0604 0.2015351.90 2.00 0.0149 0.0678355.30 1.10 0.0082 0.0456318.80 1.00 0.0075 0.0257303.80 0.00 0.0000 0.0000292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

134.00


v



441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.60 0.00 0.0000 0.0000420.20 0.00 0.0000 0.0000415.30 0.00 0.0000 0.0000401.90 0.00 0.0000 0.0000395.20 0.40 0.0035 0.0064398.60 0.40 0.0035 0.0080386.80 1.10 0.0097 0.0268401.30 27.90 0.2450 0.8165414.80 49.50 0.4346 1.4486368.00 18.10 0.1589 0.7223363.20 9.00 0.0790 0.4390323.20 5.40 0.0474 0.1635304.90 1.10 0.0097 0.0460293.10 0.40 0.0035 0.0242277.00 0.20 0.0018 0.0167274.50 0.00 0.0000 0.0000264.30 0.40 0.0035 0.0639269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

113.90


v

Product


441.80 0.00 0.0000 0.0000432.00 0.00 0.0000 0.0000423.70 0.10 0.0007 0.0007420.60 0.40 0.0030 0.0035420.60 5.30 0.0396 0.0528412.10 10.20 0.0762 0.1172414.80 20.00 0.1494 0.2716439.10 40.90 0.3055 0.6942424.80 39.10 0.2920 0.8111387.00 13.60 0.1016 0.3386368.30 3.00 0.0224 0.0747350.50 0.60 0.0045 0.0204354.60 0.40 0.0030 0.0166318.00 0.20 0.0015 0.0052303.90 0.10 0.0007 0.0036292.70 0.00 0.0000 0.0000276.80 0.00 0.0000 0.0000274.50 0.00 0.0000 0.0000263.90 0.00 0.0000 0.0000269.60 0.00 0.0000 0.0000261.80 0.00 0.0000 0.0000258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

133.90


v

Recycle


443.70 1.90 0.0133 0.0042433.60 1.60 0.0112 0.0086424.90 1.30 0.0091 0.0083421.70 1.50 0.0105 0.0124419.50 4.20 0.0294 0.0392403.60 1.70 0.0119 0.0183397.50 2.70 0.0189 0.0344403.10 4.90 0.0343 0.0780399.10 13.40 0.0938 0.2607393.50 20.10 0.1408 0.4692389.50 24.20 0.1695 0.5649364.00 14.10 0.0987 0.4488366.60 12.40 0.0868 0.4824332.60 14.80 0.1036 0.3574312.70 8.90 0.0623 0.2968298.10 5.40 0.0378 0.2608280.10 3.30 0.0231 0.2201277.00 2.50 0.0175 0.2501265.70 1.80 0.0126 0.2292270.90 1.30 0.0091 0.4791262.60 0.80 0.0056 0.3501258.30 0.00 0.0000 0.0000242.50 0.00 0.0000 0.0000

142.80


v

APPENDIX B – OPERATING DATA

FLOWS 8/4/2000 7/7/2000 8/15/2000 8/16/2000

Production Rate 28.00 26.90 27.50 29.70 t/hr3.75 4.09 4.55 4.04

Recycle Rate 105.00 110.00 125.00 120.00 t/hrSlurry to T208A - 2.80 5.20 4.50 L/sSlurry to T209A - 3.40 5.70 5.70 L/sSlurry to tee 6.00 4.80 6.40 6.30 L/sAmmonia to tee 2.52 2.30 2.55 2.60 L/sAmmonia to sparger 0.28 0.25 0.26 2.50 L/sAlum to P256/209 0.40 0.40 0.39 0.38 L/sAcid to scrubbers 0.20 0.25 0.24 0.30 L/sAcid to tee 2.60 2.60 2.60 2.60 L/sAqua to tee - 1.50 0.00 0.00 L/sWater to T210 6.50 5.20 6.80 6.50 L/s

TEMPERATURES

Solids ex Granulator 92.00 89.00 79.00 82.00 CSolids ex Dryer - - - CDryer air out 111.00 112.00 111.00 113.00 CDryer air in 480.00 481.00 481.00 489.00 CSolids Recycle at Granulator - 91.00 - - CSlurry in T210 66.00 62.00 60.00 55.00 CAmbient Temperature - - - C

SAMPLES

T210 Slurry Specific Gravity 1.06 1.05 1.035 % Water 85 87 90 pH 1.65 1.75 1.66

Product % Nitrogen 4.6 20.4 20.5 % Water 0.17 0.27 0.13 % Al 0.31 0.29 0.31 free acid - 0 0 hardness - - - size 90+ - -


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APPENDIX C – DAESIM CODE

Drum Granulator Model

(********************************************************************(* Block Name: drum_gran(*(* Description:(* The drum granulator model based on a general population(* balance (GPB) model using Hounslow's discretisation method to(* handle the birth and death terms.(* The model requires a geometric discretization of particle(* volume such that v(i)=2v(i-1) or d(i)=1.2599d(i-1).(*(* Model assumes perfect mixing in the volume and only accounts for(* aggregation mechanisms of birth and death.(* Constant mass holdup assumed in the drum. (*(* No. State Variables/Equations:(* No. Algebraic Variables:(* No. Algebraic Equations:(*(*(* Authors: Nicoleta Balliu and Ian Cameron(* Organization: CAPE / DaeSim Technologies Pty Ltd(* Version and Date: 1.1 5 September 2000(* 1.2 6 September 2000(* 1.3 8 September 2000(********************************************************************

function_block drum_gran

#include "sys_defs.h"

type ldist : structure lsize : array (1 .. nsize) of real ; end_structure ; end_type type lpar : structure lcomp : array (1 .. ncomp) of ldist ; end_structure ; end_type type


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lstream : structure ltphcomp : array (1 .. ncomp) of ldist ; (* kilograms per second solids flow *) ltphwater : real ; (* kilograms per second water flow *) end_structure ; end_type

var_in_out i1 : sol_liq_stream = ( inlet ) ; o1 : sol_liq_stream = ( outlet ) ; end_var

var_input x_holdup : lstream ; (* mass of particles in each size range [kg] *) rhogf : real ; (* density of salt kg/m3 [1600] *) rhogl : real ; (* density of solution kg/m3 [1300] *) sol : real ; (* solubility of salt in water kg/kg *) por : real ; (* porosity of granules m3/m3 [0.38] *) a : real ; (* parameter in the kernel model [1/s] *) rho : real ; (* density of the granule kg/m3 [1500] *) init_diam : real ; (* initial diameter for ranges [m] *) end_var

var_output deriv_holdup : lstream ; resid_out : lpar ; resid_water : real ; end_var

var i : int ; (* index *) j : int ; (* index *) k : int ; (* index *) k1 : int ; (* index *) birth : real ; (* mass appearing in range j due to birth *) death : real ; (* mass disappearing from range j due to death*) diameter : array (1 .. nrange ) of real ; (* [nsize+1] diameter markers for sizeranges [m] *) numb : lpar ; (* number of particles in size range j *) sum1 : real ; (* second birth term *) sum2 : real ; (* first birth term *) sum3 : real ; (* first death term *) sum4 : real ; (* second death term *) Beta0 : real ; (* the constant kernel *) ntotal : real ; (* total number of particles in drum [#]*) total_massholdup : real ; (* total kilograms in drum [kg] *) total_flow_in : real ; (* total in-flow of solids [kg/s] *) dbar : array (1 .. nsize ) of real ; (* mean granule size of the range [m] *)


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xw : real ; (* particle moisture content kg/kg [0.10]*) end_var

(* function block calculation *)

(* generate the particle size range markers *)

diameter(1) := init_diam ;for i := 2 to nrange do diameter(i) := diameter(i-1)*(2**(0.333333333)) ;end_for

(* calculate the mean particle size for each size interval *)

for k1 := 1 to nsize do dbar(k1) := (diameter(k1) + diameter(k1+1))/2 ;end_for

(* get total in-flow and total holdup in drum *)

total_massholdup := 0.0 ; total_flow_in := 0.0 ; for i := 1 to ncomp do for j := 1 to nsize do total_massholdup := total_massholdup + x_holdup.ltphcomp(i).lsize(j) ; total_flow_in := total_flow_in + i1.tphcomp(i).z_size(j) ; end_for end_for

(* moisture calculation and kernel constant estimation *)

xw := x_holdup.ltphwater/total_massholdup ; Beta0 := xw*(1+sol)*rhogf*(1-por)*a/((1-xw*sol)*rhogl*por) ;

(* Total particles *)

ntotal := 0.0 ; for i := 1 to ncomp do

for j := 1 to nsize do ntotal := ntotal + (x_holdup.ltphcomp(i).lsize(j)/(dbar(j)**3)) ;

end_forend_forntotal := ntotal * 6/(3.14159*rho) ;

(* initialization and calculation of aggregation *)

for i := 1 to ncomp do for j := 1 to nsize do


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(* Birth and death agglomeration terms based on mass using Hounslow'sdiscretization method *)

(* second birth term *)

if j > 1 then sum1 := 0.5*Beta0*(x_holdup.ltphcomp(i).lsize(j-1) *x_holdup.ltphcomp(i).lsize(j-1))

* ((6/(3.14159*rho*(dbar(j-1)**3)))**2)/ntotal ; else sum1 := 0 ; end_if

(* first birth term *)

sum2 := 0 ; if j > 2 then

for k := 1 to j-2 do sum2 := sum2 + (2.0**(k-j+1))*Beta0*(x_holdup.ltphcomp(i).lsize(j-1)/(dbar(j-1)**3))

*(x_holdup.ltphcomp(i).lsize(k)/(dbar(k)**3))*((6/(3.14159*rho))**2)/ntotal ;

end_forend_if

(* first death term *)sum3 := 0 ;if j > 1 then

for k := 1 to j-1 do sum3 := sum3 + (2.0**(k-j))*Beta0*(x_holdup.ltphcomp(i).lsize(j)/(dbar(j)**3))

*(x_holdup.ltphcomp(i).lsize(k)/(dbar(k)**3))*((6/(3.14159*rho))**2)/ntotal ;

end_forend_if

(* second death term *)sum4 := 0 ;for k := j to nsize do

sum4 := sum4 + Beta0*(x_holdup.ltphcomp(i).lsize(j)/(dbar(j)**3)) *

(x_holdup.ltphcomp(i).lsize(k)/(dbar(k)**3))*((6/(3.14159*rho))**2)/ntotal ; end_for

birth := (sum1 + sum2) ;death := (sum3 + sum4) ;


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(* The total rate of change of mass in each size range [kg/s] *)

deriv_holdup.ltphcomp(i).lsize(j) := i1.tphcomp(i).z_size(j) -o1.tphcomp(i).z_size(j)

+ (birth - death)*((3.14159*rho*(dbar(j)**3))/6);

end_forend_for

(* perfect mixing rule *)

for i := 1 to ncomp do for j := 1 to nsize do

resid_out.lcomp(i).lsize(j) := total_flow_in*x_holdup.ltphcomp(i).lsize(j) - total_massholdup*o1.tphcomp(i).z_size(j) ;

end_forend_for

(* transfer water flow from inlet to outlet stream *)

deriv_holdup.ltphwater := i1.z_tphwater - o1.z_tphwater ;resid_water := o1.z_tphwater - i1.z_tphwater ;

end_function_block


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Dryer Model

(*********************************************************************)(* Block Name: DRYER(*(* Description:(*(* Dryer model assuming plug flow (*(* No. State Variables/Equations: 0(* No. Algebraic Variables: 0(* No. Algebraic Equations: ncomp*nsize+1(*(* Author:(* Organization:(* Version and Date(********************************************************************

function_block dryer


type ldist : structure lsize : array (1 .. nsize) of real ; end_structure ; end_type type lstream : structure ltphcomp : array (1 .. ncomp) of ldist ; ltphwater : real ; end_structure ; end_type type lsep : structure lcomp : array (1 .. ncomp) of ldist ; end_structure ; end_type

var_in_out i1 : sol_liq_stream = ( inlet_1 ) ; o1 : sol_liq_stream = ( solids_outlet ) ; o2 : sol_liq_stream = ( moisture_outlet ) ; end_var


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var_input tr : real ; (* drying rate, kg H2O/kg.feed/metre of drum length *) L : real ; (* dryer length, metres *) sep_func : lsep ; (* fraction solids material to air stream *) end_var

var_output resid_solids : lstream ; resid_liquid : lstream ; end_var

var i : int ; j : int ; end_var

(* function block calculations *)

for i := 1 to ncomp do for j := 1 to nsize do resid_solids.ltphcomp(i).lsize(j) := i1.tphcomp(i).z_size(j)*(1 -sep_func.lcomp(i).lsize(j)) - o1.tphcomp(i).z_size(j) ;

resid_liquid.ltphcomp(i).lsize(j) :=i1.tphcomp(i).z_size(j)*sep_func.lcomp(i).lsize(j) - o2.tphcomp(i).z_size(j) ; end_for end_for resid_solids.ltphwater := i1.z_tphwater*(1-tr*L) - o1.z_tphwater ; resid_liquid.ltphwater := i1.z_tphwater*tr*L - o2.z_tphwater;

end_function_block

Screen Model

(******************************************************************)(* Name: SCREEN(*(* Description:(* (* The "screen" model, which uses a selection function to(* assign what fraction of the size range goes to the oversize(* stream.(* (* State Variables/Equations: 0(* Algebraic Variables: 0(* Algebraic Equations: 2*(ncomp*nsize+1)(*


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(* Author: Bob Newell, Ian Cameron, DaeSim Technologies P/L(* Version: 1.0 Rev 1: September 4, 2000(******************************************************************)

function_block screen


type ldist : structure lsize : array (1 .. nsize) of real ; end_structure ; end_type type lstream : structure ltphcomp : array (1 .. ncomp) of ldist ; ltphwater : real ; end_structure ; end_type type lsep : structure lcomp : array (1 .. ncomp) of ldist ; end_structure ; end_type

var_in_out i1 : sol_liq_stream = ( inlet ) ; o1 : sol_liq_stream = ( outlet_oversize ) ; o2 : sol_liq_stream = ( outlet_undersize ) ; end_var

var_input sep_func : lsep ; (* fraction of inlet to oversize *) end_var

var_output resid_over : lstream ; resid_under : lstream ; end_var




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for i := 1 to ncomp do for j := 1 to nsize do resid_over.ltphcomp(i).lsize(j) := o1.tphcomp(i).z_size(j) - sep_func.lcomp(i).lsize(j) * i1.tphcomp(i).z_size(j) ; resid_under.ltphcomp(i).lsize(j) := o2.tphcomp(i).z_size(j) - ( 1 - sep_func.lcomp(i).lsize(j) ) * i1.tphcomp(i).z_size(j) ; end_for end_for

(* all the water goes out the bottom *)

resid_over.ltphwater := o1.z_tphwater - 0.000000001 ; resid_under.ltphwater := o2.z_tphwater - i1.z_tphwater ;

end_function_block


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Crusher Model

(******************************************************************)(* Name: GRINDS *)(* *)(* Description: *)(* The ball mill model (solids particulate stream) *)(* *)(* State Variables/Equations: ncomp*nsize+1 *)(* Algebraic Variables: 0 *)(* Algebraic Equations: ncomp*nsize *)(* *)(* Author: Bob Newell, Ian Cameron *)(* Organization: Daesim Technologies Pty Ltd *)(* Date: 6 September 2000 *)(******************************************************************)

function_block grinds


type ldist : structure lsize : array (1 .. nsize) of real ; end_structure ; end_type type lpar : structure lcomp : array (1 .. ncomp) of ldist ; end_structure ; end_type type lstream : structure ltphcomp : array (1 .. ncomp) of ldist ; ltphwater : real ; end_structure ; end_type

var_in_out i1 : sol_liq_stream = ( inlet ) ; o1 : sol_liq_stream = ( outlet ) ; end_var

var_input x_holdup : lstream ; breakage_rate : lpar ; breakage_func : lpar ;


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selec_func : lpar ; end_var

var_output deriv_holdup : lstream ; resid_out : lpar ; resid_water : real ; end_var

var i : int ; j : int ; k : int ; n : int ; total_sholdup : real ; total_sflow_in : real ; nett_breakage : real ; breakage_out : array (1 .. nsize) of real ; breakage_in : array (1 .. nsize) of real ; broken_residue : real ; end_var


for i := 1 to ncomp do

for j := 1 to nsize do breakage_in(j) := 0.0 ; end_for breakage_out(1) := 0.0 ; for j := 1 to nsize-1 do breakage_out(j+1) := x_holdup.ltphcomp(i).lsize(j+1) * breakage_rate.lcomp(i).lsize(j+1) ; broken_residue := breakage_out(j+1) ; if j > 1 then for k := 1 to j-1 do n := j - k + 1 ; breakage_in(n) := breakage_in(n) + breakage_out(j+1) * breakage_func.lcomp(i).lsize(nsize-k) ; broken_residue := broken_residue - breakage_out(j+1) * breakage_func.lcomp(i).lsize(nsize-k) ; end_for end_if breakage_in(1) := breakage_in(1) + broken_residue ; end_for

for j := 1 to nsize do nett_breakage := breakage_out(j) - breakage_in(j);


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deriv_holdup.ltphcomp(i).lsize(j) := i1.tphcomp(i).z_size(j) - o1.tphcomp(i).z_size(j) - nett_breakage ; end_for

end_for deriv_holdup.ltphwater:= i1.z_tphwater - o1.z_tphwater;

(* totals *)

(* algebraic equations *)

total_sholdup := 0.0 ; total_sflow_in := 0.0 ; for i := 1 to ncomp do for j := 1 to nsize do total_sholdup := total_sholdup + x_holdup.ltphcomp(i).lsize(j) * selec_func.lcomp(i).lsize(j) ; total_sflow_in := total_sflow_in + i1.tphcomp(i).z_size(j) ; end_for end_for

(* perfect mixing rules *)

for i := 1 to ncomp do for j := 1 to nsize do(* use total_sflow_in instead of total_sflow_out to enforce the total_sflow_in = total_sflow_out constraint without structural or conditioning problems *)

resid_out.lcomp(i).lsize(j) := total_sflow_in * x_holdup.ltphcomp(i).lsize(j) * selec_func.lcomp(i).lsize(j) - total_sholdup * o1.tphcomp(i).z_size(j) ; end_for end_for resid_water := o1.z_tphwater - i1.z_tphwater ;

end_function_block

Mixer Model

(******************************************************************)(* Name: MIXER *)(* *)(* Description: *)(* The "mixer" model *)(* State Variables/Equations: 0 *)(* Algebraic Variables: 0 *)(* Algebraic Equations: ncomp*nsize+2 *)


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(******************************************************************)

function_block mixer


type ldist : structure lsize : array (1 .. nsize) of real ; end_structure ; end_type type lstream : structure ltphcomp : array (1 .. ncomp) of ldist ; ltphwater : real ; end_structure ; end_type

var_in_out i1 : sol_liq_stream = ( inlet_1 ) ; i2 : sol_liq_stream = ( inlet_2 ) ; o1 : sol_liq_stream = ( outlet ) ; end_var

var_output resid_out : lstream ; end_var



resid_out.ltphwater := i1.z_tphwater + i2.z_tphwater - o1.z_tphwater ; for i := 1 to ncomp do for j := 1 to nsize do resid_out.ltphcomp(i).lsize(j) := i1.tphcomp(i).z_size(j) + i2.tphcomp(i).z_size(j) - o1.tphcomp(i).z_size(j) ; end_for end_for

end_function_block


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Global Variables Code

(* global variable declarations *)

var_external

(******************************************************************** (* these are the standard declarations and should not be touched *)

initialization : bool = 0 (none) ; (* enables user code at time zero *) time : real = 0 (s) ; (* simulation time *) max_time : real = 100 (s) ; (* time for end of simulation *) print_level : int = 0 (none) ; (* degree of print output *) integrate_option : int = 2 (none) ; (* specifies the type of integrator *) special_option : int = 0 (none) ; (* special tasks during solution *) error_tolerance : real = 0.0001 (none) ; (* integration error acceptable *) no_comps : int = 1 (none) ; (* number of components in stream *) no_comps_4 : int = 1 (none) ; (* number of components in stmline *) mole_flag : int = 0 (none) ; (* mole flows = 1, mass flows = 0 *) f_errcode : int ; (* argument for system error function *)

(********************************************************************

(* add your own global variable declarations here *)

nsize : int = 6 (none) ; (* number of size fractions *) nrange : int = 7 (none) ; (* number of range markers *) ncomp : int = 1 (none) ; (* number of components *)

(********************************************************************

end_var

Solid-Liquid Source Code

(******************************************************************)(* Name: SCL_SOURCE(*(* Description:(* The source of a stream entering the flowsheet(* with its solid component rates and liquid rate (scl_)(* specified.(*(* Maximum usage is specified and will halt the simulation(* with error code 5001.(*(* State Variables/Equations: 1


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(* Algebraic Variables: 0(* Algebraic Equations: ncomp*nsize+1(* (* Authors: Bob Newell, Ian Cameron, Daesim Technologies P/L(* Version, Date: v2.0, 6 September 2000(******************************************************************)

function_block scl_source


type dist : structure lsize : array (1 .. nsize) of real ; end_structure ; end_type

type lstream : structure ltphcomp : array (1 .. ncomp) of dist ; ltphwater : real ; end_structure ; end_type

var_in_out o1 : sol_liq_stream = (outlet_stream) ; end_var

var_input x_usage : real ; (* accumulated flow *) max_usage : real ; (* source capacity *) specn : lstream ; end_var

var_output deriv_usage : real ; (* outlet flow *) resid_flow : lstream ; end_var

var i : int ; j : int ; total_flow : real ; end_var

function error ( errcode : int; ) end_function



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(* stream specification and summation *)

resid_flow.ltphwater := specn.ltphwater - o1.z_tphwater ; total_flow := o1.z_tphwater ; for i := 1 to ncomp do for j := 1 to nsize do resid_flow.ltphcomp(i).lsize(j) := specn.ltphcomp(i).lsize(j) - o1.tphcomp(i).z_size(j) ; total_flow := total_flow + o1.tphcomp(i).z_size(j) ; end_for end_for

(* mass balance *)

deriv_usage := total_flow ;

(* limiting calculations *)

if x_usage > max_usage then call error( errcode := 5001; ); end_if

end_function_block


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Solid Liquid Link Code

(********************************************************************(* Global and Link Definitions(********************************************************************

(* Solids - Liquid Stream Definition *)

type

sm_dist : structure z_size : array (1 .. nsize) of real ; end_structure ;

end_type

type

sol_liq_stream : structure tphcomp : array (1 .. ncomp) of sm_dist ; (* tonne per hour solid *) (* components *) z_tphwater : real ; (* tonne per hour water *) end_structure ;

end_type

Solid Liquid Sink Code

(******************************************************************)(* Name: SCL_SINK(*(* Description:(*(* The free sink of stream leaving the flowsheet.(* A maximum usage is specified and will halt(* the simulation with error code 1002.(*(* State Variables/Equations: 1(* Algebraic Variables: 0(* Algebraic Equations: 0(* (******************************************************************)

function_block scl_sink



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var_in_out i1 : sol_liq_stream = (inlet_stream) ; end_var

var_input x_usage : real ; (* accumulated flow (kg) *) max_usage : real ; (* sink capacity (kg) *) end_var

var_output deriv_usage : real ; (* inlet flow *) end_var

var i : int ; j : int ; total_flow : real ; end_var

function error ( errcode : int; ) end_function


total_flow := i1.z_tphwater ; for i := 1 to ncomp do for j := 1 to nsize do total_flow := total_flow + i1.tphcomp(i).z_size(j) ; end_for end_for

(* mass balance *)

deriv_usage := total_flow ;

(* limiting calculations *)

if x_usage > max_usage then call error( errcode := 5002; ); end_if

end_function_block

s ross thesis

Documents

circuit analysis

analysis of dryer

analysis errors

analysis of samples

size distribution analysis

entire granulation circuit

moisture content analysis

data collection