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An Operator Training Simulator System forMMM Comminution and Classification Circuits

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An Operator Training Simulator System for

MMM Comminution and Classification

Circuits  

Rodrigo Toro ∗ Jose Manuel Ortiz ∗ Ivan Yutronic ∗∗

∗ Honeywell Chile S.A., Santiago, Chile (e-mail: rodrigo.toro@honeywell.com, josem.ortiz3@honeywell.com)

∗∗Compa˜ nıa Minera Do  na Ines de Collahuasi, Iquique, Chile (e-mail: ipyutronic@collahuasi.cl)

Abstract: The high copper prices, together with the lack of experienced personnel in mineralprocessing operations, have lead to high investments in technology for this industry. Simulationbased training is becoming a necessary phase for new projects and adoption of new technologies –like APC (Advanced Process Control)– because it is an instance to transmit the best operational

practices over a safe environment. In this paper, an Operator Training Simulator (OTS) system–for grinding and classification circuits– is presented. A case study, in which a complete SAGand secondary grinding/classification circuit has been simulated; supervisors and operators havebeen trained in order to educate them on the best operational practices, using APC applications.The OTS sessions have lead to an increase in the utilisation of APC applications from 20% to70%, in average.

Keywords:   Simulators, population balance models, advanced process control.

1. INTRODUCTION

Operator training simulators have been widely used in thechemical industry (see Coker and Ludwig (2007)), andare being recently incorporated in the mineral processingindustry (see Malhotra et al. (2009) and Sbarbaro andVillar (2010)). There are many simulators for the miningindustry, mainly focused on the plant design and thereforebased on steady-state models (see Roine et al. (2011)),rather than dynamic models, which are the ones usedin OTS systems due to the need to simulate transientresponses and automatic control strategies.

The expected benefits of the use of OTS systems are: fasterand safer ramp-up for new projects, enhancements in thelearning process effectiveness and faster adoption of newcontrol technologies, among others.

On this paper, an OTS system for a copper grinding andclassification process is presented. The simulated equip-ment was modelled using dynamic population balancemodels, implemented on  Matlab R S-Functions. The OTSsystem was deployed using OPC connections between thesimulation server and the operator stations. Advanced pro-cess control–APC–applications, were installed in operatorstations with the purpose of instructing students in thecorrect use of those applications. An automatic evaluationmethod has been implemented in order to have indicatorsof the student performance through the pre-programmedoperational events or cases.

Work of R. Toro and J. M. Ortiz has been supported by Honeywell

Chile S.A.Work of I. Yutronic has been supported by Companıa Minera Dona

Ines de Collahuasi.

The main contribution of this paper is a confirmation of the fact that the use of OTS systems helps to transmitthe best practices and reduce the adoption time of newtechnologies–such as APC–, giving the student the confi-

dence to confront complicated operational events throughthe experimentation in a safe environment.

This paper has been structured in the following way: InSection 2, a brief description of the dynamic models used inthe simulation is presented. In Section 3, the OTS systemarchitecture is reviewed. In Section 4, a case study ispresented, after, in Section 5, some results, taken from thecase study, are discussed. Finally, in Section 6, conclusionsand future work are presented.

2. DESCRIPTION OF COMMINUTION MODELS

The population balance model was introduced by Epstein

(1947) with further development by many authors, in-cluding Kellsall et al. (1969), Whiten (1974), Herbst andFuerstenau (1968, 1973), and Austin and Weller (1982).The model concepts are widely used in modern processsimulation.

The models used to simulate the process are basedin the Modern Comminution and Classification Theory(see Gutierrez and Sepulveda (1986)). For comminu-tion/classification simulations,   n  + 1 size intervals havebeen defined, where the   ith interval is delimited by theupper bound of size   i  −  1, named   di−1, and delimited bythe lower bound of size   i, named  di, in such a way that,

d0  > d1  > . . . > di  > di+1  > . . . > dn  > dn+1,   (1)

where,

•   n, is the number of size classes; and

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•   dn+1  = 0.

At every size interval, the mass holdup in the comminutionequipment has been modelled using a general populationbalance model, defined as follows:

dH i

dt

  =  −S iH i +i−1

j=1

S ibijH j  +  M F i   − M P i   ,   (2)

where,

•   H i, is the mass holdup in the   ith size class. Hence,the total mass holdup is calculated as  H  =

i H i;

•   S i, represents the   ith term of the selection function,that is, the disappearance rate particles of size   i;

•   bij , represents the breakage function term, that is, theprobability that a particle of size   i  becomes of size  jas it breaks;

•   M F i   , is the mass flow of particles of size   i  being fedinto the comminution equipment;

•   M P i   , is the mass flow of particles of size   i  that come

out as product.For every plant, parameters  S i  and  bij  are calculated froma desired steady-state result. That is, from a particle sizedistribution of the feed and product (e.g. samples takenfrom the real plant), these parameters are adjusted usingoptimization techniques, in such a way that the desiredproduct is obtained from that particular feed.

The product mass flow value  M P i   , is calculated differentlydepending on the type of comminution equipment. Themost common ones will be analysed next.

2.1 Ball Mill 

The product mass flow is calculated considering perfectmix of the slurry in the mill. Hence,

M P i   =  H i

V  sq P ,   (3)

where V  s   is the total slurry volume contained in ball voidsand above balls level, and q P  is the volume flow of product.Since a ball mill discharges by overflow,  V  s is constant andhence, the product volume flow is equal to the feed volumeflow.

Furthermore, given that the load volume is considered con-stant, the water mass holdup is calculated as by difference:

H w  = ρw(V  s −

H 

ρo ),   (4)where ρw  is the water density and  ρo   is the ore density.

Similarly, the water mass flow of product is given by

M w  = ρw(q P  −

n

i   M P i

ρo),   (5)

where ρo   is the ore density.

The power drawn by the mill is calculated as follows:

P Net = 0.238  ·  D2.5· L  ·  nc   · ρap(J t  − 1.065J 2t ) sin α,   (6)

where   D   and   L  are the diameter and Length of the millrespectively,   nc   is the percentage of critical speed that

the mill is operating on;  ρap   is the apparent density, thatis, the density of what is contained in the mill, includingballs and slurry;  J t  is the load fraction of the mill, that is,

the fraction of the total volume that is used by the massholdup (including balls); and  α  is the lift angle of the load.

The lift angle varies with the density of the slurry. A linearrelation is adjusted in such a away that the lift angle islower for higher values of slurry density. As a result, thedenser the slurry, the lower will the power draw be. This

has correspondence with what is observed in real plants.

2.2 SAG Mill 

SAG mills have a discharge grate, which works as aclassification device.

The product mass flow is also calculated consideringperfect mix of the slurry in the mill, but in this case thedischarge grate adds the following term to equation (3),

M P i   = (1  −  C gi)H i

V  sq P ,   (7)

where C gi  is the term of the discharge grate’s classificationfunction corresponding to size   i.

In this case,   q P   depends on the friction imposed by thegrate and the pressure difference on the discharge. As aconsequence,  V  s   is calculated from the total mass holdupand the components’ densities.

The water mass holdup depends on its own mass balanceequation,

dH w

dt  = M F w   − M P w ,   (8)

where M F w  is the water mass flow fed into the mill.

The product’s water mass flow is calculated assumingperfect mix, yielding:

M P w   =   H wV  s

q P ,   (9)

2.3 Hydrocyclones 

The Hydrocyclone model is based on Plitt’s model. Theclassification curve depends on the feed pressure on thecyclone’s inlet, and the percent of solids of the slurry beingfed.

The inlet pressure will influence on how steep the classifi-cation curve will result. The higher the pressure, the closerto the ideal classifier. The pressure calculation depends onthe feed volumetric flow, the geometry of the cyclone and

the percent of solids of the feed.The percent of solids in the feed influences on the finesand water by-pass, that is, the percentage of water andfine particles that appear on the cyclone’s underflow.Moreover, the percent of solids determines the cut sizeof the classification curve.

For more details on the Hydrociclone model, see Gutierrezand Sepulveda (1986).

2.4 Other Models 

Other models used on this training include:

•  Sump: A simple tank model (basic mass balance).•   Pump: Basic time lag, with speed as input and

volumetric flow as output.

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•   Conveyor Belt: implemented using a time transport.

Other models developed by the group include: flotationcells, thickeners, and crushers.

3. THE OPERATOR TRAINING SYSTEM

3.1 Models Library 

Based on the above described models, a modular  Matlab& Simulink R library was developed. Each module can beparameterised to represent a specific site and connecteddepending on the desired plant configuration.

The implementation was based on  Matlab R S-Functions.These functions can represent any dynamic model by theuse of the following structure:

x = F(x, u)

y = G(x, u)

where   u   is the input,   y   the output, and   x   the model’sstates.

In the case of Ball and SAG Mills, the states are exactlythe mass holdups in each size interval plus the water massholdup.

Fig. 1.  View of  Simulink R developed Library.

For supporting the simulation, some extra modules weredeveloped, including: feeders, mineral and water sources,

PID controllers with PV tracking, manual and auto mode,among others. On Fig. 1, a view of the developed libraryfor grinding and classification is shown.

3.2 System Architecture 

The OTS system architecture is presented on Fig.2. It iscomposed of one simulation server, in which 6 simulationinstances are running in parallel. Inside the simulationserver, an OPC server is configured, acting as a simulationdata hub. Student stations are based on virtual machines,in which a .NET HMI of the process is installed, togetherwith the APC platform, based on Honeywell’s ProfitControllerR.

The simulation server has an 8-core Intel R XeonR CPU,running at 2.66Ghz with 8GB of RAM. Operator stations

HMI Station 01

+

 APC

HMI Station 02

+

 APC

HMI Station 06

+

 APC

   L  o  c  a   l   A  r  e  a   N  e   t  w  o  r   k

Simulation Server 

OPC

Server 

.

.

.

Instructor Station

Fig. 2.   OTS System Architecture

run on virtual machines installed on dual-core CPU workstations with 2GB of RAM.

With the above configuration, the simulation server canhandle 6 instances of the simulated process, running inreal-time, using a backward euler integrator scheme.

3.3 Evaluation Strategy 

Each case is evaluated with a score, which is calculated

using an excursion-based method, in which the studentstarts with the maximum score and every time a KPIhas an excursion from its pre-defined limits the scoreis penalised proportionally to the excursion time. Theexcursion time of every KPI is recorded, and the final scoreis calculated as follows:

Score =

i (ωi   · texi

)

ttotal   ·

i ωi

· 100,   (10)

where,

•   Score   ∈   [0, 100], is the final score for a trainingexercise;

•   texi, is time where the   ithKPI   was outside the high

or low limit;•   ttotal, is the total simulation time; and•   ωi   ∈  R

+

0  , is a weighting factor, that may be greaterthan 1.

Fig. 3.  Excursion-based Evaluation

The idea behind the weighting factor is to give the pos-sibility to prioritise one KPI above another. In Fig.3,a graphical representation of the evaluation method ispresented.

4. CASE STUDY

The case study of this paper is the grinding and classifi-cation circuit – Line 3 of Companıa Minera Dona Ines deCollahuasi (CMDIC).

Operations personnel started a project in which theywant to educate their engineers and operators on the best

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operational practices, as well as encouraging the utilisationof APC applications installed on the SAG and secondarygrinding-classification units.

SAG

1011Pebbles

Crusher 

BallMill

1012

BallMill

1013

Feeders

To

Flotation

To

Flotation

PI

PI PI

PI

LI

JI JI

DI

DI

DI

DI

To Stock

Pile

 AI

 AI

 AI

 AI

 

FIC

Dilution

Water 

WIC

SECONDARY GRINDING APC

Fig. 4.   Case Study Layout

Line 3 of grinding and classification circuit is composedof a 40x22 feet gearless SAG Mill (one of the biggestSAG mills in the world). Its product enters a screen, fromwhere the oversized particles are transported to a pebblescrusher plant and the undersize to a common sump. Four

pumps take the slurry from the sump to four hydrocyclonebatteries–with 10 hydrocyclones per battery–, overflowgoes to rougher flotation and undersize to two 26x38 feetBall Mills. The process layout is presented on Fig.4.

There are two APC applications on that unit. One is theSAG mill, presented on Toro and Yutronic (2010), andthe other is the secondary grinding and classification ap-plication, reviewed on Yutronic et al. (2011). The controlvariables associated to APC applications are the following:SAG Mill Application

•  Controlled Variables: Mill weight (as a measure of theinternal load), noise (as a measure of impacts insidethe mill), power consumption, torque, and mass flowof produced pebbles (screen oversize),

•  Manipulated Variables: fresh ore feed mass flow, Millspeed, and dilution water in terms a solid percentageratio (controlled by a PI regulator),

•  Measured Disturbances: percentage of fine ore in thefresh feed, and returned pebbles.

Secondary Grinding and Classification APC Application

•   Controlled Variables: hydrocyclone batteries pres-sure, level of centralised sump, +65# percentage (onhydrocyclone batteries overflow), solid percentage of hydrocyclone batteries feed, and power consumptionof ball mills,

•  Manipulated Variables: speed of each pump attachedto the centralised sump, mass flow of process water(recovered) added to the centralised sump,

•   Measured Disturbances: fresh ore mass flow (fed toSAG Mill), and fresh water added to the centralisedsump.

An HMI, based on the DCS schematic of CMDIC, wascreated in order to give the student a working environmentas similar to the real one as possible. Fig.5, presents

the HMI created for CMDIC OTS sessions. The mainregulatory control loops have been reproduced in thesimulation platform, giving the opportunity to transmitbasic control theory concepts to the students, such aswind-up situations, and how to carry out regulatory-control evaluations, through the APC operation interface.

Fig. 5.  OTS System HMI

4.1 OTS Scenarios 

For the training sessions, the first scenarios were centredin SAG Mill operations, followed by secondary grindingplus classification operations. The final scenario was aboutcoordinated operation. The defined scenarios were thefollowing:

(1) SAG Mill overload due to an increase on the orecoarseness;

(2) Low Stock-Pile level and its effects on the plant;(3) Constrained operation, with low water network pres-

sure;(4) Secondary grinding. Constrained operation with sud-den pump malfunction events and low water avail-ability;

(5) Operational optimisation of the integrated circuit.

4.2 Evaluation and APC Operational Reports 

For CMDIC training sessions, two types of evaluationswere carried out. The first one is the automatic evaluation,explained in Section 3.3. Also, a theoretical evaluationhas been carried out at the end of the practical sessions,with the purpose of checking if the relevant ideas weretransmitted correctly to the students.

Another feature that helped to reinforce key concepts wasthe adaptation of the APC Operational Reports, used to

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evaluate operational shifts in a daily basis. Those reportswere analysed after the application of each scenario, andextrapolated to a complete shift in order to transmit to thestudents the impact of their decisions in the operationalKPIs.

5. RESULTS

The evaluation of the impact of an OTS training is notstraight forward. Therefore, the results presented in thissection are an indirect measure of this impact.

One of the consequences of OTS sessions is an increasein the utilisation of the APC applications, mainly be-cause training gives the student a better understandingof how advanced control strategies work. In Fig.6, thedaily effective utilisation of the secondary grinding APCapplication, for a six month period starting one monthbefore the first training session took place, is presentedin grey bars. A 28-day moving average of the effectiveutilisation is presented on dashed black lines. OTS sessions

are presented as black bars, with a 140% value, labeled“Training”, and the maintenance stops, in a brick-like redcolour, with a 120% value, are labelled as “Maintenance”.Note that after the first three sessions the utilisation of the APC application increased substantially. In January,a previously scheduled maintenance in the dynamic modelsof the APC applications was carried out, which explainsthe utilisation decrease.

Jul−06 Aug−01 Sep−01 Oct−01 Nov−01 Dec−01 Jan−010

20

40

60

80

100

Maintenance

Training

Fig. 6.   Effective APC Utilisation, Maintenances and OTS Sessions

6. CONCLUSIONS AND FUTURE WORK

A confirmation of the positive impact that the OTSsessions had on the operation is the APC applicationsutilisation increase. The confidence given to the studentsto face difficult decisions in every-day opperation allowedfor safer driving of the plant towards the optimum.

OTS sessions gave operators the opportunity to becomefamiliar with the best practices, defined by operations andmetallurgical supervisors. Those practices have helped in-creasing APC applications utilisation, minimise the eventsof constrained controllers by prompt detection of equip-ment malfunction and water constraints, get an integratedview of the process, avoid production losses and maintainproduct quality.

Currently, the simulation team is migrating the  Matlab & Simulink R models to UniSim R, a Honeywell’s commercial

simulation platform. Additionally, crushing, flotation andthickening models are being developed, in order to coverthe complete sulphur-concentration process.

ACKNOWLEDGEMENTS

The authors would like to acknowledge the support andcontributions of the people involved in the development of the CMDIC project, specially to Claudio Zamora, HectorSlater, Rene Fuenzalida, Eduado–Lalo–Cubillos, CarmenGloria Cortes, Jose Olivares and Sandy Ramırez.

REFERENCES

Austin, L.G. and Weller, K.R. (1982). Simulation andscale-up of wet ball milling. In   XIV Int Min Proces Congress .

Coker, A. and Ludwig, E. (2007).  Ludwig’s applied process design for chemical and petrochemical plants . ElsevierGulf Professional Pub.

Epstein, B. (1947). The material description of certainbreakage mechanisms leading to the logaritmic-normaldistribution. Journal of the Franklin Institute , 244, 471–477.

Gutierrez, L. and Sepulveda, J. (1986).   Dimension-amiento y optimizaci´ on de plantas concentradoras me-diante tecnicas de modelaci´ on matem´ atica . CIMM.

Herbst, J.A. and Fuerstenau, D.W. (1968). The zero orderproduction of fines in comminution and its implicationin simulation.  Trans SME/AIME , 241, 531–549.

Herbst, J.A. and Fuerstenau, D.W. (1973). Mathemati-cal simulation of dry ball milling using specific powerinformation.   Trans SME/AIME , 254, 348–354.

Kellsall, D.F., Reid, K.J., and Stewart, P.S.B. (1969). The

study of grinding processes by dynamic modelling.   Elec Eng Trans Eng , EE5(1), 155–169.

Malhotra, D., Taylor, P., Spiller, E., and LeVier, M.(2009).   Recent Advances in Mineral Processing Plant Design . Society for Mining Metallurgy.

Roine, T., Kaartinen, J., and Lamberg, P. (2011). Trainingsimulator for flotation process operators. In   Preprints of the 18th IFAC World Congress .

Sbarbaro, D. and Villar, R. (2010).   Advanced Control and Supervision of Mineral Processing Plants . Advances inIndustrial Control. Springer.

Toro, R. and Yutronic, I. (2010). Design and implemen-tation of advanced automatic control strategy based on

dynamic models for high capacity sag mill. In  Proceed-ings of The Canadian Mineral Processors 42nd Annual Conference .

Whiten, W.J. (1974). A matrix theory of comminutionmachines.  Chem Eng Sci , 29, 588–599.

Yutronic, I., Espinoza, P., and Olivares, J. (2011). Sag& ball mill control by model predictive controllers on3 lines at collahuasi. In   Proceedings of The SAG 2011Congress .