"Design and Implementation of Advanced Automatic Control Strategy Based on Dynamic Models for High

Capacity SAG Mill"

Iván Yutronic (Collahuasi)Rodrigo Toro (Honeywell Chile)

Table of Contents

• Who are we?

• The Challenge

• The Solution

• The Results

Who are we?• Collahuasi is operated by a joint

venture Xstrata (44%), Anglo American (44%), and Mitsui & Co. Ltd (12%).

• Collahuasi is located in the Andean plateau of northern Chile's Tarapacá Region.

• 210 km southeast of the city of Iquique average altitude of 4300 m.a.s.l.

• Reserves – 5115 Mt at 0.81% Cu y 0.023% Mo.

• Employees Approximately 3,600 people.

• 400,000 t.p.y. of copper in concentrates, 60,000 t.p.y. of copper cathode .

CollahuasiCollahuasi

CS - 1011

CS - 1012

PP- 1023 PP- 1024

PP- 1021 PP- 1022

BM 3

SAG 1

BM 1013

SAG 1011

BM 4SAG 2

Line #1

Line #2

Line #3

TO FLOTATION

DI 01

BM 1012

DI 1051

DI 02

Fe - 003

Fe - 004

Fe - 005

Fe - 006

Fe - 007

Fe - 008

Fe - 1031

Fe - 1032

Fe - 1033

Fe - 1034

Fe - 1035

Fe - 1036

Fe - 1037 Fe - 1038

CV - 05

CV - 06

SU - 01

SU - 02

CS - 001

CS - 002

CS - 003

CS - 004

SU - 1021

CS -1013

CS - 1014

CV - 1036

CV - 1038

CV - 1037

CV - 1035

PP- 03 PP- 04

PP- 05 PP- 06

CV - 07

CV - 09

CV - 08

CV - 10

Collahuasi Grinding Circuit

• Three SAG Mill lines and produces copper and molybdenum concentrate.

• The total production of the concentrator plant is 140000 [tpd].

• SAG Mill 1011 production is 60% of total production.

THE CHALLENGE

The Challenge

• High Capacity SAG Mill– Size: 40x24 [ft]

– Power: 21500 [kW]

– Max throughput: 5300 [ton/h]

Manual Operation Automatic Operation

The Team

GSOGSOOperationsOperations

HoneywellHoneywell

Search of advanced control solutions at the market

The choice of available alternatives.

The formulation and implementation of advanced control strategies.

*GSO: Operational Services Management

• Honeywell Chile– APC(Control/Process Engineers)

• Collahuasi:– Operations(Process Engineers)

– GSO*(DCS, Automation Engineers)

Targets

• Implement an advanced control solution able to:

– Govern the SAG mill.– Stabilize the main variables.

– Handle process constraints.

• Optimize the operation – Keeping the mill weight within the

operational range (940-1020 [ton]).

– Maximizing the fresh feed rate.

– Reducing the impact of process disturbances (variations in feed particle size and recycle of pebbles).

THE SOLUTION

Advanced Process Control

• Increase the profit by:

– Carry out the process to an optimal state

– An increase of plant operational efficiency

– Give a measurement of plant performance

– Coordination between the different process units

Variable

TimePoor control

Constraint Limit

Good RegulatoryControl

Advanced Control

$$

MPC Control Strategy

• Like a chess master– A set of (optimal) movements is

calculated (based in a prediction) in order to reach the objective.

– Optimal movements are computed at each control interval in order to handle changes in the “game conditions”.

• MPC: Model based Predictive Control– A well-established industrial control technology which dates back over 30

years.– 2000+ documented industrial applications*.

• ~100 applications in mining process with Honeywell Technology since 1996.

– Refining and Petrochemical applications are typically dominant but MPC is being rapidly adopted in other markets.

* ref: Control System Design (G. C. Goodwin et al. 2001)

Honeywell’s MPC: Profit Controller

• Profit Controller (RMPCT*):– RCA: Range Control Algorithm.

– Economical optimization (e.g. minimize power consumption).

– Robust control technology.

Known values Optimal response

Unforced prediction

Past Present Future

*RMPCT: Roboust Multivariable Predictive Control Technology

The Solution: ProfitSAG

• ProfitSAG is an MPC solution for SAG Mills– Objective function designed to accomplish the goals (maximize fresh feed rate)

– Fault tolerant policies (anti-windup integration with regulatory control level)

– Fully integrated with measured disturbances

SAG 1011

• Fresh Feed• Mill Speed• Solids

• Mill’s Weight• Mill’s Power• Mill’s Noise• Mill’s Torque• Produced Pebbles

• Returned pebble• Particle size

Process Process ValueValue PredictionsPredictions

MVs

DVs

CVs

ProfitSAG Dynamical ModelsFinal Trials

CV1 -WEIGHT

CV2 -NOISE

CV3 -POWER

CV4 -PEBBLES

CV5 -TORQUE

MV1 -ORE FEED [TPH]

MV2 -MILL SPEED [RPM]

MV3 -SOLIDS [%]

DV1 -RETURNED PEBBLE [TPH]

DV2 -FEED PARTICLE SIZE (<1”) [%]

G(s) = .01951

3s + 1e

-2s0 5 10 15 20

G(s) = -16.71

4s^2 + 4.18s + 1e

-1.67s0 5.25 10.5 15.7 21

G(s) = .07051

5.85s^2 + 5.33s + 1e

-0s0 4.5 9 13.5 18

G(s) = -4.121

38.4s + 1e

-6.08s0 39.9 79.8 120 160

G(s) = -.0195-.231s + 1

14s^2 + 5.96s + 1e

-0s0 5 10 15 20

G(s) = 6.011.85s + 1

.459s^2 + 3.89s + 1e

-0s0 3.17 6.33 9.5 12.7

G(s) = -.07171

5.76s^2 + 5.98s + 1e

-0s0 5.12 10.2 15.4 20.5

G(s) = 2.121

2.56s + 1e

-3.25s0 3.37 6.75 10.1 13.5

G(s) = 6631

.49s + 1e

-0s0 1.37 2.75 4.12 5.5

G(s) = 90.11.23s + 1

.368s^2 + 1.21s + 1e

-0s0 2 4 6 8

G(s) = -36.81

1.57s + 1e

-.75s0 3 6 9 12

G(s) = 4.211

.00949s^2 + .423s + 1e

-.0833s0 .625 1.25 1.87 2.5

ls MV1 -ORE FEED [TPH]

MV2 -MILL SPEED [RPM]

G(s) = .01951

3s + 1e

-2s0 5 10 15 20

G(s) = -16.71

4s^2 + 4.18s + 1e

-1.67s0 5.25 10.5 15.7 21

G(s) = -.0195-.231s + 1

14s^2 + 5.96s + 1e

-0s0 5 10 15 20

G(s) = 6.011.85s + 1

.459s^2 + 3.89s + 1e

-0s0 3.17 6.33 9.5 12.7

RESULTS

Evaluation

Scenarios:

1. Unconstrained Process • Sending produced pebbles (~8-10%) to

crushing plant.

2. Constrained Process• Recycling pebbles to SAG mill.

Performance Index:

1. Feed rate [tph]2. Specific energy consumption [kW/tph]

Scenario 1: without pebbles recycling

3 3.5 4 4.5 5 5.5 6 6.5 7 7.50

2

4

6

8

10

Specif ic Energy Consumption [kW/TPH]

Per

cent

age

of O

ccur

renc

e

Histogram of Specif ic Energy Consumption

Prof itSAG ONMedia: 4.9639Std: 0.44711Prof itSAG OFFMedia: 5.0755Std: 0.46949

2000 2500 3000 3500 4000 4500 50000

2

4

6

8

10

12

14

Fresh Feed [TPH]

Per

cent

age

of O

ccur

renc

e

Histogram of Fresh Feed

Prof itSAG ONMedia: 3649.9041std: 347.8902Prof itSAG OFFMedia: 3567.0156std: 361.0295

• The average has been reduced by 2.2% (0.1 [kW/tph]) and its standard deviation has been reduced by 4.8% (0.02 [kW/tph]).

• The fresh feed rate has been increased by 2.3% (82 [tph]) and its standard deviation has been reduced by 3.6% (14 [tph]) .

Scenario 2: recycling pebbles

2000 2500 3000 3500 4000 45000

4

8

12

16

20

Fresh Feed [TPH]

Per

cent

age

of O

ccur

renc

e

Histogram of Fresh Feed

Prof itSAG ONMedia: 3074.3594Std: 290.5276Prof itSAG OFFMedia: 2792.3525Std: 295.7731

4 4.5 5 5.5 6 6.5 7 7.5 80

2

4

6

8

9

Specif ic Energy Consumption [KW/TPH]

Per

cent

age

of O

ccur

renc

e

Histogram of Specif ic Energy Consumption

Prof itSAG ONMedia: 5.5155Std: 0.4567Prof itSAG OFFMedia: 5.7726Std: 0.60116

• The Specific Energy Consumption average has been reduced by 4.5% (0.24 [kW/tph]) and its standard deviation has been reduced by 24% (0.15 [kW/tph]) .

• The Fresh feed rate has been increased by 10.1% (282 [tph]) and its standard deviation has been reduced by 1.7% (12 [tph]) .

Conclusions

• Successfully integration between Collahuasi Operations, GSO and Honeywell APC Chile.

• Implementation of a Fully Automatic Solution in record time (1 month).

• Excellent initial utilization 90%.

• High confidence of operators and supervision in the controller actions.

• ProfitSAG shows good disturbance rejection and handling of constraints (e.g. recycle Pebbles).

• Fresh feed rate, has been increased in a range of 2.3 to 10 % (82 to 282 [tph]) and reduced variations by 3.6 % (14 [tph]), depending on the operational conditions.

• The Specific Energy Consumption, has been decreased in a range of 2.2 to 4.5% (0.1 to 0.24 [kW/tph]) and its standard deviation has been reduced by 24%, depending of the operational conditions.

Thank you