Model Predictive Control for mining

Model Predictive Control for miningProcess optimization solution

SOURCE: World Bank Group

Minerals and metals play a central role in the global economy and will continue to provide the materials the world needs.THE WORLD IS SEEING INCREDIBLE CHANGES that require resource-intensive

goods of all sorts. Overall population is growing and shifting to urban areas,

the middle class is expanding and infrastructure development is increasing.

With this growth comes a need for resources, meaning metals like iron, copper,

aluminum and nickel will come with a much higher demand.

A new report from World Bank Group states that the production of minerals,

such as lithium, graphite and cobalt, could increase by nearly 500% by 2050, in

order to meet the growing demand for clean energy technologies. The report

estimates that more than 3 billion tons of minerals and metals will be needed to

deploy wind, solar and geothermal power, as well as energy storage, required for

achieving a below 2°C future.

Mining powers the world

2The global mining marketPG 2

Model predictive control solutionsPG 5

Model predictive control in mining applications PG8

http://pubdocs.worldbank.org/en/961711588875536384/Minerals-for-Climate-Action-The-Mineral-Intensity-of-the-Clean-Energy-Transition.pdf

Today’s process characteristics Mineral processing plants are inherently complex, facing internal and external disturbances, many recirculating loads and multiple interactions. And the declining of the ore grades and continuous variability of raw material are pushing the limits of processing plants even more.

Process objectives These process characteristics that miners are dealing with today provide opportunity to improve:

• Reduce cost per ton

• Lower plant costs

• Lower energy intensity

• Improve energy and water consumption

• Extend the life of existing ore bodies

• Continue to meet regulatory requirements

• Overcome lack of experienced and trained instrumentation technicians, operators, metallurgists

How will you achieve these process objectives in such complex operations?

Mineral processing challengesOre grades are declining and becoming more complex

33Solutions to meet steel needsPG 2

The connected steel plantPG 5

Increase operational efficiencyPG 9

Knowledge driven operationsPG 14

Modernize andempower yourworkforcePG 19

Design, build and upgrade with confidencePG 22




Although traditional optimization strategies provide adequate control in terms of plant safety, it rarely achieves optimal control of quality and economical, efficient operation.

Why? Mineral processing plants face inherent complexities that can’t be addressed through traditional technologies:

Multiple constrains

Time delay processes

Multiple variables

Key variables difficult to measure

Other potential technologies, such expert systems, can address some of these challenges but with price tag:

of mineral processing plants still use basic

optimization strategies

MOR

E THAN

75%

HIGH MAINTENANCE

HARD TO COMPREHEND

MODEL OPERATORS (INSTEAD OF PROCESSES)










MPC solution benefits for mining

INCREASED throughput

LOWER reagents consumption

BETTER recovery

OPTIMUM use of water and energy

IMPROVED process stability

REDUCES variability

ACHIEVES “plant obedience”

MANAGES the process within constraints

ACHIEVES UPLIFT - operate closer to specifications and performance limits while maintaining safety margins

Handling constraints, maximizing economics, and maintaining stable processPavilion8® Model Predictive Control (MPC) from Rockwell Automation reduces process variability and enhances stability over and above what is currently possible with more traditional control schemes. This is accomplished through our multi-variable, nonlinear, model predictive control capabilities.

Maximum profitability is realized by operating a process as close to its constraints as possible while maintaining an appropriate margin of safety.

Pavilion8 Model Predictive Control

KEY

PROD

UCT

PROP

ERTY

PER

CEN

T OF

LIM

IT

Process/specification limit

TIME

MPC ONBEFORE

Variability under operator control

Variability reduction Push process toward limits

100

98

96

94

92

90










Controller / optimizer: Pavilion8 Model Predictive Control (MPC) optimizes the process making predictions about future plant outputs responding to changes in the process input variables and disturbances.

The model: In order to operate properly, we need an accurate model of the process. Pavilion8 MPC leverages best in class machine learning technologies to build robust models of the process incorporating and combining all available knowledge of the process, such as historical data, process equations, data from plant tests and operator knowledge.

Virtual online analyzer: The efficiency of the optimization tool is related to the richness and quality of the field data. However, in most of the process some measurements are unprecise, not present, or delayed (for example data from laboratory). In this instance, Pavilion8 MPC uses an innovative virtual online analyzer that’s able to estimate such variables closing the gap of this information gap.

The console: A powerful visualization tool allowing operators to visualize the process and model KPIs for better situational awareness of the system.

Pavilion8 Model Predictive Control architecture overview

LABORATORY

PROCESS

PROCESS KNOWLEDGEPLANT TEST DATAHISTORICAL DATA

MODELING, IDENTIFICATION, ANALYSIS

VIRTUAL ONLINE ANALYZER CONTROLLER/OPTIMIZER

CONSOLE

PLANT CONTROL SYSTEMS










The controller matrix shows dynamic relationships between process outputs and inputs. The system predicts future values of the outputs by movement of all the inputs including manipulated variables and disturbance variables.

Controlled variables (CVs) Process variables to maintain at a target or within a range (can be considered outputs)

Constrain variable (CCVs) The state is forbidden to penetrate or may have physical limitations

Manipulated variables (MVs) A manipulated input is one that can be adjusted by the control system (or process operator)

Disturbance variables (DVs) Disturbance variables - these are also called “load” variables and represent input variables that can cause the controlled variables to deviate

The controller matrix

CV3 MV3

CV1CV2

DV1DV2DV3

MV1MV2

MV4 MV5DV4

CV4CV4

Illustrative example of a flotation

matrix model

MV1 MV2 MV3 MV4 MV5 DV1 DV2 DV3 DV4

Collector reagent

flow ratio

Depressor reagent

flow ratioTrailings

value Air flowFrother reagent

flow ratio

P80 from grinding circuit

Feed flow from grinding

Feed density

from grinding

pH

CV1 Concentrate grade

CV2 Metal recovery

CV3 Froth depth

CV4 Bubble speed of air

hold up

CV5Bubble size distribution or surface

area










PROCESS CHALLENGES

• Balance between the individual crushers and the overall circuit goal

• Maintain circuit efficiency

• Ensure that crushers are choked

• Crusher capacity vs. feed

• Equipment wearing

• Power consumption

RESULTS

• Maximized crusher circuit throughput

• Process stability

• Optimum energy efficiency

• Decrease equipment wear

• Fewer trips over current, high-level silos

Crusher circuits

MOD

EL P

REDI

CTIV

E CO

NTR

OL

Belt 2

Belt 3

2nd cone crusher

From primary crusher

Feed bin 1

Feed bin 2

3rd cone crusher

Belt 5

Belt 4

Belt 1

Screen 1

Screen 2

MV Speed

MV Speed

MV Speed

MV Speed

CV Level

CV Level

CV Level

MV DV CV CCVManipulated Variable

Upper & lower Constraint

Disturbance Variable

Upper Constraint

Controlled Variable

Lower Constraint

Constraint Variable

Maximize Target

T OilCCV

T OilCCV

Weight, Level, Speed, Position, Current

CCV


CCV


CCV

Weight, Level, Speed, Temperature, Position, Current

CCV

MV Speed

MV Speed

MV Speed

CV Level










POTENTIAL GAINS: Up to 10% increased throughput, 10% on reduced specific energy, with a P80 variability reduction of up to 50%

PROCESS CHALLENGES

• Complex behavior

• Process disturbances

• Relationship between variables isnon-linear

• Energy waste due to overgrinding

• Requires operators with extensiveexperience

• Balance between throughput anddownstream constraints such asoptimum particle size

RESULTS

• Maximized throughput rate

• Decrease energy/ton

• Increased stability

• Reduction in particle size variation

• Delivers grinding circuit stabilityto achieve throughput and grindsize targets

Comminution / grinding circuits

MOD

EL P

REDI

CTIV

E CO

NTR

OL Cyclones

Number of hydrocyclone

Ball mill

Flotation feed

Water

Fresh feed

Cyclone feed tank

CrusherSAG MILL

MV Flow

MV Flow

MV Flow

MV Speed

DV F80

CV Density

CV Sump level

CV P80




Upper Constraint

Controlled Variable

Lower Constraint

Constraint Variable

Maximize Target

CCV Power

CCV Pressure

PowerCCV

DensityCCV

FlowCCV

MV

DV










DV

CV Weight

CV Density

DV Flow

PROCESS CHALLENGES

• Complex process that is notfully understood

• Still relies on operator control

• A small disturbance in one processvariable propagates to the finalprocess output

RESULTS

• Increased recovery

• Increased and stabilizedcleaner grade

• Optimized reagent consumption

Flotation

MOD

EL P

REDI

CTIV

E CO

NTR

OL

POTENTIAL GAINS: Increased recovery of up to 3%, reduction of reagents of up to 3%




Upper Constraint

Controlled Variable

Lower Constraint

Constraint Variable

Maximize Target

MV Air

MV Collector

MV Valve position

MV Depressor

MV Modifier

MV Valve position

DV Feed flows

DV P80

DV Ore grades

CVMass/metal recovery

CV Bubble size

CV Bubble speed

CV pH CV Concentrate grade

CV Cell levels










MV Air

DV Ore Mineralogy

Tailings

Concentrate

MV Frother

PROCESS CHALLENGES

• Water conservation

• Optimum reagents consumption

• Little control and local knowledge

• Not often integrated into controlsystem

• Conventional control isinefficient (long residence time,large disturbances and non-linear behavior)

RESULTS

• Increased underflow density

• Decreased flocculantconsumption

• Improved overflow clarity

• Better water utilization

Thickeners

MOD

EL P

REDI

CTIV

E CO

NTR

OL

POTENTIAL GAINS: Increased water recovery of up to 3%, reduction of flocculant of around 2%




Upper Constraint

Controlled Variable

Lower Constraint

Constraint Variable

Maximize Target

MV Underflow

DV Feed density

DV Feed pH

DV Feed flow

CV Mud density

Rake arm torque

CCV

CCV Turbidity

Thickener overflow










CV Underflow pump amp

DV Rake height

CV Bed pressure

MV Flocculant flow

PROCESS CHALLENGES

• Inconsistent production rates

• Loss of production at shift change

• Conveyor system trips

RESULTS

• Maximizing ore flow

• Operate to maximum constraints

• Minimize equipment trips

• Extending equipment life

• Minimize interruptions during shiftchanges

• Energy conservation andmaintaining the maximumthroughput

Material flow management

MOD

EL P

REDI

CTIV

E CO

NTR

OL

POTENTIAL GAINS: 20% increased throughput










PROCESS CHALLENGES

• Product under specified grade

• Change in feed characteristics

• Final product variability

• Temperature control

RESULTS

• Stabilization of the operation

• Improved quality

• Product consistency

• Energy savings

• Reduce emissions

Material refining

MOD

EL P

REDI

CTIV

E CO

NTR

OL










Publication MIN-BR007B-EN-P January 2021

Copyright © 2021 Rockwell Automation, Inc. All Rights Reserved. Printed in USA.

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Model Predictive Control for mining

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