APSC 150 Engineering Case Studies Case Study 3

1
APSC 150Engineering Case StudiesCase Study 3

• Part 3
• Lecture 3.6 - Process Control in Mining
• John A. Meech
• Professor and Director of CERM3
• Centre for Environmental Research in
  Minerals, Metals, and Materials

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To Be Ore, or Not to Be?

• An ore is a mixture of minerals, one or more of
  which has value, that can be mined
• At some time
• At some place
• For a profit
• What is not ore today, may become ore in the
  future
• What is ore in one place, may not be in another

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Mineral Processing Stages

• Liberation (comminution or breaking of rock)
• Blasting
• Crushing
• Grinding
• Separation (valuable minerals from waste)
• Gravity
• Magnetic
• Electrostatic
• Flotation
• Extraction of values from mineral concentrate

4
Operating Plant Targets

• Maximize Product Quantity (Production)
• Tonnage rate of ore (say 100,000 tpd)
• Recovery of Valuable Component (say 92)
• Maximize Product Quality (customer needs)
• Concentrate grade (say 28 Cu or 54 Zn)
• Impurity component levels (Bi, Sb, Pb in ppm)
• H2O (both minimum and maximum)
• Particle size constraints (top size and
  ultra-fines)

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Grade vs. Recovery

• Often, there is a quality/quantity trade-off
• One goes up, the other goes down

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Process Instrumentation Diagrams

• Process diagrams depict a network of stages or
  events through which materials flow
• Process flowsheets represent unit operations
  through which solids, liquids, or gasses flow and
  are transformed
• Control system diagrams (or programs) represent
  stages in a system through which signals,
  information, or data flow

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What is a Process?
A Process takes inputs and combines them in a way
to produce one or more outputs In process
control, only a single input is involved in each
block
Process
Output
Input
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What is a Process?
Process
Batch
Continuous
Discrete Event
Discrete State
Shut Down
Start Up
Non- stop
after John Sowa, 2001. Processes and Causality,
ltwww.jfsowa.com/ontology/causal.htmgt
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Batch or Discrete Process
Person Waiting Person gets on bus Person
on bus
Bus Arriving Bus stops (an Event) Bus
waiting Bus starts (an Event) Bus leaving
(an Event)

• Execution of a Bus Stop Petri Net model
  (cumulative)
• - works well with discrete agents/products
  represented as tokens

after John Sowa, 2001. http//www.jfsowa.com/ontol
ogy/causal.htm
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Batch Processes in Mining
Drilling
Loading Explosives
Blasting
Digging
Loading Ore/Waste
Hauling Ore/Waste
Dumping Ore/Waste
Returning Empty
Maintenance
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What is a Control System?

• A control system tries to keep an important
  process output variable as close to a target
  level (or set point) for as much of the time as
  possible
• The system responds rapidly and stably to
  compensate for changes in other variable that
  affect the output or to desired changes in the
  target level of the output

set point
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Elements of a Control System
Load Block
System Load Variable
Process
Final Control Element
Controller
Error
System Set Point

Control Signal
Control Variable
System Output
Measuring Device
Measured Variable
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Elements of a PID Control System
Regulator Control
One or the Other
Load Block
System Load Variable
Servo Control
?
Final Control Element
Process
Error
System Set Point

Control Signal
Control Variable
System Output
Measuring Device
Measured Variable
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Response to a Set Point Step Change
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Response to a Set Point Step Change
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Response to a Set Point Step Change
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Response to a Set Point Step Change
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Response to a Set Point Step Change
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Response to a Set Point Step Change
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Response to a Load Step Change
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Response to a Load Step Change
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Response to a Load Step Change
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Response to a Load Step Change
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Response to a Load Step Change
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Response to a Load Step Change
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Unit Operation grinding

• Ball-mill
• rotating drum with steel balls cascading onto the
  rocks to break them into finer particles

Grate-Discharge
Typical Installation showing Covered Trunnion
and associated Electric Motor
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Unit Operation size separation

• Hydrocyclone separation by size

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Unit Operation slurry pump
Variable Frequency Drive Slurry Pump
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Unit Operation conveyor belt
Conveyor belt feeding a stacker/reclaimer
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Building a Flowsheet - 1
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Building a Flowsheet - 2
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Building a Flowsheet - 3
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Building a Flowsheet - 4
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Building a Flowsheet - 5
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Building a Flowsheet - 6
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Building a Flowsheet - 7
Hydro cyclone
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Building a Flowsheet - 8
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Adding Actuators andFinal Control Elements - 1
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Adding Actuators andFinal Control Elements - 2
VS Variable Speed
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Adding Actuators andFinal Control Elements - 3
VS Variable Speed
CS Constant Speed (to be ignored for this
exercise)
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Adding Actuators andFinal Control Elements - 4
VS Variable Speed
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Adding Actuators andFinal Control Elements - 5
VS Variable Speed
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Adding Instrumentation - 1
VS Variable Speed
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Adding Instrumentation - 2
VS Variable Speed
DP Direct Pressure
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Adding Instrumentation - 3
VS Variable Speed
DP Direct Pressure
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Adding Instrumentation - 4
To Flotation Separation
VS Variable Speed
DP Direct Pressure
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Adding Control - 1
To Flotation Separation
Pulp Density Set Point

Pulp Density Meter
Pulp Density Controller
H2O valve
VS Variable Speed
DP Direct Pressure
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Adding Control - 2
To Flotation Separation
Pulp Density Set Point

Pulp Density Meter
Pulp Density Controller
H2O valve
Motor Controller
-
Sump Level Set Point

VS Variable Speed
DP Direct Pressure
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Adding Control - 3
To Flotation Separation
Pulp Density Set Point

Pulp Density Meter
Pulp Density Controller
H2O valve
-
Motor Controller
Tonnage Set Point

Motor Controller
-
Sump Level Set Point

VS Variable Speed
DP Direct Pressure
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Adding Control - 3
To Flotation Separation
Pulp Density Set Point

Ratio Set Point
Pulp Density Meter
Ratio Control
Pulp Density Controller
H2O valve
-
Motor Controller
Tonnage Set Point

Motor Controller
-
Sump Level Set Point

VS Variable Speed
DP Direct Pressure
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Supervisory Control
Supervisory Computer Control
Set Point (tonnage) Set Point (sump level) Set
Point (pulp density) Set Point (water
ratio) Set Point (particle size)
Tonnage rate (tph)
Sump Level ()
CF Pulp Density (solids)
Ball Mill Power Draw (kW)
COF Particle Size (- 150 µm)
Control Goal Either 1. Maximize Tonnage Rate
or 2. Particle Size Control Constraints
Coarsest grind Minimum tonnage rate
Pulp density (min max) Pulp density (min
max) Sump level (min max) Sump level
(min max) In some types of grinding circuits,
ball mill power draw may be an important
constraint and may require consideration in
control of tonnage rate, but in this case, power
draw is dominated by the charge of steel balls
in the mill.
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System Responses

• Regulatory Loads
• Ore Feed Hardness changes
• Ore Feed Particle Size Distribution changes
• Water flowrate upsets
• Ball charge wear rate changes (small effect)
• Servo Requirements
• Flotation Circuit constraint changes
• Ore Availability changes
• Maintenance (scheduled/unplanned)

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Example Strategy maximize tonnage

• Maintain particle size (grind) by changing pulp
  density of cyclone feed (CF)
• If grind is too fine, then use tonnage rate
  changes to control grind and set CF pulp density
  to maximum
• If grind is too coarse, then use CF pulp
  density changes to control grind and set tonnage
  rate to minimum

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Example Strategy control Grind

• Adjust particle size set point to suit ore needs
• Control grind using CF pulp density changes
• Maintain constant tonnage until grind reaches
  maximum, then reduce tonnage rate
• If grind becomes too fine, then increase tonnage
  rate to suit ore conditions

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Control of tonnage and water addition
GR Grind PD Pulp Density
Normal Ore
Hardest Ore
Max GR
Add T
Add W
Add W
Grind of the Ore
coarser
Reduce T
Softest Ore
Add T
Add W
Min GR
Min PD Max PD
solids in CF
T tonnage W Water
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Programmable Logic Control
Hardness Block
Hardness Coarseness
Coarseness Block
Tonnage Control
V.S. Drive
Processes
Error
Grind Set Point

Grind Output
Logic Switch
CF Density Control
H2O Valve
Particle Size Monitor
Measured Variable
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Benefits of Optimizing Tonnage Control

• Recovery drops at high tonnage rates because
• Ore Grind is too coarse unliberated values
  are lost to tailings
• Residence time in Separation Circuit is too short

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Advantage of Grind Control
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Steps in Designing for Control

• Identify and categorize all variables
• Design variables that will not change
• Variables that can be measured and changed
• Variables that can be measured, but not changed
• Variables that cannot be measured, but inferred
• Variables that cannot be measured or inferred
• Which are Inputs, Outputs, and Loads
• Choose a goal for the system
• Select targets or set points for the outputs
• Decide what is to be maximized or minimized

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Steps in Designing for Control

• Perform system identification testwork
• Study the open-loop system response between one
  input variable and one output
• Characterize process delays (Td) and lags (Tp)
• Characterize process gains (Kp)

Output
Kp
1.0 0.0
0.632Kp
Input
0
time
Td Tp
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Steps in Designing for Control

• Choose type of controller
• Proportional (P)
• Proportional-Integral (PI)
• Proportional-Integral-Derivative (PID)
• Do not use Derivative with noisy signals
• Select controller constants (tuning) to provide
  slightly underdamped response
• Kc
• Ti
• TD
• Study effects of interacting control systems

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Steps in Designing for Control

• Examine advanced control techniques
• Cascade control (fast inner loop)
• Feed-forward control (fast and predictive)
• Adaptive Control (lags, delays, and gains are not
  constant)
• Model-based Control (updating comparing with
  process)
• Advanced Signal Filters (Kalman, Smith predictor,
  etc.)
• Intelligent Control (fuzzy, neuro-fuzzy, expert
  systems, etc.)
• Ensure system is stable under all conditions
• Set-up Alarms to detect non-standard states

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• Questions ?

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Extra Slides
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Outotecs PSI 500 Analyser

• Particle Size Analysis based on laser
  diffractometry
• Outputs both PSA and solids data
• Accuracy ? 2
• Can handle particle size distributions as low as
  500 mesh (20 microns)
• Accurate samples are diluted by 10 to 1001 so
  laser can penetrate the slurry for measurement

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NLA launder primary sampler with mechanical
cutter cleaner

• PSI 500 System with primary sampler
• Easy to use and maintain

Secondary sampling system
Probe control setup with local user interface
Diluter Unit
Optical sensor head
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Principles of Laser Diffractometry

• Small particles diffract laser beam light more
  than coarse particles.
• Diffraction pattern measured by sensor array
• Resulting signals used to calculate particle size
  distribution.
• A beam power detector measures non-diffracted
  laser light for dilution control (solids).
• LorenzMie theory, is an
• analytical solution of
• Maxwell's equations for
• scattering of EM radiation
• by spherical particles

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Example of Zn Flotation Fuzzy Control

• Sets up rule maps as below

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Control of OK Flotation Cells