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APSC 150 Engineering Case Studies Case Study 3 Part 3 Lecture 3.6 - Process Control in Mining John A. Meech Professor and Director of CERM3 Centre for.

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Presentation on theme: "APSC 150 Engineering Case Studies Case Study 3 Part 3 Lecture 3.6 - Process Control in Mining John A. Meech Professor and Director of CERM3 Centre for."— Presentation transcript:

1 APSC 150 Engineering Case Studies Case Study 3 Part 3 Lecture Process Control in Mining John A. Meech Professor and Director of CERM3 Centre for Environmental Research in Minerals, Metals, and Materials

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

3 Mineral Processing Stages Liberation (comminution or breaking of rock)Liberation (comminution or breaking of rock) BlastingBlasting CrushingCrushing GrindingGrinding Separation (valuable minerals from waste)Separation (valuable minerals from waste) GravityGravity MagneticMagnetic ElectrostaticElectrostatic FlotationFlotation Extraction of values from mineral concentrateExtraction of values from mineral concentrate

4 Operating Plant Targets Maximize Product Quantity (Production)Maximize Product Quantity (Production) Tonnage rate of ore (say 100,000 tpd)Tonnage rate of ore (say 100,000 tpd) %Recovery of Valuable Component (say 92%)%Recovery of Valuable Component (say 92%) Maximize Product Quality (customer needs)Maximize Product Quality (customer needs) Concentrate grade (say 28 %Cu or 54 %Zn)Concentrate grade (say 28 %Cu or 54 %Zn) Impurity component levels (Bi, Sb, Pb in ppm)Impurity component levels (Bi, Sb, Pb in ppm) %H 2 O (both minimum and maximum)%H 2 O (both minimum and maximum) Particle size constraints (top size and ultra-fines)Particle size constraints (top size and ultra-fines)

5 Grade vs. Recovery Often, there is a quality/quantity trade-offOften, there is a quality/quantity trade-off One goes up, the other goes downOne goes up, the other goes down

6 Process & Instrumentation Diagrams Process diagrams depict a network of stages or events through which materials flowProcess 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 transformedProcess 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 flowControl system diagrams (or programs) represent stages in a system through which signals, information, or data flow

7 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 Input Output

8 What is a Process? Process ≈≈ BatchContinuous Shut Down Start Up Non- stop Discrete Event Discrete State ≈≈≈≈ ≈≈ ≈≈≈≈ after: John Sowa, Processes and Causality,

9 Batch or Discrete Process Execution of a Bus Stop Petri Net model (cumulative) - works well with discrete agents/products represented as tokens Person Waiting Person gets on bus Person on bus Bus Arriving Bus stops (an Event) Bus waiting Bus starts (an Event) Bus leaving after John Sowa, (an Event)

10 Batch Processes in Mining Drilling LoadingExplosives Blasting Digging LoadingOre/WasteHaulingOre/WasteDumpingOre/Waste ReturningEmptyMaintenance

11 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 possibleA 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 outputThe 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

12 Elements of a Control System LoadBlock ProcessFinalControlElementController MeasuringDevice System Load Variable System Set Point System Output + – Control Variable Measured Variable Error Control Signal

13 Elements of a PID Control System LoadBlock FinalControlElement MeasuringDevice System Load Variable System Set Point System Output + – Control Variable Measured Variable Error Control Signal Integral Proportional Derivative Process t=0 1.0 ? One or the Other Servo Control Regulator Control t=0 1.0

14 Response to a Set Point Step Change

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20 Response to a Load Step Change

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26 Unit Operation – grinding Ball-millBall-mill rotating drum with steel balls cascading onto the rocks to break them into finer particlesrotating 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

27 Unit Operation – size separation Hydrocyclone – separation by sizeHydrocyclone – separation by size

28 Unit Operation – slurry pump Variable Frequency Drive Slurry Pump

29 Unit Operation – conveyor belt Conveyor belt feeding a stacker/reclaimer

30 Crushed Ore Bin Building a Flowsheet - 1 From crushers

31 Crushed Ore Bin Feed Conveyor Building a Flowsheet - 2 From crushers

32 Ball Mill Crushed Ore Bin Feed Conveyor Building a Flowsheet - 3 From crushers

33 Ball Mill Crushed Ore Bin from Mill Water Supply Feed Conveyor Building a Flowsheet - 4 From crushers

34 Ball Mill pulp SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Feed Conveyor Building a Flowsheet - 5 from Mill Water Supply From crushers

35 Ball Mill pulp SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Feed Conveyor Building a Flowsheet - 6 from Mill Water Supply From crushers

36 Ball Mill pulp SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Hydro cyclone Feed Conveyor Building a Flowsheet - 7 from Mill Water Supply From crushers

37 Ball Mill pulp To Flotation Separation SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Feed Conveyor Building a Flowsheet - 8 from Mill Water Supply Hydro cyclone From crushers

38 Electric Motor Ball Mill pulp To Flotation Separation SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Adding Actuators and Final Control Elements - 1 Feed Conveyor from Mill Water Supply Hydro cyclone From crushers

39 Electric Motor Ball Mill pulp To Flotation Separation SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Adding Actuators and Final Control Elements - 2 VS = Variable Speed Feed Conveyor VS Drive Motor from Mill Water Supply Hydro cyclone From crushers

40 Electric Motor Ball Mill pulp To Flotation Separation SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Adding Actuators and Final Control Elements - 3 VS = Variable Speed Feed Conveyor VS Drive Motor CS Drive Motor CS Drive Motor CS = Constant Speed (to be ignored for this exercise) from Mill Water Supply Hydro cyclone From crushers

41 Electric Motor Ball Mill pulp To Flotation Separation SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply VS = Variable Speed Feed Conveyor VS Drive Motor Adding Actuators and Final Control Elements - 4 from Mill Water Supply Hydro cyclone H 2 O valve From crushers

42 Electric Motor Ball Mill pulp To Flotation Separation SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply VS = Variable Speed Feed Conveyor VS Drive Motor H 2 O valve Adding Actuators and Final Control Elements - 5 from Mill Water Supply Hydro cyclone H 2 O valve From crushers

43 Electric Motor Ball Mill pulp To Flotation Separation SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Adding Instrumentation - 1 VS = Variable Speed Feed Conveyor VS Drive Motor H 2 O valve Weigh Scale from Mill Water Supply Hydro cyclone H 2 O valve From crushers

44 Electric Motor Ball Mill pulp To Flotation Separation SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Adding Instrumentation - 2 VS = Variable Speed Feed Conveyor VS Drive Motor H 2 O valve Weigh Scale Sump Level DP Cell DP = Direct Pressure from Mill Water Supply Hydro cyclone H 2 O valve From crushers

45 Electric Motor Ball Mill pulp To Flotation Separation SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Adding Instrumentation - 3 VS = Variable Speed Feed Conveyor VS Drive Motor H 2 O valve Sump Level DP Cell DP = Direct Pressure Pulp Density Meter Weigh Scale from Mill Water Supply Hydro cyclone H 2 O valve From crushers

46 Electric Motor Ball Mill pulp To Flotation Separation SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Adding Instrumentation - 4 VS = Variable Speed Feed Conveyor VS Drive Motor H 2 O valve Sump Level DP Cell DP = Direct Pressure Pulp Density Meter Weigh Scale from Mill Water Supply H 2 O valve Hydro cyclone Particle Size Monitor From crushers

47 Electric Motor Ball Mill pulp To Flotation Separation SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Adding Control - 1 VS = Variable Speed Feed Conveyor VS Drive Motor H 2 O valve Sump Level DP Cell DP = Direct Pressure Pulp Density Meter Weigh Scale Pulp Density Set Point + – PulpDensityController from Mill Water Supply H 2 O valve Hydro cyclone Particle Size Monitor From crushers

48 Electric Motor Ball Mill pulp SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Adding Control - 2 VS = Variable Speed Feed Conveyor H 2 O valve Sump Level DP Cell DP = Direct Pressure Pulp Density Meter Weigh Scale Pulp Density Set Point + – PulpDensityController Sump Level Set Point + MotorController - To Flotation Separation from Mill Water Supply H 2 O valve Hydro cyclone Particle Size Monitor VS Drive Motor From crushers

49 Electric Motor Ball Mill pulp SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Adding Control - 3 VS = Variable Speed Feed Conveyor VS Drive Motor H 2 O valve Sump Level DP Cell DP = Direct Pressure Pulp Density Meter Weigh Scale Pulp Density Set Point + – PulpDensityController Sump Level Set Point + MotorController - MotorController To Flotation Separation from Mill Water Supply H 2 O valve Hydro cyclone Particle Size Monitor Tonnage Set Point + - From crushers

50 Electric Motor Ball Mill pulp SUMP Pump Crushed Ore Bin H2OH2O from Mill Water Supply Adding Control - 3 VS = Variable Speed Feed Conveyor VS Drive Motor H 2 O valve Sump Level DP Cell DP = Direct Pressure Pulp Density Meter Weigh Scale Pulp Density Set Point + – PulpDensityController Sump Level Set Point + MotorController - MotorController To Flotation Separation from Mill Water Supply H 2 O valve Hydro cyclone Particle Size Monitor Tonnage Set Point + - Crushed Ore Bin Crushed Ore Bin Crushed Ore Bin RatioControl Ratio Set Point From crushers

51 Supervisory Control Supervisory Computer Control Tonnage rate (tph) Sump Level (%) CF Pulp Density (%solids) Ball Mill Power Draw (kW) COF Particle Size (%- 150 µm) Set Point (tonnage) Set Point (sump level) Set Point (pulp density) Set Point (water ratio) Set Point (particle size) Control Goal: Control Goal: Either:1. Maximize Tonnage Rate or2. Particle Size Control Constraints: Constraints: Coarsest “grind” Minimum tonnage rate Pulp density (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.

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

53 Example Strategy – maximize tonnage Maintain particle size (“grind”) by changing pulp density of cyclone feed (CF)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 maximumIf “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 minimumIf “grind” is too coarse, then use CF pulp density changes to control grind and set tonnage rate to minimum

54 Example Strategy – control “Grind” Adjust particle size set point to suit ore needsAdjust particle size set point to suit ore needs Control “grind” using CF pulp density changesControl “grind” using CF pulp density changes Maintain constant tonnage until “grind” reaches maximum, then reduce tonnage rateMaintain constant tonnage until “grind” reaches maximum, then reduce tonnage rate If grind becomes too fine, then increase tonnage rate to suit ore conditionsIf grind becomes too fine, then increase tonnage rate to suit ore conditions

55 %solids in CF Min PD Max PD Grind of the Ore Min GR Max GR coarser Reduce T Normal Ore Hardest Ore Softest Ore Add T Add W Control of tonnage and water addition Add W Add T T = tonnageW = Water GR = “Grind”PD = Pulp Density

56 Programmable Logic Control Particle Size Particle SizeMonitor Hardness Coarseness “Grind” Set Point “Grind” Output + – Measured Variable Error V.S. Drive H 2 O Valve CF Density Control TonnageControl Processes Logic Switch CoarsenessBlock HardnessBlock

57 Benefits of Optimizing Tonnage Control %Recovery drops at high tonnage rates because:%Recovery drops at high tonnage rates because: Ore “Grind” is too coarse – unliberated values are lost to tailingsOre “Grind” is too coarse – unliberated values are lost to tailings Residence time in Separation Circuit is too shortResidence time in Separation Circuit is too short

58 Advantage of “Grind” Control

59 Steps in Designing for Control Identify and categorize all variablesIdentify and categorize all variables Design variables that will not changeDesign variables that will not change Variables that can be measured and changedVariables that can be measured and changed Variables that can be measured, but not changedVariables that can be measured, but not changed Variables that cannot be measured, but inferredVariables that cannot be measured, but inferred Variables that cannot be measured or inferredVariables that cannot be measured or inferred Which are Inputs, Outputs, and LoadsWhich are Inputs, Outputs, and Loads Choose a goal for the systemChoose a goal for the system Select targets or set points for the outputsSelect targets or set points for the outputs Decide what is to be maximized or minimizedDecide what is to be maximized or minimized

60 Steps in Designing for Control Perform system identification testworkPerform system identification testwork Study the open-loop system response between one input variable and one outputStudy the open-loop system response between one input variable and one output Characterize process delays (T d ) and lags (T p )Characterize process delays (T d ) and lags (T p ) Characterize process gains (K p )Characterize process gains (K p ) time T d T p Input Output 0.632K p Kp

61 Steps in Designing for Control Choose type of controllerChoose type of controller Proportional (P)Proportional (P) Proportional-Integral (PI)Proportional-Integral (PI) Proportional-Integral-Derivative (PID)Proportional-Integral-Derivative (PID) Do not use Derivative with noisy signalsDo not use Derivative with noisy signals Select controller constants (tuning) to provide slightly underdamped responseSelect controller constants (tuning) to provide slightly underdamped response K cK c T iT i T DT D Study effects of interacting control systemsStudy effects of interacting control systems

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

63 Questions ?

64 Extra Slides

65 Outotec’s PSI 500 Analyser Particle Size Analysis based on laser diffractometryParticle Size Analysis based on laser diffractometry Outputs both PSA and %solids dataOutputs both PSA and %solids data Accuracy =  2%Accuracy =  2% Can handle particle size distributions as low as 500 mesh (~20 microns)Can handle particle size distributions as low as 500 mesh (~20 microns) Accurate samples are diluted by 10 to 100:1 so laser can penetrate the slurry for measurementAccurate samples are diluted by 10 to 100:1 so laser can penetrate the slurry for measurement

66 PSI 500 System with primary samplerPSI 500 System with primary sampler Easy to use and maintainEasy to use and maintain NLA launder primary sampler with mechanical cutter cleaner Probe control setup with local user interface Secondary sampling system Diluter Unit Optical sensor head

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

68 Example of Zn Flotation Fuzzy Control Sets up rule maps as belowSets up rule maps as below

69 Control of OK Flotation Cells


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