Fuzzy Sag Ppt

8/10/2019 Fuzzy Sag Ppt

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Standards

Certification

Education & Training

Publishing

Conferences & Exhibits

Fuzzy logic control: A

successful example

Copyright 2012 by ISA, www.isa.org

Presented at ISA Automation Week 2012, 24-27 September 2012

Orange County Convention Center, West Concourse, Orlando, Florida USA


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2

Presenter

Michel Ruel, P.E., founder and president of TOP Control Inc., now a member of BBA Inc. (900 p)

registered professional engineer, university lecturer, and author of several publications and

books on instrumentation and control.

for over 37 years, he has been solving unusual process control problems in several fields in

more than 16 countries

graduated from Laval University, Quebec Canada, with a Bachelor of Science, Electrical

Engineering (Process and Automation).

member of the following organizations:

ISA, Fellow (International Society for Automation);

IEEE (Institute of Electrical and Electronic Engineers)

PAPTAC (Pulp and Paper Technical Association of Canada)

TAPPI (Technical Association of the Pulp and Paper Industry)

AIChE(American Institute of Chemical Engineers) PEO (Professional Engineers of Ontario)

OIQ (Ordre des ingnieurs du Qubec)





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Agenda

SAG Mill Process and Process Control Advanced Process Control

Decision Tree

Comparison

Fuzzy Logic Control Controller Design

Commissioning and Optimization

Results

3





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Process

4

Measurements (controlled):

Pulp Density Power

Weight (bearing pressure)

Recirculation Flow

Disturbances

Ore size

Ore Hardness

Crusher Opening

Manipulated

Speed

Feed (tonnage)

Water Flow Copyright 2012 by ISA, www.isa.orgPresented at ISA Automation Week 2012, 24-27 September 2012



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Need for Advanced Process Control

Use APC to : Improve performance,

Stabilize production,

Handle constraints,

Handle interactions, Protect equipment,

Manage grade changes.

Approaches

Advanced Regulatory Control (PID control +++)

Model Predictive Control

Fuzzy Logic Control

Neural Network

5





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SISO and MIMO

SISO, Single Input, Single Output Each loop is alone

One model per loop

MIMO, Multiple Inputs, Multiple Outputs

Models for input/output + Models for interaction +

Models for distrubances

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Multi Loop Process Control, MIMO

7

SP PVCODisturbances

ProcessController





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Modeling

Small SP excitation (Closed Loop)Automated Standardized Tests

Normal Operation

Multi Loop

No need to stabilize the process

Automated

Results Models matrix

Quality of models

Error bound

8





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Agenda


Decision Tree

Comparison



Results

9





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Models are identified or calculated

1-SISO

1 PV, 1 CO

Good process model PID controller

Non linear process model or models PID controller + gain

scheduling (or PID)

2-MIMO

n PVs, mCOs

Good process models, weak interactions PID controllers

Good process models, interactions PID controllers, tuned at

different speeds PID controllers + decouplers

MPC

Good process models, strong interactions PID controllers + decouplers

MPC

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No process models identified

nor calculated

1-SISO 1 PV, 1 CO No process model PID controller, relaxed tuning

parameters

PID controller + logic + enhanced

functions

Best operator can be modeled Fuzzy logic controller

2-MIMO

n PVs, mCOs

No process model PID controllers,relaxed tuning parameters

+ logic + enhanced functions

Best operator can be modeled Fuzzy logic controller

Best operator cannot be modeled

Data available Neural network

No data Re-design! 11Copyright 2012 by ISA, www.isa.org




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PID vs. APC

Feedforward

Decoupling

Adaptive Gains

Characterizers

Is Advanced

Regulatory Control

Sufficient?

PID CanProcess be

Modeled?

NoYes

NoYes

MPC Can BestOperator

Control?

NoYes

Fuzzy Logic Neural NetworkCopyright 2012 by ISA, www.isa.org




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Comparison

13

Approach

Model

Rules

Historical

Control ARC MPC FLC Neural Network

Description PID, Controlstrategies

Process ismodelled

Operator ismodelled

Black boxapproach

Usage Few variables Good models Best operatorBased on

historical data

Development Simple Moderate Complex Black box

Commissioning Simple Moderate Long but easy Black box

Optimization Simple Part of design Cumbersome No need

Process changes Simple Re-modelReview rules and

membershipfunctions

Re-train

Maintenance Simple Needs expert Easy Re-design

Cost Low High Moderate HighCopyright 2012 by ISA, www.isa.org




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Agenda


Decision Tree

Comparison



Results

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Fuzzy vs. PID

The density

is too high. Ill increase

water flow.

DC

Bias

dt

deTdedt

T

1eKOP

t

0

pi

+

++=





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Fuzzy Control

What do high, medium and low actually mean?

These vague, subjective classifications put the fuzzy into fuzzy logic.

IF (power IS high)AND

(Weight IS low)THEN

(Speed medium), (Feed low),

FeedPower

Weight Speed





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Controller Structure (PLC function blocks)

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FuzzificationInputs) Decisions(rules)If T is

AND THEN(423)

Defuzzification Outputs)

Inputs Xnand dXn/dt

Numerical

Membership function

3 to 7 Adjustable weights

Outputs Mix of all fired rules

Membership function

Numerical

3 to 7 Adjustable weights

Rules

Logic (Inputs, Outputs)

Adjustable weightsCopyright 2012 by ISA, www.isa.orgPresented at ISA Automation Week 2012, 24-27 September 2012Orange County Convention Center, West Concourse, Orlando, Florida USA


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Agenda


Decision Tree

Comparison



Results

19

Copyright 2012 by ISA, www.isa.orgPresented at ISA Automation Week 2012, 24-27 September 2012



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Controller Structure

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FuzzificationLoad (5)

dLoad/dt (5)

OreSize (3)

dOreSize/dt (3)

Recirculation (5)

dRecirculation/dt (5)

Power (5)

dPower/dt (5)

Density (5)

dDensity/dt (5)

Decisions(rules)If T is

AND THEN(423)

Defuzzification Tonnage (7)Water flow (5)

Rotation speed(5)

Rule PressurePressure

RatePower Power Rate Tonnage

123 HH H L H/OK/L/LL L

124 HH L/LL OK/L OK/L OK

125 HH HH/H LL L/LL LLL

126 H OK OK HH/H OK

shapesweights




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Control ler Design

Objectives to reduce power consumption per ton of ore,

increase throughput,

protect linings and stabilize quality and operation

Determining Rules Design of experiments (DOE) to determine how the SAG mill

should be operated.

These tests were conducted in different conditions.

All tests were conducted during the summer of 2011

Which resulted in hundreds of rules.

Rules were then chosen to reach the selected goals and to push

the feed rate to its maximum.

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Control ler Design

Membership Functions Shapes based on DOE

Number based on expected ranges and rules

Rules

More than 500 MIMO

Structured

Programming

PLC fuzzy functions

Workarounds for bugs and optimization

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Agenda


Decision Tree

Comparison



Results

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Advisory mode 3 days, 4days, FLC was used during the day

membership functions, shapes and ranges were modified.

rules were also modified while others were added

8thday, FLC was used continuously Every week,

metallurgists validate the rules and make slight adjustments.

Training

operators, metallurgists, maintenance technicians and engine

Maintenance

Plant personnel maintain the system, modify the controller, add

rules and optimize the controller.

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Tools

Statisticals to support metallurgists Historian

Rules used (% time, strength, etc.)

Statistical data on rules and inputs

Key performance indices:

Tons/d, kW/ton, average error, etc.

Performance Monitoring Software

KPI

Utilization Performance

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Agenda


Decision Tree

Comparison



Results

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Results

Utilization > 98%

27

Commissioning andOptimization




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Conclusions

This project was carried out over six months. The team consisted of:

consultant personnel, metallurgists from the plant and operators.

Operators have quickly gained confidence and

performances have been improved: Utilization > 98%

Energy per ton has been reduced by 8%

Tonnage per day has been increased by 14%

A production record was achieved during the first week.

The savings generated by the fuzzy logic controller covered the

projects cost in less than three months.

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Thank you!

Questions?

Michel Ruel, P.E.

Top Control is now Member of BBA Inc.Department Manager, Optimization and Advanced Control(877)867-6473 office

(418)569-8543 cell

[email protected]

http://www.BBA.ca

Copyright 2012 by ISA, www.isa.orgPresented at ISA Automation Week 2012 24 27 September 2012
http://www.bba.ca/http://www.bba.ca/http://www.bba.ca/

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Fuzzy Sag Ppt

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