LUCIEIntroduction to the Expert System Theory

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niversal

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Expert System

Basic form of artificial intelligence

Decisions equivalent to those of the human bean

Developed by interviewing an experienced person

Consolidates process operating know how into a

standard product easy portable to any plant

Two key components: …

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1. The Knowledge base

A set of rules, information, facts about a certain

subject

Stored in an organized structure

Populated with both questions and answers

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2. The Inference Engine

Rule-based algorithm that interacts with a

Knowledge Base to draw conclusions about a set of

inputs

Emulates the human capability to arrive at a

conclusion by reasoning

Process Principles

LUCIE Mill

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What do you wish as Mill Operator?

The highest production of very good quality cement/raw mix under stable conditions

Is this all ?

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What do you need?

Sensors

Actuators

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What do we use …

Quality – Blaine, SO3

Mill

Separator

Fresh Feed

Finish Product

Nl1 Nl2

Mkw

Amps ElevSENSOR

ACTUATOR

Feed Rate

Sep Speed

Gypsum %

Rejects

Temp

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Control Limitations

LUCIE changes set-points ONLY!

No actual equipment control (motor starts/stops,

alarm acknowledgement)

Lucie is not hiding mechanical/process problems.

On the contrary!

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Principles

1st Stabilize Mill Throughput

2nd Increase Production Level by Optimizing Throughput

3rd Optimize Quality

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Sensor 1 Sensor 2 Sensor 3

Virtual Sensor (Estimates)

Short term Potential

Long term Potential

Set-points

Normalized values

ST-Actions

LT-Action

Time constant

Lucie Actuators Set-points

Mill Strategy Organization

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Treatment of sensors

WHY? To allow Lucie to continue to operate when a sensor

signal is no longer significant

To enable the strategy to always work with a plausible signal value

To provide the most representative information of the real state of the kiln / mill

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Treatment of sensors

HOW?

By filtering - eliminate the signal noise

By defining inside Lucie of four possible sensor “states” and two “validity” values

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FILTERS - Example

The field-valueof the sensor isnot enough filtered.

The Lucie filtered value

Sensor Field Value Set-point State Validity

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Valid

Normal

Valid

Doubtful

Valid

Invalid

Frozen

Invalid

Abnormal

Signal Treatment

Sensor

The Estimates

LUCIE Mill

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The Estimates (Virtual Sensors)

Evaluate and forecast continuously how a particular control parameter (mill throughput, material level, etc.) will vary

Are the of Lucie

All actions are determined from the estimate results

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Estimates with impact on production The Mill Throughput Estimate The Material Level Estimate The Drying Estimate

Estimates with impact on quality The Quality Estimates

The Mill Estimates

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Goal: Calculate the mill throughput deviation

from the set point Sensors: Elevator Amps,

((Rejects, Feed))

To each sensor a mono-estimate is connected

The mono-estimate converts the value from the sensor

into a common reference unit (t/h of MTP)

The Mill Throughput Estimate

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The Mono-Estimate

Mathematically expressed:

Mono-Estimation = Gain x (PV - Set Point) + Offset

The gain can be calculated:

Reference SensorGain =

Mono‘s Sensor

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The Multi-Estimate

The Mono-Estimate which is Estimating the Smallest Margin is Chosen

The output of the multi-estimation are the State and the Tendency in Normalized Values

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Normalization

Converts a particular value within a predefined range [-4 , +4]

Brings all the signal on the same “playing field”

Enables reasoning with symbolic states

error = Value - Set Point

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NNOORRMMAALLIIZZAATTIIOONN

MultiEstimate

20 t/h

-30 t/h

-24 t/h

-17 t/h

-9 t/h

9 t/h

17 t/h

24 t/h

30 t/h +4

+3

+2

+1

-1

-2

-3

-4

Normalized State

very high

high

slightly high

normal

slightly low

low

very low

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Normalized Tendency

How quickly and in what direction the error is changing

Based upon 2 errors compared ~8 minutes apart

Norm. Tendency = Norm. State (t) - Norm. State (t-)

Value between (-4 to +4)

i.e., fast filling, slow emptying

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The Material Level Estimate

Goal: Calculate the material level of the mill(security)

Sensor: Electrical ears (C1 / C2)Mill power / Amps(P)

Same treatment as done by the mill throughput estimate

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The Drying Estimate

Goal: Qualify the margin of available heat in the mill

Sensor: Gas temperature at mill exitMaterial temperature at mill exit(Gas temperature at mill inlet)

This estimate is reducing the feed if the minimum temperature is not achieved

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From each multi-estimate a potential of feed is

determined

A Short Term Potential

A Long Term Potential

Potentials

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Potential Calculation

Sum of NormalizedMill Tend.and State

from Estimate

ST/LT ActionFuzzyLogicTable

Short/Long TermAction

Potential

in tons of mill feed [- 4; +4 ]

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Major vs. Minor

Major Continuous control Potential used

Minor Security control - SAFEGUARD Potential Used IF

(State, Tendency) Exceeds Threshold

Potential Selection

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The Minimum of the short- and long term potentials is chosen

These potentials are piloting the mill

They are called the short- and long term Pilot

The Min-Action Object

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The Short Term Actions

Used to stabilize the mill

They Are: Proportional to the set point deviation Of big amplitude Temporary Superimposed on the long term actions

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The Long Term Actions

Used to maintain the long term stability

They are: Of low amplitude Cumulative Permanent

Weighted by a factor which takes into

account the past

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Major MTP estimatorMinor ML estimator which has not exceeded the threshold

ProposesProposes

+ 1 ton per hour - 3 tons per hour

Pilot estimator = Mill ThroughputResult = + 1 ton per hour

Who Is The Pilot?

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Major MTP estimatorMinor ML estimator which has exceeded the threshold

ProposesProposes

+ 1 ton per hour - 3 tons per hour

Pilot estimator = Material LevelResult = - 3 tons per hour

Who Is The Pilot?

Optimization Of Mill Throughput

LUCIE Mill

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MillThroughput

Feed

MaxFeed

Opt.Set Point

PositiveIncrement

NegativeIncrement

< 0FeedMTP> 0Feed

MTP

Relationship Feed / Mill throughput

LUCIE Calculates the Feed and MTP Set Point Variation

Same Sign -> MTP Set Point Increases

Different Sign -> MTP Set Point Decreases

The Quality Estimates

LUCIE Mill

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The Quality Estimates

Fineness, SO3 ...

Input: Sensor or Manually

Quality Target is the Set Point in LUCIE

Designed to mimic SPC control

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The Quality Estimates

Calculation:Quality Level = Input Value - Set Point

A normalized value is then calculated from this quality level

Actions triggered by control card

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NNOORRMMAALLIIZZAATTIIOONN

NormalSlightly Low

LowVery Low

NormalSlightly High

HighVery High

-350

-300

-200

-90

90

200

300

350 +4+3

+2

+1

-1

-2

-3

-4

Normalization

3750 – 3500Blaine

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State of theQuality

EstimateX

GainLong-term Increment

for separator speed

LT-FuzzyTable

Calculation Of Action

The Product Table

LUCIE Mill

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The Product Table

Add / Remove Products

Define individual recipe for each product Set Points for Mono Estimators Scale Factors for Actions Quality set points

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Recipe Files

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LUCIE

Is a tool for the plant improvement Duplicates the Operator behaviour based on

fundamental process principles Can yield higher production rates (~3%) and

lower standard deviation for quality parameters

Is dedicated to both Process and Production

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Do you know that Lucie controls

109 cement mills 34 raw mills 5 coal mills 7 vertical mills

in more than 50 plants all over the world ?

The Operator Screen

LUCIE Mill

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