Lucie Mill

LUCIE Introduction to the Expert System Theory

La f a r g e Un i v e r s a l Co n t r o l &

?

I nference

En g i n e 2

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: … 3

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 4

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

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LUCIE Mill Process Principles

What do you wish as Mill Operator?

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

Is this all ? 7

What do you need?

Sensors

Actuators

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What do we use … SENSOR Amps Elev

ACTUATOR Separator

Sep Speed

Rejects

Quality – Blaine, SO3 Finish Product

Mkw

Fresh Feed

Nl1

Feed Rate

Temp

Nl2 Mill

Gypsum %

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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! 10

Principles 

1st Stabilize Mill Throughput



2nd Increase Production Level by Optimizing Throughput



3rd Optimize Quality

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Mill Strategy Organization Sensor 1 Set-points

Sensor 2

Sensor 3

Virtual Sensor (Estimates) Normalized values

Short term Potential

Long term Potential LT-Action

STActions

Time constant

 Lucie Actuators Set-points 12

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 Sensor

Field

Value Set-point

State

Validity

The field-value of the sensor is not enough filtered.

The Lucie filtered value

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Sensor

Signal Treatment

Normal

Doubtful

Frozen

Abnormal

Valid

Valid

Valid

Invalid

Invalid

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LUCIE Mill The Estimates

The Estimates (Virtual Sensors) 

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



Are the



All actions are determined from the estimate results

of Lucie

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The Mill Estimates 

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



Estimates with impact on quality  The Quality Estimates

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The Mill Throughput Estimate 

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)

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The Mono-Estimate 

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



The gain can be calculated:

 Reference Sensor Gain =

 Mono„s Sensor 21

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 23

Normalized State 30 t/h

+4

very high

24 t/h

+3

20 t/h

high

17 t/h

slightly high Multi Estimate

9 t/h

+2 +1

normal -9 t/h

slightly low

-17 t/h

-2

low -24 t/h

very low -30 t/h

-1

-3 -4

N O R M A L I Z A T I O N

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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 (DP)



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 exit Material temperature at mill exit (Gas temperature at mill inlet)



This estimate is reducing the feed if the minimum temperature is not achieved 27

Potentials



From each multi-estimate a potential of feed is determined 

A Short Term Potential



A Long Term Potential

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Potential Calculation

Sum of Normalized Mill Tend. and State from Estimate [- 4; +4 ]

ST/LT Action Fuzzy Logic Table

Short/Long Term Action Potential

in tons of mill feed

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Potential Selection Major vs. Minor 

Major  Continuous control  Potential used



Minor  Security control - SAFEGUARD  Potential Used IF (State, Tendency) Exceeds Threshold

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The Min-Action Object 

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

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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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Who Is The Pilot? Major MTP estimator

Minor ML estimator which has not exceeded the threshold

Proposes

Proposes

+ 1 ton per hour

- 3 tons per hour

Pilot estimator = Mill Throughput Result = + 1 ton per hour

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Who Is The Pilot? Major MTP estimator

Minor ML estimator which has exceeded the threshold

Proposes

Proposes

+ 1 ton per hour

- 3 tons per hour

Pilot estimator = Material Level Result = - 3 tons per hour

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LUCIE Mill Optimization Of Mill Throughput

Relationship Feed / Mill throughput Feed



LUCIE Calculates the Feed and MTP Set Point Variation



Same Sign -> MTP Set Point Increases



Different Sign -> MTP Set Point Decreases

Max Feed

Positive Increment

Negative Increment

D Feed >0 D MTP

D Feed <0 D MTP

Opt. Set Point

Mill Throughput

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LUCIE Mill The Quality Estimates

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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Normalization 350 300 200 90

3750 – 3500 Blaine

-90 -200 -300 -350

Very High High Slightly High Normal Normal Slightly Low Low Very Low

+4 +3 +2 +1

-1 -2 -3

-4

N O R M A L I Z A T I O N

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Calculation Of Action

Gain State of the Quality Estimate

LT-Fuzzy Table

X

Long-term Increment for separator speed

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LUCIE Mill The Product Table

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 ?

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LUCIE Mill The Operator Screen

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