Post on 21-Jul-2016
description
LUCIEIntroduction to the Expert System Theory
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L
U
C
I
E
?
afarge
niversal
ontrol&
nference
ngine
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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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