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© Marc LE GOC - LSIS - DLS03 Dec Tucson

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1 © Marc LE GOC - LSIS - DLS03 Dec 3-4 - Tucson
Extensive Large Scale Real Time Knowledge Based Systems Design: the Sachem Example Marc LE GOC LSIS, Laboratory of Sciences of Information and System Marseille, France © Marc LE GOC - LSIS - DLS03 Dec Tucson

2 Sachem and the Blast Furnace Sachem Design Current Works
Content Sachem and the Blast Furnace Sachem Design Current Works © Marc LE GOC - LSIS - DLS03 Dec Tucson

3 Arcelor is the world ‘s first steel producer
45 millions tons per year, > employees, $33 billions of turnover. Estimates 43,5 28,6 20,2 19,3 19,1 17,7 16,5 15 13,3 27,1 Arcelor (Luxembourg) Posco (South Korea) Nippon Steel (Japan) NKK LMN (Ispat London) Shanghai Baosteel (China) Corus (United Kingdom) Thyssen Krupp Steel (Germany) Riva (Italy) Kawasaki © Marc LE GOC - LSIS - DLS03 Dec Tucson

4 © Marc LE GOC - LSIS - DLS03 Dec 3-4 - Tucson
1 300 MW for6,000 tons of hot metal / day ~1200 sensors Extreme conditions (high T°, dust, acidity, etc) No mathematical model of the dynamic Long period for learning (5-10 years) © Marc LE GOC - LSIS - DLS03 Dec Tucson

5 © Marc LE GOC - LSIS - DLS03 Dec 3-4 - Tucson
1 300 MW for6,000 tons of hot metal / day ~1200 sensors Extreme conditions (high T°, dust, acidity, etc) No mathematical model of the dynamic Long period for learning (5-10 years) Sachem goal: Optimize the BF operation in order to save up 1€/ton of hot metal (i.e. 1% of the cost price) © Marc LE GOC - LSIS - DLS03 Dec Tucson

6 Sachem optimises the BF by reducing the deviation of its parameters
Monitoring 1,100 data/mn ~70 Msg / day Alarms/Action Data Acquisition & Model Processing State Perception & Diagnosis State Correction Operator Communication Justifications Instrumentation computer Data Base Management Process DB Event DB ~5500 Variables ~150 Phenomenon / day ~10 Actions / day © Marc LE GOC - LSIS - DLS03 Dec Tucson

7 Sachem represents ~415 000 lines of « C » equivalent code
21 Knowledge Bases 1060 Object Classes 1100 First Order Rules 140 Chronicles Monitoring 7% Data Acquisition & Model Processing 20% State Perception & Diagnosis 33% State Correction 10% Operator Communication 20% Data Base Management 10% Development: ~30 engineers from 1991 to 1998 150 man.year (~30 m€) Knowledge acquisition & modeling: 12 Experts, ~6 knowledge engineers 14 man.year over 3 years © Marc LE GOC - LSIS - DLS03 Dec Tucson

8 With 6 Sachem in operation, the Arcelor Group earns ~18,5 m€ per year
Frequency of Incidents 5 10 15 20 25 30 Reference Level Expected level Without Sachem With Sachem Low Hot Metal T° Thermal Losses Burden Descent TOTAL Target Result Sachem save up ~1,7€ per ton of hot metal © Marc LE GOC - LSIS - DLS03 Dec Tucson

9 Sachem and the Blast Furnace Sachem Design Current Works
Extensive Large Scale Real Time Knowledge Based Systems Design: the Sachem Example Sachem and the Blast Furnace Sachem Design Current Works © Marc LE GOC - LSIS - DLS03 Dec Tucson

10 Discrete trajectories Interpretation
Sachem is designed a as recursive abstraction process of discrete events Process Phenomena Signals Logical Sensors Process State Quantization Recognition Process Behavior Signal Events Diagnose Discrete trajectories Interpretation Process Behavior Analysis Signal Phenomena Process Problems & Causes State Perception Correct Detection Process Phenomena Recommend Recommended actions & warnings Process State Logical Actuators © Marc LE GOC - LSIS - DLS03 Dec Tucson

11 CommonKads Model Transformation Process, within a spiral development
Knowledge Analysis & Modeling Requirement’s definition Software Specification Technical Design Implementation SACHEM Sachem Requirement Knowledge Model Specification Model Design Model Concept’s Model Data Model 25,000 objects: 3200 concepts, 2000 relations 75 inference structures 33 goals, 27 tasks Inference’s Model Function Model Task’s Models Behavior Model © Marc LE GOC - LSIS - DLS03 Dec Tucson

12 Sachem Template Knowledge Model for Monitoring and Diagnosing
Analysis & Modeling Requirement’s definition Software Specification Technical Design Implementation SACHEM Sachem Requirement Knowledge Model Specification Model Design Model Concept’s Model Data Model Inference’s Model Function Model Conceptual Generic Sachem Task’s Models Behavior Model Validated in collaboration with the LSIS by simulation (using Petri Nets formalism) © Marc LE GOC - LSIS - DLS03 Dec Tucson

13 Sachem Framework for an Adaptative Generic Monitoring Cognitive Agent
Fos#1 Specific Process Instrumentation Structure Functions Parameterization 150 man.year Duk#4 ~1 man.year MT#1 ~5 man.year ~0.5 man.year Fos#2 Pat#6 Generic Blast Furnace Generic Process Knowledge Knowledge Bases Configuration Generic Process#2 Generic Sachem Monitoring and Diagnosing Method Kernel Compilation Implemented Sachem Template Knowledge Model © Marc LE GOC - LSIS - DLS03 Dec Tucson

14 Sachem and the Blast Furnace Sachem Design Current Works
Extensive Large Scale Real Time Knowledge Based Systems Design: the Sachem Example Sachem and the Blast Furnace Sachem Design Current Works © Marc LE GOC - LSIS - DLS03 Dec Tucson

15 One of the advantages of discrete event representation is compactness
octets of the Sachem Process Data Base are required to produce one octet in the Sachem Event’s Data Base One year of blast furnace behavior is ~30,000 events Event’s Data Base = 4.7Mb  Sachem Digital Data base = 235,000 Mb 15 years of blast furnace (450,000 events) is only ~70Mb ! To be compared with 15*235,000Mb = 3,525Gb ! Events Log Compactness Factor = ~50 000 500 < Factor < 1000 Process Data (~1100 variables) 50 < Factor < 100 Sachem Process Data Base(~5500 variables) © Marc LE GOC - LSIS - DLS03 Dec Tucson

16 The « Elp Laboratory » aims at learning from an event flow
SACHEM ELP Lab Expert Knowledge Engineer Events Signatures New Knowledge to improve the perception of the process state A signature show the way a type of event is generated © Marc LE GOC - LSIS - DLS03 Dec Tucson

17 © Marc LE GOC - LSIS - DLS03 Dec 3-4 - Tucson
A signature show the way an event type is produced by a couple (Process, MCA) Event Flow Analysis (Markov Theory) Signatures Event’s Data base Sequences of Events Model Recognition (DEVS Simulator) ELP Lab ELP Models (DEVS formalism) Expert Discrete Event Models ELP Models Editor © Marc LE GOC - LSIS - DLS03 Dec Tucson


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