1. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Fuzzy Systems in Use for Human Reliability Analysis Myrto Konstandinidou Zoe Nivolianitou Nikolaos Markatos Christos Kyranoudis Loss Prevention Prague, June 2004

2. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Outline Introduction The Fuzzy Logic as a modeling tool Methods for Human Reliability Analysis The CREAM methodology Development of the Fuzzy Classification System Results Conclusions

3. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Introduction HRA is a critical element for PRA Most important concerns: - the subjectivity of the methods - the uncertainty of data - the complexity of the human factor per se Fuzzy logic theory has had many relevant applications in the last years

4. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Fuzzy Logic as a modeling tool (1) Fuzzy logic (FL) is a very useful tool for modeling - complex systems - qualitative, inexact or uncertain information FL resembles the way humans make inference and take decisions FL accommodates ambiguities of real world human language and logic

5. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Fuzzy Logic as a modeling tool (2) Applications - Automatic control - Data classification - Decision analysis - Computer Vision - Expert systems The most used fuzzy inference method: Mamdani’s method (1975)

6. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Fuzzy Logic as a modeling tool (3) Definitions FL allows an object to be a member of more that one sets and to partially belong to them. - Fuzzy set - Degree of membership - Partial membership

7. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Fuzzy Logic as a modeling tool (4) The 3 steps of a FL system Fuzzification: the process of decomposing input variables to fuzzy sets Fuzzy Inference: a method to interpret the values of the input vectors Defuzzification: the process of weighting and averaging the outputs Crisp Output Crisp Input Fuzzification Inference Defuzzification

8. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Methods of Human Reliability Analysis Fundamental Limitations – Insufficient data – Methodological limitations – Uncertainty Most important methods developed for HRA: – THERP – CREAM – ATHEANA

9. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering CREAM Methodology (1) The choice of CREAM was made because: 1) It is well structured and precise 2) It fits better in the general structure of FL 3) It presents a consistent error classification system 4) This system integrates individual, technological and organizational factors

10. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering CREAM Methodology (2) Control Modes 1. Scrambled 2. Opportunistic 3. Tactical 4. Strategic Definition of Common Performance Conditions (CPCs) to be used in FL model

11. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Development of a Fuzzy Classifier (1) Experience - Accident analysis - Risk assessment - Human reliability Data - Diagrams of CREAM - MARS Database - Incidents and accidents from the Greek Petrochemical Industry

12. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Development of a Fuzzy Classifier (2) The Development of the Fuzzy Classification System for Human Reliability Analysis STEP 1 Selection of input parameters STEP 2 Development of the Fuzzy sets STEP 3 Development of the Fuzzy Rules

13. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Development of a Fuzzy Classifier (3) STEP 1: Selection of the input parameters Adequacy of organization Number of simultaneous goals Crew collaboration quality Working conditions Available timeAdequacy of training Adequacy of maintenance & support Availability of procedures & plans Time of day (Circadian rhythm)

14. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Development of a Fuzzy Classifier (4) STEP 2: Development of the Fuzzy sets Each input is given a number based on its quality 0 (worst case) - 100 (best case) “Time of day” from 0:00 (midnight) to 24:00 Output scale 0.5*10 -5 - 1.0*10 0

15. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Development of a Fuzzy Classifier (5) CPCsFuzzy sets INPUTAdequacy of organization4 Working conditions3 Availability of procedures3 Adequacy of maintenance4 No of simultaneous goals3 Available time3 Time of day3 Adequacy of training3 Crew collaboration quality4 OUTPUTProbability of human erroneous action4

16. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Development of a Fuzzy Classifier (6) Output fuzzy sets: Probability of a human erroneous action Control modeAction failure probability Strategic0.5*10 -5 <p<1.0*10 -2 Tactical1.0*10 -3 <p<1.0*10 -1 Opportunistic1.0*10 -2 <p<0.5*10 0 Scrambled1.0*10 -1 <p<1.0*10 0

17. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Development of a Fuzzy Classifier (7) Input variable

18. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Development of a Fuzzy Classifier (8) Action Failure Probability 0 1 -5.30E+00-4.30E+00-3.30E+00-2.30E+00-1.30E+00-3.00E-01 Probability interval Strategic Tactical Opportunistic Scrambled Output

19. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Development of a Fuzzy Classifier (9) STEP 3: Development of the fuzzy rules Based on CREAM basic diagram Simple linguistic terms Logical AND operation

20. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering CREAM basic diagram Σ improved reliability 7. 6 5 4 3 2 1 123456789 Σ reduced reliability StrategicTacticalOpportunisticScrambled

21. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Development of a Fuzzy Classifier (10) Fuzzy model operations Probability that operator performs erroneous action Input values Fuzzification Inference Defuzzification

22. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Scenarios Five independent scenarios characterizing 5 different industrial contexts: Scenario 2 represents a best case scenario Scenario 4 represents a worst case scenario Scenarios 4 and 5 have slight differences in the values of input parameters

23. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Results of test runs 1.91*10 -1 2.02*10 -1 6.33*10 -2 9.81*10 -4 1.0*10 -2 Fuzzy Model results 1.0*10 -1 <p<1.0*10 0 1.0*10 -2 <p<0.5*10 0 0.5*10 -5 <p<1.0*10 -2 1.0*10 -3 <p<1.0*10 -1 Probability interval Scrambled 5 4 (Worst case) Opportunistic 3 Strategic 2 (Best case) Tactical 1 Control Mode Scenario

24. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Comments on the results All FL model results in accordance with CREAM Best case scenario very low action failure probability Worst case scenario very high action failure probability Small differences in input have impact to output The results can be used directly in PSA methods (event trees, fault trees, etc.)

25. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Conclusions (1) FL system to estimate the probability of human erroneous action has been developed: Based on CREAM methodology 9 input variables 1 output parameter

26. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Conclusions (2) Test runs for 5 different scenarios Very satisfactory results Main difference between FL model and CREAM: probabilities estimation are exact numbers The results can and will be used in other PSA methods

27. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Further goals 1) Model calibration with data from the Greek Petrochemical Industry 2) Addition of other CPCs or PSFs 3) Expansion to other fields of the chemical industry 4) Application in other fields of technology (e.g aviation technology, maritime transports, etc…)

28. NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Acknowledgments The Financial support of the EU Commission through project “PRISM” GTC1-2000-28030 to this research is kindly acknowledged