Modelling a Waste Vitrification Process BNFL

1. Modelling a Waste Vitrification Process BNFL Tom Musicka Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle-upon-Tyne, England

2. Outline • Introduction • Framework • Module Communication • Data Analysis • Modelling • On-Line Application • Application • BNFL • High Level Waste Process • Application of Framework

3. Data Collection New Data Data Assimilation Data Analysis Process Knowledge Data Screening Clustering Data Splitting Analysis Modelling Result Structural Framework

4. Communication Framework • Dynamic System • Continuous and Batch Modules • Communication between Modules • Causality Links • Allow Decisions • Takes Process Knowledge into Account • Updating Strategy • Defines Link Access • Application Dependent

5. Additional Considerations • Data Splitting • Reduce Size of Training Data Set • Flat Distribution Sampling • Global/Local Modelling • Aggregation • Model Stability • Two Phase Modelling • Track Drift • Model Lifetime • Future Step-Ahead Predictive Performance • Time Dynamics • Integration of Process Knowledge

6. Model Selection • Knowledge Incorporation • Data Analysis • Expert Advice • Model Aggregation • Model Comparison • Linear/Non-Linear • Complexity • Interpretation • Black/Gray/White Box • Accuracy

7. Black Box Modelling • If No First Principal Model is Available • Data Driven • Discard Process Knowledge • Non-Linear • Flexible • Universal Approximation • Non-Interpretable • ‘Meaningless’ Parameters • Fitting Data • Require Constraints • Generalisation Issues • Designate Structure

8. Objectives • Waste Vitrification Plant (WVP) • Semi-Batch Process • Highly Active Liquid Transformed into Solid (Calcine) • Vitrification - Batch Process • Storage • Stringent Control Limits - Univariate • Require Improved Efficiency • Limited Understanding of Variable Relationships • Require Prediction Model for Quality Variables • Calciner Expansion

10. Challenges – Calciner Expansion • Calciner Expansion Model Validation • Model Enhancement • Account for Datum Shift with Wear • Parallel Modelling Set-up • Compare Results to Online Measurements • Peak Analysis for Pour Start-up • Analyse Pour Fingerprints • Operating Strategy Optimisation • Investigate Current System • Fault Detection • <12mm Expansion • Reinvestigate Previous Containers with <12mm Expansion

11. Data Characteristics • Multiple Input, Single Output Models • Enormous Amount of Data • Every 5 Seconds for Approximately 200 Variables • Discrete and Continuous Variables • Missing Data, Process Noise, Outliers • Multiple Operating Regions • Local Modelling Techniques • Non-Linear Relationships

12. Data Splitting Techniques • Data is to be Divided into Training and Testing Sets • Training • Data to Build Model • Testing • Data Used to Quote Model Results • Unseen by Model in Training Phase • Possible use of Validation Set • Data used to Define Early Stopping Epoch

13. Data Selection Methods • Require Representation of Full Scope of Process Within Training Data • Random Selection • Sample from Data Distribution • Self Organising Map • Unsupervised Neural Network • Topological Map • Kennard-Stone Selection • Sample from Flat Distribution • Distance Metric for Selection • Ensures Training Set Covers Full Scope • Time Series Selection

14. Kennard-Stone Algorithm • Select Mean (or Furthest from Mean) Observation point • Initialise ‘Object’ Matrix - Index of Observations • Initialise ‘Included’ Set - Observations Already Chosen • Find Observation Furthest Away from Starting Point • Add to ‘Included’ Set • Add Index to ‘Object’ • Loop • Find Observation Furthest Away from Group • Add to ‘Included’ • Add to ‘Object’ • Repeat

15. Kennard-Stone Sampling- Example Position 1-25 Ordered in Sampling Space Sample Number – Ordered in Data Space

16. Sampling Plot

17. Duplex Method • Splitting Calibration Data Set • Training • Validation • Ensures Independent Sets • Avoids Overfitting due to Sampling Method • 2-Stage Method • Similar to Kennard-Stone • Provides 50:50 Split

18. Data Validation System • Univariate Checks • Time Series Visualisation • Multivariate Statistics • Principal Component Analysis • Outlier Detection • Process Visualisation • Multi-Modal Data • Local PCA Models • Process Grades • Fuzzy Clustering • MixPCA

19. Data Analysis - Principal Component Analysis

20. Local Principal Component Analysis

21. Modelling Calciner Expansion

22. Modelling Calciner Expansion

23. Modelling Calciner Expansion

24. Advanced Modelling Studies • Projection to Latent Structures • Linear and Non-Linear • Predictive Extension of PCA • Neural Networks • Non-linear • ‘Black-Box’ • Neuro-Fuzzy Inference Systems • Adaptive Neuro-Fuzzy Inference System - ANFIS • Fuzzy Rulebase - Model Switching / Merging • Local Linear Models • Compromise between Accuracy, Interpretability and Simplicity

25. Model Aggregation • Range of Techniques • Stacking • Hybrid Models • Bayesian Framework • Avoids Relying on Single Model • Distribution of Parameters • Model Selection • Intelligent Decisions • Mean Stacking • Average of Model Outputs • Each Observation

26. Input Data S Input Fuzzification Normalised Rulebase Local Models Network Output ANFIS Output ANFIS Structure

27. A1 w1 w1*z1 P x A2 Swi*zi S B1 / z P w2*z2 y B2 S Swi w2 ANFIS • Fuzzy reasoning B1 A1 z1 = p1*x+q1*y+r1 w1 w1*z1+w2*z2 z = w1+w2 A2 B2 z2 = p2*x+q2*y+r2 w2 y x • ANFIS (Adaptive Neuro-Fuzzy Inference System)

28. Modelling Calciner Expansion

29. Modelling Calciner Expansion

30. Model Portability • Multiple Sites • Similar Process Characteristics • Simultaneous Modelling • Multi-Group Model • Pooled Covariance Matrix • Common Eigenvector Subspace • Identical Variable Selection

31. Model Portability

32. Challenges - Dust Scrubber Recycle Vessel (DSRV) • Model DSRV • Predict Pressure Drop and Density of Gas • Model Validation • Calculate Optimal Operation Mode for the DSRV • Evaluate Current Operating Routines • Fault Detection • Blockages • Early Warning System

33. Additional Process Modelling

34. Additional Process Modelling

35. At-Line Application • Imminent Application of IP21 Data Handling System • Data Capture • Data Storage • Data Visualisation • Easily Linked to Other Applications • Data Validation System • Modelling Module • Controller User Interface • Control System • Expert System

36. Multiple Model System Model 1 Crucible Run 1 Model 2 New Run Crucible Run 2 Model Output Model n Crucible Run n New Operating Regions New Model Distance Metric Weightings

37. Challenges • Model Validation • Model Enhancement • Fault Detection • Account for Datum Shift with Wear • Peak Analysis • Operating Strategy Optimisation • Model DSRV • Predict Pressure Drop and Density of Gas • Model Validation • Ascertain Optimal Operation Mode for the DSRV • At-Line Implementation – IP21 System

38. Acknowledgements • CPACT - School of Chemical Engineering and Advanced Materials • University of Newcastle • BNFL • Sellafield • EPSRC • Funding