Multi-Phase Analysis Framework for Handling Batch Process Data
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1 Multi-Phase Analysis for Handling Batch Process Data J. Camacho, J. Picó Department of Systems Engineering and Control. A. Ferrer Department of Applied Statistics, Operations Research and Quality. Technical University of Valencia Cno. de Vera s/n, 46022, Valencia, Spain
2 ntroduction to Batch Processing Model Structures Aim of the work Multi-Phase End-quality Other Applications Conclusions
3 1. ntroduction to Batch ntroduction to Batch Processing Repetitive nature: charge, processing and discharge. Three-way data: a set of variables are measured at different sampling times during the processing of a batch, and this is repeated for a number of batches. The duration of the processing of a batch may be variable in some processes Aligment methods. After the alignment, data matrix X ( J K) contains the values of J variables at K sampling times in batches.
4 Model Structures Convert into two-way data and apply PCA, PLS, Apply three-way methods: PARAFAC, Tucker-3, Trilinear Nature???
5 Model Structures Convert into two-way data and apply PCA, PLS, Apply three-way methods: PARAFAC, Tucker-3,
6 Model Structures Convert into two-way data and apply PCA, PLS, Unfold the three-way matrix. Divide in K local matrices. Use an adaptive approach. Apply three-way methods: PARAFAC, Tucker-3,
7 Model Structures Unfold the three-way matrix. Batch-wise unfolding Thousands of variables!!!
8 Model Structures Unfold the three-way matrix. Batch-wise unfolding Dynamics are built in the model More than a half is noise!!! 2 variables x 116 sampling times Variables close in time are more related
9 Model Structures Unfold the three-way matrix. Variable-wise unfolding Low number of parameters More samples/parameter dynamics are not built in the model Constant Correlation mposed!!!
10 Model Structures Unfold the three-way matrix. Variable-wise unfolding Constant Correlation mposed!!! V-W scores
11 Model Structures Unfold the three-way matrix. Batch dynamic unfolding = VW + LMVs 1 LMV LMVs VW LMVs BW Adjust the amount of dynamic information Dynamics are imposed to be constant
12 Model Structures Divide in K matrices Local Moving Average Evolving LMVs locally Adjustable High number of models
13 Aim of the work Context: a) A large number of possible Model Strutures, each of them with advantages and drawbacks. b) Very different batch processes, (constant or varying dynamics, dynamics of different order, etc ) NO MODELLNG STRUCTURE S THE BEST ALWAYS!!! WHY DON T WE DENTFY THE MODEL STRUCTURE FOR THE CURRENT CASE STUDY???
14 ntroduction to Batch Processing Model Structures Aim of the work Multi-Phase End-quality Other Applications Conclusions
15 J X 2 X 1 J X K ndex Multi-Phase - Three-steps Analysis: a) Multi-phase Algorithm. Camacho J, Picó J, Multi-phase principal component analysis for batch processes modelling. Chemometrics and ntelligent Laboratory Systems. 2006; 81: Camacho J, Picó J. Online Monitoring of Batch Processes using Multi-Phase Principal Component Analysis. Journal of Process Control. 2006;10: Parameters X K X K-1 PCA Multi-Phase Algorithm X J K J PCA PCA PCA J J Greedy Optimization + Pattern Recognition
16 X J X 2 X n1+1 X k1-n1 X k1-1 X k1-n1+1 X k1 K X J X 1 X 2 X n1+1 X k1-n1-1 X k1-n1 X k1-1 X k1-n1 X k1-n1+1 X k1 X k1-n2+2 X k1+1 X k2-n2 X k2-1 X k2-n2+1 X k2 K X k2-n3+1 X k2-n3+2 X K-n3-1 X K-n3 X K-n3 X K-n3+1 X k1-n2+1 X k1-n2+2 X k1+1 X k2-n2-1 X k2-n2 X k2-1 X k2-n2 X k2-n2+1 X k2 X J X k2-n3+1 X k2-n3+2 X k2+1 X K-n3-1 X K-n3 X K-1 X K-n3 X K-n3+1 X K J K J J X J K ndex Multi-Phase - Three-steps Analysis: a) Multi-phase Algorithm. Camacho J, Picó J, Multi-phase principal component analysis for batch processes modelling. Chemometrics and ntelligent Laboratory Systems. 2006; 81: Camacho J, Picó J. Online Monitoring of Batch Processes using Multi-Phase Principal Component Analysis. Journal of Process Control. 2006;10: b) Merging Algorithm: T m, Criterium PCA PCA PCA Merging Algorithm PCA X 1 X 2 X n1+1 X k1-n1-1 X k1-n1 X k1-1 X k1-n1 X k1-n1+1 X k1 PCA PCA X k2-n3+1 X k2-n3+2 X K-n3-1 X K-n3 X K-n3 X K-n3+1 X k1-n2+2 X k2-n2 X k2-n2+1
17 Multi-Phase - Three-steps Analysis: a) Multi-phase Algorithm. Camacho J, Picó J, Multi-phase principal component analysis for batch processes modelling. Chemometrics and ntelligent Laboratory Systems. 2006; 81: Camacho J, Picó J. Online Monitoring of Batch Processes using Multi-Phase Principal Component Analysis. Journal of Process Control. 2006;10: b) Merging Algorithm: Why merge? Greedy Optimization Sub-optimal solution To allow obtaining sub-models with different unfolding methods c) Compromise Performance - Complexity Anova + LSD
18 Multi-Phase - Three-steps Analysis: c) Compromise Performance - Complexity Anova + LSD 5 LVs 5 LVs 15 LMVs BW 3 LVs 1 LMV
19 Multi-Phase - Three-steps Analysis: c) Compromise Performance - Complexity Anova + LSD 1 LVs VW 5 LVs 2 LVs 5 LVs BW 3 LVs 15 LMVs 1 LMV 1 LMV
20 ntroduction to Batch Processing Model Structures Aim of the work Multi-Phase End-quality Other Applications Conclusions
21 End-Quality - On-line prediction
22 End-Quality - performance: Saccha. Cerev. Waste-water treat. Anaerobic stage
23 End-Quality - Process understanding: MPPLS = VW-PLS (Anaerobic Stage) BW-PLS 1 PC = 5 parameters (5 variables) 1 PC = 1250 parameters (5 var x 250 sam. tim.)
24 Q statistic Other Applications Off-line Monitoring: Batch-Wise PCA a) The Charts of MP are more informative Phases Var Abnormality in batches #1, #2, #4 and # Sampling time Abnormality in batch #3.
25 Other Applications Off-line Monitoring: Batch-Wise PCA a) The Charts of MP are more informative. On-line Monitoring: PCA a) MP avoids problems found in some modelling structures: BW models have low detection capabilities in the D- statistic and high Overall Type Risk in the SPE. VW models have high OT Risk in the D-statistic. Local models have high OT Risk in the SPE. b) MP yields monitoring systems of fast response to faults. Estimation of trayectories (Soft-sensors): PLS a) MP yields accurate estimations, outperforming BW, VW, Local, Evolving and Adaptive approaches.
26 ntroduction to Batch Processing Model Structures Aim of the work Multi-Phase End-quality Other Applications Conclusions
27 Conclusions The Multi-phase (MP) framework, with application to off-line and on-line monitoring, final quality prediction and estimation of trajectories of variables in batch processes, has been presented. The MP approach is based on the data-driven identification of the (PCA or PLS) model structure, using pattern recognition and optimization techniques. Flexibility to adjust the structure to the case: Number of sub-models, dynamics, This approach has several general advantages: The identification of the structure of the models and the convenient use of the tools within the MP framework helps to improve the process understanding. The MP approach allows to obtain a compromise solution between complexity and performance. The MP approach yields good performance in several applications.
28 Multi-Phase Analysis for Handling Batch Process Data J. Camacho, J. Picó Department of Systems Engineering and Control. A. Ferrer Department of Applied Statistics, Operations Research and Quality. Technical University of Valencia Cno. de Vera s/n, 46022, Valencia, Spain
29 2. Aim of the work 3. Multi-Phase Algorithm 4. Merging Algorithm 5. Multi-Phase 6. On-line Monitoring 7. End-quality 8. Conclusions On-line Monitoring - Monitoring Charts: D-statistic and SPE Batch under NOC
30 2. Aim of the work 3. Multi-Phase Algorithm 4. Merging Algorithm 5. Multi-Phase 6. On-line Monitoring 7. End-quality 8. Conclusions On-line Monitoring - Monitoring Charts: D-statistic and SPE Abnormal Batch
31 2. Aim of the work 3. Multi-Phase Algorithm 4. Merging Algorithm 5. Multi-Phase 6. On-line Monitoring 7. End-quality 8. Conclusions On-line Monitoring - Case Studies:
32 2. Aim of the work 3. Multi-Phase Algorithm 4. Merging Algorithm 5. Multi-Phase 6. On-line Monitoring 7. End-quality 8. Conclusions On-line Monitoring - Preliminary Study: Saccharomyces Cerevisiae Cultivation. Overall Type Risk computed from the NOC test set, mposed significance level 1% n f number of faults Conclusion: The structure of the model is important in the on-line monitoring.
33 2. Aim of the work 3. Multi-Phase Algorithm 4. Merging Algorithm 5. Multi-Phase 6. On-line Monitoring 7. End-quality 8. Conclusions Solutions: On-line Monitoring a) To readjust the control limits of the monitoring charts using a left-one-out approach. b) To identify the covenient model structure Multi- Phase Conclusion: The structure of the model is important in the on-line monitoring.
34 2. Aim of the work 3. Multi-Phase Algorithm 4. Merging Algorithm 5. Multi-Phase 6. On-line Monitoring 7. End-quality 8. Conclusions On-line Monitoring - Nylon 6 6 Polymerization:
35 2. Aim of the work 3. Multi-Phase Algorithm 4. Merging Algorithm 5. Multi-Phase 6. On-line Monitoring 7. End-quality 8. Conclusions On-line Monitoring - Saccharomyces Cerevisiae Cultivation: - Waste-water Treatment:
Document downloaded from:
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