Process Data Mining

1. Process Data Mining Geoff C Jones

2. Data Mining using modern techniques • The truth is out there - in our plant data! • But how can we find it, and how do we get to understand it? • Is my process running normally? • Is that QC result correct for my operating conditions? • Can I predict when that column needs cleaning?

3. What do we mean by Data Mining? • Knowledge discovery from human activity (‘information from data’) • Consumer surveys, loyalty cards, credit history • Modelling behaviour - identifying relationships & trends • Prediction of buying habits, risk, etc How does Process Data Mining differ? • Knowledge discovery from systems behaviour • Data capture (on-line process & QC data) • Modelling of normal behaviour (exploratory data analysis) • Monitoring for abnormal behaviour • Prediction of quality, yield, fouling, catalyst deactivation, etc

4. How Can It Benefit My Business? • It enables the relationships between process and performance variables to be identified, that influence: • Yields & usages • Processing times, speed, throughput • Product impurities • Cost is low compared with the benefits

5. How does it differ from Conventional Process Control & SPC? • Closed-loop control: • deals with dynamic behaviour • requires sampling rate much higher than system frequency response • requires cause-effect relationships to be known • SPC: • (generally) deals with steady-state systems • sampling rate determined by cost of data collection • identifies non-random system behaviour (variation due to assignable causes) • usually configured for univariate monitoring • Process Data Mining: • identifies relationships and abnormalities in multivariate systems for process optimisation and troubleshooting • three stages: exploration, modelling, validation

6. What do I need to achieve this? • For modelling normal behaviour: • Access to historic time-stamped process & QC data • example BASF Seal Sands plant: • over 2 full years PI data for >11,000 variables, every 30 seconds • Some knowledge of what variables to monitor • Some expectation of what is to be achieved • Understanding the data • For monitoring abnormal behaviour: • A model of normal behaviour • Real-time data access • Software to identify causes of abnormal behaviour

7. Integrated network access to historic time-stamped process & QC data • Majority of process control systems (DCS & PLC) have interfaces to external systems: • normally offer ‘firewall’ security to prevent unauthorised write-back • normally offer network connectivity • Ethernet, TCP/IP, browser-configurable • Major data historian packages have standard interfaces to these systems: • client - server • data compression comes as standard • can import QC data • clients run on same platform as business systems • GUI, Excel add-in, API & web browser • Data historian systems may be plant-dedicated, shared over a business WAN, or shared over the Internet.

8. Internet Service Delivery Web Server Service Provider’s LAN PI Server LAN Firewall DMZ Internet FW Internet Internet FW DMZ LAN Firewall I/F Server Customer’s Business LAN DCS PC Business Network Service Delivery

9. Some expectation of what is to be achieved: • High Profile Targets: • % Yield Improvement • % Give-away reduction • % Utility usage reduction • % Rate or batch-speed increase • Side benefits: • Reduced ‘Grey’ material when changing grades • Reduced downtime • Reduced inventories awaiting analysis - sell from line • On-line monitoring of catalyst deactivation and process fouling

10. Constraint Poor control Improved control £ Move closer to constraint £+ Mean Process variable Time

11. Quantifying the improvement: AFTER 5% violation Dx BEFORE Dx = 1.65(s - s ) new old

12. Understanding the data: • To deal with abnormalities in the data: • Pre-Screening • Visualisation • Time-shift correction • Missing values • Outliers • Exploratory data analysis: • Projection Techniques (Chemometrics) • Clustering • Principal Component Analysis (PCA) • Regression • Least Squares (PLS)

13. Time-shift correction Raw data Cross-correlation Relationship now obvious No identifiable relationship Time-shifted cross-correlation

15. Sample with large T2 Unusual variation inside the model Sample with large Q (or SPE) Unusual variation outside the model First PC 6 4 Variable 3 Second PC 2 6 0 6 4 4 Variable 1 Variable 2 2 2 0 0 Ack to Eigenvector Research Inc.

16. Multivariate data analysis - correlation & clustering Outlier Outlier Highly correlated data

18. Real-time Modelling • Modelling for monitoring: • A subset of historic data is chosen which represents: • ‘common-cause’ process behaviour (no outliers) • the process operating within ‘control’ • The model will then be sensitive to outliers & out-of control data • Easier to say than it is to do: • many processes are ‘multi-modal’ • rate & grade changes - known as co-variates’ • co-variates must be ‘orthogonalised’ first, to give model sensitivity

19. Real-time modelling • Modelling for prediction: • Can include ‘richer’ process data for better prediction away from normal operation (but excluding outliers) • Must be validated on unseen data • Model drift is a problem, as processes change with time: • Difference between predicted & actual used for monitoring fouling & catalyst deactivation • Can be used to validate QC data

20. A selection of available software • Excel • Data analysis add-in - Moving Average, StDev, MLR • VBA programmable - on-line applications are possible • Matlab (statistics toolboxes including PCA & PLS) • http://www.ncl.ac.uk/inpact/ • http://www.eigenvector.com/ • can also be programmed for on-line monitoring • MSPC+ (off & on-line - uses embedded PI database) • http://www.mdctech.com/mspc.htm • Pirouette (exploratory data analysis) & InStep (prediction) • http://www.infometrix.com/ (demo available to download) • SIMCA-P (modelling) & SIMCA 4000 (on-line) • http://www.umetrics.com/ (demo available to download)

21. Thank you for your attention