Presentation on theme: "Toolbox Integration for Instability Prediction at Redcar Blast Furnace, Teesside Cast Products, Corus UK www.chem-dss.org."— Presentation transcript:
Toolbox Integration for Instability Prediction at Redcar Blast Furnace, Teesside Cast Products, Corus UK
REDCAR (1 Big Blast Furnace) SCUNTHORPE (3 Medium Blast Furnaces) PORT TALBOT (2 Medium Blast Furnaces) Each site produces 3.2 to 3.6 million tonnes of liquid iron per year. All sites use G2. Location of Corus UK Blast Furnaces.
Sinter,ore and coke Hot blast 1100 o C 4 bar Top gas 2 bar/120 o C 21%CO 21%CO2 5% H2 Iron and Slag 1500 o C Steel shell Water cooled pressure vessel Carbon bricks The Blast Furnace Process
Melting Zone 14 Rows of Cooling Staves 48 – 60 staves Per row = Passage of Reducing gas Melting Zone
The Objective u Predict aerodynamic instability in order to enable the controller to reduce the blast volume in time to reduce the effect. u Effect usually seen as u Sudden slip of the material in the furnace, which can lead to a Surge of gas at higher than normal pressure through the furnace stack, hence lifting the pressure relief valves. u Channelling of gas through the burden which can lead to u high local heat load onto the furnace wall cooling plates. u Poorer gas distribution in the furnace hence reduction in process efficiency.
Blast Furnace process: Combinations of Toolboxes u iMSPC alone u iMSPC with Qualtrend and G2 rules to analyse sequences of episodes u iMSPC with SALSA u Qualtrend with SALSA u (iMSPC is a multivariate SPC toolbox by Computas, Norway. G2 u Qualtrend is a qualitative trend analysis toolbox by University of Girona. G2 u Generation of objects, known as episodes, from a univariate signal u Salsa is a pattern recognition toolbox by University of Toulouse. Labviews u Toolboxes communicate using XML Blaster, freeware, using A G2 module as the ‘data store’
Salsa Classification Qualtrend Contribution analysis On PC1, PC2. Alarm Salsa iMSPC PC1, PC2 PC1, PC2, PC3, SPE Raw data Qualtrend Episodes G2 iMSPC G2 PC1, PC2 G2 rules Analyse sequences of episodes Episodes Classification Data updated Every minute
iMSPC Raw dataPrincipal Components Contribution Analysis iMSPC Alone
Principal Component Analysis Data compression without loss of information Smaller number of new variables generated called ‘Principal Components’ i.e., reduce dimensionality of the data Each principal component is a linear combination of the original normalised variables
Variables Selected for PCA Stability Index NW Row 6 to Row 9 differential pressure (Quadrant 1) NE Row 6 to Row 9 differential pressure (Quadrant 2) SW Row 6 to Row 9 differential pressure (Quadrant 3) SE Row 6 to Row 9 differential pressure (Quadrant 4) CO utilisation [100 * CO2/(CO + CO2) in off gas] Sum of CO + CO2 in off gas Permeability These 7 selected after much testing with many other variables
Top gas Composition, pressure Wall pressure tappings Blast pressure temperature volume Row 6 to 9 DP over 4 quadrants Blast Furnace Signals used for PCA Models Permeability = f(blast pressure, top pressure, blast volume)
Calculation of principal component scores PC2 factors PC1 = 0.26 * CO Utilisation * Permeability Resistance * (CO + CO2) * Row 6 to 9 DP Quadrant * Row 6 to 9 DP Quadrant * Row 6 to 9 DP Quadrant * Row 6 to 9 DP Quadrant Variables must be normalised: Normalised value = (actual value - mean) / standard deviation Mean and standard deviation derived from stable period of operation We use an adaptive mean
Inputs updated every minute Calculate 5 minute moving average Inputs to model Link to model Outputs from model Outputs to G2 object (to Qualtrend) iMSPC Model Configuration in G2
iMSPC Raw dataPrincipal Components Contribution Analysis iMSPC Alone
iMSPC Contribution Analysis u Contribution Analysis monitors the bi-variate trend of PC1 v PC2 (These 2 PC’s represent 70% of the variability in the data) u Identifies which variables have contributed the most to the change in principal component. u Alarm if 6/7 points outside action limit and significant change in at least 1 quadrant for 6-9 Differential Pressure.
iMSPC Qualtrend Raw dataPC1, PC2 Sequence of episodes analysed in G2 procedure Episodes iMPSC with Qualtrend and G2 rules
Data entry (PC1)Range check Filter Configure attributes to be stored in episodes and hold current values Calculate 1 st derivative Signal block (level = normal/low) Attributes of current episode. List of past episodes Filtered signal First derivative Limits Episode Types : Type Level First derivative 7 Normal Normal 6 Normal Low 16 Low Low 31 Low Normal
Qualtrend: development of rules 22 * 24 hour periods of 1 minute data supplied to UDG from Jan 2002 to Oct PC1 and PC2 Episodes generated in Qualtrend. Sequences of episodes analysed. Possible rules tested in Matlab. Successful rules programmed into G2 and run on line at Redcar since October Within the same G2 as iMSPC. (The live plant G2).
G2 Rules u Rule 1 looks for a sequence of episode types from PC1. u Criteria set for minimum rate of change (slope) and degree of change (amplitude). u Another rule looks for a similar sequence of episodes from PC2, and generates an alarm if the most recent episode from PC1 satisfies certain conditions. u Effectively, this detects a sequence of events in the process. u To prevent false alarms, an ‘enabler’ has been added based on the recent trend in heat flux.
2m Slip at 09: minutes warning. Current episode = 31 and Max-min of previous episode > 2.2 And min slope < Episode Types : Type Level First derivative 6 Normal Low 31 Low Normal Filtered PC1 First derivative
2m Slip at 09: minutes warning. Confirms previous message PC2 Current episode = 31 and previous episode = 6 Min slope of last episode of PC1 < And finished within 10 minutes Filtered PC2 First derivative Episode Types : Type Level First derivative 6 Normal Low 31 Low Normal
Summary Statistics Event Type Number of events Predicted by iMPSC alone Predicted by iMSPC/ Qualtrend PC1 Predicted by iMSPC/ Qualtrend PC1/PC2 Not predicted Major Minor Events detected over 22 days Jan 2002 – Oct Classed as predicted if more than 10 minutes warning. Major event: Slip >=1m and/or excessive heat flux. Minor event: Smaller slip and/or significant rise in heat flux. Sometimes alarms also generated during event (high heat flux).
Conclusion u All of the major events were predicted (19/19) u Only 3/10 of the minor events were predicted. u However, it is unlikely that action would have been taken for minor events.
iMSPC SALSA Raw data PC1, PC2, PC3, SPE Classification to Normal, Pre-slip or Slip G2 Windows iMSPC with Salsa
iMSPC and SALSA u Same data as used in for iMSPC/Qualtrend/G2 rules u (PC1 – PC4, SPE and T2 for 22 * 24 hour periods) u Best classification gained with PC1, PC2, PC3 and SPE u However, too many false alarms
Qualtrend Salsa Raw data (4 * differential Pressures) Episodes Classification to Normal, Pre-slip or Slip G2 Windows Raw data with Qualtrend and Salsa
Raw data with Qualtrend and SALSA u Classification based on data from early u Classification based on u Quantitative values (values at end of previous episode) u Qualitative values (current episode types) u So 8 inputs (4 differential pressure signals: 4 sets of episodes) Can give more advanced warning than other methods described. e.g., 4 Jan mins before iMSPC/Qualtrend. Issues SALSA on-line reliability – stalls after a day. Need to write classifications back from SALSA to DTM.
Qualtrend Alarms = 2 metre Slips SALSA alarm
Did not exceed action limits for 6 minutes, so no alarm iMSPC alone on 4 th Jan 2002.
Qualtrend Salsa Episodes Classification G2 Qualtrend iMSPC G2 PC1, PC2 G2 rules Analyse sequences of episodes Contribution analysis On PC1, PC2. Alarm Salsa iMSPC PC1, PC2 PC1, PC2, PC3, SPE Raw data Episodes Classification
Blast Furnace process Summary of Results u 1. iMSPC alone u All alarms generated by action limits are valid u Many events are missed u 2. iMSPC with Qualtrend and G2 rules to analyse sequences of episodes u Predicted remaining major events and very few false alarms once heat flux trend ‘enabler’ added u 1 and 2 predicted all the major events. u 3. iMSPC with SALSA u Many false alarms u 4. Qualtrend with SALSA u Predicts certain types of faults with good warning u Salsa not robust enough for continuous on line u Salsa needs to send classifications back to DTM