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Application of Data Mining Techniques to Industrial Processes to Improve Business Performance

Application of Data Mining Techniques to Industrial Processes to Improve Business Performance. Clive Duebel Knowledge Process Software cduebel@knowledgeprocesssoftware.com. Data is not knowledge!. Modern Process control systems collect and store large amounts of data

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Application of Data Mining Techniques to Industrial Processes to Improve Business Performance

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  1. Application of Data Mining Techniques to Industrial Processes to Improve Business Performance Clive Duebel Knowledge Process Software cduebel@knowledgeprocesssoftware.com © Knowledge Process Software 2003

  2. Data is not knowledge! • Modern Process control systems collect and store large amounts of data • A huge repository of plant operating experience • Obtained at significant expense • At most plants an under utilised resource • Lack of staff time • Many hundreds of operating variables • Different operating modes • Bad or unrepresentative data • Lack of tools to analyse data © Knowledge Process Software 2003

  3. Data Mining approach • Innovative technique to analyse plant data • Handles massive databases • Finds patterns (modes of operation) automatically • Presents patterns as rules • Improves understanding of process performance • Quickly identifies improvements • Product development supported by UK Government Energy Efficiency Best Practice Programme © Knowledge Process Software 2003

  4. Categories of Data Mining • Manual identification • Database queries to discover patterns • Semi-automatic identification • Plots for visual identification • Automatic identification • Limited user input • Rule induction © Knowledge Process Software 2003

  5. Rule Induction © Knowledge Process Software 2003

  6. Advantages of Rule induction • Automatically identifies and prioritises impact of variables on process performance • Models can be quickly generated from large data sets • Models are transparent to user • Validity of rules to split data can be validated • Automatically identifies different process operating regimes © Knowledge Process Software 2003

  7. GAS/ GAS Exchanger FEED GAS TO REFRIGERATION GAS/ Liquid Exchanger Feed Gas Pre-cooling © Knowledge Process Software 2003

  8. Outcomes and Attributes • Outcome • cooling achieved • Attributes • Disturbances: • feed rate, feed temperature, pressure • Controlled: • flow split © Knowledge Process Software 2003

  9. Performance Analysis Is it better to operate with a high flow ratio or a low flow ratio?

  10. Decision Tree

  11. Implementation • Operate with higher flow ratios at the higher feedrates • Simple change to implement – update to existing control strategy © Knowledge Process Software 2003

  12. Case Study-Power Station • Serving integrated Chemicals complex • Power station supplying steam to process site • Also generating power for the site and to export to the Grid • Objective to improve net revenue (steam and electricity out minus fuel, electricity and water in) © Knowledge Process Software 2003

  13. Define critical performance factors Acquire historical data Data mining to generate performance improvement rules Quantify benefits Methodology Implementation © Knowledge Process Software 2003

  14. Outcome and Attributes • Outcome was ‘cost per unit of steam’ • Attributes included: • Disturbances: • steam demand, ambient demand, water temp • fuel and power prices • Control settings: • manifold pressure, turbine exit pressure, boiler choice, • boiler loading, bleed steam flows, secondary turbine flows © Knowledge Process Software 2003

  15. Data Preparation & Analysis Data From Plant -10 months Data Preparation • Prepared Data Set • -7000 records • hourly averages • 27 attributes Knowledge Discovery Decision Tree - 26 operating regimes © Knowledge Process Software 2003

  16. Power Station decision tree • Part of decision tree analysing power station performance • Automatically splits the data into statistically different operating regimes • Identifies key variables impacting production costs • Identifies opportunities to operate in a more efficient operating regime © Knowledge Process Software 2003

  17. Benefits • Identified opportunities to reduce costs by 5% • Quantified savings • Mostly low cost changes to control set points of operations • Some advanced controls needed • Pay back in months © Knowledge Process Software 2003

  18. Summary • Data Mining techniques enable rapid analysis of huge plant databases • Identification of improvements in process operation based on historical performance • Unlocks best performance from existing control and process management systems • Applications • Industrial and processing companies • Utilities • Large users of energy • Oil/Gas/Petrochemical © Knowledge Process Software 2003

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