1 / 18

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

shepry
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related