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Deep reinforcement learning for industrial process automation nicolas deruytter

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Are you familiar with the fascinating world of: Artificial Intelligence? Autonomous systems? Advanced Process Control? Machine Learning? Predictive Maintenance? This presentation features them all!

Presented by Nicolas Deruytter, Founder & CEO ML6 on Supply Chain 4.0 : ready to operate in the digital era? (29 Nov, 2018)

Publicado en: Ingeniería
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Deep reinforcement learning for industrial process automation nicolas deruytter

  1. 1. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 1Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 1
  2. 2. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 2
  3. 3. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 3Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 3 ML6 AT A GLANCE Not only knowledgeable about Machine Learning, but defining the future of it 70+ years of combined Machine Learning Experience 20% of workforce fully dedicated to 3 funded ML Research tracks Premier Partner of Google Cloud since 2014 Serving Clients across 40 different countries Equipping 20% of BEL 20 companies with cutting-edge ML 4 Offices, located in Belgium, The Netherlands and Germany
  4. 4. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 4 WE WORK ACROSS THREE PILLARS RESEARCH A dedicated research division, solely focused on innovating ML algo’s enabling us to stay ahead of competition SERVICES Passionate consultants to keep the pulse on the market and set up long lasting relationships with our customers ENGINEERING Software engineers building our ML building blocks and maintaining our ML Marketplace Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 4
  5. 5. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 5 IMAGE AND VIDEO
  6. 6. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 6 HOND VS DWEIL
  7. 7. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 7 TOO EASY?
  8. 8. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 8 MUFFIN VS CHIHUAHUA
  9. 9. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 9 A DEEP LEARNING MODEL IS TRAINED, RESULTING IN TUMOR PROBABILITY MAP
  10. 10. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 10 OTHERS
  11. 11. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 11 OUR DISTRIBUTED FRAMEWORKS ALLOW US TO RAPIDLY IMPLEMENT OTHERS
  12. 12. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 12 DISRUPTING THE UTILITIES INDUSTRY WITH COMPUTER VISION
  13. 13. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 13
  14. 14. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 14
  15. 15. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 15Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 15
  16. 16. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 16 IOT & ADVANCED PROCESS CONTROL
  17. 17. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 17 THE MODEL ACCURATELY PREDICTED CONGESTIONS IN LONDON TRAFFIC
  18. 18. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 18 PHYSICS SIMULATOR
  19. 19. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 19 PHYSICS SIMULATOR
  20. 20. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information A product by
  21. 21. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information Energy Raw Material People System / Environment / Machine Production Quality Consumption Safety Days Machine Settings Manual raw material fill settings Rule based maintenance Rule based safety
  22. 22. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information Energy Raw Material People System / Environment / Machine Production Quality Consumption Safety Days Machine Learning Optimisation INLINE TRAINING Machine Settings Manual raw material fill settings Rule based maintenance Rule based safety Machine Learning Automation
  23. 23. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information
  24. 24. ALPHA PARTNER USE CASE Reducing giveaway of washing detergent 27 WHAT? ECC.AI learned to continuously control a powder pouch filling process, reducing the wastage from product giveaway due to variability of pouch weights HOW? By continuously tuning the diaphragm and centrifugal system, whilst taking into account product specific parameters and environment variables WHERE? At one of the largest FMCG production sites TIME TO MVP? 3 months INPUT NEEDED? 3 months’ historical data on input control, quality, weight and volume ROI? Improvement of 65% and growing WITHOUT ECCAI WITH ECCAI
  25. 25. ALPHA PARTNER USE CASE Increasing pump life expectancy with closed loop control 28 Breakdown Probability Optimal Settings Current Settings Threshold Predicted Maintenance Time Breakdown Probability ECCAI CONTROL ACTIVE Current Settings Threshold Time WHAT? ECC.AI learned to continuously control an air compressor pump, increasing its life expectancy: the next step in predictive maintenance HOW? By monitoring real-time demand and temperature, and continuously tuning fan speed and other control parameters to optimise the pump’s output and thus life expectancy WHERE? At one of the largest pump manufacturers TIME TO MVP? 6 months INPUT NEEDED? 1 month historical data set at millisecond level ROI? Pending
  26. 26. ALPHA PARTNER USE CASE Optimising Combined Heat and Power systems 29 WHAT? To optimise the steam generation kettle of a Combined Heat and Power (CHP) system to increase overall energy efficiency. HOW? The water level of the steam kettle appeared to be difficult to control, but using sensor data of temperature, pressure, water/steam flow and valve positions etc, our algorithms learned the dynamics to accurately control the water inlet, thus increasing its efficiency. WHERE? CHP power plant TIME TO MVP? 3 months INPUT NEEDED? 4 months’ sensor data of all relevant components (pressure, temperature, valve positions, current control units etc) ROI? TBD
  27. 27. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information The key lies in an “evolution not revolution” approach, where small pieces of process are automated and optimized individually. ECC.AI is best utilized in a dynamic environment with vast amounts of input, where today’s production teams face repetitive tasks that could be replaced by cognitive machine learning for faster, more optimal results. Blueprint & Roadmap IT/OT Connectivity Integration Real-time AnalyticsDashboard & Reporting Connectivity to Intelligent ERP Smart Predictive MES Workforce Enablement Largely Manual and Unconnected Automated Monitoring and Connected Intelligent and Predictive AI Ecosystem Integration Dynamic and Ecosystem-driven
  28. 28. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 31Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 31 ABOUT THE SMART FACTORY Suppliers & Subcontractors Suppliers & Subcontractors Customers Customers Smart Grid Factory 1 Factory 2 Factory 3
  29. 29. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information Karel Dumon (karel.dumon@ecc.ai) Rebecca Brooke (rebecca.brooke@ecc.ai) BUSINESS CASE DATA SAMPLE INFO @ ECC.AI
  30. 30. Copyright © 2018 ML6. All rights reserved. ML6 Confidential Information | 33 THANKS NICOLAS DERUYTTER NICOLAS@ML6.EU

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