CityName. Champagne with a touch of innovation. R. David & Ph. Greiner Smart-Factory Engineers

Transcription

1 Champagne with a touch of innovation R. David & Ph. Greiner Smart-Factory Engineers CityName Think Brussels / DOC ID / October 5, 2018 / 2018 IBM Corporation

2 TECHNORD IS A FAMILY BUSINESS THE FAMILY-OWNED COMPANY HAS JUST CELEBRATED ITS 30 YEARS! HQ in Belgium Staff: 380 Turnover: 75M Export outside EU: 25% 2

3 TECHNORD GROUP EVOLUTION Electricity Automation Automation Supervision D.C.S. Manufacturing Information Systems Manufacturing Operations Management Data Science Simulation IIOT FROM ELECTRICITY TO DATA ENGINEERING 3

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5 Technord Industry Process Improvement The Smart-Factory Unit of TECHNORD 5

6 TECHNORD GROUP AT A GLANCE OUR DATA COVERAGE 6

7 IoT4Factory MOM4Factory Cloud4Factory Internet Of Things Easy measurements Secure transport Link to supervision Manufacturing Operations Management MES : Manufacturing Execution System LIMS : Laboratory Information Management System Cloud Solutions Easy storage of data Architectures QuickWin4Factory Copilot4Factory Maintenance4Factory Framework Study Data Science Business Intelligence Offline GO NO GO Process Copilot Online Data Intelligence / Simulation Soft sensors Advanced Process Control CONTINUOUS IMPROVEMENT WITH TECHNORD Predictive Maintenance 7

8 METHODOLOGY: THE MODELING DIALOGUE From PRODUCTION to UNDERSTANDING Relevant data analysis Dialogue with process experts Visualisation OUTCOME: Know How Crystallization From UNDERSTANDING to IMPROVEMENT New ideas assesment Better performances scenario OUTCOME: Design Tool From IMPROVEMENT to PRODUCTION Assistance for production operators Open or closed loop control OUTCOME: Quick Win, Copilot A PATH TO CRYSTALLIZE KNOW-HOW & IDEAS 8

9 QUICKWIN4FACTORY: VISUALISATION Visualisationsenabling fast reporting and production overview Process Yield Predicted Yield for current batch Energy per ton Production time per ton 9 VISUALISATIONS TAILORED TO YOUR NEEDS 9

10 QUICKWIN4FACTORY: A BIT FURTHER THAN SIMPLE VISUALISATION Visualisationsimproving the understanding of RM properties influence Correlation matrix Scatter plots 10 VISUALISATIONS TAILORED TO YOUR NEEDS 10

11 CEMENT GRINDING MILL OPTIMIZATION Fineness Energy Ball Mill Separator Speed Optimized Variables: Cement Fineness Energy Manipulated Variables Separator Speed Flow rate of clinker feed 11 Flow Rate 11

12 MEET HYBRID MODELING, THE IDEAL BLEND OF BOTH APPROACHES Historical data Live data Process understanding DATA ANALYTICS CONCEPTUAL MODELING Insights Predictions Anomaly detection TAILORED BLEND TO GET THE MOST OF EACH APPROACH 12

13 COPILOT4FACTORY: A STEP FURTHER Based on process knowledge Data Analytics Dynamical equations Dynamical Transients management Real-time calculation Computation time related to real-time constraints Adapted to the current process equipement Tailor-made Representative of the process Copilot! A PATH TO CRYSTALLIZE KNOW-HOW & IDEAS 13

14 PROOF OF CONCEPT IN CHAMPAGNE PRODUCTION PROOF OF CONCEPT IN CHAMPAGNE PRODUCTION POC FERMENTATION

15 And now, let s make Champagne 15

16 with a touch of innovation! 16

17 PROOF OF CONCEPT IN CHAMPAGNE PRODUCTION Assistance in the live management of the grape fermentation WHY? - Numerous tanks of fermentations to monitor - High variability of the substrate for a constant wine profile HOW? - Detecting the start and the end of the fermentation - Forecast of the fermentation progress - Alarms/corrections when problems OUTCOMES : - Prediction sensor - Online measurements processing - Operational gain Financial gain Fermentation start Vmax End of fermentation 17

18 Architecture PLC Mobile Internet Gateway Watson IoT Cloudant Data Structure Machine Learning Dashboar d HARDWARE LOCAL BY 18

19 Architecture 19

20 Architecture 20

21 Architecture 21

22 Architecture 22

23 ONLINE SIMULATION Best fit selection - online - based on dynamics evolution 23

24 NEXT STEPS A new selection of data to expand the scope of the simulator (predictions of problematic situations and impact of corrections) Simulation inserted into supervision, automation and workflow Predictive sensor POC FERMENTATION 24

25 Other possibilities 25

26 SOFTWARE SENSORS - MEASURE THE UNMEASURABLE Software Sensors can estimate measurements that: cannot be made are sub-sampled Available measurements Modeling Estimate In which cases are used Software Sensors? Hardware sensors are not reliable: quality, harsh environment Hardware sensors are too expensive Laboratory analysis is too long to be used in the process Laboratory analysis is too expensive Hardware sensors can fail or must be maintained The Software sensor can detect the hardware-sensor failure by comparison The Software sensor can temporally replace it and the process must not stop MEASURE THE UNMEASURABLE

27 MAINTENANCE4FACTORY Conditional Maintenance: Analysis of data with Data Science tools (offline) Alarm when threshold is exceeded Predictive Maintenance: A step further with modeling providing predictions of the equipment behavior (online) Example: development for UPS (Uninterruptible Power Supply) for Data Center SAME LINE OF THOUGHT, BUT FOR EQUIPMENT 27

28 Take Home Messages 28

29 ROI FOR DATA SCIENCE / SIMULATION Roadmap : Framework Study of your data Consultancy service Proof Of Concept Pilot Full Scale Each project is tailor-made with generic softwares Outcomes: Based on Data: - penalties when overcoming operating thresholds - losses when production must be stopped -costsavingsof 1% energy= X /year TAKE HOME MESSAGES Audit cost savings are already evaluated 29

30 SIMULATION, FOR WHICH INDUSTRIAL CASES? Lack of repeatability in production Production bottleneck Excessive energy consumption Complaints by operators Improvement ambitions Audit results Process revamping Process scale up Overproduction of waste Human variability of piloting TAKE HOME MESSAGES 30

31 ANY QUESTIONS? Feel free to contact us : r.david@technord.com 31