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Ähnlich wie NASSCOM Design and Engineering Summit 2016: Session VI: Disruptor Byte
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NASSCOM Design and Engineering Summit 2016: Session VI: Disruptor Byte
- 1. © Flutura 2016 1 Real life story + 5 Lessons October 2016
- 2. © Flutura 2016 The Industrial Context ? ASSET powering PROCESS REACTOR COMPRESSOR QUENCH TOWER ABSORPTION TOWERS WASTE HEAT BOILERS END- PRODUCTS Butadiene Butene-1 Polyisobutylene Isobutylene Derivatives Nonene, Proyelene tertramer & Pentamer INDUSTRIAL APPLICATIONS Synthetic rubber Nylon carpet/airbags Latex Paper coating Fuel Additives Paints and Coatings Chemical Manufacturing Process
- 3. © Flutura 2016 The $32 million Problem ? How do we reduce the lost rev opportunity from reactor process going down ?
- 4. © Flutura 2016 The Solution ? Asset Landscape - Reactor - Quenching tower - Compressor - Absorption tower - Boilers Cerebra Prognostics - Episode sequence algorithm - Fault pathway progression - Blueprint of failure Industrial Process Cerebra Sensor Data Lake - 3 Billion sensor events - 10 milliseconds - Instaneous parameters - Alarms - Trips - State transitions Cerebra Algorithmic Diagnostics - Algorithmically Score Asset Health - Reactor Health score - Heat Exchanger Health Score - Absorption tower health score - Compressor health score Flow sensor Pressure sensors Temp sensors Sensor Fabric Sensor Data Lake Cerebra Diagnostics Cerebra Prognostics Reduce down time by isolating & intervening earlier in the fault pathway Aspentech IP21 Historian - Valve seal gas pressure –PSI - Valve open position - % open - Flow rates - Ambient temperature - 36 instantaneous parameters • Slow valve closing • Low ceiling pressure • Low instr air pressure • Low nitrogen pressure • Loss of steam • Feed side compressor191
- 5. © Flutura 2016 Valve Diagnostics - Deep Dive Hysteresis tests + Stiction tests + Dead time tests + Linearity tests IDEA IN BRIEF • If the valve flow characteristic is expected to be linear , the relationship between %valve opening (input) and pressure (output) is usually high • The indicators of this characteristic are : • Process gain, Kp, is the “how far” variable because it describes how far the PV will travel for a given change in CO. It is sometimes called the sensitivity of the process • Correlation (~1) MAKING IT REAL • In a dehydrogenation chemical reactor used to manufacture Butadiene from Butene, we have 7 different valves aiding the process. • The pressure and valve opening % need to be highly correlated in either positive or negative • Process Gain is the derivative or slope of the valve’s flow characteristic, or more simply, the change in pressure for a given change in valve opening IMPACT ON TANGIBLE OUTCOME • If the magnitude of process gain is high or too low. Which if further strengthened with low correlation values, indicates that Pressure change in the reactor is deviating in the chemical reaction and Quality of the output (butadiene) gets impacted • Coke gets deposited on the vessel walls ,excessive of which deactivates the catalyst • Cost of operation increases as Catalyst is the most expensive ingredient of the reaction • Timely detection of the linearity deviations and controller recalibration can bring down operational costs • Potentially Controller recalibration is required Key Episodes correlated to failure mode : • Correlation factor • # times the process gain exceeded threshold • Ratio of # of process gain deviations to the total elements
- 6. © Flutura 2016 Key to IOT Prognostics Unpack High Velocity Sensor Wave !
- 7. © Flutura 2016 Quantifiable Impact on Tangible Outcome ? Cerebra Diagnostics/Prognostics helped • Scoring Industrial Assets • Diagnosing Process bottlenecks • Trigger interventions proactively $ 6.5-7 million saving in year-1 from reduced reactor down time
- 8. © Flutura 2016 Learning-1 : Forget IIoT Remember the problem
- 10. © Flutura 2016
- 11. © Flutura 2016 What is the impact on measurable outcome? 12 possibilities 1. AAS : Asset as a service – usage pricing ? 2. MAS : Monitoring as a service ? 3. VAS : Value added service to cross sell ? 4. VDE : Verticalized Data Exchange ? 5. New age IOT Business Models ? Please elaborate 1. EXPAND Asset Life ? 2. REDUCE cost of unscheduled maint ? 3. OPTIMIZE cost of spare parts inventory ? 4. %TRANSITION to data driven inspections ? 5. AUTOMATED predictive ticket generation ? 6. TRANSFORM basic RCA to RCA 2.0 ? 7. MINIMIZE cost of asset downtime ?
- 12. © Flutura 2016 How to get started ? Mind the Gap ! Engineering mental model Digital mental model
- 13. © Flutura 2016 5. Physics + Statistics + Heuristics = Force multiplier Grey-Box Model 𝑃 = 𝑃𝑜𝑤𝑒𝑟 𝜂 = 𝑡𝑢𝑟𝑏𝑖𝑛𝑒 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 𝜌 = 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝑤𝑎𝑡𝑒𝑟 𝑔 = 𝑔𝑟𝑎𝑣𝑖𝑡𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 ℎ = ℎ𝑒𝑖𝑔ℎ𝑡 𝑞 = 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒
- 14. © Flutura 2016 Closing thoughts 3 core points • Digital features > Electro-Mech features • Expanding Revenue Pools > Squeezing Incremental Efficiency • Put the problem on the pedestal, not the solution Good luck with your IIoT Journey
- 15. © Flutura 2016 15 “The price of light is less than the cost of darkness”Arthur C. Nielsen IIoT
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