NASSCOM Design and Engineering Summit 2016: Session VI: Disruptor Byte Speaker: Derick Jose, Co-founder, Flutura Decision Sciences

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1 Real life story + 5 Lessons
October 2016

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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

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The $32 million Problem ?
How do we reduce the lost rev opportunity from reactor process going down ?

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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

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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

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Key to IOT Prognostics
Unpack High Velocity Sensor Wave !

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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

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Learning-1 : Forget IIoT
Remember the problem

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Learning-2

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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 ?

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How to get started ?
Mind the Gap !
Engineering
mental model
Digital
mental model

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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

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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 

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“The price of light is less than the cost of darkness”Arthur C. Nielsen
IIoT