Part II of Operationalized Technology with Azure/Cortanta

1. REGIONAL SEMINAR

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6. Enterprise
ready cloud

11. Cloud
On Premise
PI Integrator for Business Analytics: Data Warehouse

17. 1
8
BGE003 FI-111 02T100
AF_FLOW3 02:T103 AT401
ACEDemo.Unit1.Output
TI-178 B352_W778 0_CMP_SVLV_PCT
02F104 CD:F161
GE01_DT 409510395_Wind Speed QI-109
GE01_DT Cooling Fan-711.Feed Rate
1-8.Net
Volume
03LBA32CT0
01-2
DC.SJ.ITLoad.P
WR
TI-145
FR2001
TI-178
GE04_OS
FT9001
FT9001
FR5001
AF_NOISE
DC.SJ.PUE TI-102 DC.Zero DY-108
DC.SJ.C1.Z3.R3.PDU1.PF GE01_A_DT
GE04_Status
02:F101.C
FIC-144
02F100 fasttag
FI-151
0_ENG_AUX_STS
AQUA2-SI005.PV GE02_Energy
0_ENG_MODE_STS
FI-151 02T103.Q
DC.Z1R
0_CMP_HDR
GE05_Energy C1:14AT5 AC03.Air Flow
FeedBin.Cmt Boiler Cold Reheat Pressure
B737_FG117 DC.TimeLoad
D-110.Tank Pressure.PV
GE04_DT QI-121 GE03_V_WIN
DC.Rk07R
DC.Srv06R GE04_Energy
TI-121 FT9001
FAC.OAK.Power-Kh-Val.PV
fic1001.C GE02_OT
02F102.1HR
AVG
BGT001
PI-111
facility_output
DM-05:BW.R
DC.SJ.C1.Z1.R1.Rk06.S2.
O03.PWR
QI-111FinalProductBin.On
94:GRDIDX.ProdID
Boiler-209.Fuel
Gas Flow
GE01_A_DT
fic1001.C
02:F101.C
FR2001 TIC-121
Aso AT401 DC.Srv01R
TI-178
asset1_output
Active Meters
GE01_A_DT aso
AF_NOISE PI-115
DM-05:BW.R
FI-101
Volume
0_CLR_FINAL_OUT_B_T
MP F506_E990
339511775_Clear Sky
Global
Horiz GE01_DT
FI-111
% CO2
GE05_ES T
GE01_CON
AlarmTest.Input.
Float32.1
DC.NY.Actual.PWR.day.Tot
80-5.Net Volume
GE01_A_DT
45-2.Net Volume
Boiler-209.Fuel Gas
Flow DC.Srv01R
94:GRDIDX.Tr
trigger
AC09.Power
403511195_Wind
Speed
DC.C2Z1.Pwr.Rippl
e GE01_A_DT
AF_NOISE
1-16.Net Volume
CB1992_MS
0_CMP_FLOW_TOTAL
FT9001
fasttag
FeedBin.Cmt
DC.Zone1.Number
FT9001
AlarmTest.Input.Float32.1
FT9001
DC.Srv01R
Boiler-
125.Fuel Gas
Volume
1-13.Net
Volume
B045_FG978
AT401
Anacortes Refinery.Alkylation.Asset
Problems B210_FG005.KPIExcursion
D-110.Tank
Pressure.P
V Boiler Feed
Pump #1
Challenge: Making Sense of the Data
94:BW.R TI-101
F723_E889
369512185-Temp
Compressor-439.Feed
Rate
DC.CH.DCE FIC-
172:210
FI-121 AF_FLOW3
0_ENG_MODE_STS
GE03_Q
DY-131:166 GE01_TD
Boiler-334.Feed Rate
GE04_OSAsset1.Problem
s QI-122 FI-151
DC.SJ.SiteRealTim
eITLoad.PR FT9001
Weather
Conditions
Relative Humidity: 34%
Current Temp: 85 F
High: 92
Low: 57 F
Wind: 8 mph/N
Crude Furnace
Draft Pressure: -0.5 WC
Stack Temp: 316 F
Oxygen: 2.5%
Outlet Temp: 840 F
Cold Oil Velocity: 6 ft/sec
Crude Desalter
Operating Pressure: 110 psi
Charge Rate: 14 gph
Mix Valve Pressure: 8 psi
Water Rate: 8%
fic1001.C FR5001
AQUA2-TI-201.PV
TIC-181
BGE003 FI-111 02T100
AF_FLOW3 02:T103
ACEDemo.Unit1.Output
TI-178 B352_W778
0_CMP_SVLV_PCT
02F104 CD:F161
AQUA2-SI005.PV GE02_Energy
0_ENG_MODE_STS
FI-151 02T103.Q
DC.Z1R 0_CMP_HDR_SUC_PRS
DM-05:BW.R
DC.SJ.C1.Z1.R1.R
k06.S2.O03.PWR
QI-111FinalProductBin.On
94:GRDIDX.Pr
odID Boiler-
209.Fuel Gas
Flow
FI-101 bf5e1d1d-
39c9-4b5b-b3d3-
c2ce05fa3a26
DM-05:BW.R
AT401
AT401
DailyTrigger FrqPrbCost_ER
GE01_DT 409510395_Wind Speed
QI-109GE01_DT Cooling Fan
AlarmTest.Input.Float32.10
364511575-AC Power
DY-101 02:F101.C
0_CLR_FINAL_OUT_B
_TMP F506_E990
339511775_Clear Sky Global
Horiz GE01_DT
80-13.Net
DY-131
DC.SJ.PUE
AlarmTest.Input.Float32.1
GE01_DT
FI-101 bf5e1d1d-39c9-
4b5b-b3d3-c2ce05fa3a26
DM-05:BW.R
AT401
4-36.NetVolumeAC04.Air Flow
02T100 03LBB02CT001-2
DC.SJ.SiteRealTim
eITLoad.PR FT9001
02F102.1HRAVGBGT001
PI-111 facility_output
AQUA2-SI005.PV GE02_Energy
0_ENG_MODE_STS FI-151
02T103.Q DC.Z1R 0_CM P_ H D R _SU C _ PRS
403511195_Wind Speed
GE02_Energy
FI-101 bf5e1d1d-39c9- 4b5b-b3d3-c2ce05f
BEFORE… AFTER…

19. 03:00 03:10 03:20 03:30 03:40 03:50
Speed
Oil
Temp
Oil
Temp
Speed
Torque
03:00
03:10
03:20
03:30
03:40
03:50
OilTemp
60
60
60
120
60
OilTemp
50
50
50
50
45
Speed
10.0
10.2
9.8
10.0
10.1
Speed
9.8
10
10
9.9
9.9
Torque
25
24
25
25
25
Challenge: Preparing the Data for Advanced Analysis

21. Solution: PI Integrator
Analytics-Ready Data | Context |
Events via C-A-S-T
AnalyticsReadyData

23. This slide is for video use only.
Copyr ight © 2013, SAS Institute Inc. All rights reser ved.
Transform data into intelligent action in the cloud
Information Management
Preconfigured Solutions
Dashboards and Visualizations
Machine Learning and Analytics
Big Data Store
Personal Digital Assistant – Cortana
Perceptual Intelligence
Text AnalyticsComputer Vision APIs
Speech APIsFace APIs

24. Cortana Intelligence Suite
Action
People
Automated
Systems
Apps
Web
Mobile
Bots
Intelligence
Dashboards &
Visualizations
Cortana
Bot
Framework
Cognitive
Services
Power BI
Information
Management
Event Hubs
Data Catalog
Data Factory
Machine Learning
and Analytics
HDInsight
(Hadoop and
Spark)
Stream Analytics
Intelligence
Data Lake
Analytics
Machine
Learning
Big Data Stores
SQL Data
Warehouse
Data Lake Store
Data
Sources
Apps
Sensors
and
devices
Data

25. People
+
Data
Sources
Apps
Sensors
and
devices
On Prem or in the Cloud
INTELLIGENCEDATA ACTION
Automated
SystemsOSIsoft PI System
PI Integrator for Azure
Apps
Cortana Intelligence

26. CortanaReadyData

27. Azure SQL
Data Warehouse
INGESTPREPAREDATA SOURCES
On Premise
Machine Learning
Power BI
ANALYZE PUBLISH CONSUME
Cortana
Web/LOB Dashboards
Azure SQL
Data Warehouse
PI System
On Cloud
Azure Data Factory (Orchestration)
CLEANSE
AUGMENT SHAPE
TRANSMIT
PULL
PUSH
data
quality
normalization
model
data
compatibility
data
aggregation
Predictions as Future Data (to PI 2015)

28. PowerBIReadyData
Stream Analytics
Customer Azure Tenant
Output
Input
Event Hubs

30. Azure
Marketplace
IoT Hub
Customer Azure Tenant
Azure IoT Suite

31. Azure
Marketplace
IoT Hub Stream Analytics
SQL Database
Customer Azure Tenant
Event Hubs
Document DB
Azure Table/Blob
Output
Input
Customer Azure Tenant

33. Advanced Analytics & Power BI with PI System data | events and context
Architecture Notes




36. Picture / Image
TARGETCHALLENGE SOLUTION
39
COMPANY and GOAL
39

37. Filling Fermentation Free Rise
Up to 72h lost!!!
Diacetyl Rest
Operating Constraints
• Dependency on manual
measurements
• Only one measurement can
be taken in a shift
Challenge
• Transition happens
between measurements
Time in Phase
ApparentDegreeofFermentation
Too Soon
Transition!!!
Can this be predicted?
Too late!!!
Time wasted

38. Proposal
Hypothesis
Transition Time influenced by
• Brand of Beer
• Fermentation dynamics
(Temperatures, pressures,..)
• Vessel’s dimensions & volume
Early Density Readings Known Transition Time
ADF
Time in Phase

39. Benchmark: Create standard
to measure against based on
historical data
Predict: use 2 early
densities to estimate
transition time
Refine: base the model on
brand to generate a more
accurate prediction
ADF
ADF
Time in Phase Time in Phase
ADF
Time in Phase

40. 43
Engine 1
Engine
100

42. PI Integrator allows you to push “Analytics Ready” data directly to Cortana Intelligence