*Hannover Messe 2016, Germany

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1 REGIONAL SEMINAR

5 *Hannover Messe 2016, Germany

11 This slide is for video use only. Preconfigured Solutions Dashboards and Visualizations Machine Learning and Analytics Big Data Store Information Management Face APIs Speech APIs Personal Digital Assistant Cortana Perceptual Intelligence Computer Vision APIs Text Analytics Transform data into intelligent action in the cloud Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

13 80-5.Net Volume DC.SJ.PUE TI-102 DC.Zero DY-108 trigger GE01_A_DT GE05_Energy DC.SJ.C1.Z3.R3.PDU1.PF C1:14AT5 AC03.Air Flow GE01_A_DT AC09.Power FeedBin.Cmt Boiler Cold Reheat Pressure GE04_OSAsset1.Problem DC.SJ.SiteRealTim Anacortes Refinery.Alkylation.Asset B737_FG117 DC.TimeLoad s QI-122 FI-151 eitload.pr FT _Wind Speed Problems B210_FG005.KPIExcursion D-110.Tank Pressure.PV 02F102.1HRAVGBGT001 FI-101 bf5e1d1d-39c9- GE04_DT QI-121 GE03_V_WIN AQUA2-SI005.PV 4-36.NetVolumeAC04.Air GE02_Energy Flow PI-111 facility_output 0_ENG_MODE_STS FI T100 03LBB02CT b5b-b3d3-c2ce05fa3a26 DC.Rk07R AQUA2-TI-201.PV DC.NY.Actual.PWR.day.Tot DM-05:BW.R 02T103.Q DC.Z1R 0_CM P_ HDR _SU C _ PRS AT401 0_CLR_FINAL_OUT_B_TDC.Srv06R GE04_Energy AlarmTest.Input.Float32.10 MP F506_E990 AlarmTest.Input.Float _Clear Sky AC Power TI-121 FT9001 Global GE01_DT _Wind Speed QI-109 Horiz GE01_DT FAC.OAK.Power-Kh-Val.PV FI-101 bf5e1d1d-39c9-4b5b-b3d3-c2ce05f GE01_DT Cooling Fan-711.Feed Rate BGE003 FI T100 DC.Srv01R Boiler-209.Fuel Gas AF_FLOW3 02:T103 AT401 AQUA2-SI005.PV GE02_Energy Boiler- AlarmTest.Input.Float32.1 Flow DC.Srv01R 0_ENG_MODE_STS ACEDemo.Unit1.Output FI T103.Q 125.Fuel Gas 94:GRDIDX.Tr TI-178 B352_W778 0_CMP_SVLV_PCT DC.Z1R Volume 02F104 CD:F161 0_CMP_HDR FI-151 D-110.Tank Net DM-05:BW.R 0_ENG_AUX_STS 94:BW.R TI Net DC.SJ.C1.Z1.R1.Rk06.S2. Pressure.P F723_E889 Volume DY-131 O03.PWR Temp TIC-181 B045_FG978 QI-111 Compressor-439.Feed FinalProductBin.On AT401 94:GRDIDX.ProdID Rate Boiler-209.Fuel % CO2 DC.SJ.PUE fic1001.c GE02_OT FI-101 bf5e1d1d- DC.CH.DCE FIC- 39c9-4b5b-b3d3-172:210 GE01_DT c2ce05fa3a26 DM-05:BW.R FI-121 AF_FLOW3 AT401 0_CLR_FINAL_OUT_B 0_ENG_MODE_STS _TMP F506_E990 GE03_Q DY-131:166 GE01_TD Boiler-334.Feed Rate 02F102.1HR AVG BGT001 PI-111 facility_output AT401 FIC-144 GE04_Status 02F100 fasttag 02:F101.C AQUA2-SI005.PV GE02_Energy 0_ENG_MODE_STS FI-151 TI T103.Q DC.Z1R asset1_output 0_CMP_HDR_SUC_PRS Active Meters FI-111 GE01_A_DT aso AF_NOISE DM-05:BW.R PI-115 BEFORE _Clear Sky Global Horiz GE01_DT FI-101 Volume BGE003 FI T100 AF_FLOW3 02:T103 ACEDemo.Unit1.Output TI-178 B352_W778 0_CMP_SVLV_PCT 02F104 CD:F161 DY :F101.C Challenge: Making Sense of the Data Gas Flow AF_NOISE GE05_ES T V Boiler Feed Pump #1 1-8.Net Volume 03LBA32CT Net Volume CB1992_MS 0_CMP_FLOW_TOTAL 01-2 DC.SJ.ITLoad.P WR GE02_Energy FT9001 TI-145 fic1001.c FR5001 fasttag FR2001 FT9001 FeedBin.Cmt TI _Wind DC.Zone1.Number GE04_OS Speed FT9001 DailyTrigger FrqPrbCost_ER 45-2.Net Volume FT9001 FR5001 DC.C2Z1.Pwr.Rippl FT9001 AF_NOISE GE01_CON e GE01_A_DT AlarmTest.Input. Float32.1 DC.SJ.SiteRealTim eitload.pr FT9001 GE01_DT _Wind Speed QI-109GE01_DT Cooling Fan DM-05:BW.R DC.SJ.C1.Z1.R1.R k06.s2.o03.pwr GE01_A_DT QI-111 FinalProductBin.On fic1001.c 94:GRDIDX.Pr 02:F101.C odid Boiler- FR2001 TIC Fuel Gas Aso AT401 Flow DC.Srv01R AFTER Weather Conditions Relative Humidity: 34% Current Temp: 85 F High: 92 Low: 57 F Wind: 8 mph/n Crude Desalter Operating Pressure: 110 psi Charge Rate: 14 gph Mix Valve Pressure: 8 psi Water Rate: 8% Crude Furnace Draft Pressure: -0.5 WC Stack Temp: 316 F Oxygen: 2.5% Outlet Temp: 840 F Cold Oil Velocity: 6 ft/sec 1 3

15 Solution: PI Integrator Analytics-Ready Data Context Events via C-A-S-T Analytics Ready Data

16 REGIONAL SEMINARS 2015

18 Analytics Ready Data PI Integrator for Business Analytics: Data Warehouse

19 Red Carpet Incubation Program (RCIP) for PI Integrator for Microsoft Azure and Cortana Intelligence Reports (Power BI) Data Science VM ML Web Services Predictions as Future Data (to PI 2015 or later) Azure Data Factory (Orchestration)

20 DATA SOURCES PREPARE INGEST ANALYZE PUBLISH CONSUME Cortana Azure SQL Data Warehouse Machine Learning Azure SQL Data Warehouse Power BI Predictions as Future Data (to PI 2015) Azure Data Factory (Orchestration) On Premise Or Azure IaaS On Cloud PaaS

Leveraging the PI System and Cortana Intelligence to Increase Process Efficiency COMPANY and GOAL Deschutes Brewery is the 7th largest craft brewery in US, and wanted to maximize production with its

22 Leveraging the PI System and Cortana Intelligence to Increase Process Efficiency COMPANY and GOAL Deschutes Brewery is the 7th largest craft brewery in US, and wanted to maximize production with its existing infrastructure to fund construction of a 2 nd brewery in Roanoke, VA Company Logo Picture / Image CHALLENGE Batch s phase transition happens between manual density measurements occurring every 8-10 hours Impact: Losing up to 72 hours in production time SOLUTION Use data science to achieve accurate predictive analytics for determining a batch s density measurements PI System PI Integrator for Microsoft Azure SQL Data Warehouse Azure Machine Learning Azure Data Factory RESULTS Ability to eliminate production time losses and increase production capacity Accurate predictions of when a batch s phase transitions from fermentation to free rise EMEA USERS CONFERENCE BERLIN, GERMANY Copyright 2016 OSIsoft, LLC 22

Introduction to Analytics with the PI System

Introduction to Analytics with the PI System Landry Khounlavong, Product Marketing Manager 1 Analytics offer many choices 2 Five considerations before starting with big data 1. Gaps in data collection