SMART Manufacturing Journey Alcoa Smelting and Casting · the MS SSAS toolset (BI cube) • Automated EF structure and data transfer to the cube The goal of this initiative was to

© Copyright 2015 OSIsoft, LLC

Presented by

SMART Manufacturing Journey Alcoa Smelting and Casting

Bruno Longchamps

Pierre Boutin

SMART Manufacturing Journey Alcoa, Global Smelting and Casting

2

Bruno Longchamps, P.Eng SMART Manufacturing, Program Manager

OSIsoft User’s Conference, San Francisco, April 2015

Presentation content

Alcoa SMART Manufacturing Journey Overview

Users Engagement

Anode Tracking

Cast Data Collection & Integration

Conclusion

Questions

3

Alcoa Inc.

4

A global leader in lightweight metals

technology, engineering and manufacturing

Revenues of US$23.9 billion in 2014

59,000 employees in 30 countries

At December 31, 2014

Aluminium Production Process

5

Electrode:

Anode

production

Potroom:

Liquid aluminum

production

Casthouse

Salable solid aluminium

production

c

o

k

e

P

i

t

c

h

6

Smelting Operation

Rodded Anode

Potroom

Casthouse Furnace

Finished Products

Aluminum production requires very large manufacturing sites

With time, the process control and manufacturing systems have multiplied and become

heterogeneous

Each production system was an isolated data island with limited or no data history

Common operation best practices and new technology deployment were very difficult to implement

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Why Alcoa invested in the SMART Manufacturing Initiative ?

SMART Manufacturing Program

• Well integrated, fine

granularity, long term

production data available

• Increase collaboration and

sharing at the plants and

between locations

• Achieve significant

savings

Solution Results / benefits Challenges

• Aluminium market has

been financially challenged

for several years

• Improve competitiveness

• Enhance operational

excellence

• High retirement rates, loss

of SMEs

• Establish the SMART

Manufacturing program

• OSIsoft enterprise agreement

• Deploy a robust infrastructure

« cookie cutter » project in all

plants

• Mobilize key employees

SMART Manufacturing is about

integration of data with process expertise

to enable proactive and intelligent

manufacturing decisions in dynamic

environments

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SMART Manufacturing – Main improvement axes

Data

Maturity Model

Before:

After :

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SMART Manufacturing – Real plant wide data infrastructure

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Note for Lista: QLC not installed and the initial data collection phase is not completed

Site Name Total

GPM Global BU 1,559,207

GPM Warrick Site 1,246,100

GPM Alumar Site 1,193,900

GPM ABI Site 1,042,900

GPM Baie-Comeau Site 951,450

GPM Portland Site 675,770

GPM Mosjoen Works Site 628,190

GPM Fjardaal Site 554,000

GPM Deschambault Site 501,460

GPM Massena West Site 337,590

GPM Lake Charles Site 30,751

Warrick Power Plant 22,358

GPM Lista Site 6,548

Grand Total 8,750,224

The journey to the implementation of the SMART Mfg program

2010 Program Definition (OSIsoft support)

2011 Proof of concept (Deschambault plant, QC)

2011(Q4) Entered the OSIsoft Enterprise Program

2012-2014 Aggressive Global Deployment (13 plants)

2014 EA extension to other plants

2015 Additional deployments (2 to 5 plants)

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Approx. one deployment start-up every 2 to 3 months

Each deployment required a 6 to 9 months effort

Global BU instance added in 2014

12

SMART Mfg. – Deployment Schedule

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Deschambault

Massena West

Baie Comeau

Mosjoen

Fjardaal

Lake Charles

Warrick

Portland

ABI Schedule at Risk

Reybec Behind Schedule

Global BU Instance

Sao Luis On Hold

Lista Closed

Intalco

San Ciprian

Ma'aden

2011Site

On Schedule

2015

Project Found

and Startup

Value Realization

ACES

2012 2013 2014

Deployment &

Data collectIon

SMART Manufacturing – Geographical localisation

13 All Data is random, fictional data to show functionality only

Process Equipements

PI Data Archive

MES/LIMS

MS SQL BI

ERP

(Oracle)

AF

& E

F M

odel

& B

I Cubes

Microsoft Sharepoint PI

WebParts

MS SP

Web Parts

SSRS/Power

View Report

Microsoft Tools Excel, Power Pivot, BI Tools...

OSIsoft Tools PI ProcessBook, PI Coresight, ...

Data Collection and Analytic Data Visualisation

SMART Manufacturing – Functional model

14

SMART Manufacturing – Implantation diagram

xxx-SMART(DNS alias)

xxx-MES-SQL-CL1Windows ClusterTITLE

SMART Manufacturing System Architecture

March 12th, 2014

xxx-SMT-PI(PI Collective)

xxx-SMT-AF (NLB)

xxx-SMT-SQL-CL1(Windows Cluster)

CMI - Control System LAN (one or more)

LEGEND

· mPI = OSIsoft Enterprise Agreement (EA)

· P = Primary Machine

· S = Secondary (Redundancy)

· HA = High Availability

CMI Prod Domain - DMZ

CMI - Plant Manufacturing LAN

Office / Business LAN – (Business Domain)

PI ClientsPI ProcessBook

PI Datalink

IE (WebParts & Coresight)

Analysis Tools & Displays of Manufacturing Related Data

Active

Directory

Trusting

Domain

Print

Server

CMI DFS ServerCMI DFS

Mapped drive M:\

DFS Files Share

SMART

ManagementmPI

Excel

PI-SMT

PI Datalink

PI OLEDB Ent

Bomgar Station (support)

Admin & Maintenance Apps

xxx-SMT-MGM-01

PI ServermPI

PI Server [HA]

PI AF [HA]

PS

PI AF ServicesmPI

Pi Notification [HA]

PI ACE [HA]

PI AF Analytics (AF-01)

Calculation EngineNotifications

SMART ServicesmPI

SMART Calc engine

Site coded calculation

PI-Agent proxy for PCS LAN PI-Nodes

Real-Time Calculations PI-Agent proxy for PCS LAN

SMART SharePoint

Entreprise Server

mPI

PI WebParts

PI Datalink Server

PI OLEDB Enterprise

Real-Time & Historical Plant Floor Data

xxx-SMT-WSS-01

xxx-APM-DFS

MS-SQL SMART mPI

SQL Server 2012 Enterprise

High availibilty

Clustered

SSAS / SSIS / SSRS

AF , EF, SharePoint DBPlant MI

Datawarehouse

PS

Historian ServerPlant assets and

hierarchy

PS

xxx-SMT-PI-01

xxx-SMT-PI-02

xxx-SMT-SVC-01xxx-SMT-SQL-01

xxx-SMT-SQL-02

APMSDG Domain

QLCL&K

CMIFireWall

Site

MS-SQL MES

ServerSQL Server 2012 Std

High availibiityMES Apps and Services

xxx-SMT-MES-01

xxx-SMT-MES-02

xxx-SMT-DMZ-TS(NLB)

Client Tools TSmPI

Excel

PI ProcessBook

PI Datalink

PI AF Client

PI-SMT

SMART & MES

xxx-SMT-TS-4A

xxx-SMT-TS-4B

xxx-SMT-PCN-TS(NLB)

Terminal ServerPI-SMT

PI-Datalink

SMART & MES Apps

xxx-SMT-TS-3A

xxx-SMT-TS-3B

xxx-SMT-WEB(DNS Alias)

SMART WEBmPI

IIS SMART App. Web Service

Pi Web Services

PI Coresight

SMART DashboardAnd WEB apps.

xxx-SMT-WEB-01xxx-SMT-AF-01

xxx-SMT-AF-02

xxx-SMT-NOD-01

NodemPi

SMART NODE(VM Guest)

PS

xxx-QLC3-LDR-A

xxx-QLC3-LDR-B

mPI

Proxy: xxx-SMT-SVC-01

15

User Engagement

Pierre Boutin, P.Eng, MPM Manager, Global Manufacturing Solutions Delivery

16


Organization structure for change management

17

Sector

leaders

Plant Global

Regional leaders Global

SMART technical team Global

Technical lead Value lead

OSIsoft team

Plant sector lead/area super-users

End-users

Global

End-users

Key success factors

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Data visibility

Key success factors

19

Key success factors

20

Post installation follow-ups

Train users

Answer questions

Fix issues

Share best practices from other locations

Escalate

Detect lack of engagement before it is too late

Report value to sponsors

Keep communication channels open…

Working across silos at the plant level

21

User engagement at the plant level

Electrode Potline Casthouse Environ. Energy

SMART mfg horizontal integration

Working across silos globally

22

Anode tracking Pierre Boutin, P.Eng, MPM Manager, Global Manufacturing Solutions Delivery

23


Anode tracking R&D project

• Holistic insight over anode life

• Anode performance linked to

raw materials and anode

fabrication data

• Development of anode

manufacturing best practices

• Improved Raw Material

selection process

Solution Results / benefits Challenges Technical

• Physical anode identification

• Linking anode data available at

different point in time

• Continuous and discrete

process involved

Business

• Cost of raw materials

• Establishing anode fabrication

best practices

• Read anode numbers with OCR

camera

• Store sampled data in PI Server

• Use Event Frames to organize data

in time

• Integrate all data sources with the

MS SSAS toolset (BI cube)

The goal of the anode tracking project is to

make anode genealogy data (7 to 8 weeks

from start to finish) easily accessible to support

process improvements.

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Work breakdown structure

25

Physical Tracking

Data Processing

Process optimization

Bake Rodding

Electrolysis Paste

Anode tracking

Database

PI

System • Process modelization

• Reaction plan

• Preventive/proactive actions

• Predictions

• Process optimization

• Best practices development

• Data collection

• Structure/organize data

• Manufacturing Intelligence

Data – Continuous process

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Paste plant

Rodding

Automated reading

Electrolysis

Butt treatment

Anode numbers

Rod numbers

Rod numbers

Crane

s

Rod numbers

Anode positioning

Cranes

Rod numbers

Rodded anodes

Butts

Anode numbers

Anode weighing

Data – Discrete process

27

Challenges

28

Data processing

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Asset Framework :

equipment hierarchy

PI Data

Server

Microsoft Excel

Lab,

MES,

other

sources

Event Frames :

organize data in time

Automation

Raw data

Data extraction / exploitation

Microsoft SQL-Server

Analysis Services (SSAS)

dialog

30 All data is random, fictional data to show functionality only

Final result

31

Data sampled at different points in time are all linked to the same anode

All data is random, fictional data to show functionality only

Cast data collection and integration

Bruno Longchamps, P.Eng SMART Manufacturing, Program Manager

32


Cast data collection & integration

• Well integrated, right

granularity cast data

• Increased monitoring,

troubleshooting and

analytical capabilities

• Reduced scrap and

improved product quality

Solution Results / benefits Challenges

• Huge volume of data

• Adaptive and generic data

capture

• User driven data capture

enhancements

• Time series and relational

data integration in one spot

• Use Event Frames to capture

and organize data in time

• Extend EF internal data

capability

• Integrate all data sources with

the MS SSAS toolset (BI cube)

• Automated EF structure and

data transfer to the cube

The goal of this initiative was to deliver an

efficient and flexible cast equipment's data

collection engine and to combine resulting

data with other data sources such as

elaboration, quality, laboratory and tracking

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Casting Operation Overview

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Casting operation:

When metal preparation

completed in the furnace

The furnace is feeding the

liquid metal to a casting table

Casting table is forming and

cooling the metal

The Casting Pit elevator is

going down from the top

position to the bottom (Cast

length)

1 to 3 hours per cast

Metal

Furnace

Up to 30

Feet Long

Elevator

Casting Data Collection & Integration technical challenges

Summarize the volume of time series data: 50 sensors and set

points x 3 hours cast x 1 sec frequency = approx. ) 0.5 M values per

cast;

Ability to capture cast data for a specific internal process step like:

start-up time, steady state time, stoppage time;

Ability to capture cast data at a specific time (on critical event);

Deliver a complete process data integration (from all sources);

Be able to quickly compare, slice and dice from various angle critical

cast data;

Finally, from the cast summary data, be easily able to go back to the

detailed data in one mouse click;

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Solution : Casthouse Data Integration Concept

36

AF Data Model

PI Data

Server

Microsoft Excel

Other

sources

Event Frames

Automation

MES &

LIMS

Casthouse

Casthouse AF Data Model

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Casting Operation Event Frames Boundaries

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Cast Start =

Event Frames

(EF) Start

Cast Stop =

Event Frames

(EF) End

Cast Length

Capture data for specific process phase (time window)

39

Step 5 to 7 = Startup (ramp-up)

Step 8 = Steady phase

Cast Start Cast End

Step 5 to 7 = Startup (ramp-up) Step 5 to 7 = Start-up (ramp-up)

Step 9 to 10 = Stoppage

Capture Data during the Steady State phase only

40

Based on predefined Cast Steps (Steady phase step = 8)

Data is summarized for the

cast steady state time

window as configured: Step Start @ mm

Step Stop @ mm

Step: 2:49:59 hours

41

Cast Length >=

7,500 mm

At 3:28:54 AM

Additional Data capture capability: Data at a specific moment

Capture information at a specific moment (Like a picture)

42

Cast Length >=

7,500 mm

At 3:28:54 AM

Can always go back to the detail with imbedded PI Coresight hyperlink

43

BI Cube needed for the Integration of all data sources (EF and MES)

44

• Summary of all Cast critical data in one place • Can look at a large volume of data quickly • Can do cast to cast comparisons • Can go back to the detailed data in one click (PI Coresight hyper link)

44

Can always drill down to the detail with PI Coresight from the CUBE

45

Cast display in PI Coresight – Focus on Start and End

46

Event Frames – Furnace Preparation EF (if time allows)

47

Metal Temperature at the

silicon addition time

Important EF attribute can be displayed in a KPI (if time allows)

48

Note: Numbers displayed on this slide do not represent real operation data

780

656

700

600

© Copyright 2015 OSIsoft, LLC 49

Contact information

Bruno Longchamps, P. Eng.

[email protected]

SMART Manufacturing, Program Manager

Alcoa Global Primary Products

Pierre Boutin, P. Eng., MPM

[email protected]

Manager, Global Manufacturing Solutions Delivery

Alcoa Global Primary Products

mailto:[email protected]:[email protected]


Questions

50

Please wait for the microphone before asking your questions State your name & company

Documents

SMART Manufacturing Journey Alcoa Smelting and Casting · the MS SSAS toolset (BI cube) • Automated EF structure and data transfer to the cube The goal of this initiative was to