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SMART Manufacturing Journey
Alcoa Smelting and Casting
Presented by Bruno Longchamps
Pierre Boutin
© Copyright 2015 OSIsoft, LLC
SMART Manufacturing Journey
Alcoa, Global Smelting and Casting
Bruno Longchamps, P.Eng
SMART Manufacturing, Program Manager
OSIsoft User’s Conference, San Francisco, April 2015
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Presentation content
 Alcoa
SMART Manufacturing Journey Overview
 Users Engagement
 Anode Tracking
 Cast Data Collection & Integration
 Conclusion
 Questions
3
Alcoa Inc.
 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
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Aluminium Production Process
c
o
k
e
P
i
t
c
h
Electrode:
Anode
production
Potroom:
Liquid aluminum
production
Casthouse
Salable solid aluminium
production
5
Smelting Operation
Potroom
Rodded
Anode
Casthouse Furnace
Finished Products
6
Why Alcoa invested in the SMART Manufacturing Initiative ?
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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SMART Manufacturing Program
SMART Manufacturing is about
integration of data with process expertise
to enable proactive and intelligent
manufacturing decisions in dynamic
environments
Challenges
•
Solution
Aluminium market has
been financially challenged
for several years
•
Establish the SMART
Manufacturing program
•
OSIsoft enterprise agreement
•
Improve competitiveness
•
•
Enhance operational
excellence
Deploy a robust infrastructure
« cookie cutter » project in all
plants
•
High retirement rates, loss
of SMEs
•
Mobilize key employees
Results / benefits
•
Well integrated, fine
granularity, long term
production data available
•
Increase collaboration and
sharing at the plants and
between locations
•
Achieve significant
savings
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SMART Manufacturing – Main improvement axes
Data
Maturity Model
After :
Before:
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SMART Manufacturing – Real plant wide data infrastructure
Site Name
GPM Global BU
GPM Warrick Site
GPM Alumar Site
GPM ABI Site
GPM Baie-Comeau Site
GPM Portland Site
GPM Mosjoen Works Site
GPM Fjardaal Site
GPM Deschambault Site
GPM Massena West Site
GPM Lake Charles Site
Warrick Power Plant
GPM Lista Site
Grand Total
Total
1,559,207
1,246,100
1,193,900
1,042,900
951,450
675,770
628,190
554,000
501,460
337,590
30,751
22,358
6,548
8,750,224
Note for Lista: QLC not installed and the initial data collection phase is not completed
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The journey to the implementation of the SMART Mfg program
2010
2011
2011(Q4)
2012-2014
2014
2015
Program Definition (OSIsoft support)
Proof of concept (Deschambault plant, QC)
Entered the OSIsoft Enterprise Program
Aggressive Global Deployment (13 plants)
EA extension to other plants
Additional deployments (2 to 5 plants)
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SMART Mfg. – Deployment Schedule
Site
2011
Q1
Q2
Q3
2012
Q4
Q1
Q2
Q3
2013
Q4
Q1
Q2
Q3
2014
Q4
Q1
Q2
Deschambault
Massena West
Baie Comeau
Mosjoen
Fjardaal
Lake Charles
Warrick
Portland
ABI
Reybec
Global BU Instance
Sao Luis
Lista
Intalco
San Ciprian
2015
Q3
Q4
Q1
Q2
Q3
Q4
Project Found
and Startup
Deployment &
Data collectIon
Value Realization
On Schedule
Schedule at Risk
Behind Schedule
ACES
On Hold
Closed
Ma'aden
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
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SMART Manufacturing – Geographical localisation
All Data is random, fictional data to show functionality only
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SMART Manufacturing – Functional model
Data Collection and Analytic
Data Visualisation
ERP
(Oracle)
MES/LIMS
PI Data Archive
PI
WebParts
AF & EF Model
& BI Cubes
MS SQL BI
Microsoft Sharepoint
MS SP
Web Parts
SSRS/Power
View Report
OSIsoft Tools
PI ProcessBook, PI Coresight, ...
Microsoft Tools
Excel, Power Pivot, BI Tools...
Process Equipements
14
SMART Manufacturing – Implantation diagram
Office / Business LAN – (Business Domain)
PI Clients
PI ProcessBook
PI Datalink
IE (WebParts & Coresight)
Node
CMI DFS Server
mPi
CMI DFS
Mapped drive M:\
SMART NODE
(VM Guest)
DFS Files Share
xxx-SMT-NOD-01
xxx-APM-DFS
Site
Analysis Tools & Displays of
Manufacturing Related Data
CMI Prod Domain - DMZ
CMI
FireWall
PS
PS
xxx-SMT-SQL-01
xxx-SMT-SQL-02
xxx-SMT-PI-01
xxx-SMT-PI-02
xxx-SMT-AF-01
xxx-SMT-AF-02
xxx-SMT-SVC-01
xxx-SMT-WSS-01
xxx-SMT-TS-4A
xxx-SMT-TS-4B
xxx-SMT-MGM-01
SMART SharePoint
Entreprise Server
Client Tools TS
SMART
Management
xxx-SMT-WEB-01
MS-SQL SMART
PI Server
PI AF Services
SMART Services
SMART WEB
mPI
SQL Server 2012 Enterprise
High availibilty
Clustered
SSAS / SSIS / SSRS
mPI
PI Server [HA]
PI AF [HA]
mPI
Pi Notification [HA]
PI ACE [HA]
PI AF Analytics (AF-01)
mPI
SMART Calc engine
Site coded calculation
mPI
IIS SMART App. Web Service
Pi Web Services
PI-Agent proxy for PCS LAN PI-Nodes
PI Coresight
mPI
PI WebParts
PI Datalink Server
PI OLEDB Enterprise
AF , EF, SharePoint DB
Plant MI
Datawarehouse
Historian Server
Plant assets and
hierarchy
Real-Time Calculations
SMART Dashboard
And WEB apps.
Real-Time & Historical
Plant Floor Data
SMART & MES
xxx-SMT-SQL-CL1
(Windows Cluster)
xxx-SMT-PI
(PI Collective)
xxx-SMT-AF (NLB)
xxx-SMT-WEB
(DNS Alias)
xxx-SMART
(DNS alias)
xxx-SMT-DMZ-TS
(NLB)
mPI
Excel
PI ProcessBook
PI Datalink
PI AF Client
PI-SMT
mPI
Excel
PI-SMT
PI Datalink
PI OLEDB Ent
Bomgar Station (support)
Calculation Engine
Notifications
PI-Agent proxy for PCS LAN
Admin &
Maintenance Apps
CMI - Plant Manufacturing LAN
APMSDG Domain
QLC
L&K
PS
PS
CMI - Control System LAN (one or more)
xxx-QLC3-LDR-A
xxx-QLC3-LDR-B
mPI
Proxy: xxx-SMT-SVC-01
LEGEND
·
mPI = OSIsoft Enterprise Agreement (EA)
·
P = Primary Machine
·
S = Secondary (Redundancy)
·
HA = High Availability
TITLE
xxx-SMT-TS-3A
xxx-SMT-TS-3B
xxx-SMT-MES-01
xxx-SMT-MES-02
Terminal Server
MS-SQL MES
Server
PI-SMT
PI-Datalink
SMART & MES
Apps
xxx-SMT-PCN-TS
(NLB)
Print
Server
Active
Directory
Trusting
Domain
SQL Server 2012 Std
HighApps
availibiity
MES
and
Services
xxx-MES-SQL-CL1
Windows Cluster
SMART Manufacturing System Architecture
March 12th, 2014
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User Engagement
Pierre Boutin, P.Eng, MPM
Manager, Global Manufacturing Solutions Delivery
OSIsoft User’s Conference, San Francisco, April 2015
16
Organization structure for change management
Global
Plant
Global
End-users
Sector
leaders
Plant sector lead/area super-users
Technical lead
Global
Global
End-users
Value lead
Regional leaders
SMART technical team
OSIsoft team
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Key success factors
Data visibility
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Key success factors
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Key success factors
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…
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Working across silos at the plant level
User engagement at the plant level
Electrode
Potline
Casthouse
Environ.
Energy
SMART mfg horizontal integration
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Working across silos globally
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Anode tracking
Pierre Boutin, P.Eng, MPM
Manager, Global Manufacturing Solutions Delivery
OSIsoft User’s Conference, San Francisco, April 2015
23
Anode tracking R&D project
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.
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
Solution
•
Read anode numbers with OCR
camera
Results / benefits
•
Holistic insight over anode life
•
Anode performance linked to
raw materials and anode
fabrication data
•
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)
Development of anode
manufacturing best practices
•
Improved Raw Material
selection process
•
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Work breakdown structure
Anode tracking
Data
Processing
Physical
Tracking
Bake
Process
optimization
Rodding
Database
PI
System
Paste
Electrolysis
• Data collection
• Structure/organize data
• Manufacturing Intelligence
• Process modelization
• Reaction plan
• Preventive/proactive actions
• Predictions
• Process optimization
• Best practices development
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Data – Continuous process
Paste plant
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Data – Discrete process
Rod numbers
Crane
s
Anode numbers
Anode numbers
Rodding
Anode positioning
Rodded anodes
Electrolysis
Anode weighing
Rod numbers
Automated reading
Rod numbers
Cranes
Butts
Butt treatment
Rod numbers
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Challenges
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Data processing
Asset Framework :
equipment hierarchy
Raw data
Event Frames :
organize data in time
PI Data
Server
Automation
Lab,
MES,
other
sources
Microsoft Excel
29
Data extraction / exploitation

Microsoft SQL-Server
Analysis Services (SSAS)
dialog
All data is random, fictional data to show functionality only
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Final result
Data sampled at different points in time are all linked to the same anode
All data is random, fictional data to show functionality only
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Cast data collection and integration
Bruno Longchamps, P.Eng
SMART Manufacturing, Program Manager
OSIsoft User’s Conference, San Francisco, April 2015
32
Cast data collection & integration
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
Challenges
Solution
Results / benefits
•
Huge volume of data
•
•
•
Adaptive and generic data
capture
Use Event Frames to capture
and organize data in time
Well integrated, right
granularity cast data
•
•
User driven data capture
enhancements
Extend EF internal data
capability
•
Integrate all data sources with
the MS SSAS toolset (BI cube)
Increased monitoring,
troubleshooting and
analytical capabilities
•
Reduced scrap and
improved product quality
•
•
Time series and relational
data integration in one spot
•
Automated EF structure and
data transfer to the cube
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Casting Operation Overview
Metal
Furnace
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
Up to 30
Feet Long
Elevator
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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
AF Data Model
Event Frames
PI Data
Server
Other
sources
Automation
MES &
LIMS
Microsoft Excel
Casthouse
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Casthouse AF Data Model
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Casting Operation Event Frames Boundaries
Cast Start =
Event Frames
(EF) Start
Cast Stop =
Event Frames
(EF) End
Cast Length
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Capture data for specific process phase (time window)
Step 8 = Steady phase
Step 9 to 10 = Stoppage
Step
Step 55 to
to 77 == Start-up
Startup (ramp-up)
(ramp-up)
Cast Start
Cast End
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Capture Data during the Steady State phase only
Based on predefined Cast Steps (Steady phase step = 8)
Step: 2:49:59 hours
Step Stop @ mm
Step Start @ mm
Data is summarized for the
cast steady state time
window as configured:
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Additional Data capture capability: Data at a specific moment
Cast Length >=
7,500 mm
At 3:28:54 AM
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Capture information at a specific moment (Like a picture)
Cast Length >=
7,500 mm
At 3:28:54 AM
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Can always go back to the detail with imbedded PI Coresight hyperlink
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BI Cube needed for the Integration of all data sources (EF and MES)
•
•
•
•
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)
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Can always drill down to the detail with PI Coresight from the CUBE
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Cast display in PI Coresight – Focus on Start and End
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Event Frames – Furnace Preparation EF (if time allows)
Metal Temperature at the
silicon addition time
47
Important EF attribute can be displayed in a KPI (if time allows)
700
780
656
600
Note: Numbers displayed on this slide do not represent real operation data
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Contact information
Bruno Longchamps, P. Eng.
Bruno.Longchamps@Alcoa.com
SMART Manufacturing, Program Manager
Alcoa Global Primary Products
Pierre Boutin, P. Eng., MPM
Pierre.Boutin@Alcoa.com
Manager, Global Manufacturing Solutions Delivery
Alcoa Global Primary Products
© Copyright 2015 OSIsoft, LLC
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Questions
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© Copyright 2015 OSIsoft, LLC