A REVIEW OF PROCESS ANALYTICS IN THE YEAR 2012
Rob Dubois Jeff Gunnell Dow Chemical ExxonMobil
Chemical
In the years up to 2012 the demand for more difficult measurements
increased Matched by growth in new Process
Analytical technologies...• nanotech TDL lab-on-a-chip
microGC NMR spectrometer-on-chip eNose CRD MS-on-a-chip
… but the old technologies remained...• GC MS FT-(N)IR
CEMS water
… and it became impossible for one person to provide high level support such a wide range of techniques
Remote support requirements were met through initiatives which started at
IFPACs in 2000/1/2
NeSSI: New Sampling / Sensor Initiative
ConnI: Connectivity Initiative
Appl-I: A suite of application software
Characteristics of the Process Analytics industry
Superb science• inventing new analytical technologies
Practicality• efficient engineering of the sensor-world interface• rapid commercialization
Collaboration• selecting winning industry-wide initiatives• competing in the real value-added areas
Innovative deployment of other technologies• seizing opportunities - not reinventing
Computing developments which we used...
PDAs became a standard tool• wireless connectivity• human-machine interface for many devices• used with GPS to pinpoint equipment
Wearable PCs became common
• Heads-up displays and data entry improved– voice entry of data– voice recognition “action” commands
The web ruled...• browser access to everything from everywhere
Security became better and easier• retinal recognition
NeSSI modules are the foundation
Courtesy of CIRCOR
Modular systems built on the desktop
ConnI Architecture - 4 domains
Level 2 LAN = Field LAN
Level 3 LAN
INFORMATION D0MAIN
AnLAN
PROCESS CONTROL DOMAIN
c-LAN
ENTERPRISE DOMAIN
MEASUREMENT DOMAIN
SAM
eSAM
Level 1 SensorBus (CAN)
Level 4 Enterprise LAN
- Highbandwidth data pipelines
- Traffic routers send data to the “need to know” domains
Courtesy of P. van Vuuren and R.J. O’Reilly
Predictive maintenance replaces planned
• Intelligent diagnostics watch analyser systems...all the time...
• ‘I need help’ flags are sent in real time to wireless Personal Digital Assistants (PDAs)
………………
A typical troubleshooting task for a technician starts when her PDA buzzes and indicates a possible problem...
Please help me...
A smart application in an analyser system has detected something strange - relevant information is sent out...
The technician taps down for more info...
Background data and recent history is sent
Key sensor data have been monitored and unusual conditions have been detected...
The technician taps down for more details...
Recent history is shown graphically
The technician starts to think about the cause of the problem…
…and sees the problem…clearly...
A pinched valve is quickly discovered and the problem resolved…pronto!
Appl-I: Smart applications
Functions are part of a shell which is used by all analyser vendors…(much like Excel)
•monitoring of key parameters• flow, pressure, temperature…as well as analytical
•self-validation, both continuous and periodic
•intelligent statistical tools identify abnormal events
•users notified when trouble is suspected
•data presented in a user-friendly way
Excel is a registered trademark of Microsoft Corp.
The technician takes her toolkit, including her wearable PC, out to the
field...
Courtesy of Oxford Technologies
She has access to an on-line manual
The wearable PC means that useful information is readily available:
There are up-to-date descriptions of tasks
Video clips* show how jobs are done.* Video Courtesy of Andy Jopek
ScAN microSpectrometer
Web-based manual
Model : 42 General manualTag : NA733 Specific manual
__________________________________________________________________________
Section 4.3 Valve maintenance
h h h h h h h h h h h h h h h h h h hh h h h h h h h h h h h h h h h h h hh h h h h h h h h h h h h h h h h h hh h h h h h h h h h h
h h h h h h h h h h h h h h h h h h hh h h h h h h h h h h h h h h h h h hh h h h h h h h h h h h h h h h h
h h h h h h h h h h h h h h h h h h hh h h h h h h h h h h h h h h h h h hh h h h h h h h h h h h h h h h h h hh h h h h
h h h h h h h h h h h h h h h h h h hh h h h h h h h h h h h h h h h h h hh h h h h h h h h h h h h h h h h h hh h h h h h h h h h h h h
Valve maintenance video clip
Links to other systems facilitate work
Equipment parts lists are linked to warehousing - both in house and external.
She identifies spares which she needs and orders them as she works using the manual and warehousing tool
ScAN microSpectrometer
Web-based manual
Model : 42 General manualTag : NA733 Specific manual
__________________________________________________________________________
Section 4.8 Spares
h h h h h h h h h h h h h h h hh h h h h h h h h h hh h h h h h h h h h h h h h hh h h h h h h h hh h h h h h h h h h h h h
Record keeping is built in
Intelligent checklists and voice entry make record keeping easy:
The manual helps her fill in a work record - using voice data entry
ScAN microSpectrometer
Web-based manual
Model : 42 General manualTag : NA733 Specific manual
__________________________________________________________________________
Section 4.3 Valve maintenance checklist
Say "Voice entry" and "your name "
h h h h h h h h h h h h h h h hh h h h h h h OK
h h h h h h h h h h h h h h h hh h h OK
h h h h h h h h h h h h h h h hh h h h h
h h h h h h h h h h h
h h h h h h h h h h h h h h h hh h h h h h h h h h h h h
Say "Comments"
Wizards help with many tasks
On completion she runs the smart health check wizard
She requests two day data watch so she can personally check that everything is OK. A maintenance encounter is booked into her calendar
ScAN microSpectrometer
Web-based manual
Model : 42 General manualTag : NA733 Specific manual
__________________________________________________________________________
Section 5.1 Auto check wizard
Say "Run autocheck" and select options
h h h h h h h h h h h h h h h hh h h h h h h Yes
h h h h h h h h h h hYes
h h h h h h h h h h h h h h h hh h h h h h h h h h h h h No
Section 5.1 Data watch wizard
Say "Run data watch" and select options
h h h h h h h h h h h h h h hYes
h h h h h h h h h h h hYes
h h h h h h h h h h h h h hNo
h h h h h h h h h h hYes
In 2012 a typical engineering support problem involves...data reconciliation
A micro-spectrometer system has been installed in a new plant in China...
• The process control engineers have a model to predict process behavior - and they use the analyser to correct their model
• In testing, the analyser and model tracked well during small excursions, however there was a discrepancy during a large excursion– the analyser response was just too slow and too small
• The site team in China asked the engineer to take a look
• All folks involved were able to browse their analyser and process data base from their PC
They could see the problem but could not explain it...
Model Analyser
The engineer realized he needed expert help...
• He checked a few parameters using the analyser browser but nothing jumped out at him immediately...
• In a video conference with the China team the engineer decided to ask for additional support
Connectivity tools enabled remote teamworking
• The China team set up a data-base containing the entire data record plus design and test information
• He called up a virtual team which included• the process and analyser people in China• another site which has a similar installation• the analyser vendor’s design group
• They had a virtual conference using:• video communications• data & screen sharing
It certainly looked like an analyser problem...
• The virtual team felt pretty sure that there was something strange about the analyser, not the process...
• The vendor team asked for time to work on the data bases
• They discovered that the problem was due to hysteresis in their micro-technology, coupled with an unexpected effect in their software smoothing algorithm
The problem was fixed remotely by the vendor
• A software patch was downloaded to the analyser in China• revisions were automatically updated• a sprite to monitor performance was
included
• The vendor also sent out an alert to other clients, along with the sprite
• The vendor initiated their product improvement process
Key points: Technician level
Technicians in plants retain the prime responsibility of maintaining analyser systems
Human knowledge, skills, thought processes are needed more than ever
Logic and intelligence in machines help technicians to use their human skills better• real-time health checks of systems
– focus on predictive maintenance• data presentation helps troubleshooting• …and bureaucracy is unburdened
Key points: Engineer level
Highly complex analyser technologies abound
Sites are spread all around the globe
Technical support and troubleshooting is a team game• collaboration tools facilitate virtual teams• support round the clock, round the globe• changes in
– responsibility– behaviour– relationships
This is life in 2012 - how did we get here?
NeSSI came through:• modular sample systems• integrated with analysers sensors• in smart, field mounted units
ConnI • delivered an architecture which linked
everything together
Appl-I• provided a common application shell
…and collaboration delivered benefits for all
…and a special thanks to...
Peter van Vuuren
- for supplying the building blocks
Walter Henslee
- for widening the vision
Smart
artificially intelligent
analysers