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... GCMSFT-(N)IR CEMSwater … 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 Sensor Bus (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

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

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

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

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...

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 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