The Bottom Line on Bottom Line Automation
Mike Brown, Matrikon’s vice president of technology, opened the second day of Matrikon’s Summit 2007 with reflections on the drive towards a “business-centric” view of process automation.

“I’m hearing a recurring theme that we have to pay more attention to the profitability of automation. But my cynical bone keeps wondering if this is just marketing,” he said.

The challenge, he went on, is to measure the bottom-line ROI of automation initiatives.

Users and suppliers are beginning to rethink the role of automation and demanding to know more about how automation contributes to plant profitability and the core business drivers.

Over the past years, we’ve gone through several phases in our thinking about automation technology, he added. In the 70s, we invested in automation technology for technology’s sake. Then we moved to a network-centric approach, with open systems and on to an application- centric model with MPC, advanced control and other approaches that are standards-centric as well as open. Now, we’re being told that our systems need to be business-centric.

But, said Brown, “I confess to being confused, and I don’t really know how to do that.”

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Matrikon's VP of technology, Mike Brown, says process automation users must now tackle the real challenges in their plants and transform work processes.

Seeing Your Way Through to Fleet Optimization
Just a few years ago, John Kapron couldn’t tell how well DTE Energy’s electricity generating plants were performing when he wasn’t there, or if they were operating at all. Things have changed.

DTE’s two main divisions, Detroit Edison and Mich Con, operate nine major power plants and other facilities, and nearly all their units have ABB’s distributed control systems (DCSs) for a total of 300,000 process data tags. To the DCSs it’s installed since the early 1980s, DTE added OSI PI data historian in 1999-2000, engineering applications enabled by Matrikon’s Operational Insight and Control Performance Monitor, and finally a 24/7 Performance Center in 2005 and Performance Center Applications in 2006.

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DTE’s technological specialist John Kapron says visualization works!

Matrikon Equipment Condition Monitor — Predict and Prevent Equipment Failure
- Improve equipment performance
- Identify underperforming equipment
- Reduce unscheduled downtime
- Reduce costs with condition-based maintenance
Download the whitepaper: "Production Optimization through Advanced Condition Monitoring"

Innovation, Best Practices and Optimization on Tap
Matrikon’s Alarm Mik Marvan concluded the session on alarm management by showing how Matrikon’s Alarm Management System can help companies implement best practices. In addition, Neil Gregory from Meridian Energy gave a follow-up to yesterday’s general session discussion by Garth Dibley. Meridian Energy is NZ largest energy provider.

Meridian Energy is a “renewable” company, generating only from hydro and wind. They have huge hydro assets and operate the largest wind farm in NZ’s North Island.

Asset Management Excellence
We are trying to maximize the lifecycle and the long term asset value of the large equipment assets. We are trying to optimize our maintenance. At a strategic level we’re talking about refurbishment and replacement of aging assets. With aging assets it is all about when you do that. We have tools to help us understand the plant equipment condition.

You can replace before failure. This is a low risk option, but it is very expensive. The strength of the Predictive Asset Management system is that it works for all levels of equipment from large to small assets.

You can replace after failure. This, too, is a suboptimal choice. Emergency replacement can cost as much as five times normal purchase cycle cost, not to mention the potential environmental damage. “We have transformers in a World Heritage site,” Gregory said, “and chucking 15,000 liters of transformer oil down the ridge just isn’t right.”

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Matrikon’s Mik Marvan on alarm philosophy from the Best Practices Track.

Matrikon Process Performance Monitor — Reduce Process Variability with Multivariate Analysis
- Identify process abnormalities instantly
- Ensure environmental compliance
- Construct and deploy prediction models
- Perform online sensor validation
Download the whitepaper: "Production Optimization through Advanced Condition Monitoring"

Quicker Plant Testing and Modeling
Tesoro Corp.’s North Dakota-based Mandan refinery has a multivariable predictive control (MPC) application on its crude unit (CDU). The plant produces about 58 mbpd of light, sweet crude from western North Dakota, eastern Montana, and Canada, and its gasoline and diesel products serve 112 retail sites.

However, designed using a commercially available modeling and control package, the application was originally commissioned in 2001, and ran for about a year until process changes in the distillation section resulted in significant changes in the underlying predictive models. These caused most of the MPC controller to be turned off by the board operators. The combination of poor controller models and significant unit performance issues resulted in a very low and limited use of the MPC and thus poorer operating margins.

Greg Kautzman, Mandan’s process control engineer at Mandan, reported on May 9 at Matrikon Summit 2007 that Tesoro needed to reactivate the application, but also needed to address the maintenance challenge associated with time consuming and expensive conventional plant step tests. “We had to find the balance between managing the MPC maintenance workload, and ensuring sustained optimal performance of the MPC controllers,” he says.

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Greg Kautzman of Tesoro on how to use MPC the right way: "If you want something done right, sometimes you have to do it again."

Now for the Real-World Scenarios
In yesterday afternoon’s breakout sessions at the Matrikon Summit 2007, two Matrikon users outlined the challenges and results of their implementations of Matrikon products.

Dr. Raja Amirthalingam of the Air Liquide Research and Technology Center, in Newark, DE, described a pilot program at Air Liquide using the Tai-Ji modeling algorithms for closed-loop modeling. The goal of the program for using advanced controls in MVPC implementations at more than 50 air separation units at Air Liquide facilities worldwide was three-fold. The company wanted to reduce energy consumption by improving secondary fuel (off-gas) utilization and reducing flaring; improving reliability by reducing the variability in certain critical variables; and improving operational efficiency by smoothing production rate transitions and reducing deviations from mean values by means of continuous multivariable regulation.

In order to achieve these goals Air Liquide needed a better modeling tool and less disruptive testing methods. It tried out the closed-loop modeling tool from Tai-Ji in a pilot program at the HyCO plant. The eleven-hour test made changes to a temperature controller in step sizes in open-loop mode. The purpose of the test was to identify the models and start commissioning the controller and re-identify the models in a closed loop.

In a second presentation, Mike Tyrrell, PIMS Manager for IneosChlor Vinyls, Cheshire, U.K., talked about implementing an alarm management program to maintain uptime reliability at both new facilities and 30-year-old plants with limited manpower.

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Mike Tyrrell of IneosChlor talks real-world alarm management.