...to Tango

Model-Predictive Control and Advanced Process Control Tools Are Helping Users Gain Efficiencies and Savings in Their Process Applications. Here’s How You Can Boogie to the Optimization Beat, Too

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“The APC project allowed us to achieve record production rates by finding optimal process operating conditions. This reduced the amount of energy needed per gallon of ethanol produced,” says Sommer. “With the APC application, EKAE was able to overcome operational constraints that limited plant production efficiency.”

Reflecting Real-Time Reality

A picture may be worth 1,000 words, but it also can be a useful model that’s worth a pile to savings. For instance, Jura Cement’s Wildegg plant partnered with ABB Switzerland in 2005 to integrate model-predictive control (MPC) into ABB’s Expert Optimizer 5.0 software and extended it to include Mixed Logical Dynamic (MLD) systems. ABB says combining MLD modeling with an MPC system can make short-term predictions about process evolution. Jura uses ABB’s System 800xA as the control for its main plant, but it also integrates Siemens S7’s Cemat library into 800xA by accessing data via Siemens-based OPC servers and an “ABB Cemat Connect” library.

In Jura’s cement mills, MPC and MLD can evaluate the merits of different control decisions and then select and implement the best decisions for the future. Also, the MLD mill model is constructed in Expert Optimizer’s graphical model building toolkit, so it gives veteran operators a clear representation of the real system’s relationships with its components. After the success of the first mill operating with Expert Optimizer 5.0’s MPC and MLD control, Jura decided in May 2008 to extend the software to its other mills.

Figure 2. Mechanics, shift supervisors and managers at National Grid get visual KPIs from their electric and gas facilities using Transpara’s displays from other software data sources.
“Our operators now have diverse information tools with clearly interpretable signals,” says Jürg Hitz, Jura’s head of process at the Wildegg plant. “The new control systems manage 12,000 signals over the whole process. We profit from a more explicit overview, and this gives us more and better optimization opportunitie, as well as improved productivity due to fewer and shorter kiln stops.”

Reaching Up to the Enterprise

Once optimization and MPC tools begin to achieve efficiencies, and their users get familiar with them, it seems like they almost involuntarily seek out other applications to improve, including those leading up the management and enterprise ladder.  

To extract inaccessible DCS data needed to fix its $250,000 phase separator and keep a production unit from wearing out pumps too fast, Arkema Chemicals in Calvert City, Ky., deployed OSIsoft’s PI System software about six years ago and achieved a $1.8-million, one-time savings and annual savings of $590,000. The plant makes Forane refrigerants for automotive air conditioners, other blended refrigerants and Kynar polymers.

Dwight Stoffel, Arkema’s principal plant electrical instrumentation engineer, says it took three days to install PI, and it provides a common point of view, necessary decision points and easy access to the plant’s disparate information sources. As a result, staffers in every department use several PI client tools, including ProcessBook for graphical visualization, trending and analysis and DataLink for Excel-based reporting. “Personnel in maintenance, logistics, engineering, the laboratory, accounting, process technology and operations have access to PI information, and they all use it to do their jobs better and optimize processes,” says Stoffel.

Modeling to Simulation to Training

Because plant dynamics, such as feedstock levels and downstream flows, are so complex to coordinate, the real beauty of an MPC system is that it takes information from an application’s other models and then makes changes at the exact right time to increase that application’s throughput.

For example, John Ragone, plant process optimization manager at National Grid, says the utility tries to give its technicians and managers actionable information in role-based formats, so they can make more efficient and timely decisions. National Grid has 6,650 megawatts of generating capacity, about 4.4 million electricity customers in New England and New York State, and more than 3.4 million gas customers in the U.S.

“The two pillars of our information architecture are OSI PI and SmartSignal, and we use them for performance monitoring, predictive maintenance monitoring, DCS historian and analysis, e-notification, control room unit dispatch, energy trader analysis, fuel management and cellular access to real-time data,” says Ragone. This means mechanics, performance engineer, shift supervisors and traders receive key performance indicators (KPIs) pre-assigned for their specific jobs from original data sources via cellphones, Blackberries and other handheld tools using Transpara Corp.’s business intelligence (BI) visualization software. 

So far, Ragone adds, National Grid has saved more than $11 million, thanks in part to its role-based data display tools, mostly in heat-rate savings, ISO penalty disputes and testing, DCS historical database improvements, and identifying critical points on a fluid drive. So, can I lead next time?  

Jim Montague is Control’s executive editor 

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How to Optimize Your Application

Maina Macharia, Pavilion Technologies’ project engineering manager, says there are several basic steps in undertaking most process control optimization projects.

  • Make sure you have the right instruments and digital equipment in place, so your data acquisition (DAQ) units will have a format that’s readable by your computing devices.
  • Decide what variables you want to improve by reviewing basic business goals. Then seek related historical data and determine application’s real operating baselines and use these results to find opportunities to improve performance and quality.
  • Recruit internal and external teams of experts to identify control changes that will bring your process closer to specification and reduce operating costs.
  • Evaluate and check what instruments you’ll need and develop a model of the difficult items you need to measure. Use virtual online analytics and software devices based on previous correlations.
  • Build your new model-predictive controls into your process and use it to help perform real-time control, which Pavilion reports can reduce variability by half or more.
  • Commission your new systems and set up an adjustment schedule.
  • Update operator training.
  • Collect new data at specified periods to find new baselines and calculate economic gains.
  • After all unit processes are under control, start a plant-wide optimization project to coordinate units, production lines and other systems. This can be thought of as combining smaller models into larger, overall models.
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