CLPM: Improving process efficiency, throughput
Process manufacturers are under constant pressure to improve efficiency and throughput. Fortunately, opportunities to optimize production control are readily available. Advances in control loop performance monitoring (CLPM) software enables this technology to convert a greater share of everyday setpoint changes into opportunities to improve performance.
The same setpoint changes that occur countless times each day also provide virtually all that’s needed to improve control loop performance on a plantwide basis. It’s true that isolating such changes in controller output and calculating performance enhancements can be a cumbersome task. Fortunately, a growing number of control loop performance monitoring (CLPM) solutions make it simple for manufacturers to capitalize on these opportunities and to meet their efficiency and throughput goals.
This article draws attention to readily available data that can power gains in production efficiency and throughput. It highlights improvements in CLPM software that enable the technology to convert a greater share of everyday setpoint changes into opportunities for performance improvement.
Automating the discovery process
Improving regulatory control performance at a typical production facility can have a meaningful impact on both top-line revenue and bottom-line profit. Benefits include average increases in production output on the order of 2% to 5% as well as decreases in energy consumption estimated at 5% to 15%. Depending on one’s viewpoint, those benefits are either good or bad news for process manufacturers. On the one hand, they underscore inefficiencies that are commonplace in manufacturing. On the other hand, they identify readily available opportunities to improve performance (see Figure 1).
To put those values into context, consider an automotive parts manufacturer that reduced the cycle time of its production process by 9.3%. Its output potential swelled by 13.5%—more than twice the average increase—with no additional expenditures for capital equipment. Next, consider a ceramic proppant mill that realized an estimated energy cost reduction of $100,000 per year. The cost decrease immediately improved the mill’s bottom line. What may be more important is that the improvement was achieved with corrections to less than 6% of the facility’s proportional, integral, and derivative (PID) control loops. In both instances, the gains were profound and underscored the value of improved control loop performance—dollars and cents.
It’s clear that improved regulatory control can have a meaningful impact on a plant’s financial performance. While these two examples make a case for improved control, the process of isolating a plant’s bad actors and realizing gains such as these hasn’t always been easy. Few manufacturers have had the resources needed to proactively monitor loop performance, let alone track down the associated root causes until recently. With hundreds—if not thousands—of PID loops at a typical production facility, the odds have been stacked against them.
CLPM is a relatively new category of diagnostic technology that is reshuffling the deck in favor of continuous process improvement. CLPM solutions actively monitor a manufacturer’s regulatory control systems on a plantwide or even enterprise basis. No, this is not the same function performed by a distributed control system or model predictive control solution. Those are supervisory technologies that manipulate the underlying regulatory level systems. In contrast, CLPM focuses specifically on the health and effectiveness of regulatory control loops-both in isolation and as a network.
Recommending situation-specific corrective actions
The analytics included within a typical CLPM solution assess performance, prompting timely alerts of control-related issues and facilitating the isolation of the associated root causes. But ask anyone in operations or engineering; they already know there are problems. Fortunately, CLPM solutions recommend situation-specific corrective actions (see Figure 2). While the core technology is more than a decade old, the rate of CLPM implementations has increased only recently. The underlying technology has been improved, and claims of financial impact have piqued the interest of a growing number of manufacturers.
CLPM entered the market near the start of the new millennium. As with current solutions, early CLPM offerings required high-speed data for each of the many dynamic tags associated with a plant’s PID control loops. While the collection speed varied from temperature and level to pressure and flow, sufficient resolution was needed to accurately assess a given loop’s behavior.
Back then, the tag and collection speed requirements represented a significant financial hurdle. Remember, that was before the Industrial Internet of Things (IIoT). Sensors were expensive. Data storage was expensive. Everything about CLPM was expensive. And beyond the setup and configuration costs, early CLPM solutions lacked the advanced capabilities more common in today’s offerings, which correspond directly to the stronger value proposition of current CLPM solutions.
Identifying underperforming control loops
While mechanical problems are the source of most performance issues, poorly tuned PID controllers are another well-known contributor (see Figure 3). First introduced in the late 1950s, the electronic PID controller remains the dominant technology for maintaining safe, reliable control of a facility’s complex production processes. The challenges associated with identifying underperforming PID control loops usually is two-fold. First, with a plethora of competing responsibilities, production personnel are rarely focused on loop performance when tuning issues actually arise. Second, when schedules permit, it’s rarely clear which loop—or loops—requires their attention.
CLPM has always filled the role of a preventive technology, providing awareness of tuning issues and isolating the bad actors. But once found, poorly tuned PID controllers presented other difficulties. Some loops were deemed "off limits" due to their sensitivity and/or financial importance. What’s more, oscillatory conditions routinely limited the effectiveness of tuning software and manual techniques alike. Recent innovations, however, resolved those difficulties. In fact, they’ve helped to elevate CLPM from a simple preventative to an advanced prescriptive technology.
Modeling unruly processes
In 2008, a meaningful step forward for CLPM was achieved. The technology showcased the ability to proactively isolate and model data associated with everyday setpoint changes. Remember that the average production facility undergoes hundreds of setpoint changes each day. Those adjustments are generally performed in conjunction with load changes, product changes, and even shift changes. The innovation applied traditional tuning routines to the newly accessible step test data. Being traditional, the tuning routine was limited in its scope. Step test data that exhibited the oscillatory behavior typical of industrial control applications could not be accurately modeled. Even so, this was noteworthy progress.
The ability to accurately model oscillatory, noisy dynamics provided CLPM with a more recent step forward. The advance was introduced in 2013. Based on a proprietary modeling method, the innovation eliminated the steady-state requirement associated with traditional tuning methods. By doing so, the majority of everyday setpoint changes could be used as the basis for tuning a plant’s plentiful PID control loops (see Figure 4).
Delivering big data analytics
As a direct result of the additional models, CLPM technologies gained the ability to generate other highly relevant loop information. Specifically, the ability to aggregate models on a loop-by-loop basis and in a statistically relevant manner became possible. The capacity to catalogue parameters associated with 100, 200, or any number of models equipped CLPM users with more insight into a process’ constantly changing dynamics. In addition, it allowed CLPM to simulate the same process’ performance under specific values for each controller term: proportional, integral, and when applied, derivative (see Figure 5). This was CLPM’s first true step toward delivering big data analytics.
From increases in output potential to reductions in production-related costs, CLPM is making a strong, consistent case for improving control loop performance. Recent advances in particular have bolstered the capabilities of select CLPM solutions, and those advances enhance CLPM’s impact on day-to-day plant operations. The capabilities are there. What’s more, so too are the financials. For process manufacturers actively looking to improve efficiency and throughput, CLPM may be just the ticket.
Dennis Nash is president of Control Station Inc., based in Manchester, Conn.
Don Wilkey is managing director of Daesim Technologies Pty Ltd, based in Brisbane, Australia.
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