There is a story developing in processing plants. It began slowly over the last decade as the high-grade material got mined and the sector wrestled with having to dig deeper and work harder to extract ore. Operating costs skyrocketed until global economic pressures put the squeeze on everyone. Miners who have had the Eureka moment are suddenly developing technologies themselves or seeking them out to improve their plant performance.
It seems as though most discussions concerning condition-based maintenance quickly shift toward asset reliability and predictive analytics. There’s quite a bit of value to be gained from monitoring the health of large rotating equipment.
Ask technicians and engineers about manually tuning PIDs. Their responses usually fall into one of the three different groups: 1) Some who will say it’s a “dark art” and won’t stray from an OEM’s default parameters, 2) The majority who will describe approaches that are based on seemingly random trial-and-error, and 3) Very few who will have truly figured it out. My career has thankfully allowed me to rub shoulders with enough practitioners from that last group and with their guidance I’ve cultivated a list of tuning tips that have served me well.
This article explores the data requirements that allow plant-wide monitoring and diagnostic technologies to accurately characterize changes in performance. In particular, it examines the requirements of key performance indices (KPIs) common to control loop performance monitoring (CLPM) technologies. These KPIs are essential to identifying issues and isolating the associated root-causes that undermine both production and asset reliability.
An engineer working at a corn milling production plant receives his daily report on specific opportunities to optimize the facility’s regulatory control systems. The facility is one of dozens owned and operated by this world-class manufacturer, and it relies on 100s of PID controllers to maintain safe, profitable control. Thousands of data sets generated daily by the plant are automatically captured before being modeled and having their results aggregated.
For years, it was nessesary to steady a process before tuning software could be applied successfully. Because most industrial processes exhibit some degree of ocvillatory behavior, the steady-state tuning requirement meant tuning software could be applied only on loops that were already under reasonable control. Recent advances in process modeling have changed that requirement.
Temperature control applications can be broken into two main categories—fully continuous and semi-continuous (or batch). An example of a fully continuous temperature controller is a shell-and-tube heat exchanger. In this application, the exit temperature is controlled by adjusting the flow rate of heating fluid, such as steam, through the shell side. As the flow of heating fluid is increased, the temperature also increases until it eventually settles at a temperature well below that of the heating fluid. Similarly, by decreasing the flow of steam, the temperature drops. The change in steam flow impacts the final steady state temperature of the exit steam. Processes such as this are relatively easy to tune using the basic tuning rules available. How can practitioners apply these rules to tune more complex temperature applications?
Petrochemical manufacturers experience unique challenges when it comes to maintaining effective regulatory control. As practitioners know, the dynamics of petrochemical processes are complex. From the volatility of batch reactions and rapid responses of pressure control to the highly nonlinear and sluggish nature of temperature control, the range of both process dynamics and industry applications routinely push the common PID controller to its limits. Whereas an effectively tuned PID can enable safe and efficient production, a poorly tuned controller can inadvertently hamper quality and constrain production throughput. Innovations in process modeling technology have proven to simplify the tuning of individual PIDs. Only recently have these innovations been applied to simplifying controller optimization on a plant-wide basis.
Our President and CEO, Dennis Nash, was quoted in a recent magazine article. The article covers the up and coming Industrial Internet of Things(IIOT). Dennis provides insight on how IIOT can easily be incorporated into our product, PlantESP.
The data historian has evolved. Since its introduction in the 1980s, the historian has transitioned from a simple repository of production data to a treasure trove of operational and business intelligence. Whether required by regulatory fiat or deemed necessary for emergency backup, the historian was originally a cost center in the eyes of many and offered what was generally viewed as limited application value. Storage was expensive, so data was limited. That calculus has changed as the cost of data has dropped and the value of analytics has soared.
The process industry’s objectives are simple: increase efficiency and throughput. Some process manufacturers are increasing productivity through technological innovations. Segments such as oil and gas and power have invested in new technologies to improve control over complex, often highly interactive processes. Whether multivariable or predictive in nature, these solutions can be costly and complex, creating obstacles for adoption by other process industry sectors. While other manufacturers may lack the broader energy industry’s resources, their need for growth is equally significant.
The market for advanced process control solutions is large and growing, and it’s doing so for good reason. Demand among process manufacturers for improved production control and efficiency is tireless. Whether in response to a shrinking workforce, increased production complexity, or ever increasing expectations for productivity – quite possibly all of the above – multivariable model predictive control (MPC) solutions in particular promise significant gains by optimizing the control of complex production processes.
What is the state of the art in asset reliability? Consider a typical production plant. The human machine interface (HMI) graphics flash like a stoplight: red, yellow, and green. Performance values consistently fluctuate up and down. The data is real-time and it is highly dynamic – professionals equate it to a plant’s vital signs. All the while operators monitor the HMI waiting for indications of an excursion, and maintenance staff tend to their calendar-based maintenance schedules in an effort to ward off failure. Condition-based tools monitor the few attributes that are readily correlated. With uptime synonymous with profit, this inefficient and risky approach belies the technological advances and industry’s investments in improving asset reliability.
For predictive maintenance, systems can be in a variety of conditions, and the many components within that system must be monitored to predict successfully whether those conditions indicate continued operation or looming failure. Obviously, failure of critical equipment leads to unplanned maintenance, which can be very costly.
Now that Rockwell Automation no longer offers RSTune and RSLoop Optimizer, customers have a compelling option through the company’s partner program.
The Rockwell Automation Encompass’s Product Partner program offers a dynamic solution for Proportional-Integral-Derivative (PID) loop control tuning software through its partner company Control Station, Inc. As of September 30, 2011, Rockwell Automation has discontinued the sale of RSTune® and RSLoop Optimizer. The company will honor existing support contracts, and advises its customers to seek migration elsewhere.
This generation of manufacturing automation and controls leaders includes 19 young professionals excelling in control system design and teaching others about the fun in engineering, while resolving local and global challenges through smarter applications of automation and control technologies.
Vendors of control loop performance monitoring and optimization software describe what it does and how users benefit.
In a word, control loop monitoring and optimization is all about savings, saving energy, raw materials, and operator effort while improving quality and profitability in a plant that depends on feedback controllers to produce a product. Commercial control loop monitors help the plant’s operators meet those objectives by automatically collecting and analyzing massive amounts of loop data from which to identify opportunities for improvement.
A best-practices approach to understanding and tuning PID controllers.
In the Forward to his “PID Tuning Guide,” Control Station’s Dr. Robert Rice notes that the proportional-integral-derivative (PID) controller is here to stay. Although it remains the most widely used technology for maintaining control over business-critical production processes, the PID controller’s nuances continue to mystify practitioners.
Want to meet the next generation of manufacturing automation and controls leaders? In November 2010, Control Engineering highlights 19 young professionals from around the globe who are making their marks in everything from system design to academia. These leaders aim to inspire others to get involved in engineering and resolve local and global challenges through smarter applications of automation and control technologies.
Control Engineering 2010 Engineers’ Choice Awards highlight some of the best new control, instrumentation and automation products introduced in 2009 as chosen by Control Engineering’s print and online subscribers. Survey respondents were asked to select products based on technological advancement, service to the industry, and market impact.
Sustainability has assumed a more visceral meaning. For many in manufacturing, just holding on and keeping production facilities afloat has become the singular focus. Economic upheaval in 2009 has altered the market’s appreciation for Sustainability’s ideals. Gone – seemingly out of a sense of necessity – were long-term strategies for continuous process improvement and plant optimization. Inserted overnight were the short-term tactics for simply staying in business. Lost was the understanding that Sustainability is an answer, not simply an ideal. Sustainability presents a rational and profitable solution for manufacturers both for the short- and the long-term.
The third factor in PID is the least understood. Derivative action can do good things, but when used improperly, it causes headaches.
You’ve probably heard the expression, “a little knowledge is dangerous.” This certainly applies to PID loops, especially when you try to dabble with the derivative factor. This element of the control strategy can improve performance, but only in the right situations and when applied properly. Understanding those situations begins with a quick basic review of how PID operates.
Loop tuning package doesn’t demand steady-state operation.
The nonlinear dynamics of most exothermic and endothermic batch reactions require expert attention to assure quality production. For instance, introducing reactants often results in dramatic swings in temperature. Maintaining production tolerances in such an environment tests the effectiveness of any proportional-integral-derivative (PID) controller and the associated tuning parameters.
Training the Field Operator of the Future
Simulators are widely recognized as essential to process control training as they facilitate the propagation of a company’s standard operating procedures (SOPs). This paper explores the use of process control simulators by Chevron Products Company to challenge existing corporate SOPs and to help achieve improvements in overall production performance.
Because most processes are self-regulating, it can sometimes be challenging to tune a controller for an integrating process. The principal characteristic of a self-regulating process is that it naturally seeks a steady-state operating level if the controller output and disturbance variables are held constant for a sufficient period of time. Non-self-regulating or Integrating processes can be remarkably challenging to control. This article explores their distinctive behavior.
Control Engineering 2006 Engineers’ Choice Awards highlight some of the best new control, instrumentation and automation products introduced in 2005 as chosen by Control Engineering’s print and online subscribers. Survey respondents were asked to select products based on technological advancement, service to the industry, and market impact..