Virtual plant virtuosity

Maximizing operator and system synergy for best plant performance

By Gregory K. McMillan and Martin E. Berutti

1 of 3 < 1 | 2 | 3 View on one page

It is well known that the operator can make or break a system. A recent research study found that only 2% of process losses and safety issues could be traced to something other than human error. Operator training is recognized as essential. Not realized is that many, if not most, operator errors could have been prevented by better operator interface and alarm management systems, state-based control, control systems that stay in the highest mode and prevent activation of safety instrumented systems (SIS), continual training and improvement from knowledge gained, and better two-way communication between operators and everyone responsible for system integrity and performance.

Where are we?

We have an increasing disparity between what is potentially possible and what is actually done in terms of achieving the best plant performance. This disparity is due to the retirement of expertise without mentoring successors or even documenting to any significant degree the knowledge gained over decades of plant experience. This was partly due to the practitioners being very busy doing the actual work, and not having a marketing background, being without experience or motivation for presenting and publishing. In “Getting the best APC team,” Vikash Sanghani explains how nearly all the expertise of a major supplier of model predictive control (MPC) software, including comprehensive engineering of applications, dwindled to near zero. Thinking that their customers were going to develop in-house expertise, MPC suppliers did not try to keep or replenish their experts, when the opposite occurred.

The other side of the incredible gap between what is done and possible is the order of magnitude improvement in the performance of instrumentation, control valves, controllers, MPC software, tuning software and analytics software, and the profound increase in knowledge we have documented in ISA standards and technical reports. ISA members can now view these standards and reports online for free. Transmitter and valve diagnostics, a vast spectrum of function blocks, and advanced control and modeling tools from more than a hundred engineering years of development can be configured in a matter of minutes.

The main limits are the imagination of the practitioner and the management of risk. Unfortunately, users are not given the training, mentoring, and—most importantly—the time to develop the imagination of possibilities and the automation of risk management needed. In fact, the user has negative free time due to an overload of responsibilities in executing and managing projects.

Migration projects frequently end up being largely copy jobs based on a process and instrument diagram (P&ID) and process flow diagram (PFD) that are often more than a decade old. It may be expected that the Hazard and Operability Study (HAZOP) will catch changes needed to manage risk, and that improvements will evolve over time. But the benefits of more precise and timely measurements and valves, as well as better control strategies, often are not considered because there is no understanding of the benefits. Not fully appreciated is the consequence today of the lack of experimentation with the actual plant to develop innovations to eliminate potential operator errors. Concerns about loss of production, validation and regulatory compliance, lack of the prerequisite knowledge, and the unfamiliarity with the potential benefits typically prevent deviations from standard operation. All of these issues can be addressed by the use of a virtual plant before, during, and after the project. The deep knowledge gained by fast and free exploration with the virtual plant can result in a focused and trusted Design of Experiments (DOE) and dynamic response tests for confirmation of virtual plant results and implementation of more intelligent alarm, operator interface, and control systems.

2017 State of Technology Report: control systems

Today’s dynamic simulation software has in many cases achieved the same fidelity of steady-state simulators used for process design by incorporating physical property packages, equipment details, and the first principles from the ordinary differential equations (ODE) for material, energy and component balances, supplemented by charge balances for pH. Not as appreciated but highly significant is that modeling blocks have recently been added that can simulate automation system dynamics such as sensor, transmitter, analyzer, control valve and variable-speed drive dynamics that significantly affect the performance and tuning of most loops. Furthermore, most of the full potential of the opportunities offered by dynamic simulation can be achieved by connecting the actual configuration and displays of the control system and operator interface to the dynamic model in a virtual mode to create a virtual plant, eliminating the need for emulation and translation of complex, proprietary capabilities and the associated uncertainty. Figure 1 shows the virtualization of an actual control system, displays, alarms, historian and tools.

Most of the phenomenal capability of today’s systems for modeling and control remains untapped. Unfortunately, the loss of expertise extends to management, who in previous decades had advanced from doing applications to managing groups. Today, in many process companies, there may not even be a process modeling and control group, let alone anyone left who understands what is lost and its value.

Where we were

In an Engineering Technology department that evolved into a Process Control Improvement (PCI) group of a leading chemical company, the culture was to use modeling not only for the traditional process design and operator training, but also for improving the control system. Modeling and control were synonymous. The best control involved taking the best process, automation system and operator knowledge and implementing the essential aspects into a control system. Nearly all of the accomplishments were the result of deep knowledge gained from dynamic simulation.

Until the advent of the distributed control system (DCS) for anything more than simple PID control, we had to buy, install, wire and manually adjust separate analog control modules. This did not stop us. In many ways, the creativity in process control was greater, as seen in Shinskey’s books. The dynamic models we used for developing process and control system knowledge were the result of programming the ODE in Continuous System Modeling Program (CSMP) and then Advanced Control Simulation Language (ACSL), where they would be integrated and the results printed out and plotted. The control system functionality, including the PID controller with all of its many features, had to be emulated in code. Eventually, these models, generalized as FORTRAN subroutines, were interfaced to the inputs and outputs of the actual hardware of the DCS.

We used steady-state simulations to get us the right operating conditions and flows. Employing these as the starting points, we worked on getting the right transient response. Due to the extraordinary effort required in setting up the ODE, dynamic models tended to be small and focused on a unit operation.

We found that the new PFD simulation software, advertised to be able to be switched from a steady-state mode to a dynamic mode, did not work as intended, and we had to rebuild from scratch the models in the dynamic mode. The matrix for the pressure flow solver often developed fatal problems, shutting down the program with no diagnosis of fixes, particularly when we tried to go more plant-wide.

Despite the limitations in simulation software, advanced PID control (APC), model predictive control (MPC), and real-time optimization (RTO) thrived. A large part of the credit for the success of APC is due to Greg Shinskey, and the success of MPC and RTO is due to Charlie Cutler, who invented Dynamic Matrix Control. Since I was more into APC, I particularly appreciated the incredible spectrum of process control opportunities based on fundamental understanding of the process and the PID that Greg Shinskey developed and published. Look for a special tribute to Shinskey in the October issue of Control, written with Sigifredo Nino, Shinskey’s protégé, and with all of Shinskey’s books and many of his articles highlighted.

Where could we be?

Not well recognized is that the dynamic models often used for training operators as part of an automation project have a much wider utility that today is more important than ever.

So how do we address this increasing concern and missed opportunity?

The solution is to foster process modeling and control to maximize the synergy between operators, process control engineers, and the control systems. To start on this path, process control engineers need to be given the time to learn and use a virtual plant, and set up online metrics for process capacity and efficiency. The virtual plant offers flexible and fast exploring=> discovering => prototyping => testing => justifying => deploying => testing => training => commissioning => maintaining => troubleshooting => auditing => continuous improvement showing the “before” and “after” benefits of solutions from online metrics. Examples for major unit operations are given in the accompanying appendix, “Virtual plant in practice.”

First, we need to break the paradigm that you need to run a steady-state model for your cases, and the dynamic models are built to just show dynamic response and not process relationships. With dynamic models today having nearly the same fidelity as steady-state models, this is no longer true, which is fortunate for many reasons. With steady-state models, we were only able to find the process gain for self-regulating processes, and even here, these models were clueless as to the open-loop gain, including valve gain and measurement gain. The resulting relative gain matrix from process gains is helpful for pairing controlled variables and manipulated variables, especially for two-point concentration control in columns. However, the degree of interaction and the best decoupling is also determined by dynamics. Interaction between two loops can be minimized by making a faster loop faster and/or the slower loop slower. This is the basis of cascade control, where we want the secondary loop to be at least five times faster than the primary loop to prevent interaction between these loops. Also, the dynamic decoupling achieved inherently by MPC, or by dynamic compensation of feedforward signals acting as decouplers for APC, is based on dynamics identified. Thus, a dynamic model can tell you more about how to deal with interactions and what are the achievable performance metrics, and what the control system needs to do as the process moves to a new steady state.

Furthermore, steady states do not exist in integrating and runaway processes, nor in batch operations, transitions, startups and abnormal conditions. Most of the problems with plant operation can be traced back to these types of processes because of the abrupt changes, ramps and oscillations that result from the tuning of loops, sequences, manual actions and the lack of procedural automation and state-based control, as detailed in the presentation, “Most disturbing disturbances are self-inflicted.”

Dynamic models can find steady-state conditions and process gains, but you are relying upon the control system to achieve the new steady state, kind of like the actual plant. For very slow processes such as distillation columns and plantwide operation, steady-state models may be more useful to find the process gains and more optimum operating points, unless the dynamic models can be sped up.

In this time period, the capability of dynamic models to improve system performance has greatly increased, even though their use has focused mostly on training operators as an automation project nears completion. The virtual plant should detail the tasks needed for difficult situations from the best operator practices and process knowledge, and eliminate the need for special operator actions through state-based control. APC and MPC can deal with disturbances and address constraints intelligently, continually, automatically and with repeatability. Compare this with what an operator can do in terms of constant attention, deep knowledge and timely predictive corrections considering deadtime and multivariable situations. Some operators may do well, but this is not carried over to all operators. Then, of course, any operator can have a bad day.

Automation enables continuous improvement and recognition of abnormal conditions by a much more consistent operation. A better understanding by the operator of control system functionality and process performance from online metrics makes far less likely the disruptions caused by an operator taking a control system out of its highest mode and/or making changes in flows. Furthermore, procedural automation can eliminate manual operations during startup, when risk is the greatest compared to steady-state operation.

It is important to note that while we have singled out operators and process control engineers in this article, the need for knowledge to attain the best performance extends to maintenance technicians, process engineers, mechanical engineers and information technology specialists. Just think what can be realized if we were all on the same page, understanding the process and operational opportunities, and the value of the best measurements, valves, controllers and software.

1 of 3 < 1 | 2 | 3 View on one page
Show Comments
Hide Comments

Join the discussion

We welcome your thoughtful comments.
All comments will display your user name.

Want to participate in the discussion?

Register for free

Log in for complete access.

Comments

No one has commented on this page yet.

RSS feed for comments on this page | RSS feed for all comments