Greg: Much of the success in my career stems from developing conceptual knowledge to improve process control. I always sought to understand fundamental principles and cause-and-effect relationships for a bigger picture that allowed me to extend beyond a particular application to a spectrum of applications. I didn’t know as much as the automation and process experts I worked with about specific instruments or technologies. I focused on what knowledge was essential to increase value and usefulness. I used cognitive insight to learn “why” without knowing the answer.
Using first-principle simulations enabled experimentation and exploration to develop process control improvements. Publications written by Greg Shinskey and William Luyben helped me get on the right path. Shinskey especially helped with the process, disturbance and algorithm dynamics for minimizing load upsets, and achieving desired setpoint responses. Luyben discussed the window of allowable PID gains for runaway processes and the snowballing effect of recycle streams. My approach follows the scientific method, which is objectively establishing concepts with an open mind and extensive testing, and searching for challenges that might prove predictions wrong, along with conditions that confirm the concept’s validity and value.
I’ve had the freedom and time to pursue this approach throughout my career. These days, there’s increasing pressure on project budgets and schedules, and an incredible loss of expertise due to retirements. The hiring freeze in the 1980s and the suspension of company courses caused a loss of onsite mentoring and training. Most automation engineers today don’t feel they have the time to simulate and innovate despite the increase in modeling, control technology and digital twins.
My goal is to turn this trend around, so we don’t end up doing copy jobs, with most of the technological advances sitting on the sidelines. We first need to address the question of how to get started with simulation and find the right path.
To get started, automation and process engineers should pick a couple of unit operations that are particularly important for process efficiency or capacity. Online key performance indicators (KPI) use moving averages to provide short-term (last few minutes) and long-term (last few hours or days) recognition of present process performance that should be configured, historized and trended. Engineers should partner with a modeling specialist to develop a dynamic simulation, including fundamental first-principles process relationships, plus heat transfer lags, mixing lags, mixing nonuniformity, transportation delays, instrumentation dynamics, resolution, repeatability, rangeability, reliability and response time (5Rs), and PID algorithm preferably via digital twin with the same KPIs in the plant. Strategies documented in publications should be explored and tested to see if they improve the KPIs. Once better process control is found and verified by experimentation, the results should be shown to management, other plant engineers, and technicians to support and build a team to move forward.
To provide detailed guidance on this process and how to sustain best process control performance, we asked Edin Rakovic, president of customer success, and Spencer Absher, president of business development, both at Prosera, an industrial software advisory firm.
Edin: Initially, understanding how simulation enhances plant performance beyond control testing, while qualifying operators can be challenging. To begin effectively, a discovery process is crucial to consider various use cases and develop a vision with KPIs for the plant. The outcome is a comprehensive success plan that drives organization-wide engagement and effective adoption of the new simulation solution.
Spencer: Simulation fidelity isn’t used as the sole criterion to determine the right simulation solution path. Instead, one must consider input from multiple teams to expand the simulation's use case across the plant. The simulation solution is the foundation on which we can build the best process control performance and operator performance.
Greg: What is the value of extending use beyond operator training?
Edin: While traditionally focused on operator training, simulation solutions are critical as industries face up to 40% workforce turnover due to retirements. Expanding simulation technologies beyond training enhances plant performance.
Spencer: Using a multi-purpose dynamic simulator (MPDS) adds value by reducing engineering efforts, enabling design validation, supporting virtual commissioning and startup, ensuring operator readiness, and facilitating optimization studies. We leverage modern simulation platforms to explore proven use cases, and partner with our clients to enable innovation within industrial software.
Greg: How can simulation tools be integrated into daily plant operations?
Edin: It involves using simulation systems for ongoing training and real-time operational support. This approach ensures operators continually refine their skills, and apply learned strategies directly to actual plant scenarios.
Spencer: Software must be accessible and easy to use, which is achieved through selection of the correct platform/technology and guided training programs. Enhanced decision-making and response times across the plant are possible by embedding simulation tools into daily operational practices.
Greg: What’s the future role of finding and justifying the best instrumentation and control systems?
Edin: With advances in simulation, the best instrumentation and control systems can be selected more accurately early in the design process through digital twins and integrated models, which provide recommendations for optimal plant performance based on simulated data.
Spencer: Increasing the value of the simulation model allows us to go beyond traditional practices when selecting the best instrumentation and control systems. Instead, we use inputs from digital twins to guide these decisions without the presence of physical instrumentation and other control system assets.
Greg: How do you choose purpose and fidelity?
Edin: Designing a digital twin involves considering numerous factors, notably the purpose and model fidelity. Purpose is usually established early through discussions on use cases and direct operational impacts. Model fidelity, reflecting the realistic behavior of each process unit, should be flexible to ensure quick initial business outcomes and maximize long-term return on investment (ROI).
Spencer: Deploying high-fidelity models for the entire simulation is seldom the correct approach. This uniform approach is flawed and misses a deeper use-case understanding of the solution. We deploy flexible fidelity solutions tailored to each specific purpose.
Greg: How do you justify investment in development and maintenance?
Edin: Contrary to misconceptions, investing in digital twins is more justifiable due to technological simplifications. Solutions incorporating multiple fidelity levels let end-users maximize ROI by developing models of appropriate complexity to achieve desired outcomes. Costs are distributed across teams, enhancing operational efficiency, reducing process downtime, and aiding decision-making across plant operations.
Spencer: The right simulation solution must align directly with the ROI; simulation is more accessible than ever. We support our clients by fully implementing multiple digital twins to maximize benefits across their facility. We develop technology to reduce the human capital invested in developing and maintaining digital twins.
Greg: How do you get operators, process, automation, mechanical engineers and technicians to develop and continually use dynamic simulations?
Edin: Successful technology adoption hinges on early and relevant engagement with all stakeholders, aligning challenges with digital-twin use-cases, and providing thorough training to simplify technology use.
Spencer: Early and comprehensive engagement is critical for teams to develop solutions that address individual needs. By engaging with industry experts in the early stages of technology selection, we ensure new technology is relevant, applicable and user-friendly for all teams.
Greg: How do you sustain and continually increase the benefits of improving process safety, efficiency and capacity?
Edin: Effective, plant-lifecycle management involves setting up a multi-year success plan with clear KPIs, maintaining the simulation platform and models, and establishing clear ownership and feedback mechanisms in the organization.
Spencer: The benefits of digital twins increase by having a program for maintenance and continual improvement of implemented solutions. This is achieved by implementing technology for digital-twin maintenance or increasing equipment fidelity to unlock increased value.
Greg: What are the benefits of using high-fidelity simulations in operator training?
Edin: High-fidelity simulations provide detailed and realistic scenarios that closely mimic actual plant operations, improving training quality by exposing operators to near-real conditions without the associated risks. This level of detail enhances skill development, particularly for handling complex and hazardous situations.
Spencer: There’s a level of detail required of the model to achieve true knowledge transfer. A key area where we don’t want to compromise is the quality of simulation in areas critical to safety and operational efficiency. We often see clients invest in high-fidelity simulations to maximize training effectiveness and operational preparedness. These clients have less turnover and overall higher team satisfaction as the burden of the unknown is eliminated through successful training programs.
Greg: How does collaborative training in simulations enhance team performance?
Edin: Collaborative training using simulations fosters better communication and teamwork among operators, engineers and technicians by replicating real-life scenarios requiring team coordination. This training is essential for improving collective responses to emergencies and routine operations.
Spencer: Control complexity continues to increase, while the scope of control isn’t decreasing. Developing a training program that forges strong team dynamics and communication is critical. We often create scenarios with solutions that require cross-functional teamwork, enhancing overall plant performance.
Greg: What role does continuous feedback play in simulation training programs?
Edin: Continuous feedback in simulation training programs helps fine-tune operators’ skills by providing immediate responses to their actions during training. This feedback mechanism is crucial for iterative learning and correction, allowing trainees to understand the consequences of their decisions and improve over time.
Spencer: Process knowledge integration into the digital twin is crucial. The training sessions, which conclude with analytical feedback and review, enable the model to continue growing over time, allowing the platform to evolve in real-time with post-scenario feedback.
Greg: How can virtual reality (VR) and augmented reality (AR) technologies be leveraged in simulation training?
Edin: VR and AR can create immersive and interactive training environments that enhance learning engagement and retention. They enable operators to experience and interact with complex plant processes in a controlled, virtual space, which is particularly useful for training on equipment and scenarios that are difficult or dangerous to replicate in real life.
Spencer: Technology selection is critical and having the right partner, who understands the technology without bias, enables us to align capabilities with specific training needs. We integrate VR and AR to complement traditional training methods and address learning objectives.
Greg: This site has weekly “Ask Greg” posts about how dynamic simulation can provide the best instrumentation and control systems.
The following, recent ISA books detail the use and value of simulations:
Control feature articles showing the use and value of simulation for innovation and process control improvement:
In the Control Talk column, “Dynamic World of Modeling and Control”, Julie Smith, global automation and process control technology leader at DuPont, describes how her group uses dynamic models to develop process understanding and process control improvement (PCI), including plantwide control strategies to increase process performance and provide training to increase operator performance.
In the Control Talk column, “Simulation benefits in mineral processing”, Michael Schaffer, president of Portage Technologies, details how simulation enables greater and deeper knowledge of the process and the use of smart controls and optimization to turn what were controlled variables into manipulated variables.
Since so many of my colleagues are of retirement age, the ISA-published book I authored, Funnier Side of Retirement for Engineers and People of the Technical Persuasion with cameo appearances by the late Stan Weiner and cartoons by Ted Williams, is a good source of Top 10 lists for the next several months.
