A Roadmap for Improved Simulation Success

As the team moves into project execution, their simulation needs will change. The team will begin working with dynamic simulation software to execute detailed design, where they develop the automation system and begin testing procedures and controls, tune control loops and eventually enter the commissioning and training stage.

Each of these elements can be performed on the simulation software to reduce risk and shorten time to full production.

After project execution, the organization will continue to use and update its dynamic simulation, both to extend training new and experienced operators as roles change, and as a test bed to define and test new operating strategies to unlock constant innovation (Figure 1).

The right software at every stage

The most impactful choice a project team can make is to select steady-state and dynamic simulation tools that are designed for flexibility and seamless integration. Unfortunately, many times these choices are made for the immediate next steps without consideration for needs later in the lifecycle of the facility, or even later in the project.

One of the most important factors is re-use because users need simulation to serve as many purposes as possible to maximize its value. For steady-state simulation, teams need ties to many tools, allowing them to perform capital cost estimation—as well as risk, economic and sustainability analysis. Later in the lifecycle, a simulation that can be compared to live plant conditions to look for optimization opportunities can create value for many companies.

In dynamic simulation, one example of re-use is between steady-state simulation and dynamic simulation. When teams select integrated steady-state and dynamic simulation solutions, they can easily transfer their existing flow sheets, base configuration, equipment and instrumentation to their dynamic simulation software.

Just as with the steady-state simulation, teams want dynamic simulation to serve as many purposes as possible to maximize its value. The best dynamic modeling tools also empower project teams to work in multiple fidelities. These solutions offer simulation objects that allow teams to perform high-fidelity dynamic simulation at the core of the process but also provide objects that make it easy to build out lower-fidelity objects as users approach the edges of processes and units. The advantage of such a solution is a final product that is easier and more cost-efficient to manage.

It is important to consider the required fidelity to support individual use cases. For example, contemplate a dynamic operator training simulation where the operator learns to monitor cooling water on an exchanger. The cooling water process could be modeled in high fidelity, but doing so would be complex, and would require many variable changes in the simulation any time the process changed. Those variables, however, offer little value in the training simulation. The operator does not need to know if the cooling water is 81 or 85 degrees. The user simply needs to know if there is cooling water flow—a dynamic that can be modeled and far more easily managed long term in low fidelity.

By contrast, if the simulation is training an operator to know that a bioreactor needs to run at 100 degrees versus 101, that distinction might be critical, in which case the simulation might need to be high fidelity. However, that same high-fidelity bioreactor model might be in a training simulation with many other low- and medium-fidelity elements as well. Every model that can be created in lower fidelity reduces the number of complex interconnections in the simulation (Figure 2).

Solutions built for success

Whether an organization still has a deep bench of experienced operators or is trying to onboard a new generation of workers with limited experience, finding a safe way to test, train and tune new processes is critical. New workers will need to gain experience as quickly as possible if the plant hopes to meet the necessary performance benchmarks dictated by competition in a global economy.

Conversely, even experienced workers will have to learn many new operating procedures (on very different, and often more complex equipment than they are used to) if they hope to help their plant meet new sustainability benchmarks and comply with regulations. In either case, operators need a risk-free environment to learn, test and innovate. Such an environment cannot be provided on live equipment