Simulation technology has advanced rapidly in recent years, and teams savvy enough to take advantage of that evolution can leverage the right tools to accomplish even more. These teams have learned that piecemeal solutions cobbled together from a variety of disparate technologies complicate implementation and training, delaying return on investment. Today’s most successful project and operations teams are instead focused on a boundless automation vision—turning disparate systems into a holistic, seamless ecosystem—to create end-to-end simulation solutions.
With a holistic simulation solution, users can deliver value across a facility’s entire lifecycle, from the research and development innovation phase to engineering and construction, training and, finally, across operations. Today’s most successful teams are choosing best-in-class solutions built with the critical features users need to get the most from their investment, and intentionally designed to integrate and operate seamlessly and intuitively.
It is difficult, if not impossible, to develop good simulation models if the data—including libraries of material, fluid properties, and other parameters—underlying those models is unreliable. Choosing an effective simulation solution means selecting one with an extensive base of available physical properties on which to build design models.
These extensive databases can model a much wider range of systems, fit property models against experimental data using data regression, and include estimation systems to evaluate properties based on molecular structure. The best simulation providers have decades of experience building first-principles models by leveraging the most robust databases of physical properties. They have used years of iterative development to design efficient interfaces, making their software easy and efficient to use. By developing accurate thermodynamic models using robust simulation tools, one global chemical company has realized more than $11 million/yr. in overall benefits that can be attributed to increased production and reduced energy use.
With recent innovations, technology providers are combining data and machine learning with first principles fundamentals to create models that more closely represent real plant behavior. Fast and easy access to industrial AI (artificial intelligence) is helping companies make accurate decisions to select an optimum design quickly for improved operations. These teams use the critical data built into robust simulation systems to minimize feedstock consumption, waste, and energy use.
These accurate, high-fidelity models help engineers ensure the plant will operate well, producing enough high-quality product in the shortest amount of time for improved yield. For example, a chemical company in Asia gained as much as 1% of potential savings by optimizing their steam input using predictive insights from AI-powered models, resulting in significant savings.
As teams move into engineering, they begin to focus on new dimensions—including capital, energy, safety, yield, and environmental—to optimize designs. Capabilities within the simulation environment that seamlessly integrate all design and project cost estimate functions concurrently deliver a lower-CAPEX project and optimize operating costs, while improving safety and environmental factors.
This type of integrated model is essential in closing the gap between cost estimates and reality, which is critical to the success of any project. An error of estimation in the 50% range could easily result in millions of additional dollars in unexpected added costs across the project.
Moving the engineering from a sequential to a simultaneous system-model approach can accelerate the optimization and screening of many different alternatives, reducing the design selection time from weeks to days.
Moreover, in today’s volatile environment, many organizations are leveraging the benefits of an integrated simulation system to engineer complex processes that meet sustainability goals. These teams can make difficult energy-optimization decisions by comparing different simulation models using built-in tools to calculate and visualize scope 1 and scope 2 greenhouse-gas emissions.
Activated and integrated capabilities inside the simulation environment provide an easy and accessible tool to quickly and easily visualize everything from economics, to energy, to the feasibility of equipment, all from a single, intuitive interface.
For training, the value of seamlessly connected systems becomes even more apparent. While operators can train on steady-state models from the design and engineering systems, the most effective personnel are those who are prepared for unexpected situations.
When a facility experiences a tube leak, failed heat exchanger, stuck valve, or any of the many other potential process aberrations that create safety and production risk, operators must be able to recognize those problems and respond quickly. Such a feat can only be accomplished effectively if operators have been trained using the same dashboards and controls they will use during the operations phase.
Advanced holistic simulation environments are designed to seamlessly move steady-state engineering models into dynamic process models and integrate them with the control system to accurately replicate actual operations. These process models can be used in a training simulator’s digital twin to not only demonstrate the immediate effects of a process aberration, but also any cascading effects.
Armed with more accurate information and interfaces, operators more quickly learn exactly what to do in any situation. Trainers can simulate events, from common to rare, helping ensure operators are prepared. This training can be performed in parallel with construction, so the plant can be quickly brought online, with operators ready to perform at their best from day one.
Safe, sustainable operations
Receiving maximum ROI from a simulation solution means using the same tool across the lifecycle of operations. Using an accurate digital twin built from robust simulation models, process engineers can identify operational limits and predict potential operational issues, providing support for decisions to stabilize production and maintain safe operations. For example, a national oil company was able to increase its steam production by 36% and reduce flue gas emissions by 5% using multiple digital-twin models to evaluate different scenarios for their utility system.
All these capabilities are dependent on an up-to-date digital twin but, as the plant moves from startup to operation, changes will happen. Teams will add controllers, make process changes, add I/O, and tune control loops. As they do, the data in the digital twin will begin to drift from the current operating state of the plant. If the operations team cannot quickly and easily bring the two back in sync without added costs, potential ROI diminishes quickly.
Fortunately, today’s most effective simulation ecosystems include software to quickly and intuitively identify the differences between current control states and the active digital twin simulation. With a few clicks, the operations team can instantly bring the simulation back up to date to ensure testing and training is accurate and reliable.
Making the most of simulation technology means using it to simplify projects to get them up and running faster, training personnel quickly and thoroughly, and continuing to optimize assets across the lifecycle. Organizations can use a disparate set of simulation tools to accomplish all these goals, but doing so will be time consuming, error prone, and will lower the ROI. By focusing on building a holistic solution from the earliest stages of project development, organizations can quickly and easily enhance operations, while also justifying future investment.
By Geeta Pherwani, AspenTech and Monil Malhotra, Emerson
Geeta Pherwani is Senior Product Marketing Manager for the performance engineering suite at Aspen Technology, Bedford, MA (aspentech.com), working with companies globally to advance their profitability, sustainability, and digitalization efforts.
Monil Malhotra is the Vice President of industrial software for Emerson’s (St. Louis, emerson.com) systems and software business. He manages its strategic partnership with AspenTech and the standalone industrial software businesses including Bio-G, Performance Services, Plantweb Optics, and Zedi.