By Nathalie Marcotte, SVP & President, Process Automation at Schneider Electric
Digital simulation in industry is evolving. Over the past decade, process simulation has been increasingly deployed in the design of plants and the training of the operator. But now, modern technological advancements mean that these traditional uses are being surpassed.
Currently, there is a growing trend of integrating simulation models with running plants in real time, which can enable the training of artificial intelligence (AI) agents to perform specific tasks. This integration has the potential to unlock new value from simulation models.
The focus is now on achieving greater value by connecting these models to running plants and training operators in real time for complex procedures. By doing so, operators can complete procedures more efficiently, ultimately impacting cost and profit by increasing production output. This is the revolution that is on the horizon – so, how can you prepare?
Next-generation process simulation
Process design has traditionally been overly complicated, with various disconnected tools and complex incompatible models, siloed from other disciplines. However, digitalization is removing these siloes. For example, the use of digital twins provides engineers with one platform within which to explore all dimensions of a design, from conception to implementation and use. In the process industries, engineers can use digital twins to innovate and optimize processes, evaluate design concepts and make ongoing operational decisions quickly and safely.
For example, until recently, efforts to optimize an engineering or operational domain were done in isolation. Digitalization is removing these siloes, allowing meaningful acceleration and optimization.
Digitally integrating power and process enables the delivery of critical digital twin elements from the project phase into ops and maintenance, contributing to successful start-up and production. An electrical digital twin integrated with the process digital twin facilitates predictive operations and a holistic approach to asset and power system performance management. Across the lifecycle, this integrated approach has been shown to deliver 20% EI&C-related CAPEX cost reduction, 15% reduced downtime in ops, 10% process energy usage reduction, and 3pts profitability improvement.
Currently, there are silos between power and process, which is limiting the potential gains from simulation in the electrical and process aspects of the plant. This issue is relevant to both greenfield and brownfield projects. For greenfield projects, it is crucial to examine feasibility during the early phase of the project and to use simulation during the front-end engineering and design phase to improve design outcomes. On the other hand, for brownfield projects, it is essential to engage in better planning when making upgrades or changes to the plant, such as adding a new unit or modifying equipment. To achieve this, the models developed in the greenfield phase should be used in the brownfield phase.
Process simulation for sustainable innovation
This new approach is also crucial in meeting sustainability targets. Decarbonization is an essential goal of process engineering, however it has traditionally been very difficult to analyze. Until now, sustainability metrics have not formed part of simulations but have relied on engineers using unsophisticated methods to analyze sustainability data – often leading to time-consuming and error-prone outcomes.
Now, technologies such as digital twins, digital data transfers to main operating centers and on-premise or out-in-the-field connected devices are enabling engineers to get instant insights into calculations such as carbon emissions and energy consumption, which map to key sustainability KPIs. Through holistic simulation, sustainability is just one way in which engineers are using digital technologies to enjoy near real-time optimization across a range of interconnected KPIs.
Realizing AI capabilities
The use of data through AI is making these sustainable process simulations not only possible but infinitely more reliable and fast. The capabilities of AI have advanced to the point where we can create an operator advisory system that trains operators to perform a series of moves more efficiently and rapidly. This can result in reductions in energy usage, emissions and/or an increase in production output. Customers are now seeking technology that is specifically tailored to their plant, rather than relying on generic knowledge bases that may not be relevant.
The new approach of integrating AI with plant models offers an added advantage, as the models are built to accurately represent the dynamics of their precise plant. This results in more accurate representations and more effective solutions.
When examining the use cases for both greenfield and brownfield operations, we can identify various phases of plant maintenance that offer unique value propositions. Additionally, with the advent of new artificial intelligence capabilities, we are constantly exploring how we can create value through AI implementation in both the plant design and operation stages.
However, there are challenges associated with implementing AI in plant design and operations due to the silos present between electrical systems and process plants. To effectively train AI models, it is necessary to consider all aspects of plant design, including the process equipment, such as pumps, compressors and turbines, as well as electrical systems like motors, switch gears and transformers. Failing to consider all components of the plant design can result in incomplete or inaccurate AI models, leading to less reliable predictions and less effective results. It is crucial to model all aspects of the plant to achieve a complete understanding and ensure the effectiveness of the AI model.
A holistic approach for people and process
Holistic simulation doesn’t just deliver on profitability and sustainability metrics but is an essential part of ensuring better employee experiences. It facilitates unmanned and automated operations to reduce the time taken on tasks and, in turn, opens opportunities for more flexible working and safety improvements.
Digital solutions that can be accessed from anywhere and deliver near-real-time insights remove tedious time spent on spreadsheets and free up process engineers to concentrate on true innovation. Surprises are minimized, and opportunities for creative collaboration are maximized. This all feeds into the employee experience that today’s top talent expects. Process simulation enables the next generation of engineers to deliver increased outputs through digital tools that enable flexible, accessible and collaborative working.
We need to have complete oversight of operations to make the right decisions – not simply in terms of profitability but also sustainability, feasibility and employee wellbeing. Total simulation, therefore, delivers on all levels: process, people and planet-friendly industrial progress.
