The benefits of digital twin technology

08 March 2024

Suzanne Gill finds out why control engineers working in industrial environments should be looking at adopting digital twin technology to model their processes and what barriers might need to be overcome to get the best out of the technology.

The digital twin is a concept that combines the digital and real worlds, allowing data-driven decisions to be made throughout the lifecycle of an asset, plant or process across all functions and levels. It enables real-time monitoring, simulation, and analysis of data, which assists in understanding and predicting the performance of processes. 

“Digital twins enable control engineers to simulate and emulate virtual models of machinery in the early stages of development. This includes evaluating the design in combination with application data to ensure that the correct solution has been proposed with the most energy efficient components,” said Josh Roberts, a Product Manager a Festo. 

The resulting virtual model can also replicate the physical capabilities of the solution in combination with software development and evaluate the efficiency / output of the physical machinery. Simulation within the virtual model can also ensure that any errors are identified and corrected before a physical model is created. 

Josh pointed out that digital twins can offer benefits throughout the machine lifecycle. “For example, each sub-component within a machine can have a multitude of information (supplier details, data sheet, operating instructions, CAD models, etc) which can all be compiled within the digital twin. This removes the need to store information in different formats across the business instead it brings all this data into a central location,” he said. To enable this, the standardised Asset Administrative Shell (AAS) formats for information from suppliers ensures that all the information is captured and available in a structured format, to help reduce the time spent on documentation creation and opening the potential for algorithms to be implemented on this data in the future.

Further into the future, value added services for digital twins could become available – for example condition monitoring of the sub-components within the machinery. The functionality could automatically provide messages when a specified component had achieved a defined number of cycles (within the standardised AAS) and even order the spare part. 

The next step is then the implementation of AI to detect anomalies within the machine while operational and to provide corrective actions in a fully predictive maintenance approach to increase Overall Equipment Effectiveness (OEE).

Josh went on to identify four main barriers to the implementation of digital twins. These are:

Performance:
Reliability and robustness of the emulation; Ensuring that all of the components which have been modelled within the digital twin can achieve the application parameters; Sizing tools from suppliers providing the data into the model, but also the parameters which identify what would occur if the application data changes.

Organisation:
Specialists and expertise in the area within the business; There must be a willingness to invest in the upskilling of staff along with restructuring workforces and project approaches.

Development:
Standardisation of data across multiple platforms is key; The standardised AAS format provides the framework, but all suppliers need to adhere to this standard.

Security:
Cybersecurity is a core focus for many end users; A solution needs to be found to the application of AI in value added services for preventative and predictive maintenance in this area.

Wide-ranging applications
Arianna Locatelli, EMEA Digital Engineering Specialist at Rockwell Automation, pointed out that many control engineers are already using digital twins across a wide range of applications – ranging from validating initial design concepts through to conducting controls testing. “The primary motivation behind these use cases is risk mitigation,” she said. “For example, to reduce on-site commissioning time, engineers simulate intricate production processes to assess performance and validation of control systems against their digital counterparts.”
 
Arianna went on to argue that a digital twin is much more than just a 3D animation. She said: “A system like Emulate3D digital twin software from Rockwell Automation, for example, uses physics to allow more detailed simulation. It can determine how quickly products can be placed on a moving conveyor before falling over. It can also analyse the interactions within complex systems and the speed at which issues, like a jam in a packaging machine, propagate throughout the system. If it’s a new configuration or a very complex setup, there will be a lot to be gained from emulating the system.”

During the control testing phase, engineers can develop code to control the emulation at the same time as the physical system is being built. Onsite commissioning is usually the last step to be conducted before project completion and it is typically the most critical phase of a project. So, if it can be shortened, then projects can start up faster. “The more software testing engineers can do before they go to site the better,” concluded Arianna.

Green or brownfield?
Jan Rougoor, Head of Product Management Process Industries Software at Siemens AG, argued that digital twin technology can be applied to both greenfield and brownfield applications. “For greenfield projects in the process industry the digital twin can be created automatically during the engineering phase. By using simulations for the initial process modelling it is possible to start to optimise the design of the process,” he said. This data can be further used to create detailed diagrams describing the processes and equipment in plants. If the data is then connected to the automation engineering and simulation, creating the digital twin for various disciplines in plant projects and later operations is quite easy and virtual commissioning is possible using simulation software. Virtual reality tools can also enable early training of personnel and can help accelerate commissioning.

In brownfield projects, with lots of scattered legacy data and documentation, Jan acknowledges that things can be a bit more difficult. Explaining further, he said: “Here one might start by using AI-based tools to consolidate – often unknown or unused – information into a cloud based digital twin portal. This can be the basis for important decisions on how to improve productivity, derive optimal areas to invest in modernisations or connect information from different sources from across the plant to find areas for cost reductions, or ways to run the plant and its processes with less energy consumption or lower the carbon footprint.”

Discussing the barriers that may need to be overcome to get the best out of digital twin technology, Jan highlighted the need for standardisation of data and system integration to ensure effective utilisation of digital twin technology. “Engineers must ensure compatibility between different systems and manage the continuous maintenance of these digital twins,” he said. “Most data from older plants are stored and managed by independent applications from different providers in the form of engineering systems, process control systems, manufacturing execution, or asset management systems. Additionally, a large part of the technical plant documentation and data is often only available in paper form. 

“Such a fragmented and heterogeneous system landscape leads to data silos with low data quality and consistency, making data-driven decisions difficult,” continued Jan. “With the integration of operations intelligence software, data from various sources can now be consolidated and linked to real-time plant information to ensure that data from all systems is clearly and efficiently visualised.” A digital twin of processes and production of a brownfield plant can then be created and used as described above.

Multiple benefits
The benefits of digital twin technology are substantial, including enhanced process efficiency, reduced operational costs, increased production quality, and optimised energy consumption or lower carbon footprint and costs. 

Digital twin applications also look set to play a crucial role in reducing energy costs and monitoring emissions. AI looks set to take over many tasks, shifting them from the real plant to the virtual environment. Furthermore, the digital twin will enable global collaboration in strategic partnerships and ecosystems, breaking down barriers between different domains within the industry.


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