So, this Digital Twin (DT) is really getting a lot of attention in the digitalization age. Recently, I’ve seen numerous articles, white papers and presentations on the topic that cover various aspects of the technology. And with the move towards the Industrial Internet of Things (IIoT) and digitalization, the focus is certainly warranted as there are many benefits of employing the DT in a process environment. It is being deployed in all phases of a product, plant or process lifecycle from design to operations and maintenance.
What is the Digital Twin?
A DT is essentially nothing more than a virtual representation of a physical product or process. The DT is used to better understand and predict the performance characteristics of a finished product throughout the lifecycle. Use cases include simulation, prediction and optimization of the product as well as the entire production system before investing in any physical prototypes. And that can result in quite the cost-savings as the days of correcting issues in the commissioning phase are over if the DT is employed properly.
The concept of the DT is not new. For more than 30 years, product and process engineering teams have used 3D renderings and process simulation to validate manufacturability. What is new, however, is that several factors have now converged to bring the concept of the DT to the forefront as a disruptive trend in the process industry. The DT is not a single technology but in reality, a combination of technologies including one single database for all the plant or product engineering data, simulation software, real-time data from the production environment and much more. With our ability today to easily access data from many sources, aggregate it into a dashboard style environment and to add contextual information into the mix, the DT, however, has become more powerful today than ever.
What are the benefits of the Digital Twin?
The benefits of the DT are numerous. First and foremost, by incorporating additional disciplines such as data analytics and machine learning, the DT is able to demonstrate impacts of usage and training scenarios in a virtual setting. This enables identification of potential issues in the design phase as opposed to during commissioning, which is very common today, and with that costly changes are avoided because changes only need to be made to the virtual twin. Secondly, process and sensor data from physical objects is collected and analyzed to determine real-time performance and operating changes over time. Feeding this data back into the product lifecycle, the DT is continuously updated to reflect changes to the physical counterpart. This creates a closed-loop of virtual feedback that makes products, production, and performance optimization possible at minimal cost. Lastly, the engineering project in its entirety can be transferred to the Process Logic Controller (PLC), Distributed Control System (DCS) or SCADA for more efficient programming.