The use of digital twins in process manufacturing is rising rapidly, as companies discover just how much use they can be. As a reminder, a digital twin is a dynamic model of a physical object or a process, updated in real-time to make it an always-accurate mirror of that item or process. It’s made using data from Industrial Internet of Things (IIoT) devices, together with a fast connection so there’s no lag in updating the digital twin. 

One important feature of a digital twin is that data can travel both to and from the digital model. Data traveling to the model makes it dynamic, an accurate reflection of the physical item; data traveling from it allows users to make changes in the plant, equipment, or system by carrying them out on the digital twin

The value that digital twins deliver to plants is leading the market to grow swiftly, with Gartner predicting that it will “will cross the chasm” in 2026 to reach $183 billion in revenue by 2031.

The four types of digital twin in process manufacturing 

There can be many different types of digital twins, all of which work together to form a more powerful whole. Different industries have different ways of categorizing them, but in process manufacturing, it’s popular to classify them according to levels. 

Each type of digital twin operates at a particular level of the system, to track changes in its subjects and optimize performance for those objects or processes under its supervision. When you combine them, you can manage your plant on every level, from a granular concern for each process or item of equipment, to a holistic view of the entire plant operating as a whole. 

When you’re just beginning to implement digital twins, it’s important to choose the right type(s) for your company’s goals. 

Component or part twins

Component or part twins are the first and lowest level of digital twins. These are models of individual components, like an item of equipment or just one part of a larger system. 

A part twin enables engineers to gain a better understanding of the characteristics of that part, and to examine and test it for durability, stability, and efficiency; check whether it’s working correctly; run stress tests; and other tasks. It allows you to emulate real-world conditions for that part, so as to increase the performance and working life of the relevant component, and produce more accurate predictions about when it may need repair or replacement. 

For example, you might use a component twin for a product mill to view its performance, track degradation, and perform root cause analysis when there’s an incident or product quality falls.

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Asset or product twins

Asset or product twins are the next level up from component/part twins. Asset twins could be made up of a number of component twins, or they could draw information from the component twins. With asset twins, you group components into a larger asset or product, so you can track and explore the complete asset. 

A product twin offers users better insight into how individual parts interact with each other and their environment, and investigates how they operate as a whole. With an asset twin, you can look for potential for improvement in plant processes, and optimize each part to increase efficiency. 

Engineers typically use asset twins to reduce mean time between failures (MTBF) and mean time to repair (MTTR); find ways to cut energy or water consumption, and generally improve performance. For example, you might use an asset twin for the process of heating raw materials, grouping together component twins for the conveyor belts, oven, valves, temperature sensors, etc. to gain insight into the entire process. 

System or unit twins

The next level of digital twins are called system or unit twins. They comprise groups of asset twins that work together at a system level.

System twins are used to reveal how assets work together. Plant managers use them to optimize collaboration between assets so as to improve performance and reduce wear and tear. Unit twins allow users to experiment with systems to find ways to achieve the highest efficiency and to suggest insights for new business opportunities that optimize all involved processes. 

For example, a system twin in a chemical plant might cover all the assets and processes involved in making a particular base product, which is the first stage of producing specialized chemicals. 

Process twins

Process twins are the final and highest level of digital twins. Process twins bring together system twins into larger and more complex sets of processes or workflows, often encompassing the entire plant. 

Plant managers use process twins to understand and analyze how all the various units within the plant or process collaborate. They are used to track performance, timing, and coordination between all the units and synchronize systems; for example, if one part of the plant produces its product at too fast a rate, you could end up with an excess of certain components which leads to logistical issues like storage and transportation. 

With a process twin, you can model the impact of tweaking inputs, such as the rate of feeding in raw materials, the temperature, unit vibrations, etc., and see how they affect outputs, without risking wasting resources on failed experiments or interrupting workflows. A process twin also allows users to monitor key business metrics, supporting their business decision-making and strategy. 

The value of digital twins in process manufacturing 

Depending on which level(s) of digital twin you adopt, your plant can access a great deal of value and benefits. These include:

  • Reduced downtime and costs for maintenance and repair or replacement. By using a digital twin for predictive maintenance, remote monitoring, and simulations, you can better understand and predict the impact of wear and tear and various operational modes on each part and process, allowing you to intervene at the optimal point of degradation. 
  • More efficient use of resources, including reducing your consumption of energy, water, and raw materials, because all your processes and parts are optimized. 
  • Greater productivity by synchronizing processes and optimizing flow. Digital twins help you spot anomalies that could indicate a bottleneck more quickly, so it can be resolved and removed. 
  • Faster product development by simulating possible processes and viewing the results, helping you “fail fast” without a high price tag. 
  • Strategic planning by simulating various scenarios, changes to markets and/or demand, adjustments to plant management, etc. By viewing and analyzing the results of each change, you can improve business positioning, prepare for a pivot within the market, verify the value of new tools/processes, etc. 
  • Remote training for employees who can’t be present for on-site training, and for rare, dangerous, and/or abnormal situations which are difficult to train for in an in-person manner. 
  • Increased safety for employees by removing the need for them to enter hazardous situations. Instead of placing themselves at risk, they can inspect the situation through the digital twin. Improving the running of machinery also brings increased safety. 
  • Improved innovation through greater collaboration and knowledge-sharing between different teams and departments.

Bear in mind that your level of digital twin adoption won’t only be determined by the benefits you wish to access, but also by the plant’s level of digital maturity, the speed and latency of your connectivity, and the extent of your network of IIoT devices and sensors. 

Digital twins help process manufacturing advance

Now you are familiar with the four types of digital twins used in process manufacturing – component twins, asset twins, system twins, and process twins – and the ways that they can help your plant improve productivity and efficiency. With the help of the right digital twins, process manufacturing companies can enjoy increased performance, a high competitive edge, and ultimately, higher profits.