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Use cases for digital twin technology in process manufacturing

Updated: Feb 28

Process manufacturing involves the production of products by using a combination of chemical, biological, and physical processes. These processes are complex, and any issues can have significant consequences, including product quality issues, safety hazards, and production delays. One of the emerging technologies that can help overcome these challenges is digital twins.

Digital twins are virtual models of physical assets or systems that can be used to monitor, control, and optimize their performance. In process manufacturing, digital twins can be used to create virtual models of manufacturing processes, equipment, and systems. These models can be used to simulate various scenarios, predict performance, and optimize operations.

Here are some of the use cases for digital twins in process manufacturing:

  1. Equipment Optimization: Digital twins create virtual models of manufacturing equipment, such as reactors, pumps, and heat exchangers. These models can be used to monitor and optimize their performance. By using real-time data from sensors, digital twins can simulate the behavior of the equipment and predict the impact of various factors such as load, speed, and temperature. This information can be used to optimize production, reduce downtime, and improve safety.

  2. Process Optimization: Digital twins create virtual models of manufacturing processes. These models can be used to optimize the process parameters, such as temperature, pressure, and flow rates. By using real-time data from sensors, digital twins can simulate the process behavior and predict the impact of various factors, such as changes in raw materials or process parameters. This information can be used to optimize production, reduce downtime, and improve product quality.

  3. Quality Control: Digital twins can simulate the production of a product and predict its quality. By using real-time data from sensors, digital twins can identify issues that could impact product quality, such as impurities or deviations from the desired process parameters. This information can be used to identify potential quality issues before they occur, and take corrective actions.

  4. Predictive Maintenance: Digital twins can simulate the behavior of manufacturing equipment and predict when maintenance is required. By using real-time data from sensors, digital twins can identify potential issues before they occur, and take corrective actions. This can help reduce downtime and improve reliability. Not all digital twins support full predictive maintenance, but AI is enabling machine learning, and this will become more commonplace in the future.

  5. Energy Management: Digital twins can create virtual models of manufacturing facilities. These models can be used to optimize energy consumption, identify areas of waste, and reduce the environmental impact of the facility.

In conclusion, digital twins have significant potential in process manufacturing. By creating virtual models of manufacturing processes, equipment, and systems, digital twins can help optimize operations, reduce downtime, and improve product quality and sustainability. As the technology continues to evolve, we can expect to see more use cases emerge, and digital twin solution becoming an essential part of the process manufacturing industry.

As well as delivering on numerous other KPIs including enhanced collaboration, and better, faster decision making, VEERUM has helped numerous global clients achieve significant safety and productivity improvements, and reductions in inspection costs, contingency costs, compliance costs, and field exposure.


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