Digital Twin for Process Industry

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As per Yokogawa, a digital twin is a digital representation of a human, device, system, or process that mirrors an actual process and has full knowledge of its historical performance. By simulating devices, systems, or processes to predict future performance, digital twins drive agility and the convergence of understanding to enable effective decision-making and determine strategies to maximize safety, reliability, and profitability.

A digital twin captures data to determine real-time performance, and this data can be used across the entire life cycle of an asset for optimization and predictive maintenance. Yokogawa’s digital twins can:

  • Drive improvements through advanced data analytics and operational insight.

  • Guide day-to-day decisions with in-depth, accurate data.

  • Utilize massive amounts of plant information to enable better decision-making.

  • Ensure that actual performance meets planned performance.

The current digital twin for Plant, which is a virtual replica of an actual plant, consists of a Process Digital Twin and an Asset Digital Twin. Both twins link with real plant operations and handle the data of the real plant, continuously updated with real conditions. They are capable of replicating reality, simulating it, and optimizing the activities revolving around it. The twins constitute an evolving digital profile of the historical and current behavior of a physical object or process, and this profile helps optimize business performance.

 The Process and Asset Digital Twins will be closely connected in an integrated solution:

  • Asset integrity information is part of the feedback loop to operators, giving full visibility of process impacts on mechanical assets.

  • Process data is a critical input of predictive maintenance algorithms, allowing the recognition of process patterns which could lead to asset failure.

 Asset Digital Twin

This Twin is based on a 3D model as a repository of asset information and of cumulative data completed by properties and documents, such as manuals or operating procedures generated during all life cycle phases of the asset. The twin encompasses technical documents produced during EPC (Engineering Procurement and Construction), it grows with maintenance plans developed by vendors, and it is continuously updated with maintenance and inspection reports. It is the fundamental backbone of digital-enabled solutions during plant operations, and it is fed with key information during the EPC cycle at a marginal cost.

 Asset Digital Twin benefits:

1. Improved reliability and availability of assets: by decreasing plant upsets due to human errors in planning and execution.

2. Reduction in maintenance time, effort, and costs: by increasing the reliability and productivity of day-to-day activities.

3. Reduction in turnaround duration: by better planning of activities on a 3D model, thus increasing plant availability.

Process Digital Twin

This Twin is becoming a key enabling factor for the new technologies connected to plant operation and optimization, through the integration of the process model, the process optimization engine, and real-time data from the plant. The process optimization engine leverages thermodynamics to simulate the process and optimize its operations as precisely as possible. As further improvement, it is also possible to enhance and reinforce the thermodynamics model with machine learning algorithms to simulate the process and optimize its operations as precisely as possible, integrating production programs and economics in order to maximize the plant margin.

Process Digital Twin benefits:

1. Increase in plant margin and productivity: by support operation in day-to-day activities, optimizing the plant operating conditions, fast de-bottlenecking, and reducing the utilities consumption.

2. Support for operation decision-making: by the application of the Operator Training Simulator.

Individual point solutions with digital twins do exist today, serving different purposes such as fit-for-purpose simulation models and individual data sources. But a future digital twin will be one multi-purpose digital twin, which aligns the asset life cycle and value chain, a Multi Purpose Dynamic Simulator (MPDS), and ubiquitous data sources. It is unrealistic to assume this future state can be achieved in one step, but it becomes more likely through the connectivity of valuable high-performing individual elements.