Feb-2020

Evolution of a digital twin Part 1: the concept

A two-part article provides a step-by-step introduction to the concept, construction and application of a digital twin for the lifecycle of an ethylene plant’s steam cracker.

OTMAR LORENZ, BERND-MARKUS PFEIFFER, CHRIS LEINGANG and MATHIAS OPPELT
Siemens

Article Summary

The term ‘digital twin’ itself evokes a wide range of associations. With human twins, we think of common inherited traits, similar characteristics and characters, and the often astonishing parallels in their ways of life. Although there is still a large discrepancy between the interest in digital twins (>500 million Google links) and the number of real applications, the term ‘digital twin’ is more than just a buzzword in the process industry. There are indeed many different concepts, but also initial approaches to concrete implementations.

At first glance, the large number of different types of digital twins appears to be confusing. Depending on the viewpoint of the observer, typical terms such as product digital twin, automation digital twin, production digital twin, 3D digital twin, asset digital twin, and process digital twin can be found in literature, lectures, and conferences.

A digital twin of a process plant as an integrated concept covers three core points: the digital twin of the product, the digital twin of the production plant, and the digital modelling of the performance of the product and production.

The functional scope of a digital twin essentially depends on its purpose. In the process industry, this can be everything from safety analysis, product simulation or the optimisation of the production process, right up to economic benefit formulation.

Parts of an integrated digital twin are, among others, planning data from the design and engineering phase, plant data from the operating phase, and descriptions of plant behaviour in the form of models. The individual simulation models that belong to the digital twin are specifically tailored to the planned use and satisfy the respective requirements for accuracy in this regard.

Like the real system, the digital twin develops across the plant lifecycle and integrates currently available data and knowledge bases in a step-by-step, integrated way. It not only describes the system’s behaviour, but solutions for the real system are also derived from it.¹

The individual components of a digital twin are already largely state-of-the-art. New perspectives come from the approach of integrating the individual models and software tools into an integrated, semantically coupled system via the various hierarchical levels of a plant and via the various phases in the lifecycle of a plant.

Models and simulation in the lifecycle of a plant
Each simulation can be considered as a virtual experiment with the goal of better understanding a system.² The system’s characteristics are modelled in a sufficiently accurate mathematical representation and calculated using common computer programs. The creation of a simulation model is thus always purpose-oriented and context-specific, that is it serves to answer one or more special questions. To this end, a simulation model can, for example, describe the physical, chemical, energetic, and/or IT behaviour of a system over time.³ Simulations are more or less frequently used nowadays in all phases of the plant’s lifecycle and can be compiled into the following groups:
•    Planning simulation (design simulation)
•    Simulation for virtual commissioning
•    Training simulation (OTS: operator training system)
•    Simulation during operation

These four use cases of simulations are shown in Figure 1 over the lifecycle of a process plant.

Design simulation^{4, 5}
This is the use of a steady-state process simulation for plant engineering and design. The result is represented by energy and mass balances, the process flow diagram (PFD), and data sheets for the individual units and devices. Sometimes dynamic process simulations are already used in this phase. This enables modelling of the transient behaviour between operating points of the process for example, for a better design of start-up and shutdown behaviour.

Virtual commissioning and simulation based engineering^6-10
After the plant equipment design is completed, automation system design is performed. For safe and efficient operation of the plant, the distributed control system plays a key role. Therefore correct functioning of the system is essential. Use of simulation support is made in this phase by signal and function testing of the engineered process control system against virtual plant models. These simulation models represent the behaviour of all devices that communicate with the automation system. The configuration of the automation program that will later be used in real operations should be the one that is tested. To this end, it will either be run on real hardware (a programmable logic controller (PLC)) as a so-called hardware-in-the-loop configuration, or on emulated hardware as a so-called software-in-the-loop configuration.¹¹

Since 2013, the GMA expert committee ^6.11 has been dedicated to working out VDI/VDE guideline 369312 on the topics of virtual commissioning. Test configurations, test methods, and model types which are used in the context of virtual commissioning are introduced in sheet 1 of this guideline.

Operator training^13,14
The goal of a training simulation is to prepare operating personnel to be risk-free, efficient, and realistic in their future tasks. This encompasses both working with the process control system and with the process itself. Depending on the intended application spectrum, the simulation component requirements for user interface, model accuracy, model details, and validity differ greatly.

Operation-related decision support and optimisation^13,15
The use of simulations in the operating phase is widespread. This can vary from soft sensors for monitoring and control applications up to model predictive controllers. The operator can receive support for future decisions by examining various production scenarios before active intervention in the process.