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The term ‘digital twin’ refers to a virtual simulation/model (virtual twin) of a physical plant or object (physical twin), where data flows between the virtual and physical twins. A digital twin can be used for different purposes, such as process optimisation/control, design, training, and maintenance/service. This manuscript found an increasing number of simulation and modelling publications in literature year on year, which illustrates the current trend towards implementing digital twins in a broad range of process engineering applications. A targeted literature review into the area found several commercial off-the-shelf software solutions (COTS) for different industrial applications providing the necessary flexibility to analyse a broad range of industries. However, most of the process modelling software is designed for petroleum and fine chemicals processes. There is still a need for software solutions that can model a broader range of applications. While most of the software found was licensed, open source process modelling software was also available. There is a lack of independent research into the accuracy of these software solutions. The literature review also found that 37% of the research based on process simulations is carried out to improve energy efficiencies. In comparison, 27% of the research found Decarbonization to be a secondary "added" benefit. It can be concluded that digital twins are ideally suited for driving energy efficiency improvements and decarbonisation goals. However, none of the COTS identified in the literature meets all the requirements for a digital twin. A solution to this problem is to create a layered digital twin, combining and interfacing different tools to accomplish a visually similar, self-optimising, self-learning virtual plant.

Flexible digital twins from commercial off-the-shelf software solutions: a driver for energy efficiency and decarbonisation in process industries?