Asset Digital Twins: Integrating Process Safety Risks

Integrating quantitative risk analysis (QRA) within a digital twin framework can allow stakeholders to easily identify risk drivers and isolate accident scenarios.

Operators of chemical process facilities aim to provide a safe workplace for their staff and community. However, in the case of process safety hazards, there will always be some potential for high-consequence accidents. An important part of risk management is to make these consequences visible in both design and operations.

The chemical process industries (CPI) have developed many techniques for driving process safety hazards to the forefront. Fully quantitative risk analysis (QRA) is an important tool in assessing the impact of process releases and other safety incidents on a facility. It can be used to compare design or operational risk with defined risk-tolerability thresholds. The impact of risk-reduction measures can be studied to identify those that are most effective and practical. However, the data generated by such analysis is often buried in reports and spreadsheets, severely inhibiting the use of this information more widely in risk-based decision-making.

Since most of the information related to assets is already generated and stored digitally, companies are increasingly looking at ways to create digital twins of assets using advances in computer storage and processing power. Digital twins are real-time simulations of a physical asset that contain a broad range of asset information, which can be integrated, analyzed, and displayed in a variety of ways.

While digital twins are a relatively new concept within risk management, they are well established in other industries. For example, a smart home uses internet-connected devices to enable an individual to remotely control several aspects of their home such as power, heating, lighting, domestic appliances, and security (1). The digital twin concept has also been applied to model roads and traffic, allowing civil engineers to study new transportation systems and make recommendations for repairs (2).

Using computers to conduct a QRA and arrive at the solution and recommendations is not new. What is new is the way that cloud-based information systems have transformed the delivery and use of data by organizations. A detailed QRA contains risk data that is tagged by both physical location and process system, and considers many of the barriers that are used to control and mitigate process risks. Moving analyses such as the QRA process into cloud-based systems can revolutionize how data is delivered, and it creates the opportunity for future integration into cloud-based digital twins. In this way, an analysis such as a QRA would simply become a process, run on the cloud, that accesses required information about the facility via the digital twin, and provides access to the outputs that can be clearly presented to all stakeholders. The QRA models could then be used to quickly determine the risk impact of any changes to the digital twin due to changes in design or operating conditions. Thus, stakeholders can use the data in a wider range of studies.

This article explores how cloud-based systems can revolutionize interactive integration of risk data within a QRA. Having this information readily available will help open the path toward future integration with asset digital twins, providing a wider information and knowledge source for any asset. Creating a fully interactive digital twin of an asset is still an aspiration. This article demonstrates that we can (and have) developed cloud-based QRA tools, which are a stepping stone toward fully interactive digital twins...