Ensuring Your Digital Twin Remains Relevant
What exactly is a Digital Twin? A digital twin is a virtual model designed to accurately reflect the behaviour of interest of a physical object. The object being studied, for example, a wind turbine, is designed with various sensors related to vital areas of functionality. These sensors produce data about various aspects of the physical object’s performance, such as energy output, temperature, weather conditions and more. This data is transferred to a digital twin model to derive useful information on the performance of the system. Digital twin technology can help various industries overcome challenges to more accurately track and manage emissions and help achieve their decarbonization goals. There have been numerous and varied discussions about digital twins and their ability to assist different industries to optimize their operations and minimize Greenhouse Gas (GHG) emissions. The Process Ecology differentiation. Process Ecology has helped clients in the oil & gas sector develop digital twin models of various physical assets (pipelines, gas processing facilities, etc.) that capture key parameters related to emissions and production. The objective of the digital twin needs to be clearly defined because there is no such thing as a digital twin that would help answer all your questions. Let alone provide all solutions. Process Ecology’s approach to R&D. The most important consideration in designing and implementing a digital twin model is to select the key features of the facility/asset that must be included and those of secondary relevance that may be excluded. The level of detail that is required for the goals of the model and is consistent with the available data dictates that the model should only be as detailed as necessary and not more, even if it is feasible to continue adding more details. Keep it simple. The best way to achieve this sweet spot is to proceed in steps starting with a simpler model and adding details as the responses are analyzed and major shortcomings are identified. Process simulation models have been available for decades and have been used successfully to design and optimize processing systems of varying complexity. A challenge that has restricted the value that can be obtained from these models is to keep the model updated as the facility evolves over time. Therefore, a digital twin cannot survive for long in isolation and needs to be integrated to plant data. For the case of emissions tracking, not only historical data (operating conditions and flow rates) but also field data capture systems that deliver operating hours, fluid compositions, measured volumes, and emissions surveys. All information must be supplemented with ongoing discrete event information such as equipment blowdowns, relief events, flaring events, equipment and device replacement/retrofits, turnarounds and include reliability-driven events. Collaboration is key to hitting that sweet spot. A collaborative approach between digital twin builders and facility experts is essential for the successful delivery of a valuable tool that can assist industry in their evaluation and tracking of their emissions performance over time. Conclusion. While a digital twin has the potential to become a valuable tool to manage emissions, it is essential to understand the context of its operation, in particular aspects related to systems integration, workflows and model maintenance to build an effective emissions management strategy. |
