Digital twins expanding across the downstream industry
Advances in new-generation information technologies including big data, digital twins and smart manufacturing are becoming the focus of global manufacturing transformation and upgrading.
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Digital twin applications for continuous 24/7 chemical processes optimize assets in real time while delivering an accurate representation of an asset over its full range of operation and its full lifecycle.
According to researchers and developers in academia and industry, digital twins, when combined with data mining, and machine learning technologies, offer great potential in the transformation of the downstream value chain, including the integration of refinery and petrochemical operations. Production control in hydrocarbon processing involves complex circumstances and a high demand for timeliness.
Digital twins works in the present, mirroring the actual plant in simulated mode, but with full knowledge of its historical performance and accurate understanding of its future potential. Digital twins have been applied at several refinery and petrochemical plants. For example, Shell recently announced start-up of a four-year project to construct a digital twin of one of its major petrochemical complexes.
Shell is targeting a 2024 completion date for its Pulau Bukom petrochemical complex site in Singapore.
Upon completion, and “in pilot” between times, the virtual replica of the Pulau Bukom facility will be the first digital twin the oil and gas company has built, according to the Shell announcement. They are estimating a 25% jump in productivity, reliability and safety from the project.
Shell engineers will have the capability to test troubleshooting options in real-time in the digital twin before putting them live in the plant. In this bespoke instance, plant personnel will also be positioned to receive and respond to live data in various augmented and virtual reality platforms, allowing for more effective collaboration between experts and operators.
At this relatively early stage of development, skeptics argue that digital twins are often hindered by missing knowledge, uncertain information and computational difficulties, so the onus going forward is to resolve the challenges pertaining to the modelling aspects of digital twin applications. This involves being receptive to employing surrogate models that can be leveraged when necessary.
In traditional computational model design, a simulation model is validated with the experimental results. In contrast, a digital twin is a virtual representation of the real system or processes. The only difference is that the exchange of information is carried out in real time and is more reliable. A digital twin can therefore be described as a dynamic phenomenon connecting a real system with a planned process.
Another digital twin application is seen with development of the next generation of heat exchangers. the digitalization of heat transfer in shell and tube heat exchangers can facilitate more efficient strategies for the thermal industry. Comparative finite element analysis of shell and tube heat exchangers have been performed for the purpose of enhancing the effectiveness of the heat exchanger process.
With new challenges come new opportunities as more refinery and petrochemical infrastructure incorporates digital twins into its optimization strategies. The digital twin system is a highly accurate way to understand the refinery’s performance on a day-to-day basis with respect to air emissions, including carbon emissions, while supporting energy efficient operating strategies, such as in reactors.
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