Virtual Replicas for Real-World Solutions: Digital Twins in Action
KNOXVILLE, TN, December 12, 2024 /24-7PressRelease/ — In a significant advancement for industrial technology, scientists have introduced a revolutionary digital twin (DT) platform designed to transform the process industry. This cutting-edge platform creates a highly accurate virtual replica of physical systems, allowing companies to optimize operations, enhance productivity, and fuel innovation like never before. Equipped with advanced multi-domain and multi-time scale modeling capabilities, the new DT system offers groundbreaking features such as predictive analytics, real-time optimization, and comprehensive scenario simulation, promising to elevate decision-making and operational control.
The rise of Industry 4.0 has accelerated the demand for digital twin (DT) technology, as industries seek to harness data-driven automation and connectivity. However, despite the growing use of DTs, existing platforms often face limitations, struggling to integrate complex data streams and models from multiple sources. These challenges highlighted the urgent need for a versatile and robust DT solution, one that could seamlessly connect and optimize processes across various domains and timescales.
This need has now been met on September 3, 2024. Researchers from Zhejiang University unveiled their novel platform, published (DOI: 10.1049/dgt2.12010) in Digital Twins and Applications. Known as the Novel Generation Modelling (NGM) system, this platform introduces a groundbreaking approach to industrial modeling. Leveraging the open-source programming language Modelica, NGM offers an intuitive yet powerful modeling environment that features eight integrated modules, including editors, simulators, and compilers. It effortlessly models chemical, electrical, thermal, and fluid systems, and its advanced capabilities ensure that industrial systems are not only simulated but also optimized for peak performance.
Professor Wenhai Wang, the project’s lead researcher, emphasized the significance of their achievement: “Our NGM platform addresses longstanding integration challenges in DT technology. By offering a highly scalable and accurate modeling environment, we empower industries to achieve unprecedented levels of efficiency and reliability. This breakthrough paves the way for safer and more optimized industrial control systems.”
Beyond its engineering prowess, the NGM system has the potential to redefine industrial practices across the board. From chemical plants to energy production facilities, the platform is poised to deliver substantial benefits. By enabling predictive maintenance and continuous real-time monitoring, NGM minimizes downtime, lowers operational costs, and reduces environmental impact. As industries transition further into the digital age, technologies like this could set new standards for smart manufacturing, making factories safer, greener, and significantly more efficient.
In essence, this advancement marks not just a step, but a giant leap toward the future of industrial automation and control. The days of inefficient and error-prone industrial processes may soon be over, thanks to the power of this next-generation DT technology.
References
DOI
10.1049/dgt2.12010
Original Source URL
https://doi.org/10.1049/dgt2.12010
Funding information
Industrial Internet Development Project of the Ministry of Industry and Information Technology, Grant/Award Number: TC190A449; National Key Research and Development Program of China, Grant/Award Number: 2020YFB2010900; Special Support Plan for Zhejiang Province High‐level Talents, Grant/Award Number: 2022R52012; Major Scientific Infrastructure Project of the Zhejiang Lab, Grant/Award Number: ZJKYHTZYZ10300020201229001; National Natural Science Foundation of China, Grant/Award Number: 52177119.
About Digital Twins and Applications
Digital Twins and Applications is a gold open access journal that aims to disseminate cutting-edge developments of digital twin systems spanning multiple disciplines to achieve better monitoring, simulation, prediction, optimization, and control. The journal publishes original research findings and latest perspectives from research projects relevant to digital twin technologies. This will include a variety of digital twin technologies and their applications.
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