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Grieves, M. and Vickers, J. (2017) Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In: Kahlen, J., Flumerfelt, S. and Alves, A., Eds., Transdisciplinary Perspectives on Complex Systems, Springer, Cham, 85-113.
https://doi.org/10.1007/978-3-319-38756-7_4

has been cited by the following article:

• TITLE: Process Digital Twin and Its Application in Petrochemical Industry

  AUTHORS: Libing Gao, Mengda Jia, Dongqing Liu

  KEYWORDS: Petrochemical Industry, Intelligent Manufacturing, Digital Twin, Artificial Intelligence, Simulation, Maturity

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.15 No.8, August 3, 2022

  ABSTRACT: Digital twin (DT) is drawing significant attention both from the academia, industry and government. However, people from different fields have different understandings and cognitions about DT. In addition, most of the DT application scenarios discussed belong to discrete manufacturing and are not suitable for process manufacturing. Petrochemical industry is a typical process manufacturing with multi-scale hierarchical and functional structure in space and time. This contribution focuses on topics on the application of DT in petrochemical industry including: 1) The specific DT definition by process industry. 2) The three key elements and design of chemical DT. 3) Features and application scenarios of chemical DT from the view of model precision, model scale and asset life cycle. 4) The Four P’s maturity framework of chemical DT, and 5) Prospects for the development of chemical DT.