- Chen D. (2002) Development of failsafe control technology, Petrochem. Safety Environ. Protect., 18, 5, 48–51. [Google Scholar]
- China Petroleum and Chemical Corporation. (2015) Work safety supervision regulations. https://max.book118.com/html/2015/0105/11106987.shtm. [Google Scholar]
- Wang Z., Zhang Q., Ming X., Xiong J. (2017) Dimensionless fault diagnosis of petrochemical rotating machinery based on big data, J. Guangdong Univ. Petrochem. Technol. 27, 6, 88–92. [Google Scholar]
- Hu J., Zhang L., Wang A. (2016) Quantitative safety early warning method for chain effect of refining and chemical plant failures, J. Chem. Ind. Eng. 67, 7, 3091–3100. [Google Scholar]
- Marais H., van Schoor G., Uren K.R. (2019) The merits of exergy-based fault detection in petrochemical processes, J. Process Cont. 29, 74, 110–119. [CrossRef] [Google Scholar]
- Ding J., Yang Y., Liu L. (2017) Risk analysis and a safety control system of the injection–production project in the sugiao underground gas storage group, North China, Nat. Gas Ind. 37, 5, 106–112. [Google Scholar]
- Hu S., Sun G. (2000) Research on fault detection and identification technology based on system simulation, Syst. Eng. Theory Pract. 26, 8, 38–44. [Google Scholar]
- Song W., Xiang J., Zhong Y. (2018) A simulation model based fault diagnosis method for bearings, J. Intell. Fuzzy Syst. 34, 6, 1–11. [CrossRef] [Google Scholar]
- Song X., Long Y., Jia Z., Wang X. (2006) Application research of simulation-based fault diagnosis expert system, J. Syst. Simul. 18, 4, 1038–1040. [Google Scholar]
- Tian W., Sun S., Wang H. (2008) Dynamic Simulation-based fault diagnosis in chemical pipeline leakages, J. Beijing Univ. Chem. Technol. 35, 5, 18–23. [Google Scholar]
- Hossam A.G., Sajid H., Amir H. (2014) Simulation-based fault propagation analysis application on hydrogen production plant, Process Safety Environ. Protect. 92, 6, 723–731. [CrossRef] [Google Scholar]
- Tung T., Kass A. (2018) What’s a digital twin? https://www.accenture.com/us-en/blogs/blogs-kass-tung-digital-twin. [Google Scholar]
- Yu Y., Fan S., Peng G., Dai S., Zhao G. (2017) Application of digital twin model in product configuration management, Aviat. Manuf. Technol. 60, 7, 41–45. [Google Scholar]
- Liu D., Guo K., Wang B. (2018) Summary and prospect of digital twin technology, J. Instrum. 39, 11, 1–10. [Google Scholar]
- Zhao M., Ning Z. (2019) Four talks on “digital twins”-new research/application progress, Innovisin Ltd., Shenzhen, China. [Google Scholar]
- Xie D., Qian F., Yu J. (1993) Process mathematical model of naphtha cracking in SRT – III cracking furnace – I: Clean pipe model and calculation, Petrochemical 24, 12, 813–819. [Google Scholar]
- Du Z., Hua X., Yu J. (1989) Mathematical model and state estimation of ethylene cracking furnace, J. East China Inst. Chem. Technol. 15, 02, 176–181. [Google Scholar]
Open Access
| Issue |
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 76, 2021
|
|
|---|---|---|
| Article Number | 9 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.2516/ogst/2020095 | |
| Published online | 02 February 2021 | |
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