Abstract
A new operator training system that trains both control room and field operators by coupling dynamic processes and accident simulations, thereby preventing potential hazards in a chemical plant, is proposed. The two types of operators were trained in different training environments — a conventional distributed control system interface for the control room operators and an augmented virtual reality-based system for the field operators. To provide quantitative process changes and accident information driven by the actions of the trainees in real time, two types of simulation, dynamic processes and accidents, were implemented. The former was accomplished through a real-time dynamic process simulation using Aspen HYSYS; the latter was achieved by replacing the high-accuracy accident simulation model based on computational fluid dynamics with a variational autoencoder with deep convolutional layers and a deep neural network surrogate model. The resulting two types of outcomes were transferred across each training environment in a platform called the process and accident interactive simulation engine using object linking and embedding technology. In the last step, an augmented virtual reality-based platform was attached to the process and accident interactive simulation engine, making communication between the control room and field operators possible in the proposed operator training system platform.
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Abbreviations
- C disc :
-
discharge coefficient
- A:
-
cross-sectional area of orifice [m2]
- γ :
-
specific heat ratio
- ρ :
-
density [kg/m3]
- Q:
-
total heat flux [W/m2]
- Δxi :
-
length of cell in i-direction
- Δt:
-
simulation time step
- Kc :
-
controller gain
- τ i :
-
integral time
- z:
-
latent space
- Nz :
-
number of latent variables
- v:
-
variable space
- Nv :
-
the number of variables
- Ntrain :
-
the number of training datasets
- OTS:
-
operator training system
- DCS:
-
distributed control system
- CROP:
-
control room operator
- FOP:
-
field operator
- VR:
-
virtual reality
- AVR:
-
augmented virtual reality
- CFD:
-
computational fluid dynamics
- AE:
-
Autoencoder
- VAE:
-
variational autoencoder
- CNN:
-
convolutional neural network
- DNN:
-
deep neural network
- VAEDC:
-
variational autoencoder with deep convolutional layers
- HOD:
-
heavy oil desulfurization
- PAISE:
-
process and accident interactive simulation engine
References
M. M. Maresh, The aftermath of a deadly explosion: A rhetorical analysis of crisis communication as employed by British Petroleum and Phillips Petroleum, in, Texas Tech University (2006).
A. Antonovsky, C. Pollock and L. Straker, Human Factors, 56, 306 (2014).
T. A. Kletz, Process Saf. Prog., 17, 196 (1998).
S. A. M. Naqvi, M. Raza, S. Ghazal, S. Salehi, Z. Kang and C. Teodoriu, Process Saf. Environ. Prot., 138, 220 (2020).
Z. Ahmad, D. S. Patle and G. P. Rangaiah, Process Saf. Environ. Prot., 99, 55 (2016).
L. Marcano, F. A. Haugen, R. Sannerud and T. Komulainen, Saf. Sci., 115, 414 (2019).
S. Morgan, S. Sendelbach and W. Stewart, Hydrocarbon processing, United States (1994).
P. Rutherford, W. Persad and M. Lauritsen, Hydrocarbon processing, United Sates (2003).
C. Siminovich and S. Joao, Procedia Eng., 83, 215 (2014).
S. H. Yang, L. Yang and C. H. He, Process Saf. Environ. Prot., 79, 329 (2001).
P. V. Carvalho, M. C. Vidal and E. F. de Carvalho, Human Factors and Ergonomics in Manufacturing & Service Industries, 17, 43 (2007).
M. Cha, S. Han, J. Lee and B. Choi, Fire Saf. J., 50, 12 (2012).
J. Cibulka, P. Mirtaheri, S. Nazir, D. Manca and T. M. Komulainen, Virtual Reality Simulators in the Process Industry: A Review of Existing Systems and the Way Towards ETS, in: Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016, Linköping University Electronic Press, 2018, pp. 495–502.
D. Manca, S. Brambilla and S. Colombo, Adv. Eng. Software, 55, 1 (2013).
S. R. Hanna, M. J. Brown, F. E. Camelli, S. T. Chan, W. J. Coirier, O. R. Hansen, A. H. Huber, S. Kim and R. M. Reynolds, Bull. Am. Meteorol. Soc., 87, 1713 (2006).
S.R. Hanna, O. R. Hansen and S. Dharmavaram, Atmos. Environ., 38, 4675 (2004).
K. J. Long, F. J. Zajaczkowski, S. E. Haupt and L. J. Peltier, JCP, 4, 881 (2009).
P. Middha, O. R. Hansen, J. Grune and A. Kotchourko, J. Hazard. Mater., 179, 84 (2010).
K. Palmer and M. Realff, Chem. Eng. Res. Des., 80, 773 (2002).
T. Chen, K. Hadinoto, W. Yan and Y. Ma, Comput. Chem. Eng., 35, 502 (2011).
A. Widodo and B.-S. Yang, Mechanical Systems and Signal Processing, 21, 2560 (2007).
M. Fauvel, J. Chanussot and J. A. Benediktsson, Pattern Recognition, 45, 381 (2012).
I. A. Udugama, C. L. Gargalo, Y. Yamashita, M. A. Taube, A. Palazoglu, B. R. Young, K. V. Gernaey, M. Kulahci and C. Bayer, Ind. Eng. Chem. Res., 59, 15283 (2020).
O. T. Kajero, T. Chen, Y. Yao, Y.-C. Chuang and D. S. H. Wong, J. Taiwan Inst. Chem. Engineers, 73, 135 (2017).
J. Masci, U. Meier, D. Cireşan and J. Schmidhuber, Stacked convolutional auto-encoders for hierarchical feature extraction, in: International Conference on Artificial Neural Networks, Springer, 52 (2011).
J. Geng, J. Fan, H. Wang, X. Ma, B. Li and F. Chen, IEEE Geoscience and Remote Sensing Letters, 12, 2351 (2015).
D. P. Kingma and M. Welling, Auto-encoding variational bayes, arXiv preprint arXiv:1312.6114 (2013).
J. Na, K. Jeon and W. B. Lee, Chem. Eng. Sci., 181, 68 (2018).
A. Géron, Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems, O’Reilly, United States (2017).
S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, arXiv preprint arXiv:1502.03167 (2015).
D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980 (2014).
D.-Y. Yun, S.-K. Seo, U. Zahid and C.-J. Lee, Appl. Sci., 10, 4005 (2020).
G.A. Melhem, R. Saini and B. M. Goodwin, Fluid Phase Equilib., 47, 189 (1989).
M. Stein, Technometrics, 29, 143 (1987).
J. Maltby, S. Phipps and V. Singleton, US Patent, 6,202,100 B1 (2001).
H. W. Witlox, M. Fernández, M. Harper, A. Oke, J. Stene and Y Xu, J. Loss Prevent. Proc. Ind., 55, 457 (2018).
A. Géron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems, O’Reilly, United States (2019).
C. Ko, Risk Management of Chemical Processes Using Dynamic Simulation and CFD-based Surrogate Model Approach, in, Seoul National University (2020).
Acknowledgement
This research was supported by a grant (19IFIP-B087592-06) from the Smart Civil Infrastructure Research Program funded by the Ministry of Land, Infrastructure and Transport (MOLIT) of the Korean government and the Korea Agency for Infrastructure Technology Advancement (KAIA), a grant (NRF-2019R1A2C1085081) from the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT), and Yullin Technologies Co., Ltd.
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Ko, C., Lee, H., Lim, Y. et al. Development of augmented virtual reality-based operator training system for accident prevention in a refinery. Korean J. Chem. Eng. 38, 1566–1577 (2021). https://doi.org/10.1007/s11814-021-0804-6
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DOI: https://doi.org/10.1007/s11814-021-0804-6