(55p) Flammable Gas Release Modeling for Real-Time Analysis Using Adversarial Variational Bayes
- Conference: AIChE Spring Meeting and Global Congress on Process Safety
- Year: 2019
- Proceeding: 2019 Spring Meeting and 15th Global Congress on Process Safety
- Group: Global Congress on Process Safety
Monday, April 1, 2019 - 5:00pm-7:00pm
The acrylonitrile was studied about risk assessment for health damage like cancer hazard. But the component has flammable and toxicity, so we should calculate range of damage. The main scenario in this study is the acrylonitrile released from underground model, and the result is shown different damage following hole size, mass flow rate, wind velocity, and wind direction. The superior performance of the proposed model was exemplified by comparing with other surrogate models.
 Chen, T., Hadinoto, K., Yan, W., & Ma, Y. (2011). Efficient meta-modelling of complex process simulations with timeâspace-dependent outputs. Computers & chemical engineering, 35(3), 502-509.
 Wang, K., Chen, T., Kwa, S. T., Ma, Y., & Lau, R. (2014). Meta-modelling for fast analysis of CFD-simulated vapour cloud dispersion processes. Computers & Chemical Engineering, 69, 89-97.
 Long, K. J., Zajaczkowski, F. J., Haupt, S. E., & Peltier, L. J. (2009). Modeling a Hypothetical Chlorine Release on a College Campus. JCP, 4(9), 881-890.
 Na, J., Jeon, K., & Lee, W. B. (2018). Toxic gas release modeling for real-time analysis using variational autoencoder with convolutional neural networks. Chemical Engineering Science, 181, 68-78.
 Mescheder, L., Nowozin, S., & Geiger, A. (2017, August). Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks. In International Conference on Machine Learning (ICML) (pp. 2391-2400). PMLR.
 Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
 Haber, L. T., & Patterson, J. (2005). Report of an independent peer review of an acrylonitrile risk assessment. Hunam & Experimental Toxicology, 24, 487-527.