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Vented gas explosion overpressure prediction of obstructed cubic chamber by Bayesian Regularization Artificial Neuron Network – Bauwens model

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Authors
Shi, J.
Li, J.
Hao, Hong
Pham, Thong
Zhu, Y.
Chen, G.
Date
2018
Type
Journal Article

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Shi, J. and Li, J. and Hao, H. and Pham, T. and Zhu, Y. and Chen, G. 2018. Vented gas explosion overpressure prediction of obstructed cubic chamber by Bayesian Regularization Artificial Neuron Network – Bauwens model. Journal of Loss Prevention in the Process Industries. 56: pp. 209-216.
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Journal of Loss Prevention in the Process Industries
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Abstract

© 2018 Elsevier Ltd This study aims to develop an integrated model, namely Bauwens-BRANN model, to estimate the maximum overpressure of vented gas explosion. A series of experiments designed for cubic enclosures with and without obstacles are used in the development of Bauwens-BRANN model. Two important parameters are modified to address the pre-existing issues of Bauwens model. By incorporating the Bayesian Regularization Artificial Neuron Network (BRANN) algorithm into the Bauwens model, the Bauwens-BRANN model is developed. Improved pressure estimation accuracy is seen for the Bauwens-BRANN model in comparison with the NFPA-68 2013 model.