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Lookup NU author(s): Dr Yi Zhou
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© 2024 Elsevier LtdA time-series prediction method based on AdaMax-LSTM neural network is proposed for predicting the flame propagation speed in premixed methane gas deflagration experiments, which can provide a decision-making basis for emergency operation of FLNG combustible gas deflagration accidents. Firstly, 54 sets of premixed methane gas deflagration experiments under semi-open duct obstacle conditions were conducted to investigate the different deflagration mechanisms by changing the obstacle parameters. The experimental results demonstrate that the distance between the obstacle and ignition source, obstacle length and obstacle shape will all effect the flame propagation speed and deflagration overpressure. Secondly, the LSTM neural network is employed to setup a novel method which can predict the flame speed in time series via calculating the Reynolds number and determining the turbulence of the flame accurately. The deflagration experiments results were used as the dataset for AI training for the proposed prediction method. In addition, the AdaMax optimizer is added into the backpropagation process of the proposed LSTM neural network to maximize the prediction accuracy of the method. The analysis results indicate that the AdaMax-LSTM neural network with sigmoid activation function can achieve the highest level of accuracy prediction, with the mean R2 value reaching 0.95, and can identify anomaly data and the most different deflagration mechanisms experimental condition. The proposed method provides an efficient and accurate way to predict and analyze the deflagration mechanisms via employing cutting-edge AI technology.
Author(s): Wang B, Zhang J, Zhang B, Zhou Y, Xia Y, Shi J
Publication type: Article
Publication status: Published
Journal: Journal of Loss Prevention in the Process Industries
Year: 2024
Volume: 91
Print publication date: 01/10/2024
Acceptance date: 28/06/2024
ISSN (print): 0950-4230
ISSN (electronic): 1873-3352
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.jlp.2024.105386
DOI: 10.1016/j.jlp.2024.105386
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