Boqiao Wang , Jinnan Zhang , Bin Zhang , Yi Zhou , Yuanchen Xia , Jihao Shi
{"title":"基于 Adamax-LSTM 的液化天然气(FLNG)预混合甲烷气体爆燃实验火焰传播速度预测模型","authors":"Boqiao Wang , Jinnan Zhang , Bin Zhang , Yi Zhou , Yuanchen Xia , Jihao Shi","doi":"10.1016/j.jlp.2024.105386","DOIUrl":null,"url":null,"abstract":"<div><p>A 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 R<sup>2</sup> 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.</p></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Flame propagation speed prediction model of premixed methane gas deflagration experiments based on Adamax-LSTM for FLNG\",\"authors\":\"Boqiao Wang , Jinnan Zhang , Bin Zhang , Yi Zhou , Yuanchen Xia , Jihao Shi\",\"doi\":\"10.1016/j.jlp.2024.105386\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A 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 R<sup>2</sup> 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.</p></div>\",\"PeriodicalId\":16291,\"journal\":{\"name\":\"Journal of Loss Prevention in The Process Industries\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Loss Prevention in The Process Industries\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S095042302400144X\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Loss Prevention in The Process Industries","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S095042302400144X","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Flame propagation speed prediction model of premixed methane gas deflagration experiments based on Adamax-LSTM for FLNG
A 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.
期刊介绍:
The broad scope of the journal is process safety. Process safety is defined as the prevention and mitigation of process-related injuries and damage arising from process incidents involving fire, explosion and toxic release. Such undesired events occur in the process industries during the use, storage, manufacture, handling, and transportation of highly hazardous chemicals.