nach oben

Arabian Journal for Science and Engineering

Erschienen in:

07.04.2021 | Research Article-Systems Engineering

An SVR-based Machine Learning Model Depicting the Propagation of Gas Explosion Disaster Hazards

verfasst von: Li Liu, Jian Liu, Qichao Zhou, Min Qu

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 10/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Shock wave pressure, high temperature flames, and toxic gases are among the factors that cause mine ventilation system failure following a gas explosion. Herein, we propose a Support Vector Regression (SVR)-based machine learning model to quickly determine the propagation of these disaster-causing hazards throughout the whole ventilation network. FLACS was used to simulate the explosions in a straight roadway with different spatial geometric parameters and gas explosion equivalents. Four SVR machine learning models were constructed by incorporating the roadway’s cross-sectional area, length of gas filling, gas concentration, and the distance between the observation point and the explosion source as inputs, while the shock wave pressure, flame temperature, time to the maximum temperature, and pressure served as outputs. The proposed model can quickly and accurately predict the propagation of disaster-causing hazards for a given explosion position and equivalent. As such, it plays a significant role in determining the ventilation system failure mode and aids decision makers and rescuers in developing a rescue and refuge plan.

Vorheriger Artikel A Novel Augmented Fractional-Order Fuzzy Controller for Enhanced Robustness in Nonlinear and Uncertain Systems with Optimal Actuator Exertion

Nächster Artikel Optimized Inverse Nonlinear Function-Based Wiener Model Predictive Control for Nonlinear Systems

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

He, Z.; Wu, Q.; Wen, L.J.; Fu, G.: A process mining approach to improve emergency rescue processes of fatal gas explosion accidents in Chinese coal mines. Saf. Sci. 111, 154–166 (2019). https://doi.org/10.1016/j.ssci.2018.07.006CrossRef

Azatyan, V.V.: Features of the physicochemical mechanisms and kinetic laws of combustion, explosion, and detonation of gases. Kinet. Catal. 61(3), 319–338 (2020). https://doi.org/10.1134/s0023158420030039CrossRef

Xu, J.D.; Yang, G.Y.: Numerical simulation of the barricade encouraging effect in the process of gas explosion propagation. J. China Coal Soc. 29(1), 53–56 (2004)

He, X.Q.; Yang, Y.; Wang, E.Y.; Liu, Z.T.: Study on the influence of obstacles on the flame structure and flame propagation of gas explosion. J. China Coal Soc. 29(2), 186–189 (2004)

Jia, Z.W.; Liu, Y.W.; Jing, G.X.: Propagation characteristics of gas explosion shock wave under the condition of pipe turning. J. China Coal Soc. 36(1), 97–100 (2011)

Luo, Z.M.; Zhang, Q.; Wang, H.; Cheng, F.M.; Wang, T.; Deng, J.: Numerical simulation of gas explosion in confined space with FLACS. J. China Coal Soc. 38(8), 1381–1387 (2013)

Wang, Z.; Jiang, J.; Zheng, Y.: CFD simulation on gas explosion field in linked vessels. J. Ind. Eng. Chem. 58(4), 854–861 (2007)

Qi, Z.; Bin, Q.; Da-Chao, L.: Estimation of pressure distribution for shock wave through the bend of bend laneway. Saf. Sci. 48(10), 1263–1268 (2010). https://doi.org/10.1016/j.ssci.2010.04.003CrossRef

Gieras, M.; Klemens, R.; Rarata, G.; Wolanski, P.: Determination of explosion parameters of methane-air mixtures in the chamber of 40 dm³ at normal and elevated temperature. J. Loss Prev. Process Ind. 19(2–3), 263–270 (2006). https://doi.org/10.1016/j.jlp.2005.05.004CrossRef

10.

Hisken, H.; Enstad, G.A.; Middha, P.; van Wingerden, K.: Investigation of concentration effects on the flame acceleration in vented channels. J. Loss Prev. Process Ind. 36, 449–461 (2015). https://doi.org/10.1016/j.jlp.2015.04.005CrossRef

11.

Lin, B.Q.; Zhou, S.N.; Zhang, R.G.: Influence of barriers on flame transmission and explosion wave in gas explosion. J. China Univ. Min. Technol. 28(2), 104–106 (1999)

12.

Mittal, M.: Explosion pressure measurement of methane-air mixtures in different sizes of confinement. J. Loss Prev. Process Ind. 46, 200–208 (2017). https://doi.org/10.1016/j.jlp.2017.02.022CrossRef

13.

Salzano, E.; Cammarota, F.; Di Benedetto, A.; Di Sarli, V.: Explosion behavior of hydrogen-methane/air mixtures. J. Loss Prev. Process Ind. 25(3), 443–447 (2012). https://doi.org/10.1016/j.jlp.2011.11.010CrossRef

14.

Su, B.; Luo, Z.M.; Wang, T.; Liu, L.: Experimental and numerical evaluations on characteristics of vented methane explosion. J. Cent. South Univ. 27(8), 2382–2393 (2020). https://doi.org/10.1007/s11771-020-4456-1CrossRef

15.

Qu, Z.M.; Zhou, X.Q.; Wang, H.Y.; Ma, H.L.: Attenuation law of pressure of gas explosion shock wave. J. China Coal Soc. 33(4), 410–414 (2008)

16.

Zhou, X.Q.; Wu, B.; Xu, J.D.: Basic characteristics of gas explosion in coal mine. China Coal 28, 8–11 (2002)

17.

Tauseef, S.M.; Rashtchian, D.; Abbasi, T.; Abbasi, S.A.: A method for simulation of vapour cloud explosions based on computational fluid dynamics (CFD). J. Loss Prev. Process Ind. 24(5), 638–647 (2011). https://doi.org/10.1016/j.jlp.2011.05.007CrossRef

18.

Pang, L.; Zhang, Q.; Wang, T.; Lin, D.C.; Cheng, L.: Influence of laneway support spacing on methane/air explosion shock wave. Saf. Sci. 50, 83–89 (2012). https://doi.org/10.1016/j.ssci.2011.07.005CrossRef

19.

Jiang, B.Y.; Lin, B.Q.; Shi, S.L.; Zhu, C.J.; Ning, J.: Numerical simulation on the influences of initial temperature and initial pressure on attenuation characteristics and safety distance of gas explosion. Combust. Sci. Technol. 184(2), 135–150 (2012). https://doi.org/10.1080/00102202.2011.622321CrossRef

20.

Ke, G.; Li, S.N.; Han, R.; Li, R.Z.; Liu, Z.M.; Qi, Z.P.; Liu, Z.Y.: Study on the propagation law of gas explosion in the space based on the goaf characteristic of coal mine. Saf. Sci. 127, 5 (2020). https://doi.org/10.1016/j.ssci.2020.104693CrossRef

21.

Li, L.L.; Wang, Y.: Mechanical characteristics of partially premixed methane/air explosion in a small closed tube based on simulation technology. Energy Sources Part A- Recovery Util. Environ. Eff. 42(10), 1255–1267 (2020). https://doi.org/10.1080/15567036.2019.1604850CrossRef

22.

Xie, B.J.; Du, Y.J.; Wang, L.: Experiment and numerical simulation of gas explosion flame propagation law in bifurcated pipeline. J. Chongqing Univ. 42(6), 69–77 (2019)

23.

Zhu, Y.F.; Wang, D.M.; Shao, Z.L.; Zhu, X.L.; Xu, C.H.; Zhang, Y.T.: Investigation on the overpressure of methane-air mixture gas explosions in straight large-scale tunnels. Process Saf. Environ. Protect. 135, 101–112 (2020). https://doi.org/10.1016/j.psep.2019.12.022CrossRef

24.

Cui, G.; Yang, C.; Li, Z.L.; Zhou, Z.; Li, J.L.: Experimental study and theoretical calculation of flammability limits of methane/air mixture at elevated temperatures and pressures. J. Loss Prev. Process Ind. 41, 252–258 (2016). https://doi.org/10.1016/j.jlp.2016.02.016CrossRef

25.

Duan, X.W.; Wu, Y.L.; Jing, G.X.; Guo, S.S.; Shao, H.Y.: Study on the flame propagation law of gas and coal dust explosion in a semi-closed pipeline. J. Saf. Environ. 20(2), 524–529 (2020)

26.

Li, P.; Liu, J.; Gao, K.: Experimental study on gas explosion temperature and pressure peak in pipeline. J. Saf. Environ. 15(2), 59–63 (2015)

27.

Luo, Z.M.; Li, R.K.; Wang, T.; Cheng, F.M.; Liu, Y.; Yu, Z.J.; Fan, S.X.; Zhu, X.C.: Explosion pressure and flame characteristics of CO/CH4/air mixtures at elevated initial temperatures. Fuel (2020). https://doi.org/10.1016/j.fuel.2020.117377CrossRef

28.

Ma, Q.J.; Zhang, Q.; Li, D.; Chen, J.C.; Ren, S.Y.; Shen, S.L.: Effects of premixed methane concentration on distribution of flame region and hazard effects in a tube and a tunnel gas explosion. J. Loss Prev. Process Ind. 34, 30–38 (2015). https://doi.org/10.1016/j.jlp.2015.01.018CrossRef

29.

Wang, K.; Jiang, S.G.; Ma, X.P.; Wu, Z.Y.; Zhang, W.Q.; Shao, H.: Study of the destruction of ventilation systems in coal mines due to gas explosions. Powder Technol. 286, 401–411 (2015). https://doi.org/10.1016/j.powtec.2015.08.020CrossRef

30.

Zipf, R.K.; Gamezo, V.N.; Sapko, M.J.; Marchewka, W.P.; Mohamed, K.M.; Oran, E.S.; Kessler, D.A.; Weiss, E.S.; Addis, J.D.; Karnack, F.A.; Sellers, D.D.: Methane-air detonation experiments at NIOSH Lake Lynn laboratory. J. Loss Prev. Process Ind. 26(2), 295–301 (2013). https://doi.org/10.1016/j.jlp.2011.05.003CrossRef

31.

Zhang, L.L.; Yang, Q.Y.; Shi, B.M.; Niu, Y.H.; Zhong, Z.: Influences of a pipeline’s bending angle on the propagation law of coal dust explosion induced by gas explosion. Combust. Sci. Technol. (2019). https://doi.org/10.1080/00102202.2019.1673381CrossRef

32.

He, Y.; Zhu, C.; He, Z.; Gu, C.; Cui, J.: Big data oriented root cause identification approach based on axiomatic domain mapping and weighted association rule mining for product infant failure. Comput. Ind. Eng. (2017). https://doi.org/10.1016/j.cie.2017.05.012CrossRef

33.

Duan, P.; He, Z.; He, Y.; Liu, F.; Zhang, A.; Zhou, D.: Root cause analysis approach based on reverse cascading decomposition in QFD and fuzzy weight ARM for quality accidents. Comput. Ind. Eng. (2020). https://doi.org/10.1016/j.cie.2020.106643CrossRef

34.

Prayogo, D.; Cheng, M.Y.; Wu, Y.W.; Tran, D.H.: Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams. Eng. Comput. 36(3), 1135–1153 (2020). https://doi.org/10.1007/s00366-019-00753-wCrossRef

35.

Cortes, C.; Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/bf00994018CrossRefMATH

36.

Valente, J.M.; Maldonado, S.: SVR-FFS: a novel forward feature selection approach for high-frequency time series forecasting using support vector regression. Expert Syst. Appl. 160, 13 (2020). https://doi.org/10.1016/j.eswa.2020.113729CrossRef

37.

Sun, Z.M.; Li, D.: Coal mine gas safety evaluation based on adaptive weighted least squares support vector machine and improved Dempster-Shafer evidence theory. Discret. Dyn. Nat. Soc. (2020). https://doi.org/10.1155/2020/8782450MathSciNetCrossRef

38.

Deng, J.; Lei, C.K.; Xiao, Y.; Cao, K.; Ma, L.; Wang, W.F.; Bin, L.W.: Determination and prediction on “three zones” of coal spontaneous combustion in a gob of fully mechanized caving face. Fuel 211, 458–470 (2018). https://doi.org/10.1016/j.fuel.2017.09.027CrossRef

39.

Yu, J.Y.; Hou, B.X.; Lelyakin, A.; Xu, Z.J.; Jordan, T.: Gas detonation cell width prediction model based on support vector regression. Nucl. Eng. Technol. 49(7), 1423–1430 (2017). https://doi.org/10.1016/j.net.2017.06.014CrossRef

40.

Wang, Z.F.; Wang, S.T.; Kong, D.M.; Liu, S.Y.: Methane detection based on improved chicken algorithm optimization support vector machine. Appl. Sci.-Basel (2019). https://doi.org/10.3390/app9091761CrossRef

41.

Li, D.; Peng, S.P.; Du, W.F.; Guo, Y.L.: New method for predicting coal seam gas content. Energy Sources Part A-Recovery Util. Environ. Eff. 41(10), 1272–1284 (2019). https://doi.org/10.1080/15567036.2018.1545003CrossRef

42.

Zheng, L.G., Yu, M.G., Yu, S.J., Jia, H.L., Pan, R.K.: comparative study of nonlinear approaches for predicting explosion limits of multi-component gas mixture. In: International Symposium on Safety Science and Technology, 7, 1096-1101 (2008)

43.

Xue, Y., Zhang, T.T.: The disaster risk assessment of coal mine gas explosion based on the model of SVM classification - regression. In: 6th Annual Meeting of Risk-Analysis-Council-of-China-Association-for-Disaster-Prevention. 102, 322–327 (2014)

Titel: An SVR-based Machine Learning Model Depicting the Propagation of Gas Explosion Disaster Hazards
verfasst von: Li Liu
Jian Liu
Qichao Zhou
Min Qu
Publikationsdatum: 07.04.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Arabian Journal for Science and Engineering / Ausgabe 10/2021
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-021-05616-5

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 10/2021

A 2 mA PA Driver Capable of Handling 1μF Load Capacitance with a Three-Stage Class-AB Rail-To-Rail Amplifier

Design and Investigations of Miniaturized Dual-band Quarter Concentric Circular Ring Antenna for LTE and 5G Applications

Decentralized Robust Control of Nonlinear Uncertain Multivariable Systems

Spectral Efficient Beamforming for mmWave MISO Systems using Deep Learning Techniques

Active Power Decoupling for Single-Phase Grid-Connected PV System using a Ripple Port

Efficient Framework Analysis for Targeted Drug Delivery Based on Internet of Bio-NanoThings

Premium Partner

Marktübersichten