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THE IGNITION SENSITIVITY ANALYSIS BASED ON BAYESIAN STATISTICS APROACH
Abstract
The paperwork is based on the data emerged from a conducted research related to a moisture influence on ignition sensitivity of air-methane gas mixture. For the purpose of this demarche the Bayesian statistics was used to fit a proposed mathematical model. In the first part of the paper it was presented the spark test apparatus used for the experimentation and the results of the experimental data obtained during the tests with moisturized air-methane mixtures. In the second part it was proposed and presented the theoretical model for moisture dependence of the ignition sensitivity. Also, in this part is succinctly presented the used Bayesian statistics approach. In the third part were analyzed the results of the fitting process and performance. On this occasion it was found that moisture affects the ignition sensitivity of explosive air-methane mixture quantified by ignition probability. Thus, it was deduced that ignition sensitivity first increase and then decrease with the moisture quantified by relative humidity.
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Cioclea Doru, Artur George Găman, Ion Gherghe, Florin Rădoi,Corneliu Boantă, Vlad Pasculescu, Possibilities to priorly establish the structures of ventilation networks affected by underground explosions, The 24th International Mining Congress and Exhibition of Turkey, 14 – 17 April 2015, Antalya, Turcia, ISBN 978-605-01-0705-0, Pag. 991-997, 2015
Darie M., Ionescu J., Burian S., Csaszar T., Moldovan L., (2012), Ignition probability assessment of low current circuits designed for use in explosive atmospheres, Environmental Engineering and Management Journal, volume 11/2012, No.5
Delignette-Muller M.L., Dutang C., Denis J.B, Siberchicot A., (2017) Help to Fit of a Parametric Distribution to Non-Censored or Censored Data available https://cran.r-project.org/web/packages/fitdistrplus/index.html.
Johansmeyer U., (1994), Investigations into the Intrinsic Safety of field bus systems, PTB-Bericht.
Plummer M., Stukalov A., Denwood M., (2016), Bayesian Graphical Models using MCMC, available https://cran.r-project.org/web/packages/rjags/index.html.
Plummer M., Best N., Cowles K., Vines K., Sarkar D., Bates D., Almond R., Magnusson A., (2016) Output Analysis and Diagnostics for MCMC available https://cran.r-project.org/web/packages/coda/index.html.
Standard IEC 60079-11: 2012 - Explosive atmospheres -- Part 11: Equipment protection by intrinsic safety "i".
R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
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