{"id":5098,"date":"2023-09-08T14:35:54","date_gmt":"2023-09-08T14:35:54","guid":{"rendered":"https:\/\/www.lasar.polimi.it\/?p=5098"},"modified":"2023-09-08T14:36:39","modified_gmt":"2023-09-08T14:36:39","slug":"new-publication-8","status":"publish","type":"post","link":"https:\/\/www.lasar.polimi.it\/?p=5098","title":{"rendered":"New Publication"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><strong>Title:&nbsp;<\/strong>Combining Natural Language Processing and Bayesian Networks for the Probabilistic Estimation of the Severity of Process Safety Events in Hydrocarbon Production Assets<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Authors:&nbsp;<\/strong>Dario VALCAMONICO, Piero BARALDI, Enrico ZIO, Luca DECARLI, Anna CRIVELLARI and Laura LA ROSA<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is an open-access article that can be downloaded <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0951832023005525\">here<\/a> <\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Abstract:<\/strong>This work investigates the possibility of using the information contained in reports describing Process Safety Events (PSEs) occurred in hydrocarbon production assets to support Quantitative Risk Assessment (QRA). Specifically, a novel methodology combining Natural Language Processing (NLP) and Bayesian Networks (BNs) is proposed to estimate the probabilities of having PSEs of various classes of severity and identifying the factors that have mostly influenced their variation along the monitored period. A repository of reports of PSEs of hydrocarbons plants is considered to show the potentialities of the developed methodology. An application to a repository of reports of PSEs of hydrocarbons plants is considered to show the potentialities of the developed methodology. The results obtained in the application show that the proposed methodology allows identifying the critical factors for the severity of the consequences of PSEs. These results show that the framework can be used to inform and guide decisions about possible improvements of the system safety by mitigative and preventive barriers.<\/p>\n<div class=\"pvc_clear\"><\/div><p id=\"pvc_stats_5098\" class=\"pvc_stats all  \" data-element-id=\"5098\" style=\"\"><i class=\"pvc-stats-icon medium\" aria-hidden=\"true\"><svg aria-hidden=\"true\" focusable=\"false\" data-prefix=\"far\" data-icon=\"chart-bar\" role=\"img\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\" class=\"svg-inline--fa fa-chart-bar fa-w-16 fa-2x\"><path fill=\"currentColor\" d=\"M396.8 352h22.4c6.4 0 12.8-6.4 12.8-12.8V108.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v230.4c0 6.4 6.4 12.8 12.8 12.8zm-192 0h22.4c6.4 0 12.8-6.4 12.8-12.8V140.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v198.4c0 6.4 6.4 12.8 12.8 12.8zm96 0h22.4c6.4 0 12.8-6.4 12.8-12.8V204.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v134.4c0 6.4 6.4 12.8 12.8 12.8zM496 400H48V80c0-8.84-7.16-16-16-16H16C7.16 64 0 71.16 0 80v336c0 17.67 14.33 32 32 32h464c8.84 0 16-7.16 16-16v-16c0-8.84-7.16-16-16-16zm-387.2-48h22.4c6.4 0 12.8-6.4 12.8-12.8v-70.4c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v70.4c0 6.4 6.4 12.8 12.8 12.8z\" class=\"\"><\/path><\/svg><\/i> <img loading=\"lazy\" decoding=\"async\" width=\"16\" height=\"16\" alt=\"Loading\" src=\"https:\/\/www.lasar.polimi.it\/wp-content\/plugins\/page-views-count\/ajax-loader-2x.gif\" border=0 \/><\/p><div class=\"pvc_clear\"><\/div>","protected":false},"excerpt":{"rendered":"<p>Title:&nbsp;Combining Natural Language Processing and Bayesian Networks for the Probabilistic Estimation of the Severity of Process Safety Events in Hydrocarbon Production Assets Authors:&nbsp;Dario VALCAMONICO, Piero BARALDI, Enrico ZIO, Luca DECARLI, Anna CRIVELLARI and Laura LA ROSA This is an open-access article that can be downloaded here Abstract:This work investigates the [&hellip;]<\/p>\n<div class=\"pvc_clear\"><\/div>\n<p id=\"pvc_stats_5098\" class=\"pvc_stats all  \" data-element-id=\"5098\" style=\"\"><i class=\"pvc-stats-icon medium\" aria-hidden=\"true\"><svg aria-hidden=\"true\" focusable=\"false\" data-prefix=\"far\" data-icon=\"chart-bar\" role=\"img\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\" class=\"svg-inline--fa fa-chart-bar fa-w-16 fa-2x\"><path fill=\"currentColor\" d=\"M396.8 352h22.4c6.4 0 12.8-6.4 12.8-12.8V108.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v230.4c0 6.4 6.4 12.8 12.8 12.8zm-192 0h22.4c6.4 0 12.8-6.4 12.8-12.8V140.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v198.4c0 6.4 6.4 12.8 12.8 12.8zm96 0h22.4c6.4 0 12.8-6.4 12.8-12.8V204.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v134.4c0 6.4 6.4 12.8 12.8 12.8zM496 400H48V80c0-8.84-7.16-16-16-16H16C7.16 64 0 71.16 0 80v336c0 17.67 14.33 32 32 32h464c8.84 0 16-7.16 16-16v-16c0-8.84-7.16-16-16-16zm-387.2-48h22.4c6.4 0 12.8-6.4 12.8-12.8v-70.4c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v70.4c0 6.4 6.4 12.8 12.8 12.8z\" class=\"\"><\/path><\/svg><\/i> <img loading=\"lazy\" decoding=\"async\" width=\"16\" height=\"16\" alt=\"Loading\" src=\"https:\/\/www.lasar.polimi.it\/wp-content\/plugins\/page-views-count\/ajax-loader-2x.gif\" border=0 \/><\/p>\n<div 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