Near Miss Archive: A Challenge to Share Knowledge among Inspectors and Improve Seveso Inspections

Silvia Maria Ansaldi,Patrizia Agnello,Maria Rosaria Vallerotonda,Annalisa Pirone

doi:10.3390/su13158456

Abstract

In European Seveso Legislation for the control of the hazard of major accidents (Directive 2015/12/UE), the Safety Management System SMS is an essential obligation for managers and the authorities are required to periodically verify its adequateness through periodical inspections at Seveso sites. One of the pillars of the SMS is the collection and analysis of documents on accidents, near misses, and possible anomalies, in order to identify weaknesses and implement continuous improvement. In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes. The archive currently contains some 4000 reports, collected in 5 years by some 100 inspectors throughout Italy. This paper discusses in detail the challenges faced to extract the knowledge hidden in the documents and make it usable through the design of a robust model. For this aim, machine learning techniques have been used for preprocessing of the reports for extracting the concepts and their relations, organized into an entity-relation model. The effectiveness of this methodology and its potentiality are pointed out by investigating a few hot topics, exploiting the information contained in the repository.

Highlights

The analysis of near misses is a need in those sectors, including aeronautics and shipping, nuclear, chemical, and petrochemical, where accidents are not frequent, but when they do occur, have catastrophic consequences [1]
The first two case studies deal with known issues, the difficulties involving the permit to work and the loss of containment in ground; the aim is to understand if those problems persist despite the efforts made to ensure work safety and to limit containment losses
The third case study starts from an intuition by looking at some terms contained in the model that are apparently out of context, but since classified in the EsOpIA model by the machine learning (ML), are interesting for safety purposes

Summary

Introduction

The analysis of near misses is a need in those sectors, including aeronautics and shipping, nuclear, chemical, and petrochemical, where accidents are not frequent, but when they do occur, have catastrophic consequences [1]. The study of near misses and anomalies provides an opportunity to recognize unsafe conditions or situations and prevent incidents [2]. The context considered by this research is the process industry, including the establishments under the European Seveso Directive on the control of major accident hazards. One of the pillars of SMS is to gather and analyze reports of accidents, incidents, near misses, and anomalies occurred in the establishment with the aim to point out the weakness in the SMS for its reviewing and improvement. The reports of major accidents occurring in the chemical process industries have been systematically analyzed to learn from what happened and avoid the repetition of the same conditions and unfavorable situations. Directive 82/501/EEC since 1982, is the official European database, it collects accidents, incidents, and near misses occurred in European Seveso establishments. Other sites collecting industrial accidents are the following: Zema, the German database of Major accidents and incidents [10], the Chemical Safety Board in the United States [11], Tukes Varo registry in Finland [12], and the Japanese Failure

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Conclusion