Application of Bowtie Analysis Risk Assessment Tool and Layer of Protection Analysis (LOPA) to Reduce Fire Incidents in Heavy Equipment during Mining Operations

Abstract

The mining industry is an industry that involves a significant amount of heavy equipment in production activities. This heavy equipment is used to facilitate mining activities by speeding up the production process and reducing risks in production activities. During the operational period in 2022, one of the mining companies faced challenges in its operational activities. Favorable commodity prices and increasing demand for coal made it difficult for operational activities to meet these needs. This situation was not supported by the readiness of operational units to face these challenges. There have been various incidents of heavy equipment fires in mining operations, which have resulted in operational disruptions and financial losses for the company. To prevent these losses, an analysis was conducted using Bowtie Analysis and Layer of Protection Analysis (LOPA). Bowtie Analysis is a technique that utilizes a bow tie-shaped diagram to represent risk events in a simple term. This method aims to provide a comprehensive overview (helicopter view) of the logical sequence of various risk event scenarios and offers a clear visual explanation of the connections between risk events, their causes, and their potential consequences. Layer of Protection Analysis (LOPA) is a tool used to understand how a process deviation or change can lead to major accident hazards. From the analysis results, 17 solutions were identified to address the root cause of the problem. These 17 solutions were then implemented in operational activities, and the results were monitored to assess their effectiveness. It was reported that heavy equipment fire incidents in mining areas decreased from 16 incidents to 3 incidents. In percentage terms, the reduction in heavy equipment fire incidents was 81,25%. Additionally, there was an increase in production capacity by 0,42 milion tons compared to the previous year