Sulphur dioxide (SO₂) abatement plant equipment data

The research focused on early life reliability, availability, and maintainability (RAM) improvement for chemical processing plants, specifically using a sulfur dioxide (SO₂) abatement plant as the case study. In this research, failure and maintenance data were collected from 52 critical equipment units within a newly built sulfur dioxide (SO₂) abatement plant during its early operational phase. This early life data, although limited and often influenced by ramp-up conditions and infant mortality failures, provided a valuable opportunity to validate design assumptions and optimize maintenance strategies. The data was analyzed using experimental-numerical failure data analysis methods based on renewal and repairable systems theory to estimate failure distributions and reliability trends. These results were then used as inputs for a discrete event-based Monte Carlo simulation, which modeled the plant’s performance under both best-case and worst-case maintenance scenarios. The simulation predicted the plant’s future Key Performance Indicators (KPIs), such as reliability and availability, under steady-state conditions. Additionally, reliability-centered maintenance (RCM) was applied to the most critical equipment identified through the simulation, leading to the development of a targeted maintenance task list and an improved maintenance plan. Despite the limitations of early life data, the research successfully demonstrated that it can be effectively used to inform maintenance strategies and design validation, offering a powerful, transferable methodology applicable to various industries, including chemical processing, mining, oil and gas, and aviation.