Application of Reliability Engineering in a Chemical Plant to Improve Productivity

摘要

This paper is based on reliability case study conducted in a chemical company (Company X) based in Germiston South Africa. The work conducted focused on the causes of production loss due to poor equipment reliability that lead to downtimes. In the chemical, the production team generates works orders through an autonomous maintenance exercise which is aimed at identifying potential equipment defaults before they cause a breakdown. The works orders are categorized under corrective maintenance schedule. There are also time based preventative maintenance works orders that are created on System Application Program (SAP) for critical equipment and their components. More often, the response time from the maintenance team is slower and leads to subsequent breakdowns and production stoppages. The financial documents of the chemical plant showed that on average the plant spends $31,000 per month on maintenance cost. Projections indicate that this could easily amount to more than $376,000 per annum provided that there is no mid-term to long-term intervention to address equipment failures. The main objective of this study is to investigate the causes of reoccurring system failures using the reliability concepts and provide a solution specific to Company X which could be expanded to other companies and industries. This study followed both a qualitative and descriptive case study research approach. Data collection was carried out by attending to equipment breakdowns, observations during the normal daily operations, during production times, studying the historical available maintenance and technical relevant data, staff interviews, company internal information regarding the financial spending for the year of study. Finding indicated that the plant maintenance programmes were inadequate and needed to be revitalised by the introduction and implementation of reliability centred maintenance (RCM) process. The RCM process was suggested to address the issue of identifying key priority equipment responsible for major downtimes and analysing the failure modes so to suggest corrective actions before failure occurs.