Abstract
This dissertation focussed on improving maintenance practice for vacuum pump and ventilation fan systems. The availability and safe operation of assets and plant is a vital consideration in refineries. The optimisation of maintenance is required to improve the plant availability and performance and to reduce operating costs. This dissertation was inspired by the maintenance challenges facing a company that is involved in the refining of special metals. For such a precious metal refinery plant, fume extraction and filtration systems are important to personal safety and processing. Chemicals used in the plants are toxic and hence it is essential that the engineering systems such as extraction fans are always in good condition. Vacuums systems are used as filtration process to separate solids from waste. Product extraction requires precision systems. Proper and suitable maintenance should be deployed on these critical systems, to avoid plant delays and to provide the necessary plant operating environment. Running equipment/systems to failure affects plant performances. A key focus for the company has been installing early detection sensors to monitor and predict abnormal equipment behaviours. The aim of this dissertation was to build on from such investments and examine how maintenance could be improved and better informed by the condition monitoring systems. The research examined existing literature on condition-based maintenance, maintenance and practice at the company under study. Pareto analysis was used to define the critical assets and problems that dominated the escalating maintenance costs. This allowed focus on the big hit strategies (big gains). The research was conducted in three sections around the entire plant at Precious refinery plant, these sections are as follows, Main extraction fan system, Material handling extraction fan system and Other precious vacuum system. Process data was collected on existing sensors, analysed and used to infer condition of critical assets. New statistical tools such as process capability index were introduced to enable tracking the condition of the equipment. Process capability index values for processes in control and out of control were defined and these can be used for tracking system deterioration and enabling predictive maintenance. The focus was on investigation of failures on vacuum pumps and ventilation fans. The research demonstrated the potential for existing sensors and data to be used in predictive maintenance to alert maintenance teams to attend to vacuum pumps and fans pre-failure and hence improve plant availability, operations and reduce cost.
M.Ing. (Engineering Management)