Abstract
Decision-making for maintenance of engineering assets is a common challenge in the process industry due to ongoing degradation. With an increasing company-size, this problem becomes more complex from an operational and computational point of view. This paper introduces a case study to the academic community that represents the problem of optimal decision-making in the context of large asset fleets. The case study poses a large fleet of offshore compressors for gas production with a specific network structure. Two exemplary discrete-time mixed integer linear programming models following the Resource Task Network framework are presented. They address asset deterioration due to effects such as fouling by suggesting specific maintenance actions as a set of different countermeasures. Novel enumerator formulations are a computationally efficient and extendable way to model the various degradation types. Results show the benefit of optimal maintenance in the application to asset fleets. The decision-support that is delivered by the scheduling and planning approach helps to determine which maintenance type should be conducted and at what time. The paper demonstrates the benefits of optimal (long-term) schedules for maintenance, but indicate at the same time the need for efficient algorithms in the context of large asset fleets, in contrast to common industrial case studies that are rather small-scale.
Highlights
Industrial process plants have lifetimes of more than 30 years and they are prone to deterioration during their operation
The high prices of the longer lasting maintenance types in combination with their time duration that causes a production stop favors most of the time the short-term maintenance actions, namely on-line and off-line washing
The fundamental hypothesis is that maintenance should not just be conducted because of approaching failures, but because the degradation is forcing the assets to perform at a suboptimal level and improvements of the operational profit can be realized by finding the optimal choice of maintenance type and time when these maintenance tasks should be performed
Summary
Industrial process plants have lifetimes of more than 30 years and they are prone to deterioration during their operation. These complex networks of physical pieces of equipment can encompass hundreds of linked assets. Advanced control technologies, monitoring of machinery condition, and data analytics are essential to enhance the operational profit of aging process plants. Degradation is the process by which equipment gradually breaks down by a series of detrimental changes in its physical condition. These changes can be caused by time, use, or other external factors
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