Project Background:

Industry has been trying to move away from time-based filter change outs to utilizing predictive models for a whole range of filter applications such as MEG filters, carbon bed filters, and inlet filters. However, the application of predictive models requires study of pressure drop and flow calculations and also setting alerts on when to do a filter change. A CEED student could assist by analyzing variables on select filters and developing predictive models that can then be implemented within internal industry models.

Filter changeouts, replacements and repairs, are typically one of the highest costs incurred by equipment subclass for LNG assets. Currently, most filter changeouts are either undertaken on a scheduled time basis, or on some kind of alert / alarm based on high-high differential pressure (dP). However, this is not the optimum interval for filter changeout or replacement, usually. Either there is a lot of remaining life on the filter, or there is a considerable Lost Production Opportunity (LPO) that is sustained by feed slowdown, or filter degradation, by the time the filter is replaced.

Key deliverables from this CEED project are:

• Identify candidates for filter optimization study. ABU can provide the list of the filters and the CEED student may analyze the data and recommend which filters would be a good candidate for optimization.
• For each filter selected, a predictive model should be developed that describes the dP build up, flow rate, and cumulative loading that leads to the filtrate buildup.
• Based on the predictive model, an asset strategy needs to be recommended on what frequency and trigger mechanism should be used to replace / repair / clean the filter based on filter costs, lead time, operational impact, maintenance cost, etc.
• Outputs from the study are: a) Predictive models for each filter selected, b) Optimum maintenance strategy for repair / maintenance intervention based on life cycle costs.

This project is to be executed as a ¾ CEED project.

Project Plan

Chevron will provide the operational data and list of filters as a list of candidate equipment for selection. PI data and maintenance data for the specific equipment will also be provided.

The CEED student is expected to deliver the items as stated in Section 2 above.

There will be 3 milestones:

• Filter candidate selection
• Predictive Model construction
• Optimum asset strategy recommended on lowest life cycle costs.