Aspen Mtell Predictive Maintenance Case Study for LDPE Hypercompressor
Case study shows how Aspen Mtell delivered 27-48 days advance warning on hypercompressor failures in an LDPE process, cutting unplanned downtime and maintenance costs.
A leading energy supply and pipeline company used Aspen Mtell on a hypercompressor operating at 2800 bar in LDPE production. The pilot targeted two failure modes: central valve degradation and HP packing seal leaks. Machine learning agents identified precise failure signatures and transferred learning across assets, providing 27 days notice for valve failure and 48 days for packing issues by incorporating upstream recirculation sensors. The approach eliminated false positives and enabled production rescheduling to avoid reactor blowdown and product loss.
Aspen Mtell detected central valve failure 27 days before occurrence with zero false positives after tuning
HP packing seal leaks were predicted 48 days ahead by adding upstream recirculation process sensors
Signature-based detection enabled transfer learning from one asset to identical equipment
Early warnings allowed production rescheduling and avoided reactor blowdown losses
Pilot completed in weeks by focusing on failure signatures rather than performance model analysis
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