Transforming Process Air Compressor Reliability
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Company Background
Indorama Ventures Public Company Limited is a global petrochemical producer with operations spanning PTA, PET, and fibre manufacturing. In PTA production, Process Air Compressors (PACs) are mission-critical assets. Their performance directly influences throughput, energy intensity, and plant stability.
Because these compressor trains operate continuously and consume significant energy, even marginal efficiency losses translate into substantial financial impact.
Because these compressor trains operate continuously and consume significant energy, even marginal efficiency losses translate into substantial financial impact.
Challenges
The PAC system consists of multi-stage centrifugal compressors, steam turbines, gas expanders, and auxiliary subsystems. The operational complexity created several constraints:
- Manual efficiency calculations performed offline
- Limited real-time performance benchmarking
- Reactive maintenance triggered by alarms rather than degradation trends
- Difficulty distinguishing mechanical degradation from process variability
- Data distributed across multiple process historians
Without centralized contextual analytics, engineers relied on spreadsheets and periodic engineering reviews. Subtle efficiency drift and early-stage mechanical degradation often went undetected until performance was materially impacted.
Solution
Indorama implemented an advanced analytics environment by connecting multiple process historians into Seeq, enabling continuous, contextualized monitoring of compressor performance.
1. Historian Connectivity
Operational data streams including:
1. Historian Connectivity
Operational data streams including:
- Suction and discharge pressures
- Stage temperatures
- Flow rates
- Turbine parameters
- Vibration data
2.Self-Service Analytics Framework
Using Seeq Workbench, reliability and process engineers built their own compressor performance models without dependency on centralized data science teams. Capabilities included:
Using Seeq Workbench, reliability and process engineers built their own compressor performance models without dependency on centralized data science teams. Capabilities included:
- Expected vs. actual discharge temperature modelling
- Compressor stage efficiency calculations
- Deviation analysis across operating envelopes
- Rate-of-change monitoring for vibration and temperature
- Digitized vendor performance curve overlays
3. Predictive Condition Monitoring
Rather than relying on static thresholds, engineers deployed dynamic degradation tracking models. These identified:
- Step changes post-maintenance
- Long-term performance drift
- Early mechanical wear indicators
Benefits
Operational Efficiency
- Real-time compressor efficiency benchmarking
- Early identification of performance degradation
- Reduced energy intensity across compressor trains
Asset Reliability
- Transition from reactive to predictive maintenance
- Improved detection of mechanical degradation before failure
- Standardized health monitoring methodology across compressor stages
- Reduced energy consumption through performance optimization
- Lower carbon intensity per ton of production
- Eliminated manual performance calculations
- Engineers independently developed and deployed analytics models
- Established a repeatable compressor analytics framework scalable to other plants
Cost Saving
Energy optimization and avoided unplanned compressor failures represent significant annual financial impact.
Indorama Ventures Public Company Limited is a global petrochemical producer with operations spanning PTA, PET, and fibre manufacturing. In PTA production, Process Air Compressors (PACs) are mission-critical assets. Their performance directly influences throughput, energy intensity, and plant stability.
Because these compressor trains operate continuously and consume significant energy, even marginal efficiency losses translate into substantial financial impact.For a detailed breakdown of avoided downtime valuation, energy intensity reduction modelling, and maintenance cost savings, connect with us to review the cost analysis framework specific to compressor assets.
Because these compressor trains operate continuously and consume significant energy, even marginal efficiency losses translate into substantial financial impact.For a detailed breakdown of avoided downtime valuation, energy intensity reduction modelling, and maintenance cost savings, connect with us to review the cost analysis framework specific to compressor assets.
