Company Background – Indorama
Transforming Process Air Compressor Reliability
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.
Challenges
Unseen Degradation in Complex Compressor Networks
Managing the complexity of multi-stage centrifugal compressors and their auxiliary subsystems was hindered by critical data being trapped across disconnected process historians. Engineers were forced to rely on offline spreadsheets and manual efficiency calculations, making it difficult to distinguish standard process variability from true mechanical degradation. Without real-time, centralized analytics, the plant was stuck in a reactive maintenance cycle where subtle efficiency drifts and early-stage wear often went undetected until they materially impacted production.
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
Empowering Engineers with Real-Time Predictive Insights
Indorama implemented an advanced analytics environment by connecting multiple process historians into Seeq, enabling continuous, contextualized monitoring of compressor performance.
01
Historian Integration
To eliminate data silos, critical operational streams—including pressures, stage temperatures, flow rates, turbine parameters, and vibration data—were integrated from various historians into a single, real-time environment. This integration established a unified and continuous data layer across all compressor stages and subsystems.
02
Self-Service Analytics Framework
Reliability and process engineers were empowered to build complex performance models without relying on data science teams. By leveraging digital overlays of OEM design curves, dynamic deviation analysis, and live stage efficiency calculations, domain experts could instantly benchmark actual performance and identify off-design operations on the fly.
03
Predictive Condition Monitoring
Moving beyond static alarm thresholds, the team deployed dynamic degradation tracking models to uncover long-term performance drift, post-maintenance step changes, and early indicators of mechanical wear. Automated, real-time dashboards completely replaced periodic spreadsheet reporting, ensuring consistent, proactive monitoring across all operational shifts.
Benefits
Comprehensive Operational and Organizational Impact
Operational Efficiency
- Real-time compressor efficiency benchmarking
- Early identification of performance degradation
- Reduced energy intensity across compressor trains
Asset Reliablility
- Transition from reactive to predictive maintenance
- Improved detection of mechanical degradation before failure
- Standardized health monitoring methodology across compressor stages
Sustainability
- Reduced energy consumption through performance optimization
- Lower carbon intensity per ton of production
Organizational Impact
- Eliminated manual performance calculations
- Engineers independently developed and deployed analytics models
- Established a repeatable compressor analytics framework scalable to other plants
