Predictive Quality Stability & Remaining Useful Life (RUL) Optimization
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Company Background
SCG Chemicals Public Company Limited (SCGC) operates large-scale petrochemical assets, including steam cracker units that form the backbone of olefins production. Cracker run length directly determines plant throughput, maintenance timing, and overall profitability.
Unplanned or poorly timed shutdowns of a cracker unit can result in substantial production and margin losses.
Unplanned or poorly timed shutdowns of a cracker unit can result in substantial production and margin losses.
Challenges
The TPE operation faced two interconnected challenges:
- Product quality variability, particularly fluctuations in key chemical properties (e.g., Methanol levels in PY), leading to off-spec risk.
- Uncertainty in equipment Remaining Useful Life (RUL) due to gradual performance degradation.
Operational constraints included:
- Reactive quality adjustments based on lab results
- Limited visibility into multivariable process interactions
- Manual historian data extraction
- No continuous degradation modelling
- Data fragmented across multiple process historians
Solution
SCG TPE implemented a predictive quality and asset health framework by integrating multiple plant historians into Seeq for centralized analytics and self-service model development.
1. Historian Integration
Real-time data streams connected into Seeq included:
2. Self-Service Quality Analytics Using Seeq Workbench, process engineers independently built multivariable models to identify dominant quality drivers. Capabilities included:
1. Historian Integration
Real-time data streams connected into Seeq included:
- Reactor temperatures and pressures
- Feed composition variables
- Flow rates
- Product quality lab measurements
- Equipment condition indicators
2. Self-Service Quality Analytics Using Seeq Workbench, process engineers independently built multivariable models to identify dominant quality drivers. Capabilities included:
- Good-run vs bad-run classification
- Correlation mapping between operating variables and Methanol variability
- Early detection of drift patterns
- Statistical boundary monitoring
3. RemainingUseful Life (RUL) Modelling
Degradation trends from key equipment parameters were analysed to estimate RUL dynamically.
Engineers developed:
- Rate-of-change degradation indicators
- Threshold forecasting models
- Maintenance intervention timing guidance
Benefits
Operational Efficiency
- Reduced process variability
- Minimized reactive operating adjustments
- Improved stability of reactor conditions
- Reduced off-spec production risk
- Improved yield consistency
- Stronger compliance with customer specifications
- Early detection of equipment degradation
- Improved maintenance planning
- Reduced unplanned interruptions
- Reduced material waste from off-spec batches
- Lower reprocessing and energy consumption
- Eliminated manual historian data consolidation
- Empowered engineers with self-service predictive modelling
- Established repeatable quality analytics methodology
Cost Saving
Reduced off-spec production, improved yield stability, and optimized maintenance timing generate substantial financial impact.
For a detailed breakdown of yield improvement valuation, avoided waste modelling, and maintenance optimization savings, connect with us to review the structured cost-saving assessment framework.
For a detailed breakdown of yield improvement valuation, avoided waste modelling, and maintenance optimization savings, connect with us to review the structured cost-saving assessment framework.
