Process Health and Optimization
Increase throughput and yields, improve efficiency
by reducing waste.
Key Technology Differentiators
- Digital Twin Models built from data of your own plant that reflect conditions more accurately as opposed to time-consuming physics only models
- Adaptive, self-learning AI models constantly in sync with process dynamics and asset changes
- Harnessing of production, inspection, maintenance, planning data to provide a 360 view of process health and optimization routes
- Process FMEA for cause advisory and recommendations

Performance 360 Key Features
Process Health Dashboard
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- KPI trending and prediction
- Real-time situation awareness
- Soft sensors – estimate variables that are hard to measure
Real-time Process Monitoring
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- Digital Twins – process states
- Forecasting & What-if Analysis
- Process Templates for rapid deployment
Process Optimization
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- Predictive and prescriptive analytics
- Online Process Control
- Advisories- Operations & Maintenance


Performance 360 Architecture


Customer Success Story: Gold Mine Mill Optimizer
A large gold mine in North America, was constrained by its ability to increase gold recovery. Operations were constrained by its ability to increase throughput in the grinding mill circuits due to several assets being in single line of failure. Performance 360 solution was deployed that had AI-powered optimizers to provide real-time inputs to downstream advanced process controllers to enabling it to achieve high throughputs while maintaining mill stability and safety parameters. Outcome: $5M+ annual benefit; AI Technologies: unsupervised & supervised AI models, LSTM, Auto-encoders, KNN, non-linear optimizers
Customer Success Story: Petrochemicals Ammonia Plant CO2 Excursion Anomaly
A large 3000 ton/hour ammonia plant at a large petrochemical plant suffered from 2-3 unplanned outages every year due to abrupt CO2 excursions that were detrimental to the catalyst beds. Performance 360 deployed its digital twin solution to look out of developing anomalies ahead of time and flag alerts to operators 6-8 hours ahead to provide sufficient lead time for mitigation and avoid process shutdowns. The solution successfully flagged all CO2 excursion events and saved millions of dollars. Outcome: $2 Million; AI Technologies: unsupervised & supervised AI models, LSTM, Auto-encoders, multi-variate forecasting
