AI Platform
for Combustion
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Why Choose Pyretek?
Our platform delivers measurable benefits across key operational metrics:
Lower Energy Consumption
Optimize Productivity
Improved Safety
Reduced Downtime
Lower Emissions
Combustion Systems Data
We analyze diverse data types to deliver actionable insights:
Operational Data
Furnace temperature, pressure, and burner power.
Energy Consumption Data
Fuel, oxygen, and air flow rates.
Safety & Reliability Data
Shutoff valves, pressure/temperature switches, UV sensors.
Emission Data
NOx, O₂, and CO sensor readings.
Ecosystem Data
Operating manuals, specifications, troubleshooting guides, and more.
Energy Efficiency
Identify and rectify operational inefficiencies and maintenance errors.
Production Optimization
Reduce CO₂ and NOx emissions through precise energy management.
Safety Enhancements
Predict and prevent device failures while ensuring regulatory compliance.
Minimized Downtime
Eliminate bottlenecks and boost operational continuity.
Sustainability Goals
Achieve precise control over emissions for greener operations.
How It Works
Our platform integrates seamlessly into your operations, delivering real-time insights and automation to enhance performance and efficiency.
Lower Energy Consumption
Ensure the reliability of safety devices and prevent errors for seamless operations.
Higher Production
Optimize burner management to reduce emissions and unplanned shutdowns.
Safety Improvements
Predict failures and maintain compliance with emission permits.
Reduced Downtime
Streamline processes to maximize yield and eliminate production bottlenecks.
Lower Emissions
Improve flow control and pressure measurement for sustainable performance.
Our AI Platform
Features and Capabilities
At Pyretek, we deliver cutting-edge AI solutions tailored for burner systems. Our platform seamlessly integrates with your sensors and controls to provide comprehensive, real-time insights into your furnace operations. Through advanced analytics, predictive AI/ML models, and actionable recommendations with Generative AI, Pyretek ensures optimized performance, safety, and sustainability.
Enhanced Monitoring
Cloud dashboards offering real-time visibility into burner systems with detailed metrics, trends, and AI-driven notifications.
AI-Powered Insights
Proprietary Machine Learning (ML) models for anomaly detection, event analysis, operational efficiency, and safety improvements.
Personalized Solutions
Targeted remediation, issue triage, and personalized recommendations for individual devices and entire fleets.
Generative AI Tools
Intuitive guidance for troubleshooting, system recommendations, and step-by-step action plans within your burner ecosystem.
THEY TRUST US
Use Cases
Explore how Pyretek optimizes combustion processes across various industrial applications
Burner Flow Accuracy Analysis
Goal
Identify any issues with Natural Gas, Oxygen, and Air flows to the burner for proactive maintenance
Context
Theoretically, flows through a gas train depend only on the Flow Control Valve (FCV) opening and incoming gas pressure
Analysis
- Our AI model uses historical data to statistically predict actual burner flows (PV) using FCV and Pressure sensor readings with 3% accuracy (see chart below)
Outcomes
- If the predicted PV is more than 5% off compared to the actual PV reading, then a proactive warning will be sent to the relevant plant personnel
- Maintenance teams can then check flowmeter calibrations as well as any issues with the flow control valves or pressure sensors
Flow Control Loop Performance Analysis
Goal
Monitor and detect any issues with the burner flow control system for energy efficiency
Context
Combustion systems are considered efficient when actual burner flows (Process Values or PV) closely follow the flow set points (SP) entered by the operators
Analysis
- Overall, NG and Oxygen flow loops are extremely efficient; response times were less than a second and the average difference between SP and PV was less than 1%
- Air flow loops also showed excellent response times
- The difference between SP and PV, however, was found to be significantly higher
- We recommend servicing the combustion air flow control system ASAP
Outcomes
- Anomalies in flow control loop performance can be detected in real-time to send timely notifications for prompt fixes and maintain optimal performance
- AI based recommendation for proactive / predictive servicing based on historic patterns
Reverb Temperature Analysis
Goal
Examine the factors that have the strongest effect on the reverb furnace temperature
Context
Since the reverb furnace temperature is one of the most critical factors affecting furnace productivity, its prediction can help alleviate potentially detrimental operating conditions
Analysis
- Our AI models leverage complex relationships across 30+ variables to predict the target variable of choice and were able to predict Reverb Temps within 5% accuracy
Outcomes
- If the predicted reverb temperature is more than 10% off compared to the actual reading, then a warning will be sent to the relevant plant personnel
- Maintenance teams can then check for temperature measurement issues as well as any issues with the furnace operational parameters
NOx Measurement Analysis
Goal
Examine the factors that have the strongest effect on the furnace NOx generation
Context
Due to stringent NOx regulations, furnace NOx generation severely affects furnace productivity, therefore we believe that our ability to predict it can help alleviate operating conditions that affect NOx adversely
Analysis
- Similar to reverb temperatures, our AI models are able to predict furnace NOx generation within 5% accuracy
Outcomes
- Our model will help identify operating conditions that minimize NOx generation while maintaining optimum production levels
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