The AMN AI Turbine Energy Yield Predictor is an advanced machine learning application designed to forecast turbine energy yield based on critical operational parameters. This tool enables gas turbine engineers, energy analysts, and researchers to optimize turbine performance, reduce inefficiencies, and enhance overall energy output.
Precise Energy Yield Predictions:
Utilizes ML models to estimate Predicted Turbine Energy Yield in MWh based on input parameters like pressure, temperature, and CO levels to receive accurate energy forecasts and annual output estimates.
Anomaly Detection and Troubleshooting Recommendations:
- Real-time monitoring identifies issues early, ensuring smooth turbine operation.
- Receive tailored suggestions to quickly address problems and reduce downtime.
- Proactive Problem Solving Alerts and recommendations let you address issues before they escalate.
- Improve Reliability: Predictive analytics and anomaly detection help avoid unexpected failures.
- Maximize Efficiency: Continuous analysis ensures optimal energy production and reduced operational costs.
Intuitive Interface & Reporting:
- Easy-to-use design with robust reporting features for tracking performance.
User-Friendly Design:
- Navigate effortlessly with an intuitive interface and keyboard shortcuts for a mouse-free experience.
Our app is designed with efficiency in mind, offering a range of keyboard shortcuts to streamline your workflow. Whether you’re entering data, navigating entries, or generating reports, these shortcuts make the process fast and seamless:
Ctrl + F: Focus on the first entry for quick input.
Enter: Jump to the next entry effortlessly.
Up/Down: Navigate between entries with ease.
Ctrl + D: Clear generated output and reports instantly.
Ctrl + P: Predict turbine energy yield and generate a detailed report. (You can leave the "Expected Turbine Energy Yield" Entry blank if comparative analysis isn’t needed.)
Ctrl + S: Save your report quickly with just a keystroke.
These intuitive shortcuts empower you to work faster and focus on optimizing turbine performance without interruptions.
The alert thresholds in this system serve as general guidelines and may vary depending on the turbine model, design specifications, and operational conditions. To ensure accurate monitoring and effective alerts, it is important to adjust the threshold parameters in the AMNai.json configuration file located in the installed directory. Parameters such as afdp_thresholds(Air Filter Difference Pressure), co_thresholds(Carbon Monoxide), tit_thresholds(Turbine Inlet Temperature), and cdp_thresholds(Compressor Discharge Pressure) should be customized to reflect the specific characteristics and performance requirements of the turbine. Proper adjustments will help achieve optimal system performance and precise diagnostics.
✅ Gas Turbine Engineers and Energy Operators: Optimize turbine operations for consistent energy output.
✅ Energy Analysts: Improve efficiency and predict energy yield for better decision-making.
✅ Engineers: Improve turbine efficiency with data-driven insights.
✅ Maintenance Teams: Detect anomalies and resolve issues proactively.
✅ ML & AI Enthusiasts: Explore machine learning applications in industrial energy optimization.
1️⃣ Input turbine parameters.
2️⃣ Click Predict TEY to calculate energy yield and generate insights.
3️⃣ Compare with expected TEY and review insights.
4️⃣ Save reports for future reference and optimization.
💡 Aneesh Murali Nariyampully – Creator of both the ML model and the application.
To learn more about the machine learning model, check out our project ML Turbine Energy Yield Prediction for Gas Turbine Optimization.
🔗 GitHub: ML Turbine Energy Yield Prediction for Gas Turbine Optimization