Gas Turbine Emissions Model Refinement via Feedback Control
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Solution Overview
Problem
Gas turbine engines face challenges in accurately controlling firing temperature and emissions due to unmeasured parameters and measurement uncertainties, leading to performance inefficiencies and variations across machines.
Innovation Solution
A system utilizing a computing device to command gas turbines to adjust power output and emissions based on measured ambient conditions, updating emissions models, and refining operating conditions to align with nominal values, thereby reducing firing temperature variation and improving control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If design margins are used to accommodate worst-case operational boundaries, then reliability is improved, but productivity deteriorates due to reduced engine performance
Solution Approach 1:
The system employs feedback control by continuously measuring actual emissions and power output, comparing them to model predictions, and adjusting the emissions model accordingly. This closed-loop approach allows the system to maintain reliability through accurate modeling without requiring conservative design margins that would reduce productivity.
Solution Approach 2:
The system dynamically adjusts operating parameters based on real-time measurements and refined models. By changing parameters adaptively rather than using fixed design margins, the system achieves both high reliability and optimal productivity across varying operating conditions.
2Measurement precision
If unmeasured parameters are controlled using measured parameters, then measurement precision is improved, but reliability deteriorates due to uncertainty in indirect parameter values
Solution Approach 1:
The system uses feedback control by continuously comparing actual measurements with model predictions and adjusting the emissions model parameters accordingly. This closed-loop approach reduces uncertainty in indirect parameter estimation by constantly refining the model based on real data, thereby improving both measurement precision and reliability.
Solution Approach 2:
The system performs preliminary tuning of the emissions model using measured data before actual operation. By pre-calibrating the model with real measurements, the system reduces uncertainty in subsequent indirect parameter estimations, improving both precision and reliability.
3Adaptability or versatility
If machine component variations are accommodated, then adaptability is improved, but manufacturing precision deteriorates due to necessary tolerances
Solution Approach 1:
The system adapts to manufacturing variations by dynamically adjusting emissions model parameters for each individual machine based on its actual performance characteristics. This allows high adaptability across machines with different component tolerances while maintaining precise control of emissions and power output for each specific unit.
Solution Approach 2:
The system applies individualized tuning parameters to each gas turbine based on its specific manufacturing variations and performance characteristics. By customizing the emissions model for each machine rather than using a generic model, the system achieves both adaptability to variations and precise control for each unit.
4Measurement precision
If sensors and measurement systems are used to monitor operating parameters, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses feedback control by comparing actual sensor measurements with model predictions and using the differences to refine the emissions model. This approach maximizes the value of existing measurements without requiring additional complex sensor systems, achieving high measurement precision while minimizing device complexity.
Data Source
AI summary
Commanding GTs to base load level based upon measured ambient condition for each GT; commanding each GT to adjust a power output to match scaled power output value equal to a fraction of a difference between the respective power output and a nominal power output value, and measuring actual emissions value for each GT during the adjusting of the respective power output; adjusting operating condition of each GT based upon a difference between the respective measured actual emissions value, a nominal emissions value at the ambient condition and emissions scale factor; updating a pre-existing emissions model for each GT based upon the adjusted operating; running set of operating conditions on each GT and measuring updated parameters for each GT including an updated emissions value; and refining updated pre-existing emissions model based upon a difference between the updated emissions value and the updated pre-existing emissions model.