Gas Turbine Performance Optimization System for Greener Fuels
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Solution Overview
Problem
Existing technologies face challenges in accurately predicting and optimizing the performance of gas turbines, especially when operating with greener fuels, due to complex fuel kinetics, design constraints, and the need to prevent choking and surging.
Innovation Solution
A processor-implemented method and system, known as the Gas Turbine Performance Optimization System (GTPOS), which receives real-time sensor data and non-real-time data to estimate process parameters, generate input data, and determine compressor and turbine performance, thereby optimizing gas turbine operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If gas turbines operate with greener fuels (green hydrogen, ammonia, biogas), then environmental performance and carbon neutrality are improved, but operational stability and flame stability deteriorate due to complex fuel kinetics and higher nitrogen oxides emissions
Solution Approach 1:
The system implements real-time feedback control by continuously monitoring compressor performance parameters and comparing them against predicted values from the dynamic model. This enables automatic detection of deviations caused by greener fuel operation and triggers appropriate control actions to maintain operational stability and prevent surge conditions.
Solution Approach 2:
The system dynamically adjusts operating parameters such as inlet guide vane angles, rotor speeds, and fuel flow rates based on real-time compressor performance prediction. This allows the gas turbine to adapt to the complex combustion characteristics of greener fuels while maintaining stable operation and preventing harmful emissions.
2Use of energy by moving object
If the gas turbine operates at high pressure ratio to maximize efficiency, then energy conversion efficiency is improved, but the risk of choking and surging increases causing severe mechanical damage
Solution Approach 1:
The system performs preliminary prediction of compressor surge and choke conditions using the dynamic model before actual instability occurs. By anticipating potential surge conditions at high pressure ratios, the control system can take preventive actions such as adjusting inlet guide vanes or reducing fuel flow to avoid mechanical damage while maintaining efficient operation.
Solution Approach 2:
Real-time feedback from the dynamic model continuously monitors compressor operating points against surge and choke boundaries. This enables the control system to maintain operation at high pressure ratios for maximum efficiency while automatically detecting and preventing approach to unstable operating regions.
3Measurement precision
If experiments are conducted at off-design conditions to improve prediction accuracy, then model precision is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system replaces physical experiments with a computational dynamic model that predicts compressor performance at off-design conditions. This virtual modeling approach eliminates the need for time-consuming and expensive physical experiments while providing accurate predictions for control and optimization purposes.
Solution Approach 2:
The dynamic model creates a virtual copy of the compressor's dynamic behavior that can be used for prediction and optimization without requiring physical experiments. This digital twin approach allows accurate prediction of off-design performance while avoiding the time and cost constraints of experimental methods.
4Measurement precision
If proprietary design data and complete off-design performance maps are made available to improve prediction accuracy, then model precision is improved, but device complexity and data management requirements increase
Solution Approach 1:
The system segments the complex performance prediction task into modular components: a dynamic model for transient behavior, steady-state performance maps for baseline characteristics, and machine learning corrections for accuracy enhancement. This modular architecture manages data complexity while maintaining high prediction accuracy across different operating conditions.
Solution Approach 2:
The dynamic model serves multiple functions simultaneously: it predicts transient compressor performance, identifies surge and choke conditions, provides setpoint generation for control, and enables optimization of operating parameters. This multi-functionality reduces the need for separate specialized systems and simplifies data management.
Data Source
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AI summary
Gas turbines are one of leading sources for power generation with lower greenhouse gas emissions. However, due to environmental concerns, gas turbines are moving towards adopting greener fuels. The shift towards greener fuels comes with own set of challenges as performance of gas turbine at different operating points needs to be accurately predicted as experiments are very costly to perform. Existing arts perform their analysis at operating line and performance estimation at other operating points is not specified. Present application provides systems and methods for estimating performance of gas turbine accurately in wide operating region. The system first accurately estimates outlet conditions for each stage of compressor. The system then utilizes estimated outlet conditions to determine outlet conditions associated with other component of gas turbine. The outlet conditions are then utilized to estimate steady state and transient state variables that further helps in identifying optimal process settings for gas turbine.