Gas Turbine Plant Optimization Under Changing Environmental Conditions
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Solution Overview
Problem
Gas turbines are susceptible to environmental changes that can lead to operational deviations, such as reduced power output, combustion disturbances, and increased pollutant emissions, necessitating frequent manual tuning by specialists, which is resource-intensive and becomes obsolete over time.
Innovation Solution
A computer-implemented method using AI-based machine learning to optimize gas turbine operation by recording and analyzing operational and environmental data, applying an optimization algorithm to adjust controllable parameters like pilot gas and exhaust gas temperature, ensuring compliance with stability and emission limits, and predicting future operating scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tuning by specialists is performed to optimize gas turbine operation, then operational quality can be improved at specific points in time, but this approach requires continuous resource investment, time, and money, and becomes obsolete over time due to climatic fluctuations
Solution Approach 1:
The gas turbine system performs self-optimization through an automated control concept that continuously monitors operating parameters and environmental conditions, automatically adjusting fuel amount and combustion chamber operation without requiring external specialist intervention. The system serves itself by detecting deviations and implementing corrective measures autonomously.
Solution Approach 2:
The system implements continuous feedback loops where operating parameters (combustion chamber accelerations, pollutant emissions, power output) are monitored and compared against target values. Based on this feedback and environmental condition data, the control algorithm automatically adjusts fuel injection and combustion parameters to maintain optimal operation.
2Reliability
If conventional tuning measures are applied to meet stability and emission limits, then operational deviations can be reduced, but the tuning becomes obsolete after certain periods due to climatic fluctuations requiring further tuning measures
Solution Approach 1:
The control system dynamically adapts to changing environmental conditions by continuously monitoring parameters such as ambient temperature, air pressure, and humidity. The optimization algorithm adjusts operating parameters in real-time based on current environmental conditions, allowing the system to maintain compliance with stability and emission limits across varying climates without requiring periodic retuning.
Solution Approach 2:
The system changes operating parameters (fuel amount, pilot gas flow rate, air mass flow) based on detected environmental conditions and their influence on combustion behavior. By dynamically adjusting these parameters, the system maintains optimal performance and compliance under varying environmental conditions.
3Temperature
If the amount of fuel is adjusted to compensate for climatic changes, then corrected exhaust gas temperature can be controlled, but this may lead to increased combustion chamber accelerations, pollutant emissions, or deviations from performance guarantees
Solution Approach 1:
The system employs a multi-parameter control approach where not only fuel amount but also pilot gas flow rate, air mass flow, and injection timing are adjusted in coordinated fashion. This allows precise control of combustion characteristics to maintain exhaust temperature while minimizing harmful effects such as combustion chamber accelerations and pollutant emissions.
Solution Approach 2:
The control system continuously monitors combustion chamber accelerations, pollutant emissions, and power output as feedback parameters. When deviations are detected, the system adjusts fuel and air parameters to correct these deviations while maintaining target exhaust gas temperature, ensuring compliance with multiple operational constraints simultaneously.
Data Source
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AI summary
The invention relates to a computer-implemented method and such a system (100) for optimizing the operating behavior of a stationary gas turbine plant (10). The method comprises the following steps: (i) recording operating data of a gas turbine (11) while the gas turbine (11) is in operation, and acquiring environmental data from an environment in which the gas turbine (11) is located, (ii) storing at least a subset of the operating and environmental data recorded in step (i).(iii) the provision of historical operating data of the gas turbine (11) stored in the database (14) and/or read from an external storage device, wherein this historical operating data represents any previous operating states of the gas turbine (11), and (iv) simulation for an optimized operating point of the gas turbine (11) by means of an automated execution of an optimization algorithm (OA) taking into account the operating and environmental data of the gas turbine (11) stored in step (ii) and its historical operating data from step (iii), wherein at least one controllable operating parameter of the gas turbine (11) is changed in such a way that at least one predetermined condition with respect to the operation of the gas turbine (11) is met.