What are the recent applications of AI algorithms on steam turbine
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Recent Applications of AI Algorithms in Steam Turbines
AI for Enhancing Steam Turbine Efficiency
Optimization of Isentropic Efficiency
Artificial Intelligence (AI) has been effectively utilized to optimize the isentropic efficiency of high-pressure (HP) steam turbines in supercritical power plants. By employing advanced AI modeling algorithms such as Artificial Neural Networks (ANN) and Support Vector Machines (SVM), researchers have been able to improve the efficiency of HP turbines significantly. For instance, ANN models have been used to conduct Monte Carlo sensitivity analysis and nonlinear programming-based optimization, resulting in efficiency improvements of 1.43%, 5.09%, and 3.40% for half-load, mid-load, and full-load power generation modes, respectively. This optimization also leads to substantial reductions in CO2 and other emissions, contributing to net-zero targets in the energy sector.
Control of Steam Turbine Heating Processes
The control of steam turbine heating processes has also benefited from AI, particularly through the use of ANN. These models help manage the thermal state of turbine casings, ensuring safe and efficient start-ups even under variable weather conditions. By predicting the optimal steam temperature at the turbine inlet, ANN-based algorithms can maintain the start-up rate and safety of the machinery, thus enhancing operational flexibility.
AI in Steam Turbine Design and Monitoring
Flow Parameter Estimation in Turbine Cascades
AI, especially ANN, has been applied to estimate flow parameters such as enthalpy, entropy, pressure, velocity, and energy losses in steam turbine cascades. This application is crucial for the design process of turbine flow parts, significantly reducing the time required for design optimization when combined with evolutionary algorithms.
Vibration Reduction in Turbine Shaft Bearings
AI models, particularly ANN, have been used to develop operational strategies for reducing vibrations in steam turbine shaft bearings. By simulating various operating scenarios, ANN models can predict and implement strategies that reduce relative vibrations by up to 4.07%, ensuring the safe and reliable operation of high-speed rotating equipment.
AI for Fault Diagnosis and Life Prediction
Fault Diagnosis
AI algorithms, including improved neural networks and neuro-fuzzy systems, have been employed for fault diagnosis in steam turbines. These models can classify fault diagnosis rules and achieve a diagnosis precision of 84%, making them valuable tools for identifying and addressing potential issues in turbine operations.
Useful Life Prediction of Turbine Blades
ANN models have also been developed to predict the useful life of steam turbine blades, which are prone to failure due to resonance stress and other factors. By accurately simulating the behavior of life cycles, these models help in planning maintenance and preventing unexpected breakdowns, thereby reducing economic losses.
AI in Steam Turbine Monitoring
Real-Time Monitoring and Control
Neural Network (NN) approaches have been applied for real-time monitoring and control of steam turbines. These models can predict generated power and various steam features that are not directly measurable through sensors, such as pressures and temperatures at drum outlets. Implementing these monitoring and control algorithms directly on PLCs allows for more efficient and accurate turbine operation.
Conclusion
The integration of AI algorithms in steam turbine applications has led to significant advancements in efficiency optimization, operational control, design, fault diagnosis, and real-time monitoring. These innovations not only enhance the performance and reliability of steam turbines but also contribute to broader environmental goals by reducing emissions and improving energy efficiency. As AI technology continues to evolve, its applications in steam turbines are expected to expand, offering even more sophisticated solutions for the energy sector.
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