Optimization Technology for Steam Turbine Thermal System Operation Based on Deep Learning
With the increasing demand for energy,improving the efficiency of thermal power generation has become an urgent problem to be solved.The article proposes a deep learning based optimization technique for the operation of steam turbine thermal systems,which improves system efficiency through intelligent data acquisition and preprocessing,LSTM performance prediction models,and intelligent optimization control strategies.A 6-month comparative experiment conducted on a 300 MW subcritical unit at Taiyuan Branch of National Energy Group Science and Technology Research Institute Co.,Ltd.showed that this technology significantly improved coal consumption,thermal efficiency,and operational stability compared to traditional methods.The research results provide a new technological path for energy conservation and emission reduction in the thermal power industry,which is of great significance for promoting energy technology innovation and low-carbon and efficient development in the industrial sector.
deep learningsteam turbine thermal systemrun optimization