Study on key technologies of optimization of big data for thermal power plant performance
Mingyang Mao, Hong Yu Xiao · AIP conference proceedings · 2018
Thermal power generation accounts for 70% of China’s power generation, the pollutants accounted for 40% of the same kind of emissions, thermal power efficiency optimization needs to monitor and understand the whole process of coal combustion and pollutant migration, power system performance data show explosive growth trend, The purpose is to study the integration of numerical simulation of big data technology, the development of thermal power plant efficiency data optimization platform and nitrogen oxide emission reduction system for the thermal power plant to improve efficiency, energy saving and emission reduction to provide reliable technical support. The method is big data technology represented by “multi-source heterogeneous data integration”, “large data distributed storage” and “high-performance real-time and off-line computing”, can greatly enhance the energy consumption capacity of thermal power plants and the level of intelligent decision-making, and then use the data mining algorithm to establish the boiler combustion mathematical model, mining power plant boiler efficiency data, combined with numerical simulation technology to find the boiler combustion and pollutant generation rules and combustion parameters of boiler combustion and pollutant generation Influence. The result is to optimize the boiler combustion parameters, which can achieve energy saving.