Development and engineering application of intelligent assisted denitrification system for thermal power plants based on LSTM algorithm
With the rapid growth of grid connected electricity in new energy power plants,conven-tional thermal power units are gradually undertaking more and more grid peak shaving tasks,and boiler flue gas denitrification systems are facing more frequent and variable operating conditions.In order to im-prove the reliability of equipment operation and reduce the workload of operation and maintenance person-nel,big data analysis and artificial intelligence algorithms were used to empower traditional denitrification equipment in power plants,and an intelligent auxiliary monitoring system was developed for a 350 MW supercritical boiler.After training and optimization,the prediction accuracy of the denitrification inlet parameter prediction model,electric heater performance monitoring model,and thermal primary air flow anomaly monitoring model all meet the practical requirements.After the deployment and application of the system,there were no further incidents of insufficient electric heaters and hot air flow on site,and there were no operator over regulation or under regulation issues under peak shaving conditions.This effectively improved the reliability of the SCR system operation,reduced personnel workload,and achieved good economic and environmental benefits.
thermal power plantsSCRbig dataintelligent assistanceintelligent algorithms