Neural Network-Based Combustion Optimization Reduces Nox Emissions While Improving Performance

摘要

The NeuSIGHT neural network based system has been applied to units with tangential-, cell-, single wall-, and opposed wall-burner arrangements that have ranged in capacity from 146 to 800 MW in an advisory mode. Several sites have employed the neural network-based system for closed-loop supervisory combustion control. Boiler combustion profiles change continuously due to coal quality, boiler loading, changes in slag/soot deposits, ambient conditions, and the condition of plant equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to reduce NO_x emissions and improve heat rate simultaneously. This paper presents the benefits of applying an on-line, real-time neural network to several network to several commercially operating bituminous coal fired utility boilers. The system helps reduce NOx emissions up to 60