LiDAR measurement based on model predictive control for boiler combustion optimization
A data-driven predictive control method with laser radar material level measurement was proposed for the combustion process of a power station boiler.This control strategy uses laser radar to monitor the amount of biomass fed into the furnace online in real time,and uses the maximum mutual information coefficient(MIC)method to analyze its effect on key parameters such as main steam pressure,combustion chamber temperature,and outlet flue gas oxygen content.Combined with the characteristic parameters of distributed control system(DCS)after screening,the model data set was constructed.Based on the model data set,an auto-regressive with extra inputs model optimized by particle swarm optimization algorithm was established.Based on the boiler key parameter model,the proposed control method attempts to minimize the flue gas oxygen content deviation under the constraints of main steam pressure and combustion chamber temperature.Taking 700 t/d biomass combustion power generation boiler as the test object,the experimental results based on the actual production data,as well as the comparative analysis demonstrate:(1)The predictive model can accurately predict the boiler key parameters and meet the demands of boiler combustion process control and optimization;(2)Compared with PID control and fuzzy control,the model predictive control algorithm shows higher control performance,and the average deviation of oxygen content from its set value in the simulation results can be controlled within±25%.The proposed model predictive control method has good practical significance in theory,and can provide reference for the optimization and transformation of biomass boiler combustion in the future.