基于补偿模糊神经网络的灰循环系统控制研究

Control Study for Ash Recycling Systems Based on Compensatory Fuzzy Neural Network

针对循环系统回料量对循环流化床锅炉床温的影响,采用补偿模糊神经网络的建模方法,建立灰循环系统回料控制模型,选取锅炉床温变化及变化率作为输入、回料风量作为输出进行了仿真研究,并与常规控制进行比较.结果表明:补偿模糊神经网络控制器对参数变化的适应性明显优于常规控制器,补偿模糊神经网络方法对灰循环系统控制优化有实际意义.

To study the influence of the amount of return materials on bed temperature of related CFB boiler, a control model has been established for the return materials in ash recycling system based on compensatory fuzzy neural network （CFNN）, with which a simulation study has been carried out by taking the temperature change and temperature variation rate as the input variables, and the return air flow as output variable. Comparison resuhs between CFNN controller and coventional controller show that the adaptability of the former one to parameter change is obviously stronger than the latter one, which therefore may serve as a reference for control optimization of ash recyling systems.