基于数据驱动的CFB机组变负荷工况SO_(2)质量浓度建模

Data-driven modeling for SO_(2) mass concentration of CFB units under variable load conditions

由于循环流化床(CFB)机组在动态过程中缺乏有效的污染物生成与还原模型的指导,导致变负荷能力在一定程度上受到污染物排放水平的制约。提出了一种基于数据驱动的循环流化床机组SO_(2)质量浓度动态模型,应用极限学习机建立基础模型,根据循环流化床污染物生成与还原机理,选择合适的输入变量,并应用遗传算法对该模型加以改进,使该模型具有较高的精度,并在动态工况下有较好的建模结果。该模型可以为SO_(2)质量浓度控制系统提供有效指导。同时,在所提出的模型基础之上,在智能平行控制理论框架下,虚拟系统与实际系统相结合形成平行系统,提出了循环流化床机组SO_(2)控制系统智能平行控制方法,可为今后循环流化床机组SO_(2)低排放智能控制提供参考,在一定程度上有利于提升循环流化床机组变负荷能力。

Due to the lack of effective guidance for pollutant generation and reduction modelling in the dynamic process of circulating fluidized bed(CFB)units,the load adjusting capacity is restricted by pollutant emission to a certain extent.A data-driven SO_(2) concentration dynamic model for CFB units is established based on an extreme learning machine.According to the generation and reduction mechanisms of pollutants from CFBs,appropriate input variables are selected.The model improved by genetic algorithms is of higher accuracy and better modelling results under dynamic conditions.The model can provide effective guidance for SO_(2) concentration control systems.At the same time,a parallel system integrating a real system with its virtual counterpart is made on the basis of the proposed model under the framework of intelligent parallel control theory.The proposed intelligent parallel control for the SO_(2) control systems can provide references for the SO_(2) emission control of the following CFBs for boosting their load adjusting capacities to a certain extent.