双循环流化床混合颗粒流率实验研究与核极限学习机模型预测

Experimental study on mixed particles flow rate of dual circulating fluidized bed and prediction of kernel extreme learning machine model

两床间颗粒循环流率是保证双循环流化床正常运行的关键。在自行设计的双循环流化床(DCFB)实验系统上进行循环流率(G_(s,mix))与各控制参数(气化室风速、提升管风速、初始床层物料量、混合物料中石英砂粒径以及稻壳质量分数)变化关系的研究,并对循环物料中稻壳质量分数(X_r)变化进行分析。此外,根据实验测量数据,建立核极限学习机(KELM)模型并进行G_(s,mix)和循环物料中Xr的预测,同时与极限学习机(ELM)模型进行比较,发现KELM模型具有更小的预测平均绝对值误差和均方根误差且预测所需时间较短,表明该模型可实现各控制参数下DCFB系统中G_(s,mix)和X_r的良好预测,为DCFB系统及类似气化系统的数据模型研究提供一种新方法。

The particles circulation flow rate between the two beds is the key to ensure the normal operation of the dual circulating fluidized bed. The study of relationship between circulating flow rate(Gs,mix) and various control parameters(gasification chamber gas velocity, riser gas velocity, initial bed material mass, quartz sand particle diameter and rice husk quality in the mixture) were carried out on a self-designed dual circulating fluidized bed(DCFB) experimental system,and the change of rice husk mass fraction(Xr) in circulating materials was analyzed. In addition, based on the experimental measurement data, the KELM model was established to predict the Gs,mix and the Xrof the circulating materials, and compared with the ELM model. It was found that the KELM model had smaller predicted MAPE and RMSE values, and the required time of complete prediction was shorter than ELM. This indicated that KELM model can achieve good prediction of Gs,mix and Xrunder various control parameters and provided a new method for the research of DCFB system and similar gasification system.