LLNFM与RBR融合的生料分解过程工况辨识模型  

Condition Identification Model Based on the Fusion of LLNFM and RBR for Raw Meal Calcination Process

作　　者：乔景慧[1] 

机构地区：[1]沈阳工业大学机械工程学院,沈阳110870

出　　处：《控制工程》2016年第4期522-526,共5页Control Engineering of China

基　　金：国家自然科学基金(61573249);中国博士后科学基金资助项目(2014M561249;2015T80268);辽宁省博士科研启动基金(201501082);流程工业综合自动化国家重点实验室面上项目(资助)

摘　　要：针对水泥生料分解过程中易煅烧工况、难煅烧工况和异常工况不能及时准确判断的难题,将局部线性神经模糊模型和规则推理相结合,提出了基于局部线性神经模糊模型和规则推理的工况识别模型。局部线性神经模糊模型预测预热器C5出口温度,规则推理使用输入变量判断当前工况。该模型已经成功应用到某水泥厂水泥生料分解过程,降低了预热器C5下料管堵塞的概率。In the raw meal calcination process, there are three conditions(i.e., easy calcination condition, difficult calcination condition, and abnormal condition), however it is difficult to be estimated in time by operators. To solve this difficult problem, a prediction model has been proposed by combining local linear neuro-fuzzy model(LLNFM) with rule-based reasoning(RBR). The LLNFM is applied to the model to predict the output temperature of the preheater C5. The proposed model has been successfully applied to raw meal calcination process of Cement Plant, which reduces the failure incidence of the preheater C5.

关 键 词：生料分解过程 局部线性神经模糊模型 规则推理 工况 

分 类 号：TQ172.1[化学工程—水泥工业]