Conditions Identification Model Based on LLNFM and RBR in Cement Raw Meal Calcination Process

摘要：

　　In cement 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 combing local linear neuro-fuzzy model(LLNFM)with rule-based reasoning(RBR).The LLNFM was applied to the model to predict the output temperature of the preheater C5 using input variables.Rule-based reasoning decided conditions according to predicting output valve.The proposed model has been successfully applied to calcination process of Jiuganghongda Cement Plant in China,and the application results showed its effectiveness.

关键词： Rule-based Reasoning(RBR) Local Linear Neuro-fuzzy Model (LLNFM) Raw Meal Calcination Process

作者: Jinghui Qiao Tianyou Chai

作者单位: School of Mechanical Engineering,Shenyang University of Technology,Shenyang 110870,China State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenya

会议类型: 国际会议

会议名称: 第26届中国控制与决策会议(2014 CCDC)

会议地点: 长沙

会议语种:英文

页码: 2804-2809

在线出版日期: 2014-05-31（万方平台首次上网日期，不代表论文的发表时间）