竖炉焙烧过程生产质量监控系统  

Product Quality Monitoring System for Roasting Process of Shaft Furnace

作　　者：吴峰华[1] 岳恒[1] 柴天佑[1] 

机构地区：[1]东北大学流程工业综合自动化教育部重点实验室,辽宁沈阳110004

出　　处：《东北大学学报（自然科学版）》2007年第7期913-916,共4页Journal of Northeastern University(Natural Science)

基　　金：国家重点基础研究规划项目(2002CB312201);国家自然科学基金资助项目(60534010);国家创新研究群体科学基金资助项目(60521003);长江学者和创新团队发展计划项目(IRT0421)

摘　　要：针对竖炉焙烧过程的质量指标磁选管回收率难以实时在线测量问题,基于RBF神经网络与专家系统提出了由磁选管回收率预报模型和生产质量诊断模型构成的竖炉焙烧质量监控系统.经过现场检验,该系统能够准确实时地预报磁选管回收率,并能够对生产质量进行诊断,提出合理的参数调整方法以避免不合格产品的出现.磁选管回收率提高2%,产品合格率提高了50%,有效地保证了竖炉焙烧过程的生产质量.In the hematite ore roasting process of a shaft furnace, the product quality index, namely the magnetic tube recovery rate （MTRR）, is difficult to be measured on-line or in real time. Therefore, a quality monitoring system is developed on the basis of RBF neural network and expert system, involving a MTRR prediction model and a product quality diagnosis model. Practical applications show that the proposed system can timely predict the MTRR with great precision and well diagnose the product quality. Furthermore, the way to adjust relevant parameters is suggested for the process to avoid the unqualified products, As a result, the MTRR is increased by 2 % with the qualified product improved 50 %, thus the product quality of the roasting process of shaft furnace can be efficiently guaranteed.

关 键 词：RBF神经网络 专家规则 磁选管回收率 生产质量监控 模型 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置]