摘要
矿石焙烧竖炉燃烧过程空燃比采用定比例控制导致燃烧效率低下并且故障频发,难以适应复杂工况的变化.应用案例推理、神经网络等智能技术,提出了空燃比的智能控制方法.根据当前工况的变化趋势及燃烧过程的故障案例,采用案例推理技术对燃烧过程中的典型故障进行预报,在此基础上,通过神经网络算法实现了空燃比的在线校正.将该方法应用于竖炉焙烧燃烧过程的生产实际中,提高了燃烧温度的控制精度,降低了能耗,且故障发生率明显降低.
Due to its synthetic and complex characters, the combustion process with fixed air-fuel ratio Of shaft ore-roasting furnace is very difficult to be controlled stably, the fault is appeared frequently and lead to the combustion efficiency laigh. To deal with this problem, an intelligent control approach has been developed for the air-fuel ratio combination of case-based reasoning and neural network. The fault prediction model performs to predict the typical fault with the help of case-based reasoning technology is obtained with the working trend and the fault cases. According to these, the tuning value of air-fuel ratio are given by the algorithm based on neural network. The proposed method has been successfully applied to the combustion process of a shaft furnace, with increase of control accuracy for the combustion temperature, reduction of gas consumption and the fault ratios.
出处
《北京工业大学学报》
EI
CAS
CSCD
北大核心
2008年第3期245-250,共6页
Journal of Beijing University of Technology
基金
国家重点基础研究发展规划资助项目(2002CB312201)
国家自然科学基金(60704036)
北京工业大学博士科研启动基金(52002017200701)
北京工业大学青年基金(97002011200701)．
关键词
竖炉
燃烧过程
案例推理
神经网络
故障预报
空燃比
shaft furnace
combustion process
case-based reasoning
neural network
fault prediction
air-fuel ratio
