Monitoring near burner slag deposition with a hybrid neural network system
Monitoring near burner slag deposition with a hybrid neural network system
复制标题
使用混合神经网络系统监测燃烧器附近的炉渣沉积
DOI：
10.1088/0957-0233/14/7/332
复制
发表时间：
2003
影响因子：
2.4
通讯作者：
M. Lewitt
中科院分区：
文献类型：
--
作者：
C K Tan;S. Wilcox;J. Ward;M. Lewitt
本论文系关于发展一套系统，以侦测及监控燃烧器附近区域之熔渣成长。这些渣沉积物通常被称为“眼眉”，并且会显著影响燃烧器的稳定性。因此，该研究涉及一系列的实验，两种不同的煤在燃烧器条件的范围内，使用150千瓦pf燃烧器装有模拟眉毛。这些模拟的眼眉由安装在原始燃烧器喷口正前方的环形耐火插入物组成。通过监测火焰发出的红外辐射和声音获得的数据进行处理，以产生时域和频域特征，然后用于训练和测试混合神经网络。这种混合“智能”系统是基于自组织映射和径向基函数神经网络。该系统能够对不同大小的眉毛进行分类，成功率至少为99.5%。因此，不仅可以通过监测火焰来检测眉毛的存在，而且网络可以在相当大的条件范围内提供对存款大小的估计。
This paper is concerned with the development of a system to detect and monitor slag growth in the near burner region in a pulverized-fuel (pf) fired combustion rig. These slag deposits are commonly known as ‘eyebrows’ and can markedly affect the stability of the burner. The study thus involved a series of experiments with two different coals over a range of burner conditions using a 150 kW pf burner fitted with simulated eyebrows. These simulated eyebrows consisted of annular refractory inserts mounted immediately in front of the original burner quarl. Data obtained by monitoring the infra-red radiation and sound emitted by the flame were processed to yield time and frequency-domain features, which were then used to train and test a hybrid neural network. This hybrid ‘intelligent’ system was based on self organizing map and radial-basis-function neural networks. This system was able to classify different sized eyebrows with a success rate of at least 99.5%. Consequently, it is possible not only to detect the presence of an eyebrow by monitoring the flame, but also the network can provide an estimate of the size of the deposit, over a reasonably large range of conditions.