摘要
平均粒径是流化床聚合工艺的重要参数,亟需在环保安全、不侵入流场的条件下,快速准确地检测。流化床声信号与平均粒径相关,可对其进行小波(包)分析,提取尺度和频度特征,构建能量模式,用于粒径判别,其判别能力与尺度相关。可选定适当的尺度,采用特征筛选和Bayes方法建立判别模型,预报性能优良,计算量甚小,适于在线检测。用于聚乙烯实例,效果令人满意。
Using acoustic sensor to measure the passive acoustic emissions （AE） from a gas-solid fluidized bed. Colligating wavelet and wavelet packet decomposition energy feature, extract multi-scale information and feature vectors from the acoustic signals. By using Bayesian discrimination, average particle size can be determined with these feature vectors. The result shows that multi-scale analysis can reveal the relationship between acoustic emissions and average particle size further, and the system made of wavelet-packet energy method can measure average particle size better than that made of wavelet.
出处
《化工自动化及仪表》
CAS
2007年第4期53-56,79,共5页
Control and Instruments in Chemical Industry
基金
国家自然科学基金重大项目资助(20490200)
关键词
流化床
平均粒径
声信号
多尺度分析
能量模式
贝叶斯判别
fluidized bed
average particle size
acoustic emission （AE）
multi-scale analysis
energy pattern
Bayesian discrimination
