摘要
针对高炉炉壁温度检测系统中由于传感器故障导致的检测信息不完备问题,提出一种基于动态神经网络的不完备检测信息软测量方法。首先,依据高炉结构和炉壁温度传感器位置分布建立温度传感器位置描述模型和分区域温度检测模型;其次,根据热传递学分析炉壁分区域温度检测模型中各个传感器之间存在的相关性,并采用最大互信息非参统计量方法从传感器检测序列上定量的计算分区域温度检测模型中各传感器间的相关度;最后,依据相关性分析结果,结合温度传递规律,提出炉壁不完备温度检测信息软测量模型,采用Elman神经网络对模型的结构和参数进行辨识。通过高炉冶炼现场采集的数据仿真计算表明,提出的方法具有较好的准确度与检测精度,能够满足现场的检测精度要求,具备广泛的应用价值。
For the missing information problem caused by faulted temperature sensor in temperature detecting system on blast furnace （BF） wall, a soft-sensing method based on dynamic neural network is proposed. Firstly, the temperature sensor position model and regional temperature measurement model are built based on the structure of BF. Then, according to heat transfer mechanism, the correlation between temperature sensors in regional temperature measurement model is quantitatively calculated by using maximal information coefficient （MIC） method. Finally, the soft-sensing model for missing temperature information is proposed by using Elman neural network to identify the structure of the model. The effectiveness and feasibility of the proposed method is proved by the simulation results of the real-time producing data of blast furnace which satisfies the field detection accuracy requirement.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2016年第3期903-911,共9页
CIESC Journal
基金
国家自然科学基金项目(61203017
61333002)
中央高校基本科研业务费专项资金资助项目(2015349120)~~
关键词
高炉
温度
软测量
神经网络
模型
热力学
blast furnace
temperature
soft-sensing
neural networks
model
thermodynamics
