摘要
工业园区废气占据大气污染来源总量的70%左右,需建立科学全面的监测机制,但实际上监测区域范围大、部分区域无法布点、气体分布机理建模困难。针对此实际问题及理论分析难点,提出基于双向误差传播多层神经网络(BEMNN)的监测盲区工业废气分布分析方法。首先,针对工业园区内部无法布设监测点的实际情况,提出"边界监测-盲区推理"的废气监测方案;然后,提出一种误差双向传播的多层组合神经网络,对边界与盲区的气体分布关系进行建模,利用边界监测数据推理盲区气体分布情况;最后利用某工业园区实际监测数据训练网络并进行回归推理,所提方法回归计算的平均绝对误差小于28.83μg,均方根误差小于45.62μg,相对误差控制在8%~8.88%,说明所提方法具有可行性,准确性可满足解决实际问题的需求。
Industrial waste gas has accounted for about 70% of the atmospheric pollution sources. It is crucial to establish a full-scale and reasonable monitoring mechanism. However, the monitoring area is so large and monitoring devices can not be set up in some special areas. Besides, it is difficult to model the gas distribution according with the actual. To solve the practical and theoretical problems, an analysis method of industrial waste gas distribution for non-detection zone was proposed based on a Bi-directional Error Multi-Layer Neural Network（ BEMNN）. Firstly, the monitoring mechanism was introduced in the thought of ＂monitoring in boundary and inference of dead zone＂, which aimed to offset the lack of monitoring points in some areas. Secondly, a multi-layer combination neural network was proposed in which the errors propagate in a bidirectional mode. The network was used to model the gas distribution relationship between the boundary and the dead zone.Then the gas distribution in the dead zone could be predicted with the boundary monitoring data. Finally, an experiment was conducted based on the actual monitoring data of an industrial park. The mean absolute error was less than 28. 83 μg and the root-mean-square error was less than 45. 62 μg. The relative error was between 8% and 8. 88%. The results prove the feasibility of the proposed method, which accuracy can meet the practical requirement.
作者
汪利伟
王小艺
王立
白玉廷
卢雨田
WANG Liwei;WANG Xiaoyi;WANG Li;BAI Yuting;LU Yutian(School of Computer and Information Engineering,Beijing Technology and Business University,Beijing 100048,China;School of Automation,Beijing Institute of Technology,Beijing 100081,China)
出处
《计算机应用》
CSCD
北大核心
2018年第5期1500-1504,1522,共6页
journal of Computer Applications
基金
国家自然科学基金青年项目(61703008)
北京市教委科技计划重点项目(KZ201510011011)
北京市市属高校创新能力提升计划项目(PXM2014_014213_000033)~~
关键词
工业废气
多层神经网络
监测盲区
数据驱动
industrial waste gas
multi-layer neural network
non-detection zone
data driven
