Neural network-based source tracking of chemical leaks with obstacles  

作　　者：Qiaoyi Xu Wenli Du Jinjin Xu Jikai Dong 

机构地区：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China [2]Shanghai Institute of Intelligent Science and Technology,Tongji University,Shanghai 200092,China

出　　处：《Chinese Journal of Chemical Engineering》2021年第5期211-220,共10页中国化学工程学报（英文版）

基　　金：The work was supported by the National Natural Science Foundation of China(Basic Science Center Program:61988101;21706069),Natural Science Foundation of Shanghai(17ZR1406800);National Science Fund for Distinguished Young Scholars(61725301).

摘　　要：The leakage of hazardous gases poses a significant threat to public security and causes environmental damage.The effective and accurate source term estimation(STE)is necessary when a leakage accident occurs.However,most research generally assumes that no obstacles exist near the leak source,which is inappropriate in practical applications.To solve this problem,we propose two different frameworks to emphasize STE with obstacles based on artificial neural network(ANN)and convolutional neural network(CNN).Firstly,we build a CFD model to simulate the gas diffusion in obstacle scenarios and construct a benchmark dataset.Secondly,we define the structure of ANN by searching,then predict the concentration distribution of gas using the searched model,and optimize source term parameters by particle swarm optimization(PSO)with well-performed cost functions.Thirdly,we propose a one-step STE method based on CNN,which establishes a link between the concentration distribution and the location of obstacles.Finally,we propose a novel data processing method to process sensor data,which maps the concentration information into feature channels.The comprehensive experiments illustrate the performance and efficiency of the proposed methods.

关 键 词：OBSTACLE Optimization NEURAL networks FEATURE extraction Source TERM estimation COMPUTATIONAL fluid dynamics (CFD) 

分 类 号：TQ086[化学工程] TP183[自动化与计算机技术—控制理论与控制工程]