Recognition of Toxic Gases Emission in Power Plant Based on Artificial Neural Network

被引：2
|
作者：
Meng Xiaomin [1 ]
机构：
[1] NE Dianli Univ, Inst Chem Engn, Jilin, Peoples R China
来源：
2012 INTERNATIONAL CONFERENCE ON FUTURE ELECTRICAL POWER AND ENERGY SYSTEM, PT B | 2012年 / 17卷
关键词：
toxic gas; recognition; back-propagation neural network (BP); self-organizing feature map (SOM);
D O I：
10.1016/j.egypro.2012.02.284
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Two kinds of methods of artificial neural network, which are used to recognized toxic gas, are presented and the effects of recognition are also compared. Firstly, the composition and principle of sensor array sensitive to toxic gas are introduced. Two kinds of neural network models, Back-Propagation Neural Network (BP) and Self-Organizing Feature Map (SOM), for qualitative analysis and recognition to three kinds of gas (CO, SO2, NO2) in sensor array system are utilized. The results show that preciseness rate of the two recognitions reaches 100%, but the identify capacity of SOM, such as study time and training epochs, is better than BP in entirety. (C) 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Hainan University.
引用
收藏
页码：1578 / 1584
页数：7
相关论文
共 50 条
  • [21] Reactive Power Compensation Based on Artificial Neural Network
    Bayindir, Ramazan
    Sagiroglu, Seref
    Colak, Ilhami
    JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, 2007, 10 (02): : 129 - 135
  • [22] Power system equivalent based on an artificial neural network
    Pavic, I
    Hebel, Z
    Delimar, M
    ITI 2001: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY INTERFACES, 2001, : 359 - 365
  • [23] Electric power regulation and modeling of a central tower receiver power plant based on artificial neural network technique
    Moukhtar, Ibrahim
    Elbaset, Adel A.
    El Dein, Adel Z.
    Qudaih, Yaser
    Blagin, Evgeny
    Uglanov, Dmitry
    Mitani, Yasunori
    JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2018, 10 (04)
  • [24] Short - Term Wind Power Plant Predicting With Artificial Neural Network
    Kumar, A. Senthil
    Cermak, Tomas
    Misak, Stanislav
    PROCEEDINGS OF THE 2015 16TH INTERNATIONAL SCIENTIFIC CONFERENCE ON ELECTRIC POWER ENGINEERING (EPE), 2015, : 584 - 588
  • [25] Condition monitoring of a nuclear power plant check valve based on acoustic emission and a neural network
    Lee, MR
    Lee, JH
    Kim, JT
    JOURNAL OF PRESSURE VESSEL TECHNOLOGY-TRANSACTIONS OF THE ASME, 2005, 127 (03): : 230 - 236
  • [26] Artificial neural network for predicting nuclear power plant dynamic behaviors
    El-Sefy, M.
    Yosri, A.
    El-Dakhakhni, W.
    Nagasaki, S.
    Wiebe, L.
    NUCLEAR ENGINEERING AND TECHNOLOGY, 2021, 53 (10) : 3275 - 3285
  • [27] The Analysis of Face Recognition Based on BP Artificial Neural Network
    Du, Yang
    Guo, Fei
    PROCEEDINGS OF THE 2016 7TH INTERNATIONAL CONFERENCE ON MECHATRONICS, CONTROL AND MATERIALS (ICMCM 2016), 2016, 104 : 272 - 277
  • [28] Orbit shape automatic recognition based on artificial neural network
    Du Dongmei
    He Qing
    Proceedings of the ASME Power Conference 2005, Pts A and B, 2005, : 489 - 492
  • [29] Artificial neural network based character recognition using SciLab
    Darshni, Priya
    Dhaliwal, Balwinder Singh
    Kumar, Raman
    Balogun, Vincent Aizebeoje
    Singh, Sunpreet
    Pruncu, Catalin Iulian
    MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (02) : 2517 - 2538
  • [30] ARTIFICIAL NEURAL NETWORK BASED SPECTRUM RECOGNITION IN COGNITIVE RADIO
    Singh, Rahul
    Kansal, Sarita
    2016 IEEE STUDENTS' CONFERENCE ON ELECTRICAL, ELECTRONICS AND COMPUTER SCIENCE (SCEECS), 2016,
12345