Neural network-based intelligent integrated modeling for the CFB-FGD process

被引：0
|
作者：
Li Hongru [1 ]
Fan Liting [1 ]
Wang Fuli [1 ]
机构：
[1] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Peoples R China
来源：
WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS | 2006年
关键词：
CFB-FGD; mechanism model; intelligent integrated model; neural network;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The integration modeling method based on input weighted feed-forward neural network was proposed under the intelligent integrated modeling theory. According to the mechanism model of circulated fluidized bed for flue gas desulfurization(CFB-FGD), the influencing factor of sulfur dioxide removal was confirmed. Furthermore, depending on the effect of influencing factor, weighting coefficients of each factor were got and the intelligent integrated model based on neural network was set up. The simulation results indicate that the integrated model can simulate and predict the desulfurization efficiency perfectly, and is better than the mechanism model.
引用
收藏
页码：4686 / +
页数：3
相关论文
共 50 条
  • [31] Neural Network-Based Co-Simulation Technology for Intelligent Contactors
    Tang Longfei
    Han Zhiping
    Xu Zhihong
    IEEE TRANSACTIONS ON MAGNETICS, 2020, 56 (02)
  • [32] Intelligent neural network-based fast power system harmonic detection
    Lin, Hsiung Cheng
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2007, 54 (01) : 43 - 52
  • [33] Modeling of TIG welding process using conventional regression analysis and neural network-based approaches
    Dutta, Parikshit
    Pratihar, Dilip Kumar
    JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2007, 184 (1-3) : 56 - 68
  • [34] Intelligent Consumer Flexibility Management With Neural Network-Based Planning and Control
    Kiljander, Jussi
    Sarala, Roope
    Rehu, Jari
    Pakkala, Daniel
    Paakkonen, Pekka
    Takalo-Mattila, Janne
    Kansala, Klaus
    IEEE ACCESS, 2021, 9 : 40755 - 40767
  • [35] Modeling and identification for practical nonlinear process using neural fuzzy network-based Hammerstein system
    Li, Feng
    Zheng, Tian
    Cao, Qingfeng
    TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2023, 45 (11) : 2091 - 2102
  • [36] Realization of BP neural network modeling based on NOXof CFB boiler in DCS
    Bai, Jianyun
    Zhu, Zhujun
    Wang, Qi
    Ying, Jiang
    INTERNATIONAL CONFERENCE ON ENERGY ENGINEERING AND ENVIRONMENTAL PROTECTION (EEEP2017), 2018, 121
  • [37] Deep neural network-based predictive modeling of road accidents
    Gyanendra Singh
    Mahesh Pal
    Yogender Yadav
    Tushar Singla
    Neural Computing and Applications, 2020, 32 : 12417 - 12426
  • [38] Nonlinear Neural Network-based Modeling of Switched Reluctance Motor
    Qin, Weixian
    Shi, Xiaobo
    Chi, Hehua
    Wu, Juebo
    2010 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2010,
  • [39] Neural Network-based Methods for Microwave Active Device Modeling
    Cui, Jinyuan
    Zhang, Qi-Jun
    2022 IEEE BICMOS AND COMPOUND SEMICONDUCTOR INTEGRATED CIRCUITS AND TECHNOLOGY SYMPOSIUM, BCICTS, 2022, : 25 - 28
  • [40] Spiking neural network-based computational modeling of episodic memory
    Shrivastava, Rahul
    Chauhan, Pushpraj Singh
    COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING, 2024, 27 (15) : 2231 - 2245
12345