Results of using neural networks for technological processes control of iron mill

被引:3
|
作者
Mikhail, Zarubin [1 ]
Venera, Zarubina [1 ]
机构
[1] Rudny Ind Inst, 50 Let Oktyabrya St, Rudny 111500 38, Kazakhstan
关键词
process optimization; resource; adaptive control; artificial neural network;
D O I
10.1016/j.egypro.2016.09.077
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The article deals with the problem of optimization complex processes of mining and processing complexes of the Republic of Kazakhstan. In the article characteristics of used control systems were analyzed and it revealed the necessity of the use of non-existing approaches to "fine" adjustment of adaptive control systems. On the basis of the research the author proposes the structure of an adaptive control system grinding process, built using an artificial neural network with radial basis function. To evaluate the effectiveness of the developed system was evaluated reducing power consumption parameter and was proved the possibility of reducing power consumption using the system by 6.9%. (C) 2016 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:512 / 516
页数:5
相关论文
共 50 条
  • [21] SIMULATION OF TECHNOLOGICAL PROCESSES USING HYBRID TECHNIQUE EXPLORING MATHEMATICAL-PHYSICAL MODELS AND ARTIFICIAL NEURAL NETWORKS
    Heger, Milan
    Spicka, Ivo
    Bogar, Martin
    Stranavova, Maria
    Franz, Jiri
    METAL 2011: 20TH ANNIVERSARY INTERNATIONAL CONFERENCE ON METALLURGY AND MATERIALS, 2011, : 324 - 329
  • [22] Improvement of Shape Recognition Performance of Sendzimir Mill Control Systems Using Echo State Neural Networks
    Park, Jung-hyun
    Han, Seong-ik
    Kim, Jong-shik
    JOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL, 2014, 21 (03) : 321 - 327
  • [23] Shape Performance Improvement of a Sendzimir Mill System Using Echo State Neural Networks and Fuzzy Control
    Han, Seong Ik
    Park, Jung Hyun
    Jeong, Cheol Su
    Kim, Jong Shik
    ISIJ INTERNATIONAL, 2013, 53 (12) : 2184 - 2191
  • [24] Improvement of Shape Recognition Performance of Sendzimir Mill Control Systems Using Echo State Neural Networks
    Jung-huyn Park
    Seong-ik Han
    Jong-shik Kim
    Journal of Iron and Steel Research International, 2014, 21 : 321 - 327
  • [25] Improvement of Shape Recognition Performance of Sendzimir Mill Control Systems Using Echo State Neural Networks
    Jung-hyun PARK
    Seong-ik HAN
    Jong-shik KIM
    JournalofIronandSteelResearch(International), 2014, 21 (03) : 321 - 327
  • [26] Artificial neural networks for nonlinear control of industrial processes
    Nikravesh, M
    NONLINEAR MODEL BASED PROCESS CONTROL, 1998, 353 : 831 - 869
  • [27] Adaptive Contraction-based Control of Uncertain Nonlinear Processes using Neural Networks
    Wei, Lai
    McCloy, Ryan
    Bao, Jie
    IFAC PAPERSONLINE, 2022, 55 (07): : 987 - 992
  • [28] Estimation-Based Predictive Control of Nonlinear Processes Using Recurrent Neural Networks
    Alhajeri, Mohammed S.
    Wu, Zhe
    Rincon, David
    Albalawi, Fahad
    Christofides, Panagiotis D.
    IFAC PAPERSONLINE, 2021, 54 (03): : 91 - 96
  • [29] Analysis of critical control points in deviant thermal processes using artificial neural networks
    Chen, CR
    Ramaswamy, HS
    JOURNAL OF FOOD ENGINEERING, 2003, 57 (03) : 225 - 235
  • [30] MODELLING AND CONTROL OF DIFFERENT TYPES OF POLYMERIZATION PROCESSES USING NEURAL NETWORKS TECHNIQUE: A REVIEW
    Noor, R. A. Mat
    Ahmad, Z.
    Don, M. Mat
    Uzir, M. H.
    CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 2010, 88 (06): : 1065 - 1084