AGC-ASC Decoupled Neural Networks Predictive Control Method

Thongsuo Shi; Yikang Sun

Volume 5 Issue 3

Sep. 1998

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Abstract

International Journal of Minerals, Metallurgy and Materials > 1998 > 5(3): 184-184.

Thongsuo Shi, and Yikang Sun, AGC-ASC Decoupled Neural Networks Predictive Control Method, J. Univ. Sci. Technol. Beijing , 5(1998), No. 3, pp.184-184.

Cite this article as:

Thongsuo Shi, and Yikang Sun, AGC-ASC Decoupled Neural Networks Predictive Control Method, J. Univ. Sci. Technol. Beijing , 5(1998), No. 3, pp.184-184.

Cite this article as:

Thongsuo Shi, and Yikang Sun, AGC-ASC Decoupled Neural Networks Predictive Control Method, J. Univ. Sci. Technol. Beijing , 5(1998), No. 3, pp.184-184.

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AGC-ASC Decoupled Neural Networks Predictive Control Method

Thongsuo Shi and
Yikang Sun

Author Affilications

Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China

Available Online: 16 June 2021

Graphical Abstract

Abstract

Abstract

The coupling models for the thickness-crown objects is established. A Dynamic Matrix Controller based on the TH neural networks is given with the convergence property. The computer simulations with the AGC-ASC decoupled neural networks predictive control system is complemented and it shows that the stable states of neural networks are reached with on more that one μs, this has not only sahsfied the fast property of rolling process, but also obtained a higher control index.
- control/dynamic matrix control,
- neural networks,
- AGC-ASC synthetic system,
- convergence

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[1]	Hamid Sazegaran, Milad Hojati. Effects of copper content on microstructure and mechanical properties of open-cell steel foams [J]. International Journal of Minerals, Metallurgy and Materials, 2019, 26(5): 588-596. DOI: 10.1007/s12613-019-1767-z
[2]	Yan-mei Yu, Wei-guo Liang, Ji-shan Liu. Influence of solution concentration and temperature on the dissolution process and the internal structure of glauberite [J]. International Journal of Minerals, Metallurgy and Materials, 2018, 25(11): 1246-1255. DOI: 10.1007/s12613-018-1677-5
[3]	Mohit Sharma, O. P. Modi, Punit Kumar. Synthesis and characterization of copper foams through a powder metallurgy route using a compressible and lubricant space-holder material [J]. International Journal of Minerals, Metallurgy and Materials, 2018, 25(8): 902-912. DOI: 10.1007/s12613-018-1639-y
[4]	Elisa Lam, Qinfen Gu, Peter J. Swedlund, Sylvie Marchesseau, Yacine Hemar. X-ray diffraction investigation of amorphous calcium phosphate and hydroxyapatite under ultra-high hydrostatic pressure [J]. International Journal of Minerals, Metallurgy and Materials, 2015, 22(11): 1225-1231. DOI: 10.1007/s12613-015-1189-5
[5]	Jin-jie Shi, Wei Sun. Electrochemical and analytical characterization of three corrosion inhibitors of steel in simulated concrete pore solutions [J]. International Journal of Minerals, Metallurgy and Materials, 2012, 19(1): 38-47. DOI: 10.1007/s12613-012-0512-7
[6]	Guo-ying Zhang, Guan-zhou Liu, Hong Zhu. Segmentation algorithm of complex ore images based on templates transformation and reconstruction [J]. International Journal of Minerals, Metallurgy and Materials, 2011, 18(4): 385-389. DOI: 10.1007/s12613-011-0451-8
[7]	Chun Feng, Jing-yan Zhang, Jiao Teng, Fu-ming Wang. Study on NiO/Fe interface with X-ray photoelectron spectroscopy [J]. International Journal of Minerals, Metallurgy and Materials, 2010, 17(6): 777-781. DOI: 10.1007/s12613-010-0388-3
[8]	Shuling Shen, Wei Wu, Kai Guo, Jianfeng Chen. Low-cost preparation of mesoporous silica with high pore volume [J]. International Journal of Minerals, Metallurgy and Materials, 2007, 14(4): 369-372. DOI: 10.1016/S1005-8850(07)60073-5
[9]	Shixiong Zhang, Guozhu Zeng, Tao Peng. Space-time principles of reducing stripping in furrow pits [J]. International Journal of Minerals, Metallurgy and Materials, 2003, 10(1): 16-17.
[10]	Zaigen Mu, Zhe Wu, Long Xiu. Fuzzy optimization of space frame [J]. International Journal of Minerals, Metallurgy and Materials, 2002, 9(2): 145-148.

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Periodical cited type(16)

1.	Shuvam Gupta, Vivian Moutinho, Jose R.A. Godinho, et al. 3D mineral quantification of particulate materials with Rare Earth Mineral inclusions: Achieving sub-voxel resolution by considering the partial volume and blurring effect. Tomography of Materials and Structures, 2025. DOI:10.1016/j.tmater.2025.100050
2.	Joan Widin Schroeder, Matthew J. Burch, Mario A. Perez. Multi-scale pore network modelling to evaluate connectivity in ceramic composites. Micron, 2024, 176: 103556. DOI:10.1016/j.micron.2023.103556
3.	A. Roslin, M. Marsh, B. Provencher, et al. Processing of micro-CT images of granodiorite rock samples using convolutional neural networks (CNN), Part II: Semantic segmentation using a 2.5D CNN. Minerals Engineering, 2023, 195: 108027. DOI:10.1016/j.mineng.2023.108027
4.	Rachael Leigh Moore, Alessandra Patera, Anne Bonnin, et al. Grating-Based X-Ray Computed Tomography for Improved Contrast on a Heterogeneous Geomaterial. Frontiers in Earth Science, 2022, 10 DOI:10.3389/feart.2022.878738
5.	J. Sittner, V. Brovchenko, A. Siddique, et al. Three-Dimensional Distribution of Platinum Group Minerals in Natural MSS-ISS Ores From the Norilsk One Deposit, Russia. Frontiers in Earth Science, 2022, 10 DOI:10.3389/feart.2022.860751
6.	Pratama Istiadi Guntoro, Yousef Ghorbani, Jan Rosenkranz. 3D Ore Characterization as a Paradigm Shift for Process Design and Simulation in Mineral Processing. BHM Berg- und Hüttenmännische Monatshefte, 2021, 166(8): 384. DOI:10.1007/s00501-021-01135-w
7.	Xiuxiu Miao, Aixiang Wu, Baohua Yang, et al. Development of a 3D dual pore‒system leaching model: Application on metal extraction from oxide copper ore. International Journal of Heat and Mass Transfer, 2021, 169: 120895. DOI:10.1016/j.ijheatmasstransfer.2020.120895
8.	Sheng-hua Yin, Lei-ming Wang, Xun Chen, et al. Agglomeration and leaching behaviors of copper oxides with different chemical binders. International Journal of Minerals, Metallurgy and Materials, 2021, 28(7): 1127. DOI:10.1007/s12613-020-2081-5
9.	Shenghua Yin, Xun Chen, Rongfu Yan, et al. Pore Structure Characterization of Undisturbed Weathered Crust Elution-Deposited Rare Earth Ore Based on X-ray Micro-CT Scanning. Minerals, 2021, 11(3): 236. DOI:10.3390/min11030236
10.	Lei-ming Wang, Sheng-hua Yin, Ai-xiang Wu. Visualization of flow behavior in ore-segregated packed beds with fine interlayers. International Journal of Minerals, Metallurgy and Materials, 2020, 27(7): 900. DOI:10.1007/s12613-020-2059-3
11.	Xinshuai Gao, Honghua Wang, Xu Zhang, et al. Preparation of Amorphous Poly(aryl ether nitrile ketone) and Its Composites with Nano Hydroxyapatite for 3D Artificial Bone Printing. ACS Applied Bio Materials, 2020, 3(11): 7930. DOI:10.1021/acsabm.0c01044
12.	Y. Wang, J.D. Miller. Current developments and applications of micro-CT for the 3D analysis of multiphase mineral systems in geometallurgy. Earth-Science Reviews, 2020, 211: 103406. DOI:10.1016/j.earscirev.2020.103406
13.	Pratama Guntoro, Yousef Ghorbani, Pierre-Henri Koch, et al. X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods. Minerals, 2019, 9(3): 183. DOI:10.3390/min9030183
14.	Yi Yang, Yushuang Yang, Xiyu Gao, et al. Microstructure evolution of low-grade chalcopyrite ores in chloride leaching - A synchrotron-based X-ray CT approach combined with a data-constrained modelling (DCM). Hydrometallurgy, 2019, 188: 1. DOI:10.1016/j.hydromet.2019.06.004
15.	Pratama Istiadi Guntoro, Glacialle Tiu, Yousef Ghorbani, et al. Application of machine learning techniques in mineral phase segmentation for X-ray microcomputed tomography (µCT) data. Minerals Engineering, 2019, 142: 105882. DOI:10.1016/j.mineng.2019.105882
16.	Manu Joseph, Sushant K. Manwatkar, S. Syamkumar, et al. Advances in Non-Destructive Evaluation (NDE). Lecture Notes in Mechanical Engineering, DOI:10.1007/978-981-97-1036-2_2