Szczegóły

Tytuł artykułu

Simulation and Analysis of Sintering Furnace Temperature Based on Fuzzy Neural Network Control

Tytuł czasopisma

Archives of Foundry Engineering

Rocznik

2021

Wolumin

vo. 21

Numer

No 1

Afiliacje

Chaoxin, Zou : Guizhou Normal University, China ; Rong, Li : Guizhou Normal University, China ; Zhiping, Xie : Guizhou Normal University, China ; Ming, Su : Guizhou Normal University, China ; Jingshi, Zeng : Guizhou Zhenhua New Material Co., Ltd., China ; Xu, Ji : Guizhou Normal University, China ; Xiaoli, Ye : Guizhou Normal University, China ; Ye, Wang : Guizhou Normal University, China

Autorzy

Słowa kluczowe

Fuzzy neural network ; Furnace temperature control ; PID

Wydział PAN

Nauki Techniczne

Zakres

23-30

Wydawca

The Katowice Branch of the Polish Academy of Sciences

Bibliografia

[1] Li, H.S., Miao, Q. & Zhou, R.M. (2014). Research on control algorithm of ceramic kiln temperature control system. Journal of Wuhan University of Technology. 36(10), 135-139. DOI: 10.3963/j.issn.1671-4431.2014.10.024.
[2] Zhu, D.Q. & Jiang, K.R. (2009). Design and simulation of fuzzy neural network controller for drying process. Journal of System Simulation. 21(15), 4768-4771. DOI: 10.16182/j.cnki.joss.2009.15.079.
[3] Zhang, Z.M., Zhang, J.Y. & Feng, X.G. (2019). Design of hot blast stove temperature control system based on RBF neural network tuning. Journal of Hebei University of Science and Technology. 40(06), 503-511. DOI: 10.7535/hbkd.2019yx06007.
[4] Bai, G.Z. & Yu, J.H. (2016). PID parameter self-tuning based on improved fuzzy neural network. Computer Application Research. 33(11), 3358-3363+3368. DOI: 10.3969/j.issn.1001-3695.2016.11.035.
[5] Li, C.L. & Huang, C.Z. (2010). Design of temperature control system based on fuzzy neural network. Microcomputer Information. 26(07), 75-76+98. DOI: 10.3969/j.issn.2095-6835.2010.07.031.
[6] Li, G.Q., Tong, S.H. & Lu, L.X. (2013). Analysis of the temperature field in a continuous sintering furnace for solar cells. Computer Simulation. 30(01), 188-192+218. DOI: 10.3969/j.issn.1006-9348.2013.01.043.
[7] Huang, B., Xie, G.J., Liang, W.S. & Zhang, J.W. (2018). Application of heating furnace temperature control system based on hybrid fuzzy PID. Electric Drive. 48(02), 43-46. DOI: 10.19457/j.1001-2095.20180208.
[8] Zhou, G.L., Peng, Y.F. & Dong, H.S. (2007). Design of adaptive fuzzy PID controller based on T-S model. Industrial Instrumentation and Automation. 2, 22-25. DOI: CNKI: SUN: GYZD.0.2007-02-005.
[9] Tan, M., Cheng, C.H. & Lu, C. (2006). Research on the temperature control system of vacuum sintering furnace based on neural network. Measurement and Control Technology. 2, 31-32+53. DOI: 10.19708/j.ckjs.2006.02.010.
[10] Wang, J.P. & Ku, M.S. (2010). Application of FNN on atmospheric heating furnace of distillation unit. Computer Measurement and Control. 18(11), 2649-2651. DOI: 10.16526/j.cnki.11-4762/tp.2010.11.003. [11] Chen, B.F., Yin, P.L. & Ma, L. (2010). Research on temperature control system based on fuzzy neural network. Computer and Digital Engineering. 38(07), 54-57. DOI: 10.3969/j.issn.1672-9722.2010.07.016.
[12] Li, M.H. & Li, Z.Q. (2012). Research on Decoupling Control Strategy of Injection Molding Machine Barrel Temperature Based on Neural Network. Ceramics. 4, 17-19. DOI: 10.19397/j.cnki.ceramics.2012.04.004.
[13] Hu, Y.N. & Ma, W.M. (2017). Research and application of paper quantitative moisture control strategy based on FNN decoupling. China Paper. 36(07), 48-53. DOI: 10.11980/j.issn.0254-508X.2017.07.009. [14] Zhang, L., Zhang, J.C., Han, H.G. & Qiao, J.F. (2020). Process control of biochemical phosphorus removal in sewage treatment based on fuzzy neural network. CIESC Journal. 71(03). 1217-1225. DOI: 10.11949/0438-1157.20191514.
[15] Tao, X.M., Luo, L. & Liu, Z.G. (2015). Research and simulation of injection molding machine barrel temperature control algorithm based on segmented PID. Plastics. 44(03), 68-70. DOI: CNKI: SUN: SULA.0.2015-03-022.
[16] Zhou, P.Q. (2016). Research on synchronization control technology of double winches based on fuzzy neural network. Machine Design and Manufacture. 9, 64-68. DOI: 10.19356/j.cnki.1001-3997.2016.09.017.
[17] Li, J.J., Xu, Y., Zhang, G., Wei, Z.Y. & Zhang, Y.B. (2015). Irrigation controller design based on BP neural network prediction and fuzzy control. Machine Design and Research. 31(05), 150-154. DOI: 10.13952/j.cnki.jofmdr. 2015.0207.
[18] Luo, C.N., Hao, R.K. & Yang, W. (2018). Boiler temperature control simulation in industrial production process. Computer simulation. 35(09), 358-362. DOI: 10.3969/j.issn.1006-9348.2018.09.074.
[19] Zhao, J. & Xu, H. (2016). Computer simulation study on segmental control of barrel temperature of injection molding machine. Synthetic Resin and Plastics. 33(05), 61-63. DOI: 10.3969/j.issn.1002-1396.2016.05.018.

Data

2021.02.12

Typ

Article

Identyfikator

DOI: 10.24425/afe.2021.136074 ; ISSN 2299-2944

Źródło

Archives of Foundry Engineering; 2021; vo. 21; No 1; 23-30

Polityka Open Access

Archives of Foundry Engineering is an open access journal with all content available with no charge in full text version.
The journal content is available under the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/).
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