Simulation Analysis on Radial Diffusion Model for Solidification Process of Carbon Fiber Production

Abstract

Abstract: Considering that Polyacrylonitrile Dimethyl Sulfoxide solution and Dimethyl Sulfoxide aqueous solution were used as the spinning dope and coagulating liquid respectively, the solidification process in spinning system of carbon fiber was studied. The radial concentration distribution and the curve of time-dependence concentration were proposed. The influence of the spinning conditions on the solidification process was investigated by changing the parameters of the model. The results show that extending the coagulation time by using coarser article spinning or increasing the solid content in dope can help to obtain the fiber with uniform structure and densification. The proposed method has good generality and can be used for solving the mathematical models of other spinning systems.

Key words: Polyacrylonitrile, solidification process, radial diffusion, spinning system

CLC Number:

TP391.9

Ren Lihong, Xu Feng, Ding Yongsheng, Zhang Tao. Simulation Analysis on Radial Diffusion Model for Solidification Process of Carbon Fiber Production[J]. Journal of System Simulation, 2015, 27(3): 486-493.

References

[1] Paul D R.Diffusion during the coagulation step of wet-spinning[J]. Journal of Applied Polymer Science (S0021-8995), 1968, 12(3): 383-402.
[2] 杨东洁, 辛长征. 纤维纺丝工艺与质量控制 [M]. 北京: 中国纺织出版社, 2008.
[3] Chen H, Qu R J, Liang Y, et al. Kinetics of diffusion in polyacrylonitrile fiber formation[J]. Journal of Applied Polymer Science (S0021-8995), 2005, 96(5): 1529-1533.
[4] Chen J, Wang C G, Dong X G, et al. Study on the coagulation mechanism of wet-spinning PAN fibers[J]. Journal of Polymer Research (S1022-9760), 2006, 13(6): 515-519.
[5] 张磊. 不确定信息在碳纤维凝固浴成型机制建模中的应用研究 [D]. 上海: 东华大学, 2013.
[6] Zhang T, Jin Y C, Ding Y S, ,et al. A cytokine network inspired cooperative control system for multi-stage stretching processes in fiber production [J]. Soft Computing. A cytokine network inspired cooperative control system for multi-stage stretching processes in fiber production [J]. Soft Computing (S1432-7643), 2015, in press.
[7] Ding Y S, Liang X, Hao K R, et al. An intelligent cooperative decoupling controller for coagulation bath in polyacrylonitrile carbon fiber production[J]. IEEE Transactions on Control Systems Technology (S1063-6536), 2013, 21(2): 467-479.
[8] Reid R C.The Properties of gases and liquids[M]. New York, USA: McGraw-Hill, 1987.
[9] Ziabicki A.Fundaments of fiber Formation[M]. New York, USA: A Wiley-Interscience Publication, 2000.
[10] Qian B J, Pan D, Wu. Z Q.The mechanism and characteristics of dry-jet wet-spinning of acrylonitrile fibers[J]. Adv, Polym. Technol., 1986, 6(4): 509-529.
[11] 王玥, 吴承训. 聚丙烯腈碳纤维原丝在干湿法纺丝中非溶剂扩散过程的研究[J]. 合成技术与应用, 2007, 22(4): 28-31.
[12] 丁莉萍, 顾劲华,潘鼎. PAN碳纤维原丝干湿纺凝固动力学的新模型 [C]// 第十四届全国复合材料学术会议论文集. 北京: 中国宇航出版社, 2006: 41-46.
[13] 陈娟, 王成国. PAN湿法纺丝中凝固成纤过程的研究进展[J]. 材料导报, 2006, 20(9): 26-30.

[1]	Zhijie Li, Haoqi Shi, Changhua Li, Jie Zhang. Image Center Layout Optimization Method Based on Improved Genetic Algorithm [J]. Journal of System Simulation, 2022, 34(6): 1173-1184.
[2]	Bin Chen, Yue Liu, Yalei Yang. Airport Flight Transit Support Time Collaborative Planning Modeling Based on STN [J]. Journal of System Simulation, 2022, 34(6): 1196-1207.
[3]	Kai Yang, Chunyi Chen, Xiaojuan Hu, Haiyang Yu. Denoising Algorithm Based on Multi-feature Non-local Mean Filtering for Monte Carlo Rendered Images [J]. Journal of System Simulation, 2022, 34(6): 1259-1266.
[4]	Qi Chen, Haoyang Cui. Visual inspection model of UAV cluster based on improved pigeon flock hierarchy [J]. Journal of System Simulation, 2022, 34(6): 1275-1285.
[5]	Muqing Wang, Lei Zhang, Xiumin Fan, Xiaomeng Luo, Wenmin Zhu. Simulation Method of Virtual Human Pose Optimization Based On VR Peripherals [J]. Journal of System Simulation, 2022, 34(6): 1296-1303.
[6]	Cheng Lu, Xuesheng Jin. Design of Interactive Simulated Water Gun Fire Fighting Training System Based on Steam VR [J]. Journal of System Simulation, 2022, 34(6): 1312-1319.
[7]	Hongnai Gao, Lijiang Fu, Qian Xia, Ya Guo. Application of Observability in Performance Evaluation of Photosynthesis Model [J]. Journal of System Simulation, 2022, 34(6): 1330-1342.
[8]	Lingjia Ni, Xiaoxia Huang, Hongga Li, Zibo Zhang. Research on Fire Emergency Evacuation Simulation Based on Cooperative Deep Reinforcement Learning [J]. Journal of System Simulation, 2022, 34(6): 1353-1366.
[9]	Dun Meng, Zhuo Hu, Huajun Zhang. Simulation of Multi-layer Ship Evacuation System Based on Improved A* Algorithm [J]. Journal of System Simulation, 2022, 34(6): 1375-1382.
[10]	Yufei Guo, Kang Zhao, Yongqing Hai. Triangular Mesh Boolean Operation Method for Finite Element Analysis [J]. Journal of System Simulation, 2022, 34(5): 1003-1014.
[11]	Tong Wu, Qinghui Wang, Zhijia Xu. Study on the Scale Characteristics of Permeability of TPMS Porous Materials [J]. Journal of System Simulation, 2022, 34(5): 1015-1024.
[12]	Yangsheng Jiang, Sichen Wang, Kuan Gao, Meng Liu, Zhihong Yao. Cellular Automata Model of Mixed Traffic Flow Composed of Intelligent Connected Vehicles’ Platoon [J]. Journal of System Simulation, 2022, 34(5): 1025-1032.
[13]	Jiangtao Liang, Huiqin Wang. Study on Building Fire Evacuation Path Planning Based on Improved Ant Colony Algorithm [J]. Journal of System Simulation, 2022, 34(5): 1044-1053.
[14]	Qiwen Zhang, Bin Zhang. Teaching-Learning-Based Optimization Algorithm for Permutation Flowshop Scheduling [J]. Journal of System Simulation, 2022, 34(5): 1054-1063.
[15]	Genshang Xing, Fang Lu, Shushan Li, Dingti Luo. Supply Chain Delivery Model and Simulation Based on Product Experience [J]. Journal of System Simulation, 2022, 34(5): 1064-1075.