Real-time operation guide system for sintering process with artificial intelligence

Abstract

In order to optimize the sintering process, a real-time operation guide system with artificial intelligence was developed, mainly including the data acquisition online subsystem, the sinter chemical composition controller, the sintering process state controller, and the abnormal conditions diagnosis subsystem. Knowledge base of the sintering process controlling was constructed, and inference engine of the system was established. Sinter chemical compositions were controlled by the strategies of self-adaptive prediction, internal optimization and center on basicity. And the state of sintering was stabilized centering on permeability. In order to meet the needs of process change and make the system clear, the system has learning ability and explanation function. The software of the system was developed in Visual C++ programming language. The application of the system shows that the hitting accuracy of sinter compositions and burning through point prediction are more than 85%; the first-grade rate of sinter chemical composition, stability rate of burning through point and stability rate of sintering process are increased by 3%, 9% and 4%, respectively.

Keywords

sintering process / process control / artificial intelligence

References

[1] Minoru W, Yutake S, Minorn S, et al. Development of operation guide system and its application to Chiba No. 4 sintering plant[A]. 4th International Symposium on Agglomeration[C]. Toronto, Canada, 1985.

[2] Unaki H, Miki K, Sakimura H, et al. New control system of sinter plants at Chiba works[A]. IFAC Automation in Mining, Mineral and Metal Processing [C]. Tokyo, Japan, 1986.

[3] KouichiM, NaokiT, KazuoN, et al.. Modeling for control knowledge in sintering process using neural network and fuzzy inference[J]. Iron and Steel, 1992, 78(7): 1045-1052 in Chinese)

[4] DawsonP R. Recent developments in iron ore sintering: (Part 4) [J]. Ironmaking and Steelmaking, 1993, 20(2): 150-159

[5] ShaoJun-li, ZhangJing, WeiChang-hua, et al.The Base of Artificial Intelligence[M], 2000, Beijing, Electronics Industry Press(in Chinese)

[6] CaiZi-xing, XuGuang-youArtificial Intelligence and its Application[M], 1996, Beijing, Tsinghua University Press(in Chinese)

[7] ShenBing-xin, FanXiao-hui, ChenXu-ling, et al.. Adaptive prediction system of sinter chemistry based on artificial neural network[J]. Sintering and Pelleting, 2002, 27(5): 1-3(in Chinese)

[8] FanXiao-hui, WangHai-dong, HuangJiao-zheng, et al.. Expert system for sinter chemical composition control centred on basicity [J]. Sintering and Pelleting, 1997, 22(4): 1-3(in Chinese

[9] FanXiao-hui, WangHai-dongMathematical Model and Artificial Intelligence of Sintering Process[M], 2002, Changsha, Central South University Press(in Chinese)

[10] JiangBoStudy of Operation Guide System and Multilevel Fuzzy Integrated Judgment of Sintering Permeability[D], 1999, Changsha, School of Mineral Engineering, Central South University of Technology(in Chinese)

[11] StrakaG. Process control model for sinter machine speed[J]. Metallurgical Plant and Technology International, 1992, 8(5): 78-80

[12] FanXiao-hui, HuangTian-zheng, XhenJin, et al.. Expert system for sinter processing control (III) [J]. Journal of Central South University of Technology(Naturnal Science), 1998, 29(6): 535-537(in Chinese)

[13] ZhengYao-dong, FanXiao-hui, ChenXu-ling, et al.. Establishing of expert system for sintering process control based on database technology [J]. Sintering and Pelleting, 2004, 29(1): 1-3(in Chinese)

[14] WuXian-yi, QuHong-tao, XieYing. Knowledge representation and its structure of expert system development tool [J]. Information Technology, 2003, 27(5): 85-88(in Chinese)

[15] ChenDe-yun. Study of visual inference of expert system[J]. Computer Engineering and Application, 2000, 36(7): 57-60(in Chinese)

[16] ZhuDong-hai, LiuLiang-hua, DongHai-ning, et al.Thorough Explain of Visual C++ 6.0[M], 1999, Beijing, China Machine Press(in Chinese)