A Fuzzy Modeling Method Based on T-S Model　for Blast Furnace Gas System

Abstract

Abstract: Aiming at the modeling problem for blast furnace gas system in steel industry, a class of complex non-linear system, a data-based fuzzy modeling method was proposed. Firstly, the proposed method establishes the identification model based on T-S fuzzy model. Considering the manual interference from the adjustable gas users, a conditional fuzzy clustering is adopted to partition the input and output space. With the introduction of fuzzy concept, the proposed model is adaptive for industrial noises. Then, a Bayesian linear regression is proposed to determine the parameters of the consequent part in this study, which can effectively avoid the ill-conditioned phenomenon. A series of simulation verification by using the industrial data of a certain blast furnace gas system demonstrate that the proposed method exhibits well performance for identifying the blast furnace gas system, and can also be used to optimize, control and schedule the blast furnace gas system.

Key words: blast furnace gas, nonlinear, system modeling, fuzzy clustering

CLC Number:

TP 274

SHENG Chun-Yang, ZHAO Jun, WANG Wei, LIU Ying. A Fuzzy Modeling Method Based on T-S Model　for Blast Furnace Gas System[J]. Journal of Shanghai Jiaotong University, 2012, 46(12): 1907-1913.

References

［1］ZHANG Xiao-ping, ZHAO Jun, WANG Wei, et al. An optimal method for prediction and adjustment on byproduct gas holder in steel industry ［J］. Expert Systems with Applications, 2011, 38: 45884599.
［2］张琦, 谷延良, 提威, 等. 钢铁企业高炉煤气供需预测模型及应用［J］. 东北大学学报, 2010, 31(12): 17371740.
ZHANG Qi, GU Yanliang, TI Wei, et al. Supplydemand forecasting model of blast furnace gas in iron & steel works and its application ［J］. Journal of Northeastern University, 2010, 31(12):17371740.
［3］ZHAO Jun, WANG Wei, LIU Ying, et al. A twostage online prediction method for a blast furnace gas system and its application ［J］. IEEE Transactions on Control Systems Technology, 2011, 19: 507520.
［4］Schiavo A, Luciano A. Powerful and flexible fuzzy algorithm for nonlinear dynamic system identification ［J］. IEEE Transactions on Fuzzy Systems, 2001, 9(6): 828835.
［5］Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control ［J］. IEEE Transactions on Systems, Man, and Cybernetics, 2011, 15(1): 116132.
［6］Abdelazim T, Malik O P. Identification of nonlinear systems by TakagiSugeno fuzzy logic grey box modeling for realtime control ［J］. Control Engineering Practice, 2005, 13: 14891498.
［7］LI Chaoshun, ZHOU Jianzhong, LI Qingqing, et al. A new TS fuzzy modeling approach to identify a boilerturbine system ［J］. Expert Systems with Applications, 2010, 37: 22142221.
［8］Kukolj D, Levi E. Identification of complex systems based on neural and takagisugeno fuzzy model ［J］. IEEE Transactions on Systems, Man, and CyberneticsPart B: Cybernetics, 2004, 34(1): 272283.
［9］DU Haiping, ZHANG Nong. Application of evolving TakagiSugeno fuzzy model to nonlinear system identification ［J］. Applied Soft Computing, 2008, 8: 676686.
［10］Pedrycz Witold. Conditional fuzzy Cmeans ［J］. Pattern Recognition Letters, 1996, 17: 625631.
［11］Pedrycz Witold. Knowledgebased clustering: From data to information granules ［M］. Canada: Wiley, 2005.
［12］蒋强, 肖建, 何都益, 等. 基于TS模型的模糊系统辨识方法综述［J］. 计算机应用研究, 2009, 26(6): 20082012.
JIANG Qiang, XIAO Jian, HE Duyi, et al. Overview of methods of fuzzy system identification on TS model ［J］. Application Research of Computers, 2009, 26(6): 20082012.
［13］刘颖, 赵珺, 王伟, 等. 基于数据的改进回声状态网络在高炉煤气发生量预测中的应用［J］. 自动化学报, 2009, 35(6): 731738.
LIU Ying, ZHAO Jun, WANG Wei, et al. Improved echo state network based on datadriven and its application to prediction of blast furnace gas output ［J］. Acta Automatica Sinica, 2009, 35(6): 731738.

[1]	Chen Qiuzhuo, Zhu Biao, Ma Lixiang, Liu Bingyou, Wan Luanfei. Underactuated System Control Based on Improved Active Disturbance Rejection Control [J]. J Shanghai Jiaotong Univ Sci, 2026, 31(2): 428-439.
[2]	Peng Cheng, Chen Li, Fu Shenglai. Nonlinear Disturbance Observer of Clutch Slipping Torque for Multi-Mode Hybrid Electric Vehicles [J]. J Shanghai Jiaotong Univ Sci, 2026, 31(2): 420-427.
[3]	Liu Qunpo, Li Jiakun, Fei Shumin, Bo Xuhui, Naohiko Hanajima. Design of Composite Two-Channel Disturbance Estimation Adaptive Controller for Backlash-Like Hysteretic Nonlinear Systems [J]. J Shanghai Jiaotong Univ Sci, 2026, 31(1): 106-116.
[4]	SHEN Fu, YANG Zhiwen, XU Xiaoyuan, ZHANG Wei, WANG Zhe, CAO Yang. Unit Model of Integrated Energy System Based on Statistical Clustering [J]. Journal of Shanghai Jiao Tong University, 2025, 59(9): 1348-1358.
[5]	LIU Biao, LIU Guangyu, FENG Wei, WANG Shuai, ZHOU Bao, ZHAO Enming. Undecimated Dual-Tree Complex Wavelet Transform and Fuzzy Clustering-Based Sonar Image Denoising Technique [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(5): 998-1008.
[6]	HU Anfeng, CHEN Yuchao, XIAO Zhirong, XIE Senlin, GONG Zhaoqi. Analysis of Nonlinear Consolidation Characteristics of Soil Around Tunnels Considering Self-Gravity Stress [J]. Journal of Shanghai Jiao Tong University, 2025, 59(4): 503-512.
[7]	Wang Jiayu, Wang Shuyi, Wei Yongxu, Liao Chencong, Shang Hanbing, Wang Xue, Kang Ning. Tumor Displacement Prediction and Augmented Reality Visualization in Brain Tumor Resection Surgery [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(4): 733-743.
[8]	LI Shuheng, HE Defeng, LIAO Fei, MU Jianbin. Backstepping Control for Attitude Stabilization of Hexacopter Based on Nonlinear Feedforward Compensation [J]. Journal of Shanghai Jiao Tong University, 2025, 59(12): 1891-1900.
[9]	HUANG Tao, HE Zeqing, SONG Lin. Vibration Characteristics of Cylindrical Membrane [J]. Journal of Shanghai Jiao Tong University, 2024, 58(4): 428-437.
[10]	HUANG Heyuan1, QIU Jiangliang1, LI Zhongsheng1, LIU Jinjian2. Hydrodynamic Study on the Probes of Expendable Bathythermograph [J]. Ocean Engineering Equipment and Technology, 2024, 11(4): 67-74.
[11]	JIN Feiyu (金飞宇), CHEN Longsheng^∗ (陈龙胜), LI Tongshuai (李统帅), SHI Tongxin (石童昕). Distributed Cooperative Anti-Disturbance Control for High-Order MIMO Nonlinear Multi-Agent Systems [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(4): 656-666.
[12]	ZHANG Weizhen, HE Zhen, TANG Zhangfan. Reinforcement Learning Control Design for Perching Maneuver of Unmanned Aerial Vehicles with Wind Disturbances [J]. Journal of Shanghai Jiao Tong University, 2024, 58(11): 1753-1761.
[13]	SHANG Xianling, LI Chunlei, WANG Leqin, FAN Zeren, HAN Yunrui, YU Long. Numerical Analysis of J-Lay on a Nonlinear Seabed [J]. Ocean Engineering Equipment and Technology, 2024, 11(1): 99-105.
[14]	YU Te, LIU Jiapeng, WU Chao, ZHOU Chang, ZHOU Shengzeng, WANG Lei. USV and AUV Cooperative Control Strategy Based on Nonlinear Disturbance Observer [J]. Journal of Shanghai Jiao Tong University, 2023, 57(S1): 114-123.
[15]	CAO Tian, ZHANG Xianku. Nonlinear Feedback Control of MIMO Unstable System for Ship Longitudinal Motion [J]. Journal of Shanghai Jiao Tong University, 2023, 57(8): 972-980.