A predictive control strategy for burn-through point in sintering process

This paper presents a predictive control strategy named as generalized predictive control with closed-loop model identification for burn-through point (BTP) in sintering process. The closed-loop model identification method describes the dynamic characteristics of sintering process. Based on the model, the BTP generalized predictive control model predicts BTP accurately and computes the strand velocity. A BTP control system is established and implemented in an iron and steel plant. The running results show that the system effectively guarantees the stability of sintering process, sufficiently suppresses the fluctuation of BTP, and greatly increases the quantity and quality of sinters.

[1]  Quanmin Zhu,et al.  A generalized procedure in designing recurrent neural network identification and control of time-varying-delayed nonlinear dynamic systems , 2010, Neurocomputing.

[2]  Gopal S. Upadhyaya,et al.  Some issues in sintering science and technology , 2001 .

[3]  Sirkka-Liisa Jämsä-Jounela Current status and future trends in the automation of mineral and metal processing , 2001 .

[4]  U. Ammann,et al.  Model Predictive Control—A Simple and Powerful Method to Control Power Converters , 2009, IEEE Transactions on Industrial Electronics.

[5]  R. P. Rusin,et al.  Combined-Stage Sintering Model , 1992 .

[6]  Wook Hyun Kwon,et al.  An application of min–max generalized predictive control to sintering processes , 1997 .

[7]  Furong Gao,et al.  Closed-loop step response identification of integrating and unstable processes , 2010 .

[8]  Yutian Liu,et al.  Online Recursive Closed-Loop State Space Model Identification for Damping Control , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.

[9]  Manfred Morari,et al.  Modeling and Control of Thermal Printing , 2010, IEEE Transactions on Control Systems Technology.

[10]  Jun Liu,et al.  Data-driven prediction of sintering burn-through point based on novel genetic programming , 2010 .

[11]  Ardalan Vahidi,et al.  Predictive Control of Voltage and Current in a Fuel Cell–Ultracapacitor Hybrid , 2010, IEEE Transactions on Industrial Electronics.

[12]  Ching-Chih Tsai,et al.  Generalized predictive control using recurrent fuzzy neural networks for industrial processes , 2007 .

[13]  J. Q. Hu,et al.  Predictive fuzzy control applied to the sinter strand process , 1997 .

[14]  Wushan Cheng Modeling of Real-Time Double Loops System in Predicting Sintering’s BTP , 2010 .

[15]  Lennart Ljung,et al.  Closed-loop identification revisited , 1999, Autom..

[16]  Nirupam Chakraborti,et al.  Dynamic process modelling of iron ore sintering , 1997 .

[17]  Wu Min An Intelligent Integrated Predictive Method Based on Gas Temperature Profile for Burn-through Point , 2007 .

[18]  Min Xu,et al.  Practical generalized predictive control with decentralized identification approach to HVAC systems , 2007 .