Hybrid Intelligent Control Strategy of the Laminar Cooling Process

2006 ◽  
pp. 858-863
Author(s):  
Minghao Tan ◽  
Shujiang Li ◽  
Tianyou Chai
Keyword(s):  
Intelligent Control ◽  
Control Strategy ◽  
Laminar Cooling ◽  
Cooling Process

scholarly journals Intelligent Model Building and GPC-PID Based Temperature Curve Control Strategy for Metallurgical Industry

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Shuanghong Li ◽  
Xi Li ◽  
Yupu Yang
Keyword(s):  
Control Strategy ◽  
Large Scale ◽  
Model Building ◽  
Control Method ◽  
Temperature Curve ◽  
Learning Ability ◽  
Laminar Cooling ◽  
Cooling Process ◽  
Plate Temperature ◽  
Ts Fuzzy Model

Laminar cooling process is a large-scale, nonlinear system, so the temperature control of such system is a difficult and complex problem. In this paper, a novel modeling method and a GPC-PID based control strategy for laminar cooling process are proposed to control the global temperature curve to produce high quality steel. First, based on the analysis of the cooling process of laminar flow, a new TS fuzzy model which possesses intelligence and self-learning ability is established to improve the temperature prediction accuracy. Second, the target temperature curve can be divided into several subgoals and each subgoal can be described by a CARIMA type of model. Then, by the decentralized predictive control method, GPC-PID based control strategy is introduced to guarantee the laminar cooling process to achieve subtargets, respectively; in that way the steel plate temperature will drop along the optimal temperature curve. Moreover, by employing the dSPACE control board into the process control system, the matrix process ability is added to the production line without large-scale reconstruction. Finally, the effectiveness and performance of the proposed modeling and control strategy are demonstrated by the industrial data and metallography detection in one steel company.

Intelligent Setting Method of Laminar Cooling Process for Hot Rolled Strip

2013 ◽  
Vol 756-759 ◽  
pp. 4377-4381
Author(s):  
Jing Hou ◽  
Jin Xiang Pian ◽  
Yan Ling Sun ◽  
Ke Xu
Keyword(s):  
Working Condition ◽  
Cooling Water ◽  
Water Volume ◽  
Rolled Strip ◽  
Model Parameters ◽  
Case Based Reasoning ◽  
Laminar Cooling ◽  
Cooling Process ◽  
Coiling Temperature ◽  
Operation Condition

In order to improve the control accuracy of the coiling temperature of strip in the laminar cooling process when working condition is varying, an intelligent setting method of the cooling water volume is researched in this paper. The strip coiling temperature mechanism model is built firstly. Secondly, the key model parameters are identified with case-based reasoning (CBR) technology to improve the model accuracy. Lastly, the cooling water volume setting method based the model is proposed where disturbance input method is applied. The simulation result showed that the proposed method can improve the strip coiling temperature accuracy when the operation condition is changing. The strip coiling temperature accuracy can be improved due to the CBR technology which can adjust the key model parameters according to the varying operation condition. So, the setting values based the improved model are adjusted with the changing working condition, with self-adaptive ability.

FE Analysis of Hot Strips’ Temperature Distribution in Laminar Cooling Process

2014 ◽  
Vol 900 ◽  
pp. 647-650
Author(s):  
Yu Qing Zheng ◽  
Jing Yu Cui
Keyword(s):  
Heat Transfer ◽  
Temperature Distribution ◽  
Rolled Strip ◽  
Fe Model ◽  
Laminar Cooling ◽  
Cooling Process ◽  
Transfer Coefficients ◽  
Position Effects ◽  
Simulation Accuracy ◽  
Hot Rolled

The temperature distribution of the hot-rolled strip in the ROT cooling process was calculated and analyzed using ABAQUS in this paper. The complicated heat transfer coefficients of hot strip considering the position effects of top and bottom nozzles, and the non-uniform heat transfer situation along the width direction were defined by user subroutine. The simulation results were in good correlation with test results. It’s helpful for further analysis to improve the temperature distribution uniformity and the simulation accuracy for FE model, and guide the on-site production.

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