Temperature Uniformity Control of Large-Scale Vertical Quench Furnaces for Aluminum Alloy Thermal Treatment

The thermal treatment of aluminum alloy workpieces requires strict temperature uniformity in large-scale vertical quench furnaces. To achieve the desired temperature uniformity in a large-scale spatial setting, a temperature uniformity control strategy combining workpiece temperature compensation and intelligent proportional-integral-derivative (PID) decoupling control is presented. The temperature compensation of the workpiece is realized by establishing an air heat conduction model. Moreover, an intelligent PID decoupling control system based on a novel self-growing radial basis function neural network (SGRBFNN) is developed to eliminate the strong coupling effects of multiheating zones. SGRBFNN, with the structure being dynamically adjusted by a hybrid semifuzzy Gustafson-Kessel clustering algorithm, is proposed to realize the online tuning of the parameters of the PID controller. Both the simulation and industrial experiment results demonstrate that the proposed temperature control system can effectively achieve smooth regulations and significantly improve temperature uniformity. The application also demonstrates the validity and better control performance of the proposed system compared with conventional control systems.

[1]  Hyochoong Bang,et al.  The finite element method using MATLAB (2nd ed.) , 2000 .

[2]  Guiyong Zhang,et al.  Analysis of elastic-plastic problems using edge-based smoothed finite element method , 2009 .

[3]  Yaman Arkun,et al.  A new approach to defining a dynamic relative gain , 2001 .

[4]  Naoharu Yoshitani,et al.  Model-based control of strip temperature for the heating furnace in continuous annealing , 1998, IEEE Trans. Control. Syst. Technol..

[5]  He Jian-jun,et al.  Temperature Intelligent Control System of Large-Scale Standing Quench Furnace , 2005 .

[6]  Xuan Zhou,et al.  Multivariable temperature measurement and control system of large-scaled vertical quench furnace based on temperature field , 2004 .

[7]  George E. Tsekouras,et al.  On training radial basis function neural networks using optimal fuzzy clustering , 2009, 2009 17th Mediterranean Conference on Control and Automation.

[8]  Arun Ghosh,et al.  Open-Loop Decoupling of MIMO Plants , 2009, IEEE Transactions on Automatic Control.

[9]  Manuel Berenguel,et al.  Constrained Temperature Control of a Solar Furnace , 2012, IEEE Transactions on Control Systems Technology.

[10]  Tianyou Chai,et al.  Application of multivariable technique in temperature control of reheating furnaces , 1999, Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328).

[11]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[12]  Daejun Chang,et al.  A numerical analysis of slab heating characteristics in a walking beam type reheating furnace , 2010 .

[13]  Qi Huang,et al.  Power decoupling control of a solid oxide fuel cell and micro gas turbine hybrid power system , 2011 .

[14]  Alberto Cardoso,et al.  Affine Neural Network-Based Predictive Control Applied to a Distributed Solar Collector Field , 2014, IEEE Transactions on Control Systems Technology.

[15]  Donald Gustafson,et al.  Fuzzy clustering with a fuzzy covariance matrix , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[16]  Antonio Cammi,et al.  A preliminary approach to the ALFRED reactor control strategy , 2014 .

[17]  Yongsheng Ding,et al.  An Intelligent Bi-Cooperative Decoupling Control Approach Based on Modulation Mechanism of Internal Environment in Body , 2011, IEEE Transactions on Control Systems Technology.

[18]  Xiaofeng Meng,et al.  Temperature decoupling control of double-level air flow field dynamic vacuum system based on neural network and prediction principle , 2013, Eng. Appl. Artif. Intell..

[19]  Ahmad Saboonchi,et al.  Heating characteristics of billet in a walking hearth type reheating furnace , 2014 .

[20]  Alireza Fatehi,et al.  Descriptive vector, relative error matrix, and interaction analysis of multivariable plants , 2011, Autom..

[21]  Jin-Hua She,et al.  Integrated Hybrid-PSO and Fuzzy-NN Decoupling Control for Temperature of Reheating Furnace , 2009, IEEE Transactions on Industrial Electronics.

[22]  George E. Tsekouras,et al.  A novel training algorithm for RBF neural network using a hybrid fuzzy clustering approach , 2012, Fuzzy Sets Syst..

[23]  Robert L. Kosut,et al.  Modeling and control of distributed thermal systems , 2003, IEEE Trans. Control. Syst. Technol..

[24]  Xiaofeng Meng,et al.  Decoupling control of double-level dynamic vacuum system based on neural networks and prediction principle , 2011 .

[25]  J. Ward,et al.  Zone modelling of the thermal performances of a large-scale bloom reheating furnace , 2013 .

[26]  Zi-Li Deng,et al.  Multivariable decoupling pole assignment self-tuning feedforward controller , 1991 .

[27]  Takatsugu Ueyama,et al.  Optimal slab heating control with temperature trajectory optimization , 1994, Proceedings of IECON'94 - 20th Annual Conference of IEEE Industrial Electronics.

[28]  Wei-hua Gui,et al.  Vertical quench furnace Hammerstein fault predicting model based on least squares support vector machine and its application , 2009, 2009 Chinese Control and Decision Conference.