Research for swing heating furnace temperature optimization system of plate roller-hearth normalizing furnace

2012 ◽  
Author(s):  
Jing Li ◽  
Qiushi Wang ◽  
Huachen Liao
Keyword(s):  
Heating Furnace ◽  
Furnace Temperature ◽  
Optimization System ◽  
Temperature Optimization ◽  
Roller Hearth

Research for Swing Heating Furnace Temperature Optimization System of Plate Roller-Hearth Normalizing Furnace

2013 ◽  
Vol 380-384 ◽  
pp. 4232-4236
Author(s):  
Jing Li ◽  
Qiu Shi Wang ◽  
Hua Chen Liao
Keyword(s):  
Heat Treatment ◽  
Continuous Improvement ◽  
Treatment Process ◽  
Heating Furnace ◽  
Matlab Simulation ◽  
Furnace Temperature ◽  
Temperature Prediction ◽  
Temperature Optimization ◽  
Prediction Curve ◽  
Roller Hearth

In recent years, with the continuous improvement of the quality requirements of the plate heat treatment, the swing heated gradually become the focus of attention of many researchers. Based on the above considerations, in this paper, we established the furnace temperature pre-setting system, using the intelligence algorithm of improved PSO, under the premise of swing heating of roller-hearth normalizing furnace; and studied the optimal furnace temperature optimization curve, namely the optimal furnace temperature system, under the heat treatment process of the swing of the whole furnace and steel mixed. Through the Matlab simulation, we obtained the steel Temperature Prediction curve under the optimal furnace temperature system. By comparison, verifying the accuracy of the researched optimal furnace temperature optimization curve.

Induction Heating Furnace Temperature Control Based on the Fuzzy PID

2012 ◽  
Vol 217-219 ◽  
pp. 2463-2466 ◽  
Author(s):  
Xue Gang Hou ◽  
Cheng Long Wang
Keyword(s):  
Fuzzy Control ◽  
Temperature Control ◽  
Induction Heating ◽  
Pid Control ◽  
Pid Controller ◽  
Heating Furnace ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Furnace Temperature ◽  
Run Up

Induction heating furnace temperature control is a complex nonlinear hysteretic inertial process, it's difficult to obtain an accurate mathematical model because the temperature and disturb from outside is complicated. The normal PID control algorithm is hard to satisfy the standards of control. The fuzzy PID controller provided in this article is a combination between fuzzy control and the traditional PID control. The Fuzzy control theory is used to setting the ratio, the integral and the differential coefficient of the PID control. In the run-up stage, rapidity is guaranteed, overstrike and the steady-state error is up to the mustard. An example indicates that fuzzy PID control is superior to the normal PID controller.

Design on Stepping Type Heating Furnace Control System

2012 ◽  
Vol 268-270 ◽  
pp. 1367-1370
Author(s):  
Yan Wang ◽  
Cheng Lin Liu ◽  
Bin Lu ◽  
Meng Xia Pei
Keyword(s):  
Control System ◽  
Heating Furnace ◽  
System Stability ◽  
Control Mode ◽  
Furnace Temperature ◽  
Temperature Environment ◽  
Control Target ◽  
Furnace Control ◽  
Save Energy ◽  
And Control

This paper introduces design and implementation of a kind of stepping type heating furnace automatic control system. In this system, the instrument collects variables of all kinds of process, and then put into PLC, PLC drives corresponding actuator respectively according to the setting control mode and control target value. The requirements of this design are that the control system can control temperature accurately in high temperature environment, in order to realize the furnace temperature uniformity and achieve the requirements of higher-level processing as well as improve the system stability and save energy.

Research of Furnace Temperature Optimization Control Method in Hot Rolling Process

2011 ◽  
Vol 230-232 ◽  
pp. 7-11
Author(s):  
Jun Yan Xu ◽  
Jian Rong Bu
Keyword(s):  
Control Method ◽  
Control Strategies ◽  
Production Capacity ◽  
Electricity Consumption ◽  
Rolling Process ◽  
High Energy ◽  
Furnace Temperature ◽  
Rbf Network ◽  
Temperature Optimization ◽  
Control Accuracy

To effectively solve the problems of high energy consumption, low control accuracy and time control-delay for furnace, this paper proposes the intelligent control strategies based on the process feature of walking beam furnace, namely fuzzy-RBF network self-learning and self-optimizing function, which is combined with dynamic PID feedback compensation strategy. The experiment shows that the system not only guarantees the furnace temperature control accuracy and increases the temperature up and down rate under working condition fluctuation, which reduces unit fuel consumption, unit electricity consumption and billet burning loss, but also improves the furnace production capacity.

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