Abstract: Traditional PID control is not timely enough in controlling sudden temperature changes, resulting in poor heating effect. Fuzzy control can effectively solve the temperature fluctuation problem caused by various operating conditions and achieve real-time fluctuation adjustment. This article has great reference value for steel rolling heating control. Keywords: Heating Zone, PID, Fuzzy Control [b]1 . Introduction [/b] Currently, the main problems with the 1500 medium-wide strip heating furnace at Laiwu Steel are uneven heating temperature and insufficient heating capacity. The actual heating capacity of the two furnaces is currently 300-450 t/h, lower than the design capacity of 480-520 t/h (cold billet to hot billet). The temperature difference between slab furnaces is 25-35℃, and the temperature difference within the same slab is 20-45℃. However, the heating quality indicators for similar domestic production lines are ≤15℃ for both slab furnace temperature difference and temperature difference within the same slab temperature difference. In-depth research revealed that the design philosophy of the imported Stein heating furnace control system is not fully compatible with the existing operating conditions at Laiwu Steel. Furthermore, the frequent changes in the rolling rhythm on-site prevent it from meeting the changing operating conditions, and the actual production process lacks necessary statistical analysis data and on-site testing methods. Changes in production plan, heating steel grade, size, billet entry temperature, waiting (stopping) rolling time, and starting rolling temperature all require a period of time for the heating furnace temperature to rise slowly to avoid a strong impact on the entire gas system. However, due to the increased on-site pace, operators cannot wait for the temperature to rise slowly, nor can they adjust the heating strategy in a timely and accurate manner. Additionally, human factors (experience, sense of responsibility, day/night shift), inconsistent operation among shifts and individuals, and long idle burning times ultimately lead to fluctuations in furnace and steel temperatures, poor heating quality, high unit fuel consumption, excessive billet oxidation and burning loss, and poor product quality stability. Therefore, the Stein program cannot adapt to the actual production conditions of the broadband heating furnace. Therefore, fuzzy control theory is introduced into the heating furnace control system to simplify the original program and adapt it to the actual production needs of the broadband furnace. 2. Control Implementation of the Heating Furnace The furnace body consists of the following heating zones: preheating zone; heating zone; and insulation section. Each zone is heated by combustion gas and air. The combustible gas flow is adjusted according to the set values ​​in several control loops. Figure 1 shows a schematic diagram of the heating control: [b]3. Traditional PID Control[/b] The output signal form of the traditional control device is as follows: Where: Kp - proportional gain Ti - integral time Kp and Ti are internal PI parameters, adjusted once by the operator. The output signal u(t) is converted into heating demand according to the following formula: Where: a, b = constant. This value will not exceed the set limit. The «y» value is used to determine the set value of the gas valve opening control loop. The «y1» value (air flow control loop) is calculated using the «y» value, and its purpose is to maintain the air/gas ratio within a specified time interval. These two loops are assembled in a cross-control manner, the purpose of which is to check this ratio. The signals that directly control the process flow are issued from these two control loops, named Q_gas (gas flow rate) and Q_air (air flow rate). The system operates well when the state is stable. However, the following factors can interfere with the process: (1) Production delay (planned or unplanned), which triggers transients not only at the start of production but also at the start of the delay, causing abrupt switching of the transient gas flow. (2) Production changes - this means that different types of products enter the furnace in sequence, resulting in different heating demands. (3) Changes in the weight of products in the furnace. 4. Improved Fuzzy Control Fuzzy control is introduced into the intelligent control system of the heating furnace. The original control loop cannot overcome the limitations of the complexity and uncertainty of the process, and the proportional-integral (PI) control device cannot correctly control the development of the process. Interferences in production changes, such as changes in timing, products (type, size, quantity), and the use of different production methods (short delay, long delay, low flame), all cause conversions, which were not carefully considered in the original control. By analyzing the transfer function of the process and the operator's on-site experience, the coefficients of the proportional-integral-derivative (PID) control device for the complete process are obtained. The coefficients of the control device are calculated using a standard adjustment formula, combining the parameters of the process mathematical model with the parameters of the control device and the operator's experience parameters. This achieves adjustable control of the PI parameters, finding a balance between speed and precision in the control loop, meeting the requirements of different production rhythms for wide strip steel. Using a fuzzy management program, the control device operates according to the traditional PI (proportional-integral) parameters determined by actual operation. Data is extracted from system observation, experience, and process understanding to form a special database for the fuzzy logic management program. The fuzzy logic blocks determine and adapt to necessary changes in the proportional-integral control device based on the input values. A system diagram is shown in Figure 2. Fuzzy control is a monitoring-level control. During control, the parameters of the proportional-integral-derivative control device are calculated online. This control device is used to measure the temperature of the standard control loop. The variables considered are: setpoint; measured temperature; measured temperature variable within a specified time step; instantaneous regional load; and actual step value. The fuzzy control level can be connected or disconnected using only simple on/off commands. If the fuzzy control level is disconnected, the proportional-integral-derivative (PID) parameters will be set to their default values ​​using the traditional method. To ensure normal operation, the fuzzy logic controller requires the following data: input variables described by a fuzzy subset; error; dynamic data of the measured temperature; weight of the product in that segment; actual step speed; output described by the fuzzy subset; proportional gain: Kp; integral time: Ki; and type rules. The principle diagram of the fuzzy block is shown in Figure 3. The fuzzy controller has two modes: "steady-state mode" and "transient mode." When the measured value is not significantly different from the set value, we consider the system to be in a steady state (fuzzy inference). In the steady state, the adjustment of Kp and Ki is accomplished through the temperature error. When the error is too large, we consider the system to enter a transient state. In this case, it is necessary to dynamically control the measured temperature to match the furnace response. The transition between operating modes is accomplished by a fuzzy interruptor, which ensures a balanced transition from one mode to another. Through these modes, we can obtain the initial values ​​of Kp and Ki. In the second mode group, these values ​​will be adjusted, and the deviations of Kp and Ki (weight and step speed) in the actual working condition function will also be calculated. Finally, the intermediate results of the steady-state and transient mode groups will be summed. [b]5. Conclusion[/b] The use of fuzzy control makes the system control more reliable and stable, the temperature error is significantly reduced, and excellent control effect is achieved, which provides a guarantee for subsequent rolling processes, reduces steel accumulation, and greatly improves economic efficiency. References: [1] Peng Jian. Research on asymmetric cross rolling [D]. Beijing: Tsinghua University, 1990. [2] Lu Binglin. Technology of asymmetric cross control of roll shape [J]. Rolling Steel, 1994 Special Issue: 356-365. [3] Lu Binglin. Cross angle control model of asymmetric cross rolling [J]. Iron and Steel, 1996, 31(2): 30-33.