热轧带钢层流冷却过程建模与控制方法研究
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摘要
层流冷却工艺是现代钢铁工业中通过轧后强制水冷来改善带钢的组织性能,提高带钢质量和产量的过程。层流冷却控制系统通过控制喷水集管阀门的开闭状态控制冷却水量,使带钢从终轧机出口带钢温度(800～900℃)冷却到工艺要求的卷取温度(550～700℃)。层流冷却是影响热轧带钢组织性能的关键工序之一,而冷却后的卷取温度是决定成品带钢加工性能、力学性能的重要参数之一,卷取温度过低或者过高都将降低带钢质量。
冷却过程传热机理复杂、水冷换热系数、热导率等具有随工况条件的变化而非线性变化的特性,难以建立精确的层流冷却过程模型。现有的层流冷却模型只描述了在单个冷却单元下的冷却动态过程,不能直接计算带钢卷取温度,且水冷换热系数和热导率的选择忽略了变化工况对关键参数的影响,导致现有层流冷却模型精度较低。
因受冷却过程中高温水汽的影响,冷却区中难以逐点对运行状态的热轧带钢温度进行连续检测,且冷却水量与带钢卷取温度之间的关系具有复杂的非线性特性,现有的基于表格查询和带钢温度模型的控制方法受限于表格的规模和带钢温度模型精度,不能适应变化频繁的工况条件,导致卷取温度控制精度较低。
本文依托973项目(No.2002CB312200)“复杂生产制造过程实时、智能优化与控制理论和方法研究”,针对热轧带钢层流冷却过程建模和控制中存在的问题,以提高带钢卷取温度控制精度为目标,开展了热轧带钢层流冷却过程的建模和控制方法的研究。具体研究工作包括：
1.提出了由冷却单元阀门开闭状态模型、任意段带钢所处冷却单元位置计算模型、冷却单元下的冷却过程动态模型切换机制、冷却单元下的带钢上表面温度模型组成的带钢卷取温度模型。其中冷却单元阀门开闭状态模型根据喷水控制系统给出的喷水集管阀门开启总数、上下起始阀门开启位置和喷水模式确定每段带钢经过各冷却单元时对应的喷水控制阀门开闭状态；每段带钢所处冷却单元位置计算模型根据带钢进入冷却区的初始速度和设定的加速度计算每段带钢在任意时刻所处冷却区域内的位置；冷却单元冷却过程动态模型切换机制根据阀门开闭状态确定在该冷却单元发生的换热方式；冷却单元带钢上表面温度模型根据确定的换热方式选择具体的水冷或空冷换热模型计算带钢上表面温度。
2.给出了水冷换热系数模型、热导率模型中关键参数随带钢工况条件变化的确定方法,特别是给出了水冷换热系数模型中的比例参数随每段带钢的工况条件的变化而变化的方法,改进了水冷换热系数和热导率模型,从而提高了带钢卷取温度模型精度。最后采用国内某大型钢厂实际运行数据进行了模型实验研究,结果表明,与文[34]忽略每段带钢工况条件的变化对水冷换热系数模型的影响的方法相比,采用本文方法改进水冷换热系数模型、热导率模型之后计算的卷取温度与实测卷取温度之间的偏差均方根误差从11.8℃下降到4.2℃。
3.提出了层流冷却过程混合智能控制方法,其中喷水集管控制阀门开启总数设定方法由喷水集管阀门开启总数的预设定模型、卷取温度预报模型、预报补偿模型、带钢批次间补偿模型组成,其中预设定模型根据目标卷取温度、终轧机出口带钢厚度预估值、温度预估值、带钢头部速度预估值计算喷水集管阀门开启总数的预设定值；卷取温度预报模型根据阀门开启总数的设定值预报带钢的卷取温度值；预报补偿模型根据预报卷取温度偏差,结合案例推理技术和常规PI调整算法计算喷水集管阀门开启总数的前馈补偿值；带钢批次间补偿模型根据已经冷却后的带钢实测卷取温度与目标卷取温度之间的实际偏差,采用案例推理技术和PI迭代学习方法,在不同批次带钢之间进行迭代运算,最终求得使实际卷取温度偏差被控制在一定范围内的喷水集管阀门开启总数批间补偿值。随工况条件的变化,该方法能够自动调整喷水集管阀门开启总数设定值,再将该设定值送给过程控制系统进行任意时刻的喷水控制阀门的开闭状态计算并执行,从而实现冷却水量的调整,保证卷取温度被控制在目标温度范围内。
4.基于本文提出的层流冷却过程模型和控制方法,设计开发了虚拟对象仿真软件和设定控制软件,并将开发的软件与现有的由设定控制计算机、过程控制系统(包括PLC控制系统和过程监控系统)、仪表与执行机构虚拟装置、虚拟对象计算机组成的分布式仿真实验平台相集成,建立了层流冷却过程控制仿真实验系统。
5.在上述仿真实验系统上进行了本文提出的混合智能控制方法的实验研究,结果表明,与国内某大型钢厂现有控制方法相比,采用本文方法使实际卷取温度偏差被控制在±10℃的命中率从42.9%提高到84.1%,均方根误差由21.1℃下降为8.21℃。
In order to enhance the quality and yield of strip, the laminar cooling technique is used to improve the metallurgical properties of the hot rolled strips by water-cooling after rolling in modern iron and steel industry. The strips are cooled from the austenitic finishing temperature (800～900℃) down to the ferrite coiling temperature (550～700℃) by controlling the cooling water quantity in the laminar cooling system. Laminar cooling process is one of the key procedures influencing the microstructure and properties of hot rolled strips. The coiling temperature is one of the important factors that determines the processing properties and mechanics properties of the strip product, and too high or too low of the coiling temperature will downgrade the final strip product quality.
The heat transfer in cooling process is extremely complex. What's more, the heat transfer coefficient and thermal conductivity change nonlinearly along with the operation conditions. It's difficult to establish the accurate mathematical model for the strip cooling process. The cooling process models reported in the literature so far are only for dynamic cooling process in a single coiling unit. Those models cannot be used to compute the strip coiling temperature directly. The choice of water-cooling heat transfer parameter and thermal conductivity doesn't consider the influences of varying process condition, and thus results less accurate models.
The strip temperature is difficult to be measured online in the cooling zone because of the high temperature vapour generated. The relationship between cooling water quantity and strip coiling temperature is nonlinear. For the above reasons, the existing control methods based on looking up tables and strip temperature models can't adapt the changing operation condition and lead to low control accuracy.
Supported by the national project 973 (No.2002CB312200) of "Research on the real-time, intelligent optimal and control theory and methods for complex production manufacturing process" and aiming at the above problems in the modeling and control of laminar cooling processe, this paper carries out the research on the modeling and control of the laminar cooling process, and the target is to increase the control precision of the strip coiling temperature. The main works of this research are summarized as follows.
1. A strip coiling temperature model is proposed consisting of four parts:the status of cooling unit valves calculated model, the strip segment track model, the model switching mechanism and the top surface temperature model of the strip in a cooling unit. The status of cooling unit valves calculated model is used to determine the valve status when the strip segment passes by every cooling unit, according to the setting values given by the control system of the sprayers, such as the number of active valves, the initial active valve location both side of the runnout table, and the spray mode. The strip segment track model is to compute the location of the strip segment, according to the initial velocity and the setting accelerations. The model switching mechanism is to decide the heat transfer style in the cooling unit. The top surface temperature model for the strip is to choose the air-cooling or water-cooling model according to the heat transfer style and to compute the top surface temperature of the strip.
2. A method to determine the key coefficient of the water-cooling heat transfer parameter and thermal conductivity is proposed according to the fluctuating operation condition. Especially, the key coefficient of the water-cooling heat transfer parameter varys with the segment strip's operation conditon. The water-cooling heat transfer parameter and thermal conductivity can be modified and the coiling temperature model precision is improved. Simulation is conducted using the industrial operating data from a large strip plant, and the results show that root mean square (RMS) of the error decreases from 11.85℃to 4.2℃, where the error is between the measured coiling temperature and the computed coiling temperature based on the improved water-cooling heat transfer parameter and thermal conductivity.
3. A hybrid intelligent control method for laminar cooling process is developed. The proposed setting model for the number of active valves is consisted of four parts: the number of active valves pre-setting model, coiling temperature prediction model, prediction compensator and batch to batch compensator. The number of active valves pre-setting model calculates the pre-setting values of the number of active valves according to the target coiling temperature, the estimated values at the outlet of the finish train, such as the strip thickness, temperature and the running velocity of the strip head. The coiling temperature prediction model is to predict the coiling temperature according to the setting number of active valves. The prediction compensator computes the active valves compensated according to the predicted coiling temperature deviation, where the case-based reasoning and general PI tuning algorithm are used. The batch to batch compensator is to compute the compensated number of the active valves according the cooled strips'measured coiling temperature deviations. The case-based reasoning technology and PI iteration learning algorithm are adopted to conduct the iterative calculation among different strips, to control the measured coiling temperature deviation in a limited range. The number of active valves setting model can automatically adjust the number of active sparyers according to the varying operating condition. The control system of spary computes the states of the spary head valve according the new setting value and realize the cooling water quantity, in order to control the coiling temperature within the acceptable range of their target values.
4. Based on the proposed model of the strip coiling temperature and the hybrid intelligent control method, a virtual object software and a setting control software are designed and developed. A simulation system for laminar cooling system is developed by integrating with an existing distributed experimental platform, which consists of a setting computer, a process control system(such as PLC system and the process monitor system), virtual instruments and actuators computer, and a virtual object computer.
5. The proposed hybrid intelligent control method is simulated using the above simulation system. Contrasting with the existing control method, the simulation results show that the hit rate increased from 42.9% to 84.1% where the coiling temperature errors are controlled in the range of±10℃, and the root mean square (RMS) of the error decreases from 21.1℃to 8.21℃.
冷却过程传热机理复杂、水冷换热系数、热导率等具有随工况条件的变化而非线性变化的特性,难以建立精确的层流冷却过程模型。现有的层流冷却模型只描述了在单个冷却单元下的冷却动态过程,不能直接计算带钢卷取温度,且水冷换热系数和热导率的选择忽略了变化工况对关键参数的影响,导致现有层流冷却模型精度较低。
因受冷却过程中高温水汽的影响,冷却区中难以逐点对运行状态的热轧带钢温度进行连续检测,且冷却水量与带钢卷取温度之间的关系具有复杂的非线性特性,现有的基于表格查询和带钢温度模型的控制方法受限于表格的规模和带钢温度模型精度,不能适应变化频繁的工况条件,导致卷取温度控制精度较低。
本文依托973项目(No.2002CB312200)“复杂生产制造过程实时、智能优化与控制理论和方法研究”,针对热轧带钢层流冷却过程建模和控制中存在的问题,以提高带钢卷取温度控制精度为目标,开展了热轧带钢层流冷却过程的建模和控制方法的研究。具体研究工作包括：
1.提出了由冷却单元阀门开闭状态模型、任意段带钢所处冷却单元位置计算模型、冷却单元下的冷却过程动态模型切换机制、冷却单元下的带钢上表面温度模型组成的带钢卷取温度模型。其中冷却单元阀门开闭状态模型根据喷水控制系统给出的喷水集管阀门开启总数、上下起始阀门开启位置和喷水模式确定每段带钢经过各冷却单元时对应的喷水控制阀门开闭状态；每段带钢所处冷却单元位置计算模型根据带钢进入冷却区的初始速度和设定的加速度计算每段带钢在任意时刻所处冷却区域内的位置；冷却单元冷却过程动态模型切换机制根据阀门开闭状态确定在该冷却单元发生的换热方式；冷却单元带钢上表面温度模型根据确定的换热方式选择具体的水冷或空冷换热模型计算带钢上表面温度。
2.给出了水冷换热系数模型、热导率模型中关键参数随带钢工况条件变化的确定方法,特别是给出了水冷换热系数模型中的比例参数随每段带钢的工况条件的变化而变化的方法,改进了水冷换热系数和热导率模型,从而提高了带钢卷取温度模型精度。最后采用国内某大型钢厂实际运行数据进行了模型实验研究,结果表明,与文[34]忽略每段带钢工况条件的变化对水冷换热系数模型的影响的方法相比,采用本文方法改进水冷换热系数模型、热导率模型之后计算的卷取温度与实测卷取温度之间的偏差均方根误差从11.8℃下降到4.2℃。
3.提出了层流冷却过程混合智能控制方法,其中喷水集管控制阀门开启总数设定方法由喷水集管阀门开启总数的预设定模型、卷取温度预报模型、预报补偿模型、带钢批次间补偿模型组成,其中预设定模型根据目标卷取温度、终轧机出口带钢厚度预估值、温度预估值、带钢头部速度预估值计算喷水集管阀门开启总数的预设定值；卷取温度预报模型根据阀门开启总数的设定值预报带钢的卷取温度值；预报补偿模型根据预报卷取温度偏差,结合案例推理技术和常规PI调整算法计算喷水集管阀门开启总数的前馈补偿值；带钢批次间补偿模型根据已经冷却后的带钢实测卷取温度与目标卷取温度之间的实际偏差,采用案例推理技术和PI迭代学习方法,在不同批次带钢之间进行迭代运算,最终求得使实际卷取温度偏差被控制在一定范围内的喷水集管阀门开启总数批间补偿值。随工况条件的变化,该方法能够自动调整喷水集管阀门开启总数设定值,再将该设定值送给过程控制系统进行任意时刻的喷水控制阀门的开闭状态计算并执行,从而实现冷却水量的调整,保证卷取温度被控制在目标温度范围内。
4.基于本文提出的层流冷却过程模型和控制方法,设计开发了虚拟对象仿真软件和设定控制软件,并将开发的软件与现有的由设定控制计算机、过程控制系统(包括PLC控制系统和过程监控系统)、仪表与执行机构虚拟装置、虚拟对象计算机组成的分布式仿真实验平台相集成,建立了层流冷却过程控制仿真实验系统。
5.在上述仿真实验系统上进行了本文提出的混合智能控制方法的实验研究,结果表明,与国内某大型钢厂现有控制方法相比,采用本文方法使实际卷取温度偏差被控制在±10℃的命中率从42.9%提高到84.1%,均方根误差由21.1℃下降为8.21℃。
In order to enhance the quality and yield of strip, the laminar cooling technique is used to improve the metallurgical properties of the hot rolled strips by water-cooling after rolling in modern iron and steel industry. The strips are cooled from the austenitic finishing temperature (800～900℃) down to the ferrite coiling temperature (550～700℃) by controlling the cooling water quantity in the laminar cooling system. Laminar cooling process is one of the key procedures influencing the microstructure and properties of hot rolled strips. The coiling temperature is one of the important factors that determines the processing properties and mechanics properties of the strip product, and too high or too low of the coiling temperature will downgrade the final strip product quality.
The heat transfer in cooling process is extremely complex. What's more, the heat transfer coefficient and thermal conductivity change nonlinearly along with the operation conditions. It's difficult to establish the accurate mathematical model for the strip cooling process. The cooling process models reported in the literature so far are only for dynamic cooling process in a single coiling unit. Those models cannot be used to compute the strip coiling temperature directly. The choice of water-cooling heat transfer parameter and thermal conductivity doesn't consider the influences of varying process condition, and thus results less accurate models.
The strip temperature is difficult to be measured online in the cooling zone because of the high temperature vapour generated. The relationship between cooling water quantity and strip coiling temperature is nonlinear. For the above reasons, the existing control methods based on looking up tables and strip temperature models can't adapt the changing operation condition and lead to low control accuracy.
Supported by the national project 973 (No.2002CB312200) of "Research on the real-time, intelligent optimal and control theory and methods for complex production manufacturing process" and aiming at the above problems in the modeling and control of laminar cooling processe, this paper carries out the research on the modeling and control of the laminar cooling process, and the target is to increase the control precision of the strip coiling temperature. The main works of this research are summarized as follows.
1. A strip coiling temperature model is proposed consisting of four parts:the status of cooling unit valves calculated model, the strip segment track model, the model switching mechanism and the top surface temperature model of the strip in a cooling unit. The status of cooling unit valves calculated model is used to determine the valve status when the strip segment passes by every cooling unit, according to the setting values given by the control system of the sprayers, such as the number of active valves, the initial active valve location both side of the runnout table, and the spray mode. The strip segment track model is to compute the location of the strip segment, according to the initial velocity and the setting accelerations. The model switching mechanism is to decide the heat transfer style in the cooling unit. The top surface temperature model for the strip is to choose the air-cooling or water-cooling model according to the heat transfer style and to compute the top surface temperature of the strip.
2. A method to determine the key coefficient of the water-cooling heat transfer parameter and thermal conductivity is proposed according to the fluctuating operation condition. Especially, the key coefficient of the water-cooling heat transfer parameter varys with the segment strip's operation conditon. The water-cooling heat transfer parameter and thermal conductivity can be modified and the coiling temperature model precision is improved. Simulation is conducted using the industrial operating data from a large strip plant, and the results show that root mean square (RMS) of the error decreases from 11.85℃to 4.2℃, where the error is between the measured coiling temperature and the computed coiling temperature based on the improved water-cooling heat transfer parameter and thermal conductivity.
3. A hybrid intelligent control method for laminar cooling process is developed. The proposed setting model for the number of active valves is consisted of four parts: the number of active valves pre-setting model, coiling temperature prediction model, prediction compensator and batch to batch compensator. The number of active valves pre-setting model calculates the pre-setting values of the number of active valves according to the target coiling temperature, the estimated values at the outlet of the finish train, such as the strip thickness, temperature and the running velocity of the strip head. The coiling temperature prediction model is to predict the coiling temperature according to the setting number of active valves. The prediction compensator computes the active valves compensated according to the predicted coiling temperature deviation, where the case-based reasoning and general PI tuning algorithm are used. The batch to batch compensator is to compute the compensated number of the active valves according the cooled strips'measured coiling temperature deviations. The case-based reasoning technology and PI iteration learning algorithm are adopted to conduct the iterative calculation among different strips, to control the measured coiling temperature deviation in a limited range. The number of active valves setting model can automatically adjust the number of active sparyers according to the varying operating condition. The control system of spary computes the states of the spary head valve according the new setting value and realize the cooling water quantity, in order to control the coiling temperature within the acceptable range of their target values.
4. Based on the proposed model of the strip coiling temperature and the hybrid intelligent control method, a virtual object software and a setting control software are designed and developed. A simulation system for laminar cooling system is developed by integrating with an existing distributed experimental platform, which consists of a setting computer, a process control system(such as PLC system and the process monitor system), virtual instruments and actuators computer, and a virtual object computer.
5. The proposed hybrid intelligent control method is simulated using the above simulation system. Contrasting with the existing control method, the simulation results show that the hit rate increased from 42.9% to 84.1% where the coiling temperature errors are controlled in the range of±10℃, and the root mean square (RMS) of the error decreases from 21.1℃to 8.21℃.
引文
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