带钢层流冷却装置

1、引言 精轧后的带钢层流冷却装置是控制冷却制度和保证卷取温度的设备,它与带钢的质量有着密切的关系。要想得到机械性能良好     

热轧带钢更换规格卷取温度分析及其补偿研究

热轧带钢卷取温度是反映热轧带钢性能指标的重要参数之一,介绍了某700 mm热连轧机层流冷却的组成及其工作原理,针对其具体情况,基于实时数据,应用数据挖掘工具,从理论和工艺的角度分析了控冷过程中热轧带钢更换规格温差大问题产生的原因,得出了热轧带钢更换规格的规律,并应用回归统计建立了温度补偿模型,进行了实验研究,实验结果表明,应用数据挖掘技术从立体的现场数据中得出热轧层流冷却控制模型的做法,对提高控冷精度,优化生产工艺具有一定意义。     

新钢1580热轧层流冷却温度控制的研究

主要介绍了新钢层流冷却设备及其自控制系统,根据现场使用的工况采用了先进的数学模型与控制技术,优化参数与现场工艺装备,提高了卷取温度命中率和产品性能.     

热轧板卷取电机的自动控制与应用

热车板是以板坯为原料,经加热后由粗轧机组及精轧机组制成带钢,从精轧最后一架轧机出来的热钢带通过层流冷却至设定温度,由卷取机卷成钢带卷。通过论述卷取电机的速度与张力控制原理,结合PLC控制,实现对热轧卷取的高精度控制。     

中宽带轧机层流冷却装置

层流冷却装置位于精轧机后卷取机前,用于控制冷却速度,保证卷取温度.简要介绍中宽带热连轧机层流冷却装置的工作原理及设备组成.     

浮选药剂智能优化设定控制方法的研究

针对浮选过程精矿品位和尾矿品位与浮选药剂之间具有强非线性、不确定性,难以用精确数学模型来描述,常规控制方法难以给出药剂量优化设定值的难题,将建模与控制相结合,提出了浮选药剂智能优化设定控制方法.该方法将基于案例推理的单位药剂预设定模型、基于规则推理的反馈补偿模型有机集成,在工况发生变化时,能够自动调整浮选药剂量设定值,实现浮选药剂的优化控制.该方法已在某选矿厂浮选过程中成功应用,稳定了精矿品位和尾矿品位,降低了浮选药剂消耗,取得了显著的应用效果.     

基于神经网络的热轧卷取温度模型辨识

针对热轧带钢卷取温度模型具有高度非线性的特点，利用神经网络具有逼近任何非线性函数及预报的性质，采用附加动量BP算法，准确预报卷取温度，进而应用最小二乘辨识方法对卷取温度统计模型进行参数辨识，辨识结果与设定结果的比较表明此方法行之有效。这种神经网络预报与最小二乘线性辨识相结合的方法为热轧带钢卷取温度模型的辨识优化提供了新的途径。     

基于神经网络的热轧带钢卷取温度预测

热轧带钢卷取温度是影响成品带钢性能指标的重要工艺参数之一,其层流控制系统具有高度的非线性。采用附加动量BP算法,建立了基于神经网络前馈与数学模型反馈的联合层流控制系统,仿真结果表明,采用神经网络预测的卷取温度与实测温度相近,结果可信,为层流数学模型参数的在线辨识打下了坚实的基础。     

基于集成模型的集气管压力设定值稳态优化研究

某钢铁企业炼焦生产过程中集气管压力为人工经验设定,难以根据不同工况进行实时调整,使得蝶阀容易工作在极值范围,无法达到期望生产目标,提出以降低焦炉能耗为优化目标,蝶阀开度范围和集气管压力工艺设定值范围为约束条件的稳态优化思想.首先采用基于线性回归与最近邻聚类学习算法的RBF神经网络方法建立焦炉集气管压力设定值动态模型,得到集气管压力设定估值,然后以降低能耗为目标函数,蝶阀开度范围和集气管压力工艺设定值范围为约束条件,采用改进的粒子群算法对目标函数寻优得到集气管压力的稳态优化设定值.实际应用结果表明,采用该方法获得的优化结果满足了企业的要求,取得了较好的工业应用效果.     

边部遮挡功能在热轧带钢轧制中的应用

介绍了层流冷却区冷却水梁及边部遮挡功能的应用方法及控制程序。边部遮挡功能在很大程度上解决了热轧带钢的"边浪"问题。     

PID控制在卷取温度控制中的应用

卷取温度是影响带钢组织性能的重要工艺参数之一。将经典PID反馈控制应用到热轧带钢卷取温度的控制中,通过合理地设置温度采样时间和调节周期,并且考虑不同化学成分、不同厚度等带钢规格参数分别调整比例、积分和微分系数,取得了良好的效果,提高了卷取温度控制精度和产品成材率。     

带钢热连轧层流冷却智能控制技术研究

介绍层流冷却系统的组成，工艺控制要求，冷却区各段的控制实现方式，对带钢内部温度模型、空气冷却模型和水冷却模型进行分析。     

A downcoiler simulator for high performance coiling in hot strip mill lines

The objective of this study is to develop a coiling process simulator that can be used to test suggested coiling control algorithms, and thereby improve overall coiling performance. To accomplish this, a thorough study of the pinching and coiling mechanism of the downcoiler has been carried out, and then used to derive an appropriate mathematical model. An additional torque compensation algorithm is proposed to improve the shape of the strip to be coiled. The simulation environment permits the coiling process to be estimated offline, using the actual system parameters necessary for successful coiling. These parameters include strip tension, pinch roll force, and driving motor power for pinching and coiling. For effective coiling control, the additional compensated torque algorithm can be used to reduce the irregular shape of the loose coil that sometimes arises from insufficient coiling torque. In order to evaluate the performance and practicality of both the developed software and the proposed coiling control algorithm, experiments were conducted using the downcoiler in the No. 2 Hot Strip Mill at Pohang Works, where coiling operations are normally carried out at high strip tension compared to other downcoilers. The simulations and experimental results show that the set-up value for the unit tension can actually be lowered, which could lead to significant improvement in coiling strip quality, as well as reduced power consumption. In addition, a test coiling operation with the additional torque compensation algorithm resulted in even more effective coiling and a reduction in bad telescope quality of the coiled strip.     

中速柴油机冷却系统恒温控制设计与研究

针对柴油机不同的工况对冷却水温度要求不同,原冷却系统不能随工况的变化自动调节温度,设计了柴油机智能冷却系统恒温控制系统。通过热平衡实验获得柴油机各工况最佳工作状态的冷却水温度和流量,利用单片机控制变频器水泵转速和电控三通旁通阀的开度对冷却水温度自动控制。实验结果表明:该系统可以随柴油机工况变化将冷却水温度恒定控制在最佳工作温度,达到了节油效果,最大节油率为5.4%,平均节油率为3.6%。     

基于径向基函数神经网络和NLJ优化算法的精馏塔控制

精馏塔是一个非常重要的操作单元,具有较强的非线性和时变性,很难进行基于机理建模分析的实时优化控制.通过对精馏塔的相关过程变量进行主元分析确定了5～6个关键变量作为神经网络的输入,建立了精馏塔多个质量指标的RBF神经网络的软仪表模型,实现了这些质量指标的在线估计.选取其中部分软仪表模型作为优化控制系统中的约束条件函数模型和目标函数模型,采用NLJ优化算法(变收缩系数的随机搜索算法)获取最优的决策变量设定值,从而得到了满足生产质量要求的精馏塔产品的最大采出,实现了精馏塔的卡边优化控制.     

制冷机房的运行能耗优化

为优化制冷机房运行能耗,采用系统辨识的方法建立了制冷机房运行能耗模型,模型中包含负荷率、冷却水进水温度、冷冻水流量和冷却水流量4个因素。相关优化计算结果表明,冷却水和冷冻水同时变流量运行时,冷水机组的COP随冷水流量降低而略有减少,但由于泵耗的大幅度降低,对整个系统而言,总能耗减少。文中所提的制冷机房,冷冻水泵和冷却水泵功率占制冷机房总功率的20%左右,在部分负荷率50%～90%范围内,冷却水温度在23℃～31℃的范围内,能耗优化后节能率在5%～14%之间。若泵耗所占比例越大,则节能效率越高。     

A novel optimized hybrid fuzzy logic intelligent PID controller for an interconnected multi-area power system with physical constraints and boiler dynamics

In the fast developing world nowadays, load frequency control (LFC) is considered to be a most significant role for providing the power supply with good quality in the power system. To deliver a reliable power, LFC system requires highly competent and intelligent control technique. Hence, in this article, a novel hybrid fuzzy logic intelligent proportional-integral-derivative (FLiPID) controller has been proposed for LFC of interconnected multi-area power systems. A four-area interconnected thermal power system incorporated with physical constraints and boiler dynamics is considered and the adjustable parameters of the FLiPID controller are optimized using particle swarm optimization (PSO) scheme employing an integral square error (ISE) criterion. The proposed method has been established to enhance the power system performances as well as to reduce the oscillations of uncertainties due to variations in the system parameters and load perturbations. The supremacy of the suggested method is demonstrated by comparing the simulation results with some recently reported heuristic methods such as fuzzy logic proportional-integral (FLPI) and intelligent proportional-integral-derivative (PID) controllers for the same electrical power system. the investigations showed that the FLiPID controller provides a better dynamic performance and outperform compared to the other approaches in terms of the settling time, and minimum undershoots of the frequency as well as tie-line power flow deviations following a perturbation, in addition to perform appropriate settlement of integral absolute error (IAE). Finally, the sensitivity analysis of the plant is inspected by varying the system parameters and operating load conditions from their nominal values. It is observed that the suggested controller based optimization algorithm is robust and perform satisfactorily with the variations in operating load condition, system parameters and load pattern.