球磨机物料填充率检测器及其应用

介绍的磁电式球磨机物料填充检测器具有简单可靠、灵敏度高、稳定性好和全天侯结构等特点,可用于磨内物料填充率的显示,记录和报警,现可作为操作员提供判断磨机工况并据其进行操作的依据,又可作为磨机负荷控制的主控或监控参数,优化粉磨过程,实现粉磨过程自动化。     

火电厂球磨机非线性自适应控制

建立了球磨机特征模型,分析了运行调节需求,设计了以给煤量非线性自适应PID控制为主体,多种控制方式相结合的球磨机控制系统。该系统由磨风量控制、磨给煤量控制和风煤协调控制三部分构成,合理组合了非线性自适应PID控制、解耦控制、比值控制和仿人控制算法优势。工程应用表明,该控制系统能够解决球磨机运行优化和控制优化的双重问题,控制系统鲁棒性强,具有工况自适应能力。     

多传感器数据融合技术在球磨机负荷检测中的应用

在水泥生产过程中,球磨机的负荷检测和控制非常重要,球磨机的负荷决定了水泥生产的连续性和水泥质量的稳定性。目前,没有直接检测球磨机负荷的方法,常用磨音、电机电流、提升机功率等间接方法获得,精确性和可靠性都较低,在生产实际中只能作为参考数据。提出一种多传感器数据融合的方法,采用自适应加权数据融合算法,选择磨音、磨机出口提升机功率、磨机电机电流、粗粉回粉流量4种传感器来综合检测磨机负荷,提高了球磨机负荷检测的准确性和可靠性。经过在水泥磨系统中的实际应用,验证了该方法的有效性。     

矿渣粉磨智能控制系统的研究及应用

立磨矿渣粉磨系统经常出现立磨振动大,磨机负荷波动频繁等故障,同时立磨系统又是一个多变量、强耦合、非线性系统.针对以上问题,采用专家系统和模糊控制设计了立磨优化控制方案,利用现场实际数据辩识出立磨系统近似数学模型,并以此通过仿真实验验证了立磨优化控制算法.通过加入阈值和限幅控制将优化控制算法应用于现场实际控制中,结果表明立磨优化控制比人工手动控制的料层厚度波动范围和磨机振动值小,立磨没有出现异常振动停机.     

球磨机的筒体部分设计

在工业生产中,磨矿是常见的工作。例如发电厂,就需要先用一系列磨煤机械将原煤粉碎。炼铁时,可以直接送入高炉冶炼的富铁矿,必须先粉磨筛分成合格的粒级。即使筑路,也要粉磨出合用的碎石。因此,在许多基础工业部门里,破磨物料常常是重要工序,所用的机器大都属于主要设备。下面介绍一下球磨机的筒体部分设计的依据。     

基于神经网络的球磨机出力软测量模型研究

球磨机出力检测和控制是球磨机自动控制的重要内容,然而,目前在实际生产过程中,球磨机出力缺少有效可靠的检测手段,因此很难实现优化控制.结合基于神经网络的软测量和混沌信息处理技术两者的优点,建立球磨机出力软测量模型.该模型不仅能预估稳态下球磨机出力,且对动态过程中球磨机出力的在线估计也切实有效,从而为球磨机的出力监测、给煤...     

基于功率谱分析的球磨机负荷模型

建立了基于功率谱分析的球磨机负荷模型,分析了球磨机负荷、磨音声强和磨音频谱分布三者之间的关系:磨音检测点处的声压与球磨机筒体振动速度的平方成正比,声音频率与球磨机筒体振动频率成正比,随着球磨机负荷的增加,磨音信号逐渐减弱;磨音频谱主要分布在1 500Hz以下,当球磨机正常运行时,磨音频谱分布较为均匀,当球磨机欠磨运行时,磨音频谱主要分布在0～1 000Hz。Matlab仿真结果验证了所建立的球磨机负荷模型的可行性和有效性。     

便携式球磨机监测系统的设计与实现

通过对球磨机的轴承振动信号、球磨机进出口差压、电流信号的综合分析,提出了便携式球磨机优化监测系统的软件和硬件设计方案,实现了对球磨机料位的在线监测.该系统还能对断煤、堵磨、球磨机着火和爆炸、一次风管堵塞等故障工况在线进行识别和报警.经在某电厂的完整工况测试与实践证明,本系统设计的软硬件完全能适应便携式球磨机监测系统的要求,特别适合于现场的测试与实验.     

情感智能的球磨机系统内模控制研究

针对球磨机系统控制过程中存在的多变量、强耦合、模型时变性的特点,提出基于情感智能的内模控制方案,采用大脑情感学习模型(BEL模型)对球磨机模型进行在线辨识,根据辨识结果设计BEL控制器,建立球磨机的BEL内模控制系统。仿真结果表明该控制方法跟踪快、解耦性能好,控制品质优于传统的解耦控制方法,能够较好地解决球磨机多变量、时变性、耦合性等问题。     

矿渣粉磨控制系统的设计与实现

根据矿渣粉磨生产工艺特点,设计开发了矿渣粉磨生产集散控制系统,完成了软、硬件系统设计与组态,实现了对生产过程的集中管理和设备的分散控制.现场实际运行证明了该套控制系统的可靠性和有效性.     

Modelling and prediction of copper concentration of a grinding process

The mathematical model of a grinding process in copper concentrator is presented. With the aid of a dynamic model and a Kalman filter the copper concentration of the output of this process is predicted. The process includes ball mill, autogenous mill and separator. The autogenous mill is working as a secondary grinding mill for the underflow coming from the classifier.To obtain the mathematical model of grinding process several practical tests including screen analysis, puls and impulse tests, were performed. It has been shown that the dynamics of one ball mill can be described with two perfect mixers and plug flow in series and the autogenous mill with two perfect mixers in series. A linear matrix vector model has been used in a Kalman filter for estimating the copper concentration in the input flow of the flotation process.     

一种实用的磨矿多模型预测控制策略研究

房矿过程是选矿过程中的重要一环,属多输入多输出系统,并存在严重的扰动变量(诸如硬度等)和时变参数.介绍了一种磨矿控制系统中实用的多变量动态矩阵控制的多模型控制策略.该方法基于不同的矿石硬度对球磨过程建立不同的阶跃模型进行多模型预测控制.仿真证实了该方法的有效性.     

Soft sensor for parameters of mill load based on multi-spectral segments PLS sub-models and on-line adaptive weighted fusion algorithm

The parameters of mill load (ML) not only represent the load of the ball mill, but also determine the grinding production ratio (GPR) of the grinding process. In this paper, a novel soft sensor approach based on multi-spectral segments partial least square (PLS) model and on-line adaptive weighted fusion algorithm is proposed to estimate the ML parameters. At first, frequency spectrums of the shell vibration acceleration signals are obtained. Then the PLS sub-models are constructed with the low, medium and high frequency spectral segments. At last, the PLS sub-models are fused together with a new on-line adaptive weighted fusion algorithm to obtain the final soft sensor models. This soft sensor approach has been successfully applied in a laboratory-scale wet ball mill grinding process.     

Expert system based adaptive dynamic matrix control for ball mill grinding circuit

Ball mill grinding circuit is a multiple-input multiple-output (MIMO) system characterized with couplings and nonlinearities. Stable control of grinding circuit is usually interrupted by great disturbances, such as ore hardness and feed particle size, etc. Conventional model predictive control usually cannot capture the nonlinearities caused by the disturbances in real practice. Multiple models based adaptive dynamic matrix control (ADMC) is proposed for the control of ball mill grinding circuit. The novelty of the strategy lies in that intelligent expert system is developed to identify the current ore hardness and then select a proper model for ADMC. Compared with the various nonlinear DMC strategies, the approach can synthesize and analyze as many variables and status as possible to adequately and reliably identify the process conditions, and it does not introduce additional computational complexity, which makes it readily available to the industrial practitioner. Simulation results and industrial applications demonstrate the effectiveness and practicality of this control strategy.     

A mathematical model for simulating and manufacturing ball end mill

The performance of ball end mill cutters in cutting operations is influenced by the configuration of the rake and clearance faces in the ball component. From the mathematical design of a cutting edge curve, the rake face can be defined by the rake angle and the width of the rake face at each cross section along the cutting edge. We propose the fundamental conditions that must govern the engagement between the grinding wheel and the designed rake face in order to avoid interference while machining a ball end mill. As a result, a new mathematical model for determining the wheel location and a software program for simulating the generation of the rake face of a ball end mill are proposed. In addition, methods for grinding the clearance face in both concave and flat-shapes are introduced. The flute surface generated by a disk wheel during the grinding process is determined on the basis of a tangency condition. The results of the experiment and the simulation are compared to validate the proposed model.     

基于多模态特征子集选择性集成建模的磨机负荷参数预测方法

如何融合球磨机系统研磨过程所产生的多模态机械信号构建磨机负荷参数预测(Mill load parameter forecasting, MLPF)模型是当前研究的热点. 针对上述问题, 本文提出一种基于多模态特征子集选择性集成(Selective ensemble, SEN)建模的MLPF方法. 首先, 对多模态机械信号进行时频域变换得到高维频谱数据; 接着, 采用相关系数法和互信息法对多模态频谱进行线性和非线性特征子集的自适应选择; 最后, 采用优化和加权算法对上述特征子集的候选子模型进行自适应地选择与合并, 得到基于SEN机制的MLPF模型. 采用磨矿过程实验球磨机的机械信号仿真验证了所提方法的有效性.     

Disturbance observer based multi-variable control of ball mill grinding circuits

Ball mill grinding circuits are essentially multi-variable systems characterized with couplings, time-varying parameters and time delays. The control schemes in previous literatures, including detuned multi-loop PID control, model predictive control (MPC), robust control, adaptive control, and so on, demonstrate limited abilities in control ball mill grinding process in the presence of strong disturbances. The reason is that they do not handle the disturbances directly by controller design. To this end, a disturbance observer based multi-variable control (DOMC) scheme is developed to control a two-input-two-output ball mill grinding circuit. The systems considered here are with lumped disturbances which include external disturbances, such as the variations of ore hardness and feed particle size, and internal disturbances, such as model mismatches and coupling effects. The proposed control scheme consists of two compound controllers, one for the loop of product particle size and the other for the loop of circulating load. Each controller includes a PI feedback part and a feed-forward compensation part for the disturbances by using a disturbance observer (DOB). A rigorous analysis is also given to show the reason why the DOB can effectively suppress the disturbances. Performance of the proposed scheme is compared with those of the MPC and multi-loop PI schemes in the cases of model mismatches and strong external disturbances, respectively. The simulation results demonstrate that the proposed method has a better disturbance rejection property than those of the MPC and PI methods in controlling ball mill grinding circuits.     

Intelligent optimal control system for ball mill grinding process

Operation aim of ball mill grinding process is to control grinding particle size and circulation load to ball mill into their objective limits respectively, while guaranteeing producing safely and stably. The grinding process is essentially a multi-input multi-output system (MIMO) with large inertia, strong coupling and uncertainty characteristics. Furthermore, being unable to monitor the particle size online in most of concentrator plants, it is difficult to realize the optimal control by adopting traditional control methods based on mathematical models. In this paper, an intelligent optimal control method with two-layer hierarchical construction is presented. Based on fuzzy and rule-based reasoning (RBR) algorithms, the intelligent optimal setting layer generates the loops setpoints of the basic control layer, and the latter can track their setpoints with decentralized PID algorithms. With the distributed control system (DCS) platform, the proposed control method has been built and implemented in a concentration plant in Gansu province, China. The industrial application indicates the validation and effectiveness of the proposed method.     

基于SVM逆方法的球磨机制粉系统预测控制

根据球磨机制粉过程的特点及相应的动态数学模型,证明了系统的可逆性.进一步利用支持向量机(SVM)对非线性系统具有良好逼近能力的特性,通过其来辩识球磨机制粉过程的逆系统,可以很好解决球磨机制粉系统逆系统建模难的问题,将该逆模型与原系统串联可构成解耦后的伪线性复合系统.同时为了克服逆系统的建模误差,通过设计预测控制器对该复...