基于集员估计在球磨机料位软测量建模中的应用研究

发布时间：2018-01-27 00:46

  本文关键词： 软测量建模 球磨机料位 极限学习机 深度极限学习机 最优定界椭球 动态软测量建模　出处：《太原理工大学》2017年硕士论文　论文类型：学位论文

【摘要】：球磨机是工业生产过程中对物料进行研磨破碎的关键设备,被普遍地使用于冶金、电力、选矿及化工等行业。其经济性与内部料位相关,料位过低导致当前工作效率低,能源利用率不高,料位过高容易造成球磨机堵磨,存在安全隐患。因此,准确地测量筒内料位对实现球磨机的优化控制至关重要。但是由于球磨机的密闭旋转特性,在实际运行过程中,料位很难通过相关传感器直接测量,所以采用数据驱动的建模方法,建立软测量模型,通过输入与球磨机料位相关的辅助变量来预测其料位。传统的软测量建模方法有很多种,包括支持向量机、偏最小二乘法、神经网络以及主元回归分析法等,皆被广泛地应用在球磨机料位的建模过程中。作为神经网络的一种,极限学习机(Extreme Learning Machine,ELM)以其简洁高效的训练机制,避免了传统神经网络的反向微调过程,从而提高模型的学习速率和泛化性,因此得到广泛地应用。然而,其前馈神经网络的隐含层输出是通过某种概率进行随机选取,造成训练好的模型随机性很大,预测结果不稳定。此外ELM单隐含层的神经网络结构也限制了其特征提取的能力。为了可以更好地通过无监督训练过程学习高维数据中隐藏的抽象特征表示,研究学者又提出一种新的深度极限学习机算法(Deep Extreme learningmachine,delm),其采用多个自编码器(autoencoder,ae)堆叠而成,逐层利用elm算法进行误差重构,并将前一层ae的输出作为后一层ae的输入,最终通过多层的ae获得数据中更加抽象的特征表示。但是由于delm的每层网络权值都是通过elm算法进行计算得到的,其elm算法的随机性,导致网络权值也存在随机性而并非最优,最终造成delm模型训练的不稳定。球磨机在实际地运行过程中,其存在着时变和工况迁移等复杂因素的影响。而传统的软测量建模方法,利用已有的离线数据进行建模,模型一旦建立不再改变,对于新进来的测试集不能很好地跟踪当前对象,从而造成模型预测性能的下降。因此,必须不断地对软测量模型进行更新和校正。集员估计是一种在给定数据集,模型结构以及噪声边界的条件下,描述可行参数集合的方法,该集合内的参数可以看作是模型参数辨识的有效参数。而最优定界椭球算法(optimalboundingellipsoid,obe)是集员估计理论中经典算法之一,将其应用在软测量模型的参数优化中,可以在给定误差边界的条件下,对模型参数进行约束优化,不仅改善模型的鲁棒性,还能提高其预测精度。基于此,本文主要做了以下研究:(1)在球磨机实验过程中,针对elm预测球磨机料位结果不稳定的缺点,本文采用obe,在误差未知但有界的条件下,对训练好的elm网络模型进行优化,提高模型的预测准确度和稳定性,并通过实验证明,该方法的有效性。(2)利用深度网络对球磨机数据进行软测量建模时,为了更好抽取样本中最高层次的抽象表达,本文提出一种多层OBE-ELM算法(Multi-Layer OBE-ELM,ML-OBE-ELM),基于自编码器重构思想,采用OBE迭代算法学习输入数据的高层特征表示,最后利用ELM算法得到高层特征与样本标签的关系式。为了验证该算法的有效性,选用传统的UCI数据集和实际球磨机数据集作为实验数据,分别验证了该算法在回归和分类中都有较好的预测性能。(3)为了解决球磨机,料位中时变和工况迁移的问题,本文提出基于OBE-PLS的动态软测量模型,首先利用离线数据训练软测量模型,当新的查询样本到达时,利用OBE在原有模型的基础上动态地调整参数,从而实现该模型对查询样本的实时跟踪,并通过数值例子和小型球磨机实验对该方法的有效性进行验证。
[Abstract]:The ball mill is a key equipment for grinding of materials in the process of industrial production, is widely used in metallurgy, electricity, mineral and chemical industry. Its economy and the internal material of related material, resulting in low current and low working efficiency, the energy utilization rate is not high, the material level is too high can easily cause plugging ball mill. There are security risks. Therefore, accurate measurement of cylinder material to realize the optimization control of ball mill is very important. But the mill closed rotation characteristics, in the actual operating process, the material level is very difficult by the relevant sensor, so the data driven modeling method, a soft measurement model, through the input and load of ball mill the auxiliary variables to predict the material level. There are many kinds of soft measurement of traditional modeling methods, including support vector machines, partial least squares, neural networks and principal component regression analysis, all Is widely used in the modeling process of ball mill bit. As a kind of neural network, the extreme learning machine (Extreme Learning Machine, ELM) with its simple and efficient training mechanism, to avoid the reverse process of fine-tuning the traditional neural network model, so as to improve the learning rate and generalization, therefore widely used however, the feedforward neural network hidden layer output is randomly selected by some probability, caused by the trained model of great randomness, the forecasting result is stable. In addition the ability of ELM neural network with one hidden layer also limit the feature extraction. In order to be better through unsupervised learning process abstract characteristics. In the high dimensional data representation, research scholars and put forward a new algorithm of machine learning depth limit (Deep Extreme learningmachine, delm), which uses multiple self encoder (autoencoder, AE) Stacked layer by layer, using elm algorithm for error reconstruction, and will enter a layer of AE AE as the output of the previous layer, the final features more abstract data obtained by multi AE said. But because each layer of the network weights of delm are calculated by elm algorithm, the random elm algorithm, also cause network weights are random and not optimal, resulting in delm model training is not stable. In the actual operation of the ball mill process, the influences of time-varying and migration condition of complex factors. The traditional methods of soft measurement modeling, off-line modeling by using existing data, model once no longer change, the new incoming test set cannot properly track the object, resulting in a decline in the prediction performance. Therefore, must be updated and correction of the soft measurement model of set membership estimation is constantly. In a given data set, the model structure and the noise boundary conditions, method of describing the feasible parameter set, the parameters in the collection can be regarded as effective parameter identification of model parameters. The optimal bounding ellipsoid algorithm (optimalboundingellipsoid, OBE) is a member of one of the classic estimation algorithm theory, its application in parameter optimization of soft measurement model, can in the given error boundary conditions for constrained optimization of the model parameters, not only improve the robustness of the model, but also improve the prediction accuracy. Based on this, this paper mainly do the following research: (1) in the ball mill in the course of the experiment, the elm prediction of ball mill level unstable result the shortcomings of the OBE, the error is unknown but bounded under the condition of the trained elm network model optimization, improve the prediction accuracy and stability, and proved by experiments, the 娉曠殑鏈夋晥鎬，

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