Predicting the Operating States of Grinding Circuits by Use of Recurrence Texture Analysis of Time Series Data

Grinding circuits typically contribute disproportionately to the overall cost of ore beneficiation and their optimal operation is therefore of critical importance in the cost-effective operation of mineral processing plants. This can be challenging, as these circuits can also exhibit complex, nonlinear behavior that can be difficult to model. In this paper, it is shown that key time series variables of grinding circuits can be recast into sets of descriptor variables that can be used in advanced modelling and control of the mill. Two real-world case studies are considered. In the first, it is shown that the controller states of an autogenous mill can be identified from the load measurements of the mill by using a support vector machine and the abovementioned descriptor variables as predictors. In the second case study, it is shown that power and temperature measurements in a horizontally stirred mill can be used for online estimation of the particle size of the mill product.

[1]  M. Ausloos,et al.  RECURRENCE PLOT AND RECURRENCE QUANTIFICATION ANALYSIS TECHNIQUES FOR DETECTING A CRITICAL REGIME. EXAMPLES FROM FINANCIAL MARKET INIDICES , 2004, cond-mat/0412765.

[2]  Yigen Zeng,et al.  Monitoring grinding parameters by vibration signal measurement - a primary application , 1994 .

[3]  Manuel A. Duarte-Mermoud,et al.  Control of a grinding mill circuit using fractional order controllers , 2017 .

[4]  Chris Aldrich,et al.  Froth image analysis by use of transfer learning and convolutional neural networks , 2018 .

[5]  Cordelia Schmid,et al.  Constructing models for content-based image retrieval , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[6]  Yong Li Cheng Shao.  APPLICATION OF GREY RELATION ANALYSIS AND RBF NETWORK ON GRINDING-CONCENTRATION'S SOFT SENSING , 2005 .

[7]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[8]  Lars Lundberg,et al.  Classifying environmental sounds using image recognition networks , 2017, KES.

[9]  Mostafa Mehdipour-Ghazi,et al.  Plant identification using deep neural networks via optimization of transfer learning parameters , 2017, Neurocomputing.

[10]  B. Julesz.  A brief outline of the texton theory of human vision , 1984, Trends in Neurosciences.

[11]  Chris Aldrich,et al.  Monitoring of mineral processing systems by using textural image analysis , 2013 .

[12]  Simone Marinai,et al.  Deep neural networks for record counting in historical handwritten documents , 2019, Pattern Recognit. Lett..

[13]  Chris Aldrich,et al.  Identification of dynamic process systems with surrogate data methods , 2001 .

[14]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2004, International Journal of Computer Vision.

[15]  Jefersson Alex dos Santos,et al.  Towards better exploiting convolutional neural networks for remote sensing scene classification , 2016, Pattern Recognit..

[16]  Chris Aldrich,et al.  Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods , 2013, Advances in Computer Vision and Pattern Recognition.

[17]  Vibration signal emission from mono-size particle breakage , 1996 .

[18]  Jian Tang,et al.  Feature extraction and selection based on vibration spectrum with application to estimating the load parameters of ball mill in grinding process , 2012 .

[19]  Ali Işın,et al.  Cardiac arrhythmia detection using deep learning , 2017 .

[20]  Ying Wah Teh,et al.  Time-series clustering - A decade review , 2015, Inf. Syst..

[21]  Minho Lee,et al.  Deep learning with support vector data description , 2015, Neurocomputing.

[22]  Wei Yu,et al.  Visualizing and Comparing AlexNet and VGG using Deconvolutional Layers , 2016 .

[23]  Chris Aldrich,et al.  Acoustic estimation of the particle size distributions of sulphide ores in a laboratory ball mill , 2000 .

[24]  Jian Tang,et al.  Experimental analysis of wet mill load based on vibration signals of laboratory-scale ball mill shell , 2010 .

[25]  Chris Aldrich,et al.  Estimating size fraction categories of coal particles on conveyor belts using image texture modeling methods , 2012, Expert Syst. Appl..

[26]  Stan Sclaroff,et al.  Do less and achieve more: Training CNNs for action recognition utilizing action images from the Web , 2015, Pattern Recognit..

[27]  Tianyou Chai,et al.  Multi-frequency signal modeling using empirical mode decomposition and PCA with application to mill load estimation , 2015, Neurocomputing.

[28]  Jitendra Malik,et al.  Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.

[29]  Yigen Zeng,et al.  Application of vibration signals to monitoring crushing parameters , 1993 .

[30]  Robert P. W. Duin,et al.  Support Vector Data Description , 2004, Machine Learning.

[31]  Katherine R. Storrs,et al.  Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments , 2017, Front. Psychol..

[32]  Olaf Hellwich,et al.  Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology , 2017, Comput. Medical Imaging Graph..

[33]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[34]  Chris Aldrich,et al.  Visualization of the controller states of an autogenous mill from time series data , 2014 .

[35]  B. Julesz.  Textons, the elements of texture perception, and their interactions , 1981, Nature.

[36]  Douglas W. Fuerstenau,et al.  The energy efficiency of ball milling in comminution , 2002 .