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Chris Aldrich - One of the best experts on this subject based on the ideXlab platform.

  • Monitoring of Flotation Systems by Use of Multivariate Froth Image Analysis
    'MDPI AG', 2021
    Co-Authors: Chris Aldrich, Xiu Liu
    Abstract:

    Froth image analysis has been considered widely in the identification of operational regimes in Flotation circuits, the characterisation of Froths in terms of bubble size distributions, Froth stability and local Froth velocity patterns, or as a basis for the development of inferential online sensors for chemical species in the Froth. Relatively few studies have considered Flotation Froth image analysis in unsupervised process monitoring applications. In this study, it is shown that Froth image analysis can be combined with traditional multivariate statistical process monitoring methods for reliable monitoring of industrial platinum metal group Flotation plants. This can be accomplished with well-established methods of multivariate image analysis, such as the Haralick feature set derived from grey level co-occurrence matrices and local binary patterns that were considered in this investigation

  • Flotation Froth image recognition with convolutional neural networks
    Minerals Engineering, 2019
    Co-Authors: Y. Fu, Chris Aldrich
    Abstract:

    Abstract Computer vision systems designed for Flotation Froth image analysis are well established in industry, where their ability to measure Froth flow velocities and stability are used to control recovery. However, the use of Froth image analysis to estimate the concentrations of mineral species in the Froth phase is less well established and the reliability of these algorithms depends on the quality of the features that can be extracted from the Froth images. Over less than a decade, convolutional neural networks have significantly pushed the boundaries with regard to image recognition in range of technical applications, notably cancer diagnosis, face recognition, remote sensing, as well as applications in the food industry. With the exception of the exploration geosciences, they are yet to make meaningful inroads in the mineral process industries. In this study, the use of three pretrained neural networks architectures to estimate Froth grades from industrial image data, namely AlexNet, VGG16 and ResNet is considered. In its pretrained format, AlexNet outperformed previously proposed methods by a significant margin. This margin could be increased markedly via partial retraining of the VGG16 and ResNet34 networks.

  • Robust Block-Matching Motion Estimation of Flotation Froth Using Mutual Information
    2016
    Co-Authors: Chris Aldrich, Anthony Amankwah
    Abstract:

    In this paper, we propose a new method for the motion estimation of Flotation Froth using mutual information with a bin size of two as the block matching similarity metric. We also use three-step search and new-three-step-search as a search strategy. Mean sum of absolute difference (MAD) is widely considered in blocked based motion estimation. The minimum bin size selection of the proposed similarity metric also makes the computational cost of mutual information similar to MAD. Experimental results show that the proposed motion estimation technique improves the motion estimation accuracy in terms of peak signal-to-noise ratio of the reconstructed frame. The computational cost of the proposed method is almost the same as the standard machine vision methods used for the motion estimation of Flotation Froth

  • Motion estimation in Flotation Froth images based on edge detection and mutual information
    2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
    Co-Authors: Anthony Amankwah, Chris Aldrich
    Abstract:

    Motion Estimation is an important research field with many applications including remote sensing, surveillance, navigation, process control, and image compression. One of the popular methods used for motion estimation is intensity based block motion estimation. A common drawback with intensity based block motion estimation is that the velocity of blocks located at the boundaries of moving objects is not estimated accurately. This is mainly because blocks do not fully surround moving objects. In this paper, a novel method for the estimation of motion using edge matching is presented. The Canny edge detector is used to create the edges. The image is divided into nonoverlapping rectangular blocks. The best match to the current block is searched for in the previous of frame of the video within a search area for the location of the current block. Mutual information (MI) with a bin size of two is used as the matching criterion. Experimental results from test image sequences show that the proposed edge-based motion estimation technique improves the motion estimation accuracy in terms of the peak signal-to-noise ratios of reconstructed frames.

  • The interpretation of Flotation Froth surfaces by using digital image analysis and neural networks
    Chemical Engineering Science, 1995
    Co-Authors: D. W. Moolman, Jannie S. J. Van Deventer, Chris Aldrich, D J Bradshaw
    Abstract:

    The rapid developments in computer vision, computational resources and artificial intelligence, and the integration of these technologies are creating new possibilities in the design and implementation of commercial machine vision systems. In chemical and minerals engineering, numerous opportunities for the application of these systems exist, of which the characterization of Flotation Froth structures is a good example of the utilization of visual data as a supplement to conventional plant data. In this paper images from pyrite batch Flotation tests conducted after a factorial design as well as images from a copper Flotation plant were used to understand the relationship between Froth characteristics and Flotation performance better. The results show that a significant amount of data can be extracted from Flotation surface Froths. Techniques have been developed to characterize chromatic information, average bubble size, Froth texture, Froth stability and mobility of surface Froths. It has been shown that most of the Froth characteristics of this study can be explained in terms of the concentration of solids in the Froth and the factors that affect the solids concentration. The techniques developed proved to be useful in investigating the effect of a mixed collector and the addition of copper sulphate. The depressing effect of the copper sulphate and the higher grades and recoveries made possible by the mixed collector under these conditions were explained by analysis of the Froth features. Excellent results were obtained in modelling the relation between Froth characteristics or Froth grade and recovery by using a backpropagation neural network. A sensitivity analysis showed that the most important Froth features for the experimental conditions of this study are the Froth stability, mobility and average bubble size. This computer vision system constitutes a powerful research tool for the investigation and interpretation of the effect of various Flotation parameters. This paper also shows how the rapid development in computer technology and related disciplines can be used to transform recently developed concepts and available technology into a new generation of intelligent automation systems. © 1995.

Nana Shen - One of the best experts on this subject based on the ideXlab platform.

  • Research Article Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion
    2016
    Co-Authors: Jiesheng Wang, Shuang Han, Nana Shen
    Abstract:

    Copyright © 2014 Jie-sheng Wang et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For predicting the key technology indicators (concentrate grade and tailings recovery rate) of Flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the Flotation Froth image. Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the Flotation Froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex.The ESN soft-sensor model of Flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the Flotation process. 1

  • Research Article Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm
    2016
    Co-Authors: Jiesheng Wang, Shuang Han, Nana Shen
    Abstract:

    Copyright © 2014 Jie-sheng Wang et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For meeting the forecasting target of key technology indicators in the Flotation process, a BP neural network soft-sensor model based on features extraction of Flotation Froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of Flotation Froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensormodel. Simulation results show that themodel has better generalization results and prediction accuracy. 1

  • features extraction of Flotation Froth images and bp neural network soft sensor model of concentrate grade optimized by shuffled cuckoo searching algorithm
    The Scientific World Journal, 2014
    Co-Authors: Jiesheng Wang, Shuang Han, Nana Shen
    Abstract:

    For meeting the forecasting target of key technology indicators in the Flotation process, a BP neural network soft-sensor model based on features extraction of Flotation Froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of Flotation Froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.

  • Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion
    Hindawi Limited, 2014
    Co-Authors: Jiesheng Wang, Shuang Han, Nana Shen
    Abstract:

    For predicting the key technology indicators (concentrate grade and tailings recovery rate) of Flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the Flotation Froth image. Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the Flotation Froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of Flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the Flotation process

Mohammad Hamiruce Marhaban - One of the best experts on this subject based on the ideXlab platform.

  • An image segmentation algorithm for measurement of Flotation Froth bubble size distributions
    Measurement: Journal of the International Measurement Confederation, 2017
    Co-Authors: A Jahedsaravani, M Massinaei, Mohammad Hamiruce Marhaban
    Abstract:

    The bubble size distribution at the Froth surface of a Flotation cell is closely related to the process condition and performance. The Flotation performance can be reasonably predicted through continuous measuring the bubble size distribution by a machine vision system. In this work a new watershed algorithm based on whole and sub-image classification techniques is introduced and successfully validated by several laboratory and industrial scale Froth images taken under different process conditions. The results indicate that the developed algorithms, in particular the sub-image classification based segmentation algorithm, can accurately and reliably identify the individual small and large bubbles in the actual Froth images, which is often problematic.

D. W. Moolman - One of the best experts on this subject based on the ideXlab platform.

  • relationship between surface Froth features and process conditions in the batch Flotation of a sulphide ore
    Minerals Engineering, 1997
    Co-Authors: C Aldrich, D. W. Moolman, S J Bunkell, M C Harris, D A Theron
    Abstract:

    Flotation processes occurring in the bulk and Froth phases have a characteristic influence on the structural features and dynamics of the Flotation Froth. In principle the Froth features can therefore be used as a useful indicator of the performance of the Flotation system. In this study the surface Froth features and dynamics are represented by three features extracted from the digitized images of the Froths, viz. a statistical feature which is a rough indication of the average bubble size of the Froth, a measure of the Froth stability, as well as the average grey level of the Froth, which is an indication of mineral loading. The effect of high intensity conditioning on the batch Flotation of a sulphide ore from the Merensky reef in South Africa was investigated, and the significantly beneficial effect of high intensity conditioning on the performance of the Flotation was clearly reflected in the smaller bubble size distributions and greater stability of the Flotation Froths.

  • the significance of Flotation Froth appearance for machine vision control
    International Journal of Mineral Processing, 1996
    Co-Authors: D. W. Moolman, Jacques Eksteen, C Aldrich, J S J Van Deventer
    Abstract:

    Abstract The development of robust automatic control systems has proved difficult because of the complexity of the problem. Flotation is notorious for its susceptibility to process upsets and consequently its poor performance, making successful Flotation control systems an elusive goal. Machine vision systems provide a novel solution to several of the problems encountered in conventional Flotation systems for monitoring and control. In previous work powerful techniques have been developed for the extraction of Flotation Froth appearance features such as average bubble size, Froth mobility and stability, chromatic information and textural properties of surface Froth. A methodology has been developed for the classification of Froths, based on appearance and metallurgical significance. The objective of this paper is to provide a clear framework and motivation for the development of a machine vision system for Flotation control. A systematic discussion of the diffuse literature descriptions about the relation between Froth appearance and fundamental Flotation principles is presented. A preliminary classification strategy for Flotation Froths is proposed and an example of how process deviations can be related to Froth appearance is provided. Design constraints and principles imposed on a vision system by Flotation are also discussed.

  • The interpretation of Flotation Froth surfaces by using digital image analysis and neural networks
    Chemical Engineering Science, 1995
    Co-Authors: D. W. Moolman, Jannie S. J. Van Deventer, Chris Aldrich, D J Bradshaw
    Abstract:

    The rapid developments in computer vision, computational resources and artificial intelligence, and the integration of these technologies are creating new possibilities in the design and implementation of commercial machine vision systems. In chemical and minerals engineering, numerous opportunities for the application of these systems exist, of which the characterization of Flotation Froth structures is a good example of the utilization of visual data as a supplement to conventional plant data. In this paper images from pyrite batch Flotation tests conducted after a factorial design as well as images from a copper Flotation plant were used to understand the relationship between Froth characteristics and Flotation performance better. The results show that a significant amount of data can be extracted from Flotation surface Froths. Techniques have been developed to characterize chromatic information, average bubble size, Froth texture, Froth stability and mobility of surface Froths. It has been shown that most of the Froth characteristics of this study can be explained in terms of the concentration of solids in the Froth and the factors that affect the solids concentration. The techniques developed proved to be useful in investigating the effect of a mixed collector and the addition of copper sulphate. The depressing effect of the copper sulphate and the higher grades and recoveries made possible by the mixed collector under these conditions were explained by analysis of the Froth features. Excellent results were obtained in modelling the relation between Froth characteristics or Froth grade and recovery by using a backpropagation neural network. A sensitivity analysis showed that the most important Froth features for the experimental conditions of this study are the Froth stability, mobility and average bubble size. This computer vision system constitutes a powerful research tool for the investigation and interpretation of the effect of various Flotation parameters. This paper also shows how the rapid development in computer technology and related disciplines can be used to transform recently developed concepts and available technology into a new generation of intelligent automation systems. © 1995.

Aldrich Chris - One of the best experts on this subject based on the ideXlab platform.

  • Froth image analysis by use of transfer learning and convolutional neural networks
    'Elsevier BV', 2018
    Co-Authors: Fu Y., Aldrich Chris
    Abstract:

    Deep learning constitutes a significant recent advance in machine learning and has been particularly successful in applications related to image processing, where it can already surpass human accuracy in some cases. In this paper, the use of a convolutional neural network, AlexNet, pretrained on a database of images of common objects was used as is to extract features from Flotation Froth images. These features could subsequently be used to predict the conditions or performance of the Flotation systems. Two case studies are considered. In the first, Froth regimes in an industrial Flotation plant could be identified significantly more reliably with the features generated by AlexNet than with previous state-of-the-art approaches, such as wavelets, grey level co-occurrence matrices or local binary patterns. In the second case study, the arsenic concentration in the batch Flotation of realgar-orpiment-quartz mixtures could be predicted more accurately than was possible with features extracted by wavelets, grey level co-occurrence matrices, local binary patterns or by use of colour. These results suggest that feature extraction with convolutional neural networks trained on complex data sets from other domains can serve as more reliable methods than previous state-of-the-art approaches to Froth image analysis

  • Flotation Froth Image Analysis by Use of a Dynamic Feature Extraction Algorithm
    'Elsevier BV', 2016
    Co-Authors: Fu Y., Aldrich Chris
    Abstract:

    Froth image analysis has been well established as a means to infer the performance of Froth Flotation cells in real time. Apart from linking the appearance of the Froth to the behavior of the Flotation system, the dynamic behaviour of the Froth is also an important determinant of the performance of the Flotation cell, and ideally, this information should also be taken into consideration. In this investigation, the dynamic behaviour of the Froth was incorporated implicitly in the features extracted from the images. As a case study, mineral mixtures consisting of realgar, orpiment and quartz were floated in a laboratory batch Flotation cell. Videographic mages of the Froths generated by the experiments and a dynamic local binary pattern algorithm (LBP-TOP) was used to extract features from the video data. A random forest model could subsequently be built to reliably classify the conditions prevailing in each of the batch runs. The dynamic LBP algorithm did not perform significantly better than its 2D equivalent that did not incorporate the temporal behaviour of the Froth, as both approaches could very reliably identify the different Froth classes

  • Motion estimation in Flotation Froth using the Kalman filter
    'Institute of Electrical and Electronics Engineers (IEEE)', 2015
    Co-Authors: Amankwah A., Aldrich Chris
    Abstract:

    © 2015 IEEE. Machine vision systems have been used to monitor mineral Froth Flotation systems since the 1990s and their ability to track key performance indicators of the systems online is critical to improved plant operation. One of the challenges faces by these computer vision systems, is estimation of the motion of the Froth, which is hindered by the simultaneous deformation, bursting and merging of bubbles. In this paper, we propose a block based motion estimation method using Kalman filtering to improve the motion vector estimates resulting from the new-three-step-search technique. Experimental results derived from Flotation Froth video sequences are presented