Machine vision in measurement and control of mineral concentration process

This thesis considers machine vision in the context of the mining, mineral and metal industry (MMMI). Even though MMMI might be seen as a rather conservative industry branch, in many cases it is not. One motivation for constant research and development is the large amount of ore processed on a yearly basis, which means that even a slight improvement in performance can lead to substantial economical benefits. Another point, related more closely to the thesis, is that the development in camera and information technology has enabled the integration of machine vision based applications into many different industry branches, MMMI being one of them. Machine vision and its utilization in measurement and control of a modern flotation plant is studied in detail. The research was started in the late 90's with the development of an image analysis platform for flotation froths, which was later extended to cover multiple flotation cells. The resulting image analysis based variables were studied and new results regarding their usefulness both in single and multi-camera settings were obtained. The most important variables are shown to the plant operators and used in closed loop control. Furthermore, an image history database and a tool for its utilization were created, as well as a new type of froth level measurement technique introduced. The research done with the image analysis of flotation froths provided strong evidence of the importance of the froth colour as an indicator of grade. This motivated further studies carried out with a spectrophotometer, which is a more accurate instrument for colour measurements. As a result, a new type of on-line measurement technique was created to be used as a supplement to existing X-Ray fluorescence (XRF) analyzers to reduce their typical sampling interval of 10-20 minutes to a virtually continuous measurement. Another field of research presented is the particle size distribution analysis of crushed ore from a moving conveyor belt in a contact-free manner, for which two new measurement techniques are presented. This information, when measured already in the mine, can be used in the flotation plant to gain better grinding results, and geologists can use it in mine planning

Deep Learning Approaches to Image Texture Analysis in Material Processing

Texture analysis is key to better understanding of the relationships between the microstructures of the materials and their properties, as well as the use of models in process systems using raw signals or images as input. Recently, new methods based on transfer learning with deep neural networks have become established as highly competitive approaches to classical texture analysis. In this study, three traditional approaches, based on the use of grey level co-occurrence matrices, local binary patterns and textons are compared with five transfer learning approaches, based on the use of AlexNet, VGG19, ResNet50, GoogLeNet and MobileNetV2. This is done based on two simulated and one real-world case study. In the simulated case studies, material microstructures were simulated with Voronoi graphic representations and in the real-world case study, the appearance of ultrahigh carbon steel is cast as a textural pattern recognition pattern. The ability of random forest models, as well as the convolutional neural networks themselves, to discriminate between different textures with the image features as input was used as the basis for comparison. The texton algorithm performed better than the LBP and GLCM algorithms and similar to the deep learning approaches when these were used directly, without any retraining. Partial or full retraining of the convolutional neural networks yielded considerably better results, with GoogLeNet and MobileNetV2 yielding the best results

Advanced Techniques and Efficiency Assessment of Mechanical Processing

Mechanical processing is just one step in the value chain of metal production, but to some exten,t it determines an effectiveness of separation through suitable preparation of the raw material for beneficiation processes through production of required particle sze composition and useful mineral liberation. The issue is mostly related to techniques of comminution and size classification, but it also concerns methods of gravity separation, as well as modeling and optimization. Technological and economic assessment supplements the issue

Reflectance spectrum analysis of mineral flotation froths and slurries

The global demand of mining products has increased during recent years, and there is pressure to improve the efficiency of mines and concentration processes. This thesis focuses on froth flotation, which is one of the most common concentration methods in mineral engineering. Froth flotation is used to separate valuable minerals from mined ore that has been crushed, mixed with water and ground to a small particle size. The separation is based on differences in the surface chemical properties of the minerals. Monitoring and control of flotation processes mainly relies on the on-line analysis of the process slurry streams. Traditionally, the analysis is performed using X-ray fluorescence (XRF) analyzers that measure the elemental contents of the solids in the slurries. The thesis investigates the application of visual and near-infrared (VNIR) reflectance spectroscopy to improve the on-line analysis of mineral flotation froths and slurries. In reflectance spectroscopy the sample is illuminated and the spectrum of the reflected light is captured by a spectrograph. The main benefits of VNIR reflectance spectroscopy with respect to XRF-based analysis are the relatively low cost of the equipment required and the easy and fast measurement process. As a consequence, the sampling rate of the reflectance spectrum measurement is radically faster than in the XRF analysis. Data-based modeling is applied to the measured VNIR spectra to calculate the corresponding elemental contents. The research is conducted at a real copper and zinc flotation process. The main results of the thesis show that VNIR reflectance spectroscopy can be used to measure temporal changes in the elemental contents of mineral flotation froths and slurries in the analyzed process. Especially the slurry measurements from the final concentrates provide accurate information on the slurry contents. A multi-channel slurry VNIR analyzer prototype is developed in this thesis. When combined with an XRF analyzer, it is able to measure the slurry lines with a very fast sampling rate. This considerably improves the monitoring and control possibilities of the flotation process. The proposed VNIR analyzer is adaptively calibrated with the sparse XRF measurements to compensate for the effect of changes in other slurry properties. The high-frequency slurry analysis is shown to reveal fast grade changes and grade oscillations that the XRF analyzer is unable to detect alone. Based on the new measurement, a plant-wide study of the harmful grade oscillations is conducted in order to improve the performance of the flotation process

Texture analysis and Its applications in biomedical imaging: a survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021; date of current version January 24, 2022. This work was supported in part by the Portuguese Foundation for Science and Technology (FCT) under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors