Mineral Froth Image Classification and Segmentation

Accurate segmentation of froth images is always a problem in the research of floating modeling based on Machine Vision. Since a froth image is with the characteristic of complexity and diversity, it is a feasible research idea for the workflow of which the froth image is firstly classified and then segmented by the image segmentation algorithm designed for each type of froth images. This study proposes a new froth image classification algorithm. The texture feature is extracted to complete the classification. Meanwhile, an improved method based on the original valley‐edge detection algorithm is also proposed in the study. Firstly, the fractional differential is introduced to design the new valley‐edge detection templates which can extract more information on bubble edges after the enhancement of the weak edges, and finally the close bubble boundaries are obtained by carrying out the improved deburring and gap connection algorithms. Experimental results show that the new classification method can be used to distinguish the types of small, middle and large bubble images. The improved image segmentation algorithm can well reduce the problems of over‐segmentation and under‐segmentation, and it is in higher adaptability

Sulfur Flotation Performance Recognition Based on Hierarchical Classification of Local Dynamic and Static Froth Features

© 2018 IEEE. This paper proposes a flotation performance recognition system based on a hierarchical classification of froth images using both local dynamic and static features, which includes a series of functions in image extraction, processing, and classification. Within the integrated system, to identify the abnormal working condition with poor flotation performance (NB it could be significantly different with the dynamic features of the froth in abnormal working condition), it is functioned first with building up local dynamic features of froth image from the information including froth velocity, disorder degree, and burst rate. To enhance the dynamic feature extraction and matching, this system introduces a scale-invariant feature transform method to cope with froth motion and the noise induced by dust and illumination. For the performance subdividing under normal working conditions, bag-of-words (BoW) description is utilized to fill the semantic gap in performance recognition when images are directly described by global image features. Accordingly typical froth status words are extracted to form a froth status glossary so that the froth status words of each patch form the BoW description of an image. A Bayesian probabilistic model is built to establish a froth image classification reference with the BoW description of images as the input. An expectation-maximization algorithm is used for training the model parameters. Data obtained from a real plant are selected to verify the proposed approach. It is noted that the proposed system can reduce the negative effects of image noise, and has high accuracy in flotation performance recognition

Probability density function of bubble size based reagent dosage predictive control for copper roughing flotation

As an effective measurement indicator of bubble stability, bubble size structure is believed to be closely related to flotation performance in copper roughing flotation. Moreover, reagent dosage has a very important influence on bubble size structure. In this paper, a novel reagent dosage predictive control method based on probability density function (PDF) of bubble size is proposed to implement the indices of roughing circuit. Firstly, the froth images captured in the copper roughing are segmented by using a two-pass watershed algorithm. In order to characterize bubble size structure with non-Gaussian feature, an entropy based B-spline estimator is hence investigated to depict the PDF of the bubble size. Since the weights of B-spline are interrelated and related to the reagent dosage, a multi-output least square support vector machine (MLS-SVM) is applied to depict a dynamic relationship between the weights and the reagent dosage. Finally, an entropy based optimization algorithm is proposed to determine reagent dosage in order to implement tracking control for the PDF of the output bubble size. Experimental results can show the effectiveness of the proposed method

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

Value of Mineralogical Monitoring for the Mining and Minerals Industry In memory of Prof. Dr. Herbert Pöllmann

This Special Issue, focusing on the value of mineralogical monitoring for the mining and minerals industry, should include detailed investigations and characterizations of minerals and ores of the following fields for ore and process control: Lithium ores—determination of lithium contents by XRD methods; Copper ores and their different mineralogy; Nickel lateritic ores; Iron ores and sinter; Bauxite and bauxite overburden; Heavy mineral sands. The value of quantitative mineralogical analysis, mainly by XRD methods, combined with other techniques for the evaluation of typical metal ores and other important minerals, will be shown and demonstrated for different minerals. The different steps of mineral processing and metal contents bound to different minerals will be included. Additionally, some processing steps, mineral enrichments, and optimization of mineral determinations using XRD will be demonstrated. Statistical methods for the treatment of a large set of XRD patterns of ores and mineral concentrates, as well as their value for the characterization of mineral concentrates and ores, will be demonstrated. Determinations of metal concentrations in minerals by different methods will be included, as well as the direct prediction of process parameters from raw XRD data

Monitoring and Control of Hydrocyclones by Use of Convolutional Neural Networks and Deep Reinforcement Learning

The use of convolutional neural networks for monitoring hydrocyclones from underflow images was investigated. Proof-of-concept and applied industrial considerations for hydrocyclone state detection and underflow particle size inference sensors were demonstrated. The behaviour and practical considerations of model-free reinforcement learning, incorporating the additional information provided by the sensors developed, was also discussed in a mineral processing context

Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

Coal is heterogeneous in nature, and thus the characterization of coal is essential before its use for a specific purpose. Thus, the current study aims to develop a machine vision system for automated coal characterizations. The model was calibrated using 80 image samples that are captured for different coal samples in different angles. All the images were captured in RGB color space and converted into five other color spaces (HSI, CMYK, Lab, xyz, Gray) for feature extraction. The intensity component image of HSI color space was further transformed into four frequency components (discrete cosine transform, discrete wavelet transform, discrete Fourier transform, and Gabor filter) for the texture features extraction. A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development. The datasets of the optimized features were used as an input for the model, and their respective coal characteristics (analyzed in the laboratory) were used as outputs of the model. The R-squared values were found to be 0.89, 0.92, 0.92, and 0.84, respectively, for fixed carbon, ash content, volatile matter, and moisture content. The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression, support vector regression, and radial basis neural network models. The study demonstrates the potential of the machine vision system in automated coal characterization

AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing

In the last few years, jargon, such as machine learning (ML) and artificial intelligence (AI), have been ubiquitous in both popular science media as well as the academic literature. Many industries have tried the current suite of ML and AI algorithms with various degrees of success. Mineral processing, as an industry, is looking at AI for two reasons. First of all, as with other industries, it is pertinent to know if AI algorithms can be used to enhance productivity. The second reason is specific to the mining industry. Of late, the grade of ores is reducing, and the demand for ethical mining (with as little effect on ecology as possible) is increasing. Thus, mineral processing industries also want to explore the possible use of AI in solving these challenges. In this review paper, first, the challenges in mineral processing that can potentially be solved by AI are presented. Then, some of the most pertinent developments in the domain of ML and AI (applied in the domain of mineral processing) are discussed. Lastly, a top-level modus operandi is presented for a mineral processing industry that might want to explore the possibilities of using AI in its processes. Following are some of the new paradigms added by this review. This review presents a holistic view of the domain of mineral processing with an AI lens. It is also one of the first reviews in this domain to thoroughly discuss the use of AI in ethical, green, and sustainable mineral processing. The AI process proposed in this paper is a comprehensive one. To ensure the relevance to industry, the flow was made agile with the spiral system engineering flow. This is expected to drive rapid and agile investigation of the potential of applying ML and AI in different mineral processing industries

XVIII International Coal Preparation Congress

Changes in economic and market conditions of mineral raw materials in recent years have greatly increased demands on the ef fi ciency of mining production. This is certainly true of the coal industry. World coal consumption is growing faster than other types of fuel and in the past year it exceeded 7.6 billion tons. Coal extraction and processing technology are continuously evolving, becoming more economical and environmentally friendly. “ Clean coal ” technology is becoming increasingly popular. Coal chemistry, production of new materials and pharmacology are now added to the traditional use areas — power industry and metallurgy. The leading role in the development of new areas of coal use belongs to preparation technology and advanced coal processing. Hi-tech modern technology and the increasing interna- tional demand for its effectiveness and ef fi ciency put completely new goals for the University. Our main task is to develop a new generation of workforce capacity and research in line with global trends in the development of science and technology to address critical industry issues. Today Russia, like the rest of the world faces rapid and profound changes affecting all spheres of life. The de fi ning feature of modern era has been a rapid development of high technology, intellectual capital being its main asset and resource. The dynamics of scienti fi c and technological development requires acti- vation of University research activities. The University must be a generator of ideas to meet the needs of the economy and national development. Due to the high intellectual potential, University expert mission becomes more and more called for and is capable of providing professional assessment and building science-based predictions in various fi elds. Coal industry, as well as the whole fuel and energy sector of the global economy is growing fast. Global multinational energy companies are less likely to be under state in fl uence and will soon become the main mechanism for the rapid spread of technologies based on new knowledge. Mineral resources will have an even greater impact on the stability of the economies of many countries. Current progress in the technology of coal-based gas synthesis is not just a change in the traditional energy markets, but the emergence of new products of direct consumption, obtained from coal, such as synthetic fuels, chemicals and agrochemical products. All this requires a revision of the value of coal in the modern world economy