Innovative Solutions for Measurements, Modelling and Control in Mineral Processing

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Processing and Extractive Metallurgy".

Deadline for manuscript submissions: closed (17 September 2023) | Viewed by 12419

Special Issue Editors


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Guest Editor
Department of Measurements and Control Systems, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: multi-objective control; indirect measurements and soft sensing; process modeling; Industry 4.0; IoT; Edge Computing solutions; raw materials processing; design of innovative devices, circuits, and control strategies for mineral processing

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Guest Editor
Department of Environmental Engineering, AGH University of Science and Technology, 30-059 Cracow, Poland
Interests: processing of mineral resources; optimization of technological processes; mineral engineering; geometallurgy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The constantly growing presence of exponential technologies in everyday life, new solutions for green energy production, and the electric vehicle market expanding in the unprecedented rate that it does have all increased the need for primary and secondary material recovery. At the same time, mineral processing operations serve as significantly high-energy and cost-consuming processes which impact the environment. Thus, the economic and environmental issues point to the relevance of the efficiency of valuable material recovery. Percentage enlargement of valuable material in the final product improves profits. Lower energy and consumables related to a production unit increase the economic effects even more. Efficiency improvement of mineral processing remarkably affects the saving of natural resources, and reduction of CO2 emission. Innovative solutions may also result in lower waste production, which in turn influences the environment.

The above objectives can be obtained by applying novel and more effective processing technologies, devices, and circuits but can also be achieved with operation optimization using dedicated measurements, modeling, and control techniques. This Special Issue of Minerals is dedicated to the latter and relates to Industry 4.0 solutions for mineral processing. Therefore, the Editors especially welcome papers describing research on indirect measurements, soft-sensing techniques, vision systems, IoT solutions, edge, fog and cloud computing, signal processing, static and dynamic modeling, digital twins, advanced control, and optimization techniques applied in any stage of the mineral processing operations. Industrial solutions are mostly welcomed; however, laboratory results and simulations involving industrial data are appreciated as well.

Dr. Szymon Ogonowski
Prof. Dr. Dariusz Foszcz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mineral processing
  • process control
  • optimization
  • modelling
  • measurements
  • soft sensing
  • IoT
  • vision systems
  • edge/fog/cloud computing

Published Papers (4 papers)

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Research

13 pages, 7078 KiB  
Article
Estimation of Final Product Concentration in Metalic Ores Using Convolutional Neural Networks
by Jakub Progorowicz, Artur Skoczylas, Sergii Anufriiev, Marek Dudzik and Paweł Stefaniak
Minerals 2022, 12(12), 1480; https://doi.org/10.3390/min12121480 - 22 Nov 2022
Cited by 1 | Viewed by 1283
Abstract
Although artificial neural networks are widely used in various fields, including mining and mineral processing, they can be problematic for appropriately choosing the model architecture and parameters. In this article, we describe a procedure for the optimization of the structure of a convolutional [...] Read more.
Although artificial neural networks are widely used in various fields, including mining and mineral processing, they can be problematic for appropriately choosing the model architecture and parameters. In this article, we describe a procedure for the optimization of the structure of a convolutional neural network model developed for the purposes of metallic ore pre-concentration. The developed model takes as an input two-band X-ray scans of ore grains, and for each scan two values corresponding to concentrations of zinc and lead are returned by the model. The whole process of sample preparation and data augmentation, optimization of the model hyperparameters and training of selected models is described. The ten best models were trained ten times each in order to select the best possible one. We were able to achieve a Pearson coefficient of R = 0.944 for the best model. The detailed results of this model are shown, and finally, its applicability and limitations in real-world scenarios are discussed. Full article
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11 pages, 1441 KiB  
Article
Machine Learning Technique for Recognition of Flotation Froth Images in a Nonstable Flotation Process
by Jacek Galas and Dariusz Litwin
Minerals 2022, 12(8), 1052; https://doi.org/10.3390/min12081052 - 20 Aug 2022
Cited by 3 | Viewed by 2320
Abstract
The paper is focused on the analysis of the relation between the stability of the flotation process and the efficiency of Machine Learning (ML) algorithms based on the flotation froth images. An ML process should enable researchers to construct Artificial Intelligence (AI) algorithms [...] Read more.
The paper is focused on the analysis of the relation between the stability of the flotation process and the efficiency of Machine Learning (ML) algorithms based on the flotation froth images. An ML process should enable researchers to construct Artificial Intelligence (AI) algorithms for flotation process control. The image of the flotation froth includes information characterizing the flotation process. The information can be extracted with the aid of the Image Recognition (IR) algorithms based on the ML. This enables construction of a flotation process control system in the mineral processing plant, which is based on the recognition of images of the flotation froth. The IR algorithms do not provide stable image recognition results and are not efficient in the situation where the parameters of the flotation process are highly unstable. The classification results were equal to 75.11% and 69.62% for a stable and unstable process, respectively. The experimental data collected at the Polish Pb/Zn mineral processing plant provided better insight to the relationships between the flotation process parameters and ML efficiency. These relationships were analyzed, and guidelines for the construction of the ML process for flotation process control have been formulated. Full article
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14 pages, 2413 KiB  
Article
Comparative Analysis of Dust and Noise Emission in Aggregate Production Systems
by Agnieszka Saramak, Tomasz Gawenda and Daniel Saramak
Minerals 2022, 12(4), 452; https://doi.org/10.3390/min12040452 - 7 Apr 2022
Viewed by 1653
Abstract
This paper concerns investigations on dust particles and noise emission in mineral aggregate production. Two technological circuits of aggregate production were under investigation. The first circuit was based on a two-stage screening system, while the other was designed on a basis of a [...] Read more.
This paper concerns investigations on dust particles and noise emission in mineral aggregate production. Two technological circuits of aggregate production were under investigation. The first circuit was based on a two-stage screening system, while the other was designed on a basis of a patented solution of regular aggregate production. Results of investigations show that an innovative circuit allows for reduction of screening stages which results in shortening the entire circuit. The quality of obtained products is better, while the environmental footprint of the latter circuit is lower. Results of investigations showed that reduction both in terms of dust particle emission and in noise generation was achieved. Full article
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21 pages, 5496 KiB  
Article
Digitalization Solutions in the Mineral Processing Industry: The Case of GTK Mintec, Finland
by Alona Nad, Mohammad Jooshaki, Emilia Tuominen, Simon Michaux, Arno Kirpala and Johanna Newcomb
Minerals 2022, 12(2), 210; https://doi.org/10.3390/min12020210 - 7 Feb 2022
Cited by 11 | Viewed by 5680
Abstract
The technologies used in mineral process engineering are evolving. The digital mineral processing solutions are based on advances in our ability to instrumentally measure phenomena at several stages of the beneficiation circuit, manage the data in real-time, and to analyze these data using [...] Read more.
The technologies used in mineral process engineering are evolving. The digital mineral processing solutions are based on advances in our ability to instrumentally measure phenomena at several stages of the beneficiation circuit, manage the data in real-time, and to analyze these data using machine learning to develop the next generation of process control. The main purpose of this study is to overview various digital solutions for mineral processing plants and characterization laboratories while emphasizing their utilization in the current state of the digitization process of the GTK Mintec. This study highlights the specialized digital technologies that are particularly relevant for mineral processing and beneficiation. The digital solutions studied in this article include digital twin, machine vision, information management system, sensors, smart equipment, machine learning techniques, process control system, robotic cell, and Internet of Things applied across the whole chain of studying materials from the mineralogical examinations through the bench-scale studies to the pilot test trials. The aim is to provide a clear view on the different aspects of digitizing mineral processing plants based upon the lessons learned from the development plans in GTK Mintec. Full article
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