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Minerals
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Process Optimization in Mineral Processing, Volume II
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Process Optimization in Mineral Processing, Volume II

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 (24 June 2022) | Viewed by 11466

Special Issue Editor

Prof. Dr. Saija Luukkanen

E-Mail Website
Guest Editor
Oulu Mining School, University of Oulu, 90570 Oulu, Finland
Interests: process optimization; process chemistry; mine to mill; water and tailings management; metallurgical testwork
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The theme of the Special Issue is process optimization in mineral processing, a vitally important and comprehensive area of research. Multidisciplinary collaboration is required, since production of saleable concentrate of high quality is the sum of many factors and requires wide understanding of the technical and economical aspects of mineral processing and the stages linked to it.

In simple terms, the primary aim of process control is to maximize efficiency of the process: achieving maximum production at minimum cost. The quality of the final concentrate determines the success of further downstream process and the optimum outcome requires proper characterization and optimization of the process. Successful performance of large complex industrial plants depends upon precise measurements and control of a number of process variables. Variability of ore feed, complex mineralogy, quality of process water, reagents—all these impact process performance and pose challenges for process optimization and control. To optimize the process in the best possible way, integrated and frequent mineralogy-based analysis, reliable real-time information from the various process stages, and optimized data management play key roles: Repeatable measurements provide the control system with essential information for a stable operation.

Prof. Dr. Saija Luukkanen
Guest Editor

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

  • process control
  • mineralogy process
  • chemistry sensors and measurements
  • process automation
  • data management

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Published Papers (4 papers)

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Research

18 pages, 4186 KiB  
Article
Mill Feed Control System and Algorithm Based on Python
by Wenkang Zhang, Dan Liu, Yu Du, Ruitao Liu, Daqian Wang, Longzhou Yu and Shuming Wen
Minerals 2022, 12(7), 804; https://doi.org/10.3390/min12070804 - 24 Jun 2022
Cited by 1 | Viewed by 2306
Abstract
Grinding is an important link in the process of mineral processing. It plays a vital role in mineral processing by optimizing the grinding process, improving the quality of grinding products and ensuring the follow-up operation indicators. In this paper, the Python language, intelligent [...] Read more.
Grinding is an important link in the process of mineral processing. It plays a vital role in mineral processing by optimizing the grinding process, improving the quality of grinding products and ensuring the follow-up operation indicators. In this paper, the Python language, intelligent theoretical control technology and mineral processing were combined to solve the problem of ore feeding control in mineral processing. Using error factor analysis, an extended control algorithm was designed. The NumPy library and data collected from the Yuan YangMou concentrator in China were used to quantitatively analyze the factors affecting the error of electronic belt scales. This paper introduces the use of Kalman filtering for electronic belt scale weight data to reduce the effect of noise and hence reduce errors. The factors affecting the process of mill feeding are also analyzed. The core ideas and methods of fuzzy control theory are summarized, and a Python-based fuzzy controller suitable for the mill feeding process that improves the overall robustness and accuracy of feeding system is implemented. Full article
(This article belongs to the Special Issue Process Optimization in Mineral Processing, Volume II)
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11 pages, 2555 KiB  
Article
Recovering Iron Concentrate from Low-Grade Siderite Tailings Based on the Process Mineralogy Characteristics
by He Wan, Peng Yi, Saija Luukkanen, Juanping Qu, Chonghui Zhang, Shenghong Yang and Xianzhong Bu
Minerals 2022, 12(6), 676; https://doi.org/10.3390/min12060676 - 27 May 2022
Cited by 4 | Viewed by 2246
Abstract
Refractory iron ore is often discarded as tailings. This causes a great waste of iron resources. In this paper, the flash roasting-magnetic separation process was designed by combining the magnetic separation process of magnetite and the process mineralogy of iron tailings. The flash [...] Read more.
Refractory iron ore is often discarded as tailings. This causes a great waste of iron resources. In this paper, the flash roasting-magnetic separation process was designed by combining the magnetic separation process of magnetite and the process mineralogy of iron tailings. The flash suspension roasting effects with 3–4 s roasting time were evaluated by magnetic separation. The MLA results show that the tailings are ground to a fineness of P90 −75 μm, where the distribution of siderite and M/H in the −75 μm particle size is 85.37% and 92.75%, respectively. Moreover, M/H and siderite are mainly associated with muscovite and quartz. This indicates that regrinding for contiguous bodies of M/H and siderite is beneficial for improving the grade and recovery of iron concentrates. The results of the flash roasting-magnetic separation process show that a mixed iron concentrate containing 60.10% Fe with an iron recovery of 81.13% would be achieved after selective grinding and staged magnetic separation of the roasted ore. The result indicates that the flash suspension roasting effects with 3–4 s roasting time are achievable. The study provides an efficient approach for recovering refractory iron from tailings. Full article
(This article belongs to the Special Issue Process Optimization in Mineral Processing, Volume II)
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18 pages, 10256 KiB  
Article
Efficient and Lightweight Framework for Real-Time Ore Image Segmentation Based on Deep Learning
by Guodong Sun, Delong Huang, Le Cheng, Junjie Jia, Chenyun Xiong and Yang Zhang
Minerals 2022, 12(5), 526; https://doi.org/10.3390/min12050526 - 23 Apr 2022
Cited by 12 | Viewed by 2880
Abstract
Image segmentation approaches have been utilized to determine the particle size distribution of crushed ores in the past decades. It is not possible to deploy large and high-powered computing equipment due to the complex working environment, so existing algorithms are difficult to apply [...] Read more.
Image segmentation approaches have been utilized to determine the particle size distribution of crushed ores in the past decades. It is not possible to deploy large and high-powered computing equipment due to the complex working environment, so existing algorithms are difficult to apply in practical engineering. This article presents a novel efficient and lightweight framework for ore image segmentation to discern full and independent ores. First, a lightweight backbone is introduced for feature extraction while reducing computational complexity. Then, we propose a compact pyramid network to process the data obtained from the backbone to reduce unnecessary semantic information and computation. Finally, an optimized detection head is proposed to obtain the feature to maintain accuracy. Extensive experimental results demonstrate the effectiveness of our method, which achieves 40 frames per second on our new ore image dataset with a very small model size. Meanwhile, our method maintains a high level of accuracy—67.68% in AP50box and 46.73% in AP50mask—compared with state-of-the-art approaches. Full article
(This article belongs to the Special Issue Process Optimization in Mineral Processing, Volume II)
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17 pages, 3619 KiB  
Article
Crusher to Mill Transportation Time Calculation—The Aitik Case
by Balázs Varannai, Daniel Johansson and Håkan Schunnesson
Minerals 2022, 12(2), 147; https://doi.org/10.3390/min12020147 - 25 Jan 2022
Cited by 3 | Viewed by 3050
Abstract
Comminution is a major contributor to the production costs in a mining operation. Therefore, process optimization in comminution can significantly improve cost efficiency. The mine-to-mill concept can be utilized to optimize the comminution chain from blasting to grinding. In order to evaluate the [...] Read more.
Comminution is a major contributor to the production costs in a mining operation. Therefore, process optimization in comminution can significantly improve cost efficiency. The mine-to-mill concept can be utilized to optimize the comminution chain from blasting to grinding. In order to evaluate the mill performance of the ore from a specific location of the deposit, a direct link needs to be established between the mill performance and the place of origin in the mine. Today, technology enables the accurate positioning of drilling, loading, and dumping points in the mine, making the ore flow between loading and crushing more transparent. However, the material flow from the crusher to the mill is not yet fully understood and monitored. This paper presents the development of an ore transportation model, based on the virtual silo concept, between the crusher and the mill for Boliden’s Aitik mine in northern Sweden. The proposed model helps to establish a link between in situ ore location and mill performance. Two transportation time calculations are used, one based on mass balance, and one based on momentary values. Historical data are used to test the capabilities of the model and the results are compared with the transportation time calculated from the mean capacity values, commonly used in previous studies to connect mill parameters with in situ ore location. The comparison of the results show that the mean parameter-based values can be as much as 50% lower than the transportation times, even in normal operation. In the tested times, the transportation time based on momentary values systematically underestimated the cumulated times. The developed model will also serve as a starting point to analyze the effect of geotechnical parameters, in addition to drill and blast design, on the mill performance of the blasted ore. Full article
(This article belongs to the Special Issue Process Optimization in Mineral Processing, Volume II)
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