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Minerals
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Advances in Industrial Flotation Applications
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Advances in Industrial Flotation Applications

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

Deadline for manuscript submissions: 31 January 2025 | Viewed by 5320

Special Issue Editors

Dr. Miguel Maldonado

E-Mail Website
Guest Editor
Department of Metallurgical Engineering, University of Santiago. Avda. Libertador Bernardo O'Higgins 3363, Santiago, Chile
Interests: flotation reagents; flotation machines; advanced control strategies; supervision; hydrodynamics; machine learning; sensors; circuits; modeling
Dr. Luis Vinnett

E-Mail Website
Guest Editor
Department of Chemical and Environmental Engineering, Universidad Técnica Federico Santa María, Av. España 1680, Valparaíso, Chile
Interests: flotation; modelling and simulation; optimization; hydrodynamics and gas dispersion; process control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Flotation plants face multiple challenges, such as processing extensive amounts of ever-decreasing-grade ores that exhibit complex, varying mineralogy and demand large quantities of water that may be scarce and/or have low metallurgical quality. Efficiently treating these ores requires advances in different fields, such as developing novel chemical reagents and flotation machines with enhanced hydrodynamics for fine and coarse particle recovery. In addition, plant operators must search for optimal metallurgical performance with limited real-time information. Therefore, advances in real-time sensing technology for characterizing mineralogy, water quality, gas dispersion, and mineral suspension properties; CFD modeling; process supervision incorporating recent advances in machine learning techniques; and optimizing control strategies are also required. Thus, we invite researchers and professionals to contribute articles describing recent industrial flotation applications. 

Dr. Miguel Maldonado
Dr. Luis Vinnett
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

  • flotation reagents
  • flotation machines
  • advanced control strategies
  • supervision
  • hydrodynamics
  • machine learning
  • sensors
  • circuits
  • modeling

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Published Papers (3 papers)

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Research

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28 pages, 5677 KiB  
Article
Effective Outlier Detection for Ensuring Data Quality in Flotation Data Modelling Using Machine Learning (ML) Algorithms
by Clement Lartey, Jixue Liu, Richmond K. Asamoah, Christopher Greet, Massimiliano Zanin and William Skinner
Minerals 2024, 14(9), 925; https://doi.org/10.3390/min14090925 - 10 Sep 2024
Viewed by 835
Abstract
Froth flotation, a widely used mineral beneficiation technique, generates substantial volumes of data, offering the opportunity to extract valuable insights from these data for production line analysis. The quality of flotation data is critical to designing accurate prediction models and process optimisation. Unfortunately, [...] Read more.
Froth flotation, a widely used mineral beneficiation technique, generates substantial volumes of data, offering the opportunity to extract valuable insights from these data for production line analysis. The quality of flotation data is critical to designing accurate prediction models and process optimisation. Unfortunately, industrial flotation data are often compromised by quality issues such as outliers that can produce misleading or erroneous analytical results. A general approach is to preprocess the data by replacing or imputing outliers with data values that have no connection with the real state of the process. However, this does not resolve the effect of outliers, especially those that deviate from normal trends. Outliers often occur across multiple variables, and their values may occur in normal observation ranges, making their detection challenging. An unresolved challenge in outlier detection is determining how far an observation must be to be considered an outlier. Existing methods rely on domain experts’ knowledge, which is difficult to apply when experts encounter large volumes of data with complex relationships. In this paper, we propose an approach to conduct outlier analysis on a flotation dataset and examine the efficacy of multiple machine learning (ML) algorithms—including k-Nearest Neighbour (kNN), Local Outlier Factor (LOF), and Isolation Forest (ISF)—in relation to the statistical 2σ rule for identifying outliers. We introduce the concept of “quasi-outliers” determined by the 2σ threshold as a benchmark for assessing the ML algorithms’ performance. The study also analyses the mutual coverage between quasi-outliers and outliers from the ML algorithms to identify the most effective outlier detection algorithm. We found that the outliers by kNN cover outliers of other methods. We use the experimental results to show that outliers affect model prediction accuracy, and excluding outliers from training data can reduce the average prediction errors. Full article
(This article belongs to the Special Issue Advances in Industrial Flotation Applications)
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13 pages, 2687 KiB  
Article
The Impact of Restricting Air Intake in Self-Aspirated Flotation Cells at Los Pelambres Concentrator
by Michel Morales Gacitúa, Miguel Maldonado Saavedra and Luis Vinnett
Minerals 2023, 13(11), 1375; https://doi.org/10.3390/min13111375 - 28 Oct 2023
Cited by 1 | Viewed by 1361
Abstract
This article describes the impact of restricting the air intake in industrial 250 m3 WEMCO flotation cells at Los Pelambres concentrator. The influence of air restriction on the hydrodynamic and metallurgical performance of this type of machine was evaluated. The experiments were [...] Read more.
This article describes the impact of restricting the air intake in industrial 250 m3 WEMCO flotation cells at Los Pelambres concentrator. The influence of air restriction on the hydrodynamic and metallurgical performance of this type of machine was evaluated. The experiments were conducted in single flotation cells and entire rougher banks. In all cases, the gas holdup was measured to estimate the effectiveness of the obstruction system to decrease the air concentration. In single cells, axial profiles for solid percentage and particle size were evaluated. In addition, mass balances were conducted to assess the copper recoveries and concentrate features. In individual cells, air restriction led to a decrease in the gas holdup. However, this slight change was enough to obtain a more stable froth zone and a better solid suspension. The latter was observed as: (i) a higher P80 below the pulp–froth interface, (ii) a less diluted pulp at this level, (iii) a slightly higher Cu recovery, and (iv) a coarser concentrate product. A mineralogical analysis of the concentrate sample also showed the presence of coarser liberated Cu-sulfide particles. The results in single cells suggested an improvement in the recovery of coarse particles via more intense solid suspension. The air intake was also restricted in three rougher banks to assess the impact of air obstruction on the overall performance of the respective circuit. Eleven out of fourteen cells were operated with air restriction, which led to a significant improvement in recovery of 0.9%–1.6% (absolute), at a 95% confidence level. Size-by-size mass balances were also conducted for the rougher circuits, which proved that the recovery improvements were justified by the simultaneous increase in the recovery of coarse and fine particles. Thus, a restriction in the air intake showed that a decrease in the gas holdup (and in the bubble surface area flux) was compensated by better solid suspension and a higher collision efficiency in the draft tube. The former promoted the recovery of coarse particles in the quiescent zone, whereas the latter improved the interaction between bubbles and fine particles. Further developments are being made to implement a regulatory control strategy for the air intake in self-aspirated flotation cells and to use this approach for optimizing industrial flotation banks. Full article
(This article belongs to the Special Issue Advances in Industrial Flotation Applications)
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Review

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18 pages, 2383 KiB  
Review
Advancements in Machine Learning for Optimal Performance in Flotation Processes: A Review
by Alicja Szmigiel, Derek B. Apel, Krzysztof Skrzypkowski, Lukasz Wojtecki and Yuanyuan Pu
Minerals 2024, 14(4), 331; https://doi.org/10.3390/min14040331 - 24 Mar 2024
Cited by 4 | Viewed by 2335
Abstract
Flotation stands out as a successful and extensively employed method for separating valuable mineral particles from waste rock. The efficiency of this process is subjected to the distinct physicochemical attributes exhibited by various minerals. However, the complex combination of multiple sub-processes within flotation [...] Read more.
Flotation stands out as a successful and extensively employed method for separating valuable mineral particles from waste rock. The efficiency of this process is subjected to the distinct physicochemical attributes exhibited by various minerals. However, the complex combination of multiple sub-processes within flotation presents challenges in controlling this mechanism and achieving optimal efficiency. Consequently, there is a growing dependence on machine learning methods in mineral processing research. This paper provides a comprehensive overview of machine learning and artificial intelligence techniques, presenting their potential applications in flotation processes. The review demonstrates advancements discussed in scholarly research over the past decade and highlights a growing interest in utilizing machine learning methods for monitoring and optimizing flotation processes, as demonstrated by the increasing number of studies in this field. Recent trends also suggest that the course of flotation process monitoring, and control will increasingly focus on the refinement and deployment of deep learning networks developed specifically for froth image extraction and analysis. Full article
(This article belongs to the Special Issue Advances in Industrial Flotation Applications)
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