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Separations
Special Issues
Mineral Flotation Separation and Resource Utilization
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Mineral Flotation Separation and Resource Utilization

A Special Issue of Separations (ISSN 2297-8739) belonging to the section "Separation Engineering".

Deadline for manuscript submissions: 10 March 2027 | Viewed by 388

Editors

Dr. Yafeng Fu

E-Mail Website
Guest Editor
Institute of Minerals Research, University of Science and Technology Beijing, Beijing, China
Interests: surface interactions and electrochemistry in flotation; genetic mineral processing engineering; recycling and high-value utilization of solid waste; wastewater purification treatment
Prof. Dr. Ximei Luo

E-Mail Website
Guest Editor
State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Faculty of Land and Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
Interests: bubble-particle interactions; froth flotation; gas-liquid interface control; development of novel flotation reagents
Special Issues, Collections and Topics in MDPI journals
Dr. Haoran Sun

E-Mail Website
Guest Editor
School of Materials Science and Engineering, Shenyang Ligong University, Shenyang 110159, China
Interests: non-metallic mineral flotation; cationic collectors; calcium depressant; surface magnetization of silicate minerals

Special Issue Information

Dear Colleagues,

Mineral resources form an indispensable foundation for modern industry and strategic emerging technologies. However, as high-grade and easily processable reserves deplete, the global mining industry is increasingly confronted with the challenge of processing complex, low-grade, and fine-grained ores. Advancing flotation separation technologies, particularly through fundamental innovations in interface regulation, pulp rheology, and the mitigation of slime-prone mineral entrainment, is vital to improving resource utilization efficiency. Furthermore, the integration of Artificial Intelligence (AI) and emerging models such as Genetic Mineral Processing Engineering (GMPE) is revolutionizing the field, enabling intelligent flowsheet design and process optimization based on inherent mineralogical characteristics. In the context of global sustainability and carbon reduction goals, the development of green, intelligent, and highly efficient processing technologies, alongside the comprehensive utilization of secondary resources, has become a pressing research priority.

We invite you to submit original research papers, letters, or reviews for this Special Issue, with topics centered on the fundamental mechanisms, intelligent innovations, and advanced applications of mineral flotation and resource utilization. Your contributions will play an important role in advancing the science and technology that underpin the efficient, green, and sustainable processing of complex mineral resources.

Dr. Yafeng Fu
Prof. Dr. Ximei Luo
Dr. Haoran Sun
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Separations 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 2600 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
  • separation
  • leaching
  • intelligent decision-making systems
  • resource utilization
  • functional materials
  • metallurgical slag
  • solid waste

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Published Papers (1 paper)

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Research

21 pages, 11145 KB  
Article
A Dynamic Relationship-Aware Approach to Flotation Concentrate Grade Prediction Using DGraFormer
by Lingyu Zhao, Fuming Qu, Hui Chen, Zhengyu Liu, Yaming Ji and Xiaoming Liu
Separations 2026, 13(9), 261; https://doi.org/10.3390/separations13090261 - 14 Sep 2026
Viewed by 128
Abstract
Accurate prediction of flotation concentrate grade is essential for maintaining product quality and supporting timely process adjustment. However, mechanistic prediction remains difficult because industrial flotation involves nonlinear multivariable interactions and partially observed operating states. Recent advances in machine learning have increased interest in [...] Read more.
Accurate prediction of flotation concentrate grade is essential for maintaining product quality and supporting timely process adjustment. However, mechanistic prediction remains difficult because industrial flotation involves nonlinear multivariable interactions and partially observed operating states. Recent advances in machine learning have increased interest in data-driven methods, with encouraging results. Although recent studies have incorporated temporal dependencies into flotation-grade prediction, explicitly modeling the condition-dependent evolution of multivariate process–quality relationships under changing plant conditions remains challenging. In this study, industrial flotation records were systematically analyzed from a data-driven perspective to identify challenges affecting concentrate-grade prediction. Statistical analyses showed that variable–grade relationships are condition-dependent and vary across operating periods, indicating that measured variables only partially characterize evolving flotation states and that fixed input–output mappings may be inadequate. Accordingly, a DGraFormer-based model was developed to capture evolving inter-variable dependencies and multi-scale temporal patterns. For one-hour-ahead forecasting, the model achieved an RMSE of 0.7587 and an R2 of 0.5423 for iron concentrate grade, and an RMSE of 0.6917 and an R2 of 0.6384 for silica concentrate grade, outperforming the evaluated machine-learning and deep-learning baselines overall. These results underscore the importance of dynamic multivariate modeling for accurate concentrate-grade forecasting. Full article
(This article belongs to the Special Issue Mineral Flotation Separation and Resource Utilization)
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