ABSTRACT
The investigation of the compounding mechanisms of mixed collectors in low-rank coal flotation has gradually progressed from a macro to a micro level, and the accuracy of research outcomes significantly depends on the development of micro models. While All-Atom Molecular Dynamics (AAMD) simulations have been used for this purpose, the employment of an excessive amount of collector in the system leads to a failure in replicating the actual flotation process, which in turn, limits the practical applicability of research findings. To overcome this drawback, this paper suggests using the MARTINI Coarse-Grained force field (CGFF), developed by the S.J.Marrink group, to construct a simulation system that accurately mimics the amount of collector employed in practical flotation processes. In light of this, to make the MARTINI force field more applicable for research in the low-rank coal field, we employ density functional theory (DFT) and coarse-grained molecular dynamics (CGMD) methods to investigate water cluster interactions, identify magic number clusters, and fit the corresponding CGFF parameters to develop a CG water model suitable for exploring the compounding mechanism of mixed collectors. This research lays the groundwork for future investigations into the aggregation behavior of mixed collectors under actual usage conditions, as well as their synergistic adsorption at the coal/water interface.
HIGHLIGHTS
We developed a coarse-grained (CG) water model to investigate the compounding mechanism of mixed collectors in flotation.
The model was then used to simulate the phase behavior of the collectors in water, providing insights into their behavior in flotation.
By fitting the CG water model to experimental data, we can obtain a reliable and computationally efficient results of the interaction between water and the collectors.
Acknowledgements
This research was funded by the National Natural Science Fundamental of China (No. 52104281 and No. 51974324), the Fundamental Research Funds for the Central Universities (No. 2022×JHH01), and the Open Foundation of the State Key Laboratory of Mineral Processing (No. BGRIMM-KJSKL-2020-22).
Author Contributions
Data curation, P. Wang; Formal analysis, P. Wang; Investigation, P. Wang, and X. Sun; Methodology, W. Liu. and Q. Zhuo; Project administration, W. Liu. and Q. Zhuo; Resources, W. Liu. and Q. Zhuo; Software, P. Wang; Validation, X. Sun; Writing – original draft, P. Wang; Writing – review & editing, P. Wang.
Disclosure Statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data Availability
Data will be made available on request.