Grinding effect of sodium silicate on muscovite and its mechanism analysis

Fan, Chenyu; Ren, Liuyi; Zhang, Yimin; Bao, Shenxu

doi:10.1016/j.mineng.2023.108106

Cited by 13 publications

(2 citation statements)

References 28 publications

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“…With the continuous depletion of resources and the increasing pressure of environmental protection and production costs, the production mode of mining enterprises is changing from an extensive production mode driven by human experience to an intelligent production mode driven by knowledge and data. The popularization of basic industrial process automation technology and the rapid development of big data and intelligent control technology also provide basic conditions and technical support for the intelligent operation of mineral processing [1,2]. More and more production problems that are difficult to solve by traditional methods need to be solved by artificial intelligence methods such as machine learning [3,4].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Intelligent Control Method for Mineral Particle Size Based on Machine Learning

Zou,

Zhou,

Song

et al. 2023

Minerals

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Mineral particle size is an important parameter in the mineral beneficiation process. In industrial processes, the grinding process produces pulp with qualified particle size for subsequent flotation processes. In this paper, a hierarchical intelligent control method for mineral particle size based on machine learning is proposed. In the machine learning layer, artificial intelligence technologies such as long and short memory neural networks (LSTM) and convolution neural networks (CNN) are used to solve the multi-source ore blending prediction and intelligent classification of dry and rainy season conditions, and then the ore-feeding intelligent expert control system and grinding process intelligent expert system are used to coordinate the production of semi-autogenous mill and Ball mill and Hydrocyclone (SAB) process and intelligently adjust the control parameters of DCS layer. This paper presents the practical application of the method in the SAB production process of an international mine to realize automation and intelligence. The process throughput is increased by 6.05%, the power consumption is reduced by 7.25%, and the annual economic benefit has been significantly improved.

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Section: Introductionmentioning

confidence: 99%

Hierarchical Intelligent Control Method for Mineral Particle Size Based on Machine Learning

Zou,

Zhou,

Song

et al. 2023

Minerals

View full text Add to dashboard Cite

show abstract

“…Many studies have also aimed to solve the fine-particle flotation problem by exploiting microbubble flotation equipment; for instance, the Reflux™ Flotation Cell was used to recover and clean fine hydrophobic particles, resulting in the stripping of fewer hydrophobic particles [10][11][12][13]. Further, it is well known that nanobubbles, referring to bubbles measuring only a few hundred nanometers wide, can extend the lower particlesize limits for the effective flotation of coal, phosphate, iron ore, and a number of other typical oxidized minerals [14][15][16][17][18]. With a wealth of research experience in microbubbleenhanced fine-particle flotation, the author is dedicated to demonstrating how nanobubbles offer an effective means of enhancing fine-particle flotation recovery, making it a subject of significant interest.…”

mentioning

confidence: 99%