Effect of hydroxamic acid polymers on reverse flotation of bauxite

Li, Hai-pu; Hu, Yuehua; Wang, Dianzuo; Xu, Jun

doi:10.1007/s11771-004-0059-5

Cited by 15 publications

(5 citation statements)

References 10 publications

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“…Kaolinite, pyrophyllite and illite are the most common aluminosilicates in bauxite. 17,18 Crystal structure diaspore…”

Section: Discussionmentioning

confidence: 99%

Comminution property of bauxite and selective separation of Al and Si in bauxite

Ou¹,

Feng²,

Zhang³

et al. 2008

Mineral Processing and Extractive Metallurgy

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This paper makes research on solving the problem in China concerning the suitable particle size for flotation and the optimum particle size of the concentrate required by the subsequent Bayer process. Grinding experiments under different conditions and flotation tests had been used to investigate the comminution property of bauxite and aluminosilicate as well as their separation by flotation. The results showed that different kinds of grinding medium led to different grinding effects, the following being the order of the selective grinding effect: (columnzball).ball.rod, and the separation between Al and Si in bauxite has been improved after selective grinding. It is indicated that the crystal structural difference between diaspore and aluminosilicate leads to their difference of comminution property. A demo plant built by the novel process of 'selective grinding-flotation' had been successfully commissioned in China since 2003.

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“…Kaolinite, pyrophyllite and illite are the most common aluminosilicates in bauxite. 17,18 Crystal structure diaspore…”

Section: Discussionmentioning

confidence: 99%

Comminution property of bauxite and selective separation of Al and Si in bauxite

Ou¹,

Feng²,

Zhang³

et al. 2008

Mineral Processing and Extractive Metallurgy

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“…The variation of surface potential was also the largest, increasing by 49.77%. Due to the strong hydrophobicity of the surface of rare earth minerals, more Mg 2+ entered the particle surface, and the ionized impurity ions were adsorbed onto the particle surface to compress the double layer, resulting in a decrease in the absolute value of Zeta potential [5,38,39].…”

Section: Comparative Analysis Of Zeta Potential and Surface Potential...mentioning

confidence: 99%

Calculation of Shear Layer Thickness of Ionic Rare Earth Particles in Mixture Electrolytes during In-Situ Leaching Process

Gao,

Rao,

Zhang

et al. 2023

Minerals

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During in-situ mining and leaching of ionic rare earth ore, a chemical replacement reaction occurs between the leaching agent and rare earth ore. The thickness of the shear layer on the surface of colloidal particles is an important physical parameter. Based on the Gouy–Chapman double-layer theory and Poisson–Boltzmann equation, the relational expression for the thickness of the shear layer in the electric double layer on the particle surface under the condition of 2:2 + 3:2 mixture electrolytes (MgSO4 + RE2(SO4)3) is derived. On this basis, an indoor column leaching experiment of MgSO4 solution is conducted, and the surface Zeta potential of rare earth ore particles is measured using a Zetaprobe potential analyzer. The surface potential and the thickness of the shear layer in the leaching process with different concentrations solutions (2.5%, 3.0%, and 3.5%) are calculated. The effects of a MgSO4 solution concentration and particle surface potential on the thickness of the shear layer in the electric double layer are analyzed. It provides a theoretical basis for the study of the internal seepage of the ore body under the condition of the coexistence of multiple ions in the leaching process.

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“…As a consequence of this, there has been a paradigm change in the medical field, which has led to the development of machine learning as an important instrument for enhancing the management of neurological illnesses. [5] The field of artificial intelligence encompasses a subfield known as machine learning. A new age of precision medicine is being ushered in with the integration of clinical information with algorithms for machine learning.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Enhanced Decision Support for Neurological Disorders Management in Healthcare

Dr. Divya Jain, Dr. Vibha Tiwari, Akshra Tiwari

2023

tjjpt

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In a nutshell: Neurological illnesses, which include a wide range of ailments from Parkinson's disease to epilepsy, present considerable problems to the administration of healthcare. The purpose of this abstract is to present an overview of a unique technique called Machine Learning-Enhanced Decision Support (ML-EDS) for Neurological Disorders Management, which aims to improve patient treatment as well as patient outcomes. Within the scope of this investigation, we have suggested a complete ML-EDS framework that integrates cutting-edge machine learning strategies with more conventional approaches to medical treatment. The framework provides a comprehensive solution that makes use of several types of patient data, such as clinical records, neuroimaging data, genetic information, and data from wearable sensors. The Random Forest, Long Short-Term Memory (LSTM), and k-Nearest Neighbours (k-NN) algorithms are integrated into our methodology in order to improve illness progression prediction, seizure event detection, and individualized medication suggestion. In comparison to more conventional approaches, the suggested framework reveals noteworthy advantages. When compared to conventional algorithms, it demonstrates superior accuracy, sensitivity, and specificity in the prediction of illness development. Additionally, its F1 score is significantly higher. In addition, the application of LSTM for the identification of seizure episodes demonstrates the effectiveness of the method in determining crucial occurrences, which in turn enables prompt intervention. The possibility of tailored medical treatment based on k-means clustering is also highlighted in this research. Patients with comparable clinical characteristics can be grouped together to facilitate the development of personalized treatment methods, which can then provide individualized care pathways. The performance evaluation demonstrates that k-NN is capable of making therapy recommendations with high precision, hence contributing to an overall improvement in the health of the patient. Additionally, the proposed framework performs very well in terms of computational efficiency, reducing the amount of time required for both training and prediction, which positions it as a viable option for healthcare applications that are used in the real world. In summation, the use of machine learning in decision support for the management of neurological disorders in healthcare is a ground-breaking strategy that is reshaping the therapeutic landscape. It does this by using the power of machine learning, which enables it to provide more accurate illness progression predictions, rapid seizure event detection, and highly tailored medical care. The findings and efficiency metrics conclusively indicate that this strategy is superior to existing procedures, suggesting a paradigm change in the therapy of neurological disorders.

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Effect of hydroxamic acid polymers on reverse flotation of bauxite

Cited by 15 publications

References 10 publications

Comminution property of bauxite and selective separation of Al and Si in bauxite

Comminution property of bauxite and selective separation of Al and Si in bauxite

Calculation of Shear Layer Thickness of Ionic Rare Earth Particles in Mixture Electrolytes during In-Situ Leaching Process

Machine Learning-Enhanced Decision Support for Neurological Disorders Management in Healthcare

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