Improvement of the grindability of vanadium-bearing shale and the direct vanadium leaching efficiency of grinded product via microwave pretreatment with particle size classification

Yuan, Yi; Zhang, Yimin

doi:10.1016/j.colsurfa.2022.128979

Cited by 7 publications

(2 citation statements)

References 38 publications

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“…At present, green microwave technology has received widespread attention in the field of metallurgy. Microwave heating can improve the grinding efficiency because it changes the original strength and stress distribution of the mineral (Yuan and Zhang 2022).…”

Section: Introductionmentioning

confidence: 99%

Experimental studies for thin-layer model validation and microwave drying characteristics of manganese ore considering parameters optimization

Huang,

Zhou,

Tian

et al. 2023

Preprint

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The extraction and utilization of low-grade manganese ore can alleviate the industrial impact of the extreme lack of high-grade pyrochlore resources. The dry pre-processing of minerals is a necessary condition to ensure the safe and efficient operation of the subsequent extraction process. In this paper, green microwave technology was utilized for studying the dynamics of drying experiments at the different initial moisture content of 10%, 15%, 20%, 25%, 30%, and 35%, respectively. The experimental results explain that the initial moisture content of a mineral was positively correlated with its drying rate. Furthermore, the basic theoretical features and dynamics of microwave drying of manganese ore were discussed by fitting and analyzing experimental data with a model of thin-layer drying dynamics. The average correlation coefficient (R2) of the Quadratic model was 0.976. The RSS values obtained by fitting the Quadratic model were 0.01368, 0.01939, and 0.01986, respectively. Compared to the other two models, the Quadratic model can more accurately describe the microwave drying kinetic process of manganese ore than the other two models. Interestingly, the infrared spectrum tests indicated that microwave drying can basically remove the O-H bond, and the change of mineral weight in the drying process was entirely caused by water loss. Through microscopic characterization of minerals, it has been found that microwave drying can reduce the size of mineral particles, which is beneficial to the subsequent roasting and leaching process. The interesting and important findings from this work can be applied to explore more extraction methods for manganese.

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Section: Introductionmentioning

confidence: 99%

Experimental studies for thin-layer model validation and microwave drying characteristics of manganese ore considering parameters optimization

Huang,

Zhou,

Tian

et al. 2023

Preprint

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show abstract

“…Thus, grinding can entail high costs of more than 50% in terms of energy consumption and media use (de Bakker, 2013;de Carvalho and Tavares, 2013;Gupta and Sharma, 2014). Thus, it is essential to investigate saving and consumption reduction of energy (Shi and Xie, 2016;Yuan and Zhang, 2022).…”

Section: Introductionmentioning

confidence: 99%

Prediction and optimization of tower mill grinding power consumption based on GA-BP neural network

Wang,

Hou,

Sobhy

2023

Physicochem. Probl. Miner. Process.

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Grinding is commonly responsible for the liberation of valuable minerals from host rocks but can entail high costs in terms of energy and medium consumption, but a tower mill is a unique power-saving grinding machine over traditional mills. In a tower mill, many operating parameters affect the grinding performance, such as the amount of slurry with a known solid concentration, screw mixer speed, medium filling rate, material-ball ratio, and medium properties. Thus, 25 groups of grinding tests were conducted to establish the relationship between the grinding power consumption and operating parameters. The prediction model was established based on the backpropagation “BP” neural network, further optimized by the genetic algorithm GA to ensure the accuracy of the model, and verified. The test results show that the relative error of the predicted and actual values of the backpropagation “BP” neural network prediction model within 3% was reduced to within 2% by conducting the generic algorithm backpropagation “GA-BP” neural network. The optimum grinding power consumption of 41.069 kWh/t was obtained at the predicted operating parameters of 66.49% grinding concentration, 301.86 r/min screw speed, 20.47% medium filling rate, 96.61% medium ratio, and 0.1394 material-ball ratio. The verifying laboratory test at the optimum conditions, produced a grinding power consumption of 41.85 kWh/t with a relative error of 1.87%, showing the feasibility of using the genetic algorithm and BP neural network to optimize the grinding power consumption of the tower mill.

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Process mineralogical characterization and vanadium extraction from vanadium-bearing shale by oxidation roasting-acid leaching

Zhou

Jin

Zhu

et al. 2022

Advanced Powder Technology

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Improvement of the grindability of vanadium-bearing shale and the direct vanadium leaching efficiency of grinded product via microwave pretreatment with particle size classification

Cited by 7 publications

References 38 publications

Experimental studies for thin-layer model validation and microwave drying characteristics of manganese ore considering parameters optimization

Experimental studies for thin-layer model validation and microwave drying characteristics of manganese ore considering parameters optimization

Prediction and optimization of tower mill grinding power consumption based on GA-BP neural network

Process mineralogical characterization and vanadium extraction from vanadium-bearing shale by oxidation roasting-acid leaching

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