Prediction of yttrium, lanthanum, cerium, and neodymium leaching recovery from apatite concentrate using artificial neural networks

Jorjani, E.; Bagherieh, A.H.; Mesroghli, Sh.; Chelgani, S. Chehreh

doi:10.1016/s1005-8850(08)60070-5

Cited by 21 publications

(5 citation statements)

References 19 publications

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“…Rare earth elements may also be extracted as a byproduct from processing of minerals, such as copper, gold, uranium, and phosphate ores, with phosphate perhaps having the greatest potential. Certain phosphate deposits, specifically the fluorapatite ores, contain significant amounts of the rare earths (Jorjani et al, 2008;Becker, 1983;Preston et al, 1996). Table 1 shows lanthanide content in some phosphate rock (Altschuler et al, 1967a(Altschuler et al, , 1967bBliskoyskii et al, 1969).…”

Section: Introduction 11 Rare Earth Elements In Phosphatementioning

confidence: 99%

Rare Earths in Phosphate: Characterization & Extraction

Zhang¹

2023

Preprint

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Much of this chapter is based on results from a multi-year research project supported by the Critical Materials Institute (CMI), an Energy Innovation Hub funded by the US Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office. It also draws substantial materials from two projects funded by the Florida Industrial and Phosphate Research (FIPR) Institute, Florida Polytechnic University. Under the CMI project, FIPR Institute was engaged in three major activities: 1) chemical and mineralogical characterization of rare earth elements (REEs) in different phosphate processing streams, 2) concentration of uranium and REE (rare earth element)-containing materials from the various processing streams, and 3) extraction of REEs and uranium from the concentrated materials. This chapter covers the main findings of those three efforts, and is divided into four parts. Part I covers chemical analysis and basic properties of different samples. Part II is a detailed process mineralogy study of the amine flotation tails. Part III focuses on isolation and characterization of rare earth (RE) mineral particles in three samples using two advanced techniques, dual energy (DE) rapid scan radiography and high resolution X-ray microtomography (HRXMT). Part IV presents process development work on the concentration of REE-containing materials and extraction of REEs.In Part I, six samples were collected from a central Florida phosphate operation, including 2000 pounds of amine flotation tails, 5 full 5-gallon buckets of waste clay, 100 pounds of ground phosphate rock, 2 full 5-gallon buckets of phosphoric acid, 50 pounds of wet phosphogypsum, and several barrels of phosphoric acid sludge. These samples were analyzed for rare earth elements, uranium, thorium, routine chemical compositions, and radioactivity. Results show total REEs of 70-2600 ppm in the samples, with uranium ranging from 25-120 ppm. Radium-226 analyzed about 20 pCi/g in phosphogypsum, 28 in phosphate rock, and 0.2 in phosphoric acid, and the corresponding uranium-238 numbers are 2.8, 20 and 36 pCi/g. Simple sizing and chemical analysis of phosphogypsum (PG) revealed an extremely encouraging piece of information on REEs in PG. About 65% of the REEs in PG is concentrated in the minus 500 mesh (approximately 30 microns) fraction that represents less than 10% of the total PG mass. Another fact is that the finest fraction also contains most of the thorium but little uranium.In Part II, a detailed process mineralogy study was conducted on the amine flotation tails sample using a Mineral Liberation Analyzer, the most advanced instrument for this type of study. Two rare earth minerals were detected in the amine tails including monazite and xenotime. The monazite monomers average 1.27%

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Section: Introduction 11 Rare Earth Elements In Phosphatementioning

confidence: 99%

Rare Earths in Phosphate: Characterization & Extraction

Zhang¹

2023

Preprint

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“…The good performance of ESMs has made them popular in different scientific fields, including mining and mineral processing. For instance, Jorjani et al [13] used artificial neural networks to simulate the process of leaching rare earth elements from apatite concentrate on an industrial scale and showed that a reasonably accurate model could be developed using the improved ANN algorithm. In a later study, Milivojevic et al [14] simulated the nickel ore leaching process to demonstrate that expert systems are more reliable than linear regression-based statistical models.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Serving of DOE and RNN-Based Methods to Optimize and Simulate a Copper Flotation Circuit

Gholami

Movahedifar

Khoshdast³

et al. 2022

Minerals

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Prediction of metallurgical responses during the flotation process is extremely vital to increase the process efficiency using a proper modeling approach. In this study, two new variants of the recurrent neural network (RNN) method were used to predict the copper ore flotation indices, i.e., grade and recovery within different operating conditions. The model input parameters including pulp pH and solid content as well as frother and collector dosages were first analysed and then optimized using a two-step factorial approach. The statistical analysis showed a reliable correlation between operating parameters and copper grade and recovery with coefficients of 99.86% and 94.50%, respectively. The main effect plots indicated that pulp pH and solid content positively affect copper grade while increasing the frother and collector dosages negatively influenced the quality of the final concentrate. Despite the same effect from pulp pH, reverse effects from other variables were observed for copper recovery. Process optimization revealed that maximum copper recovery of 44.39% with a grade of 11.48% could be achieved under the optimal condition as pulp pH of 10, solid content of 20%, and frother and collector concentrations of 25 g/t and 9.9 g/t, respectively. Then, the predictive efficiency of long short-term memory (LSTM) and gated recurrent unit (GRU) networks with proper structure were evaluated using mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R2). The simulation results showed that the LSTM network with higher R2 of 0.963 and 0.934 for copper grade and recovery, respectively, was more effective than the GRU algorithm with the corresponding values of 0.956 and 0.919, respectively. The results show that the LSTM model could be useful in predicting the copper flotation behaviour in response to changes in the operating parameters.

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“…The precipitated rare earth salt forms so that this recovery is typical after dissolve through acid and alkali solvent [6,7,8]. Using this method, First step is that phosphor powder is recovered after physical separation in order to recover rare earth elements from end of life fluorescent lamps.…”

Section: Introductionmentioning

confidence: 99%

Study On The Separation And Extraction Of Rare-Earth Elements From The Phosphor Recovered From End Of Life Fluorescent Lamps

Shin

Kim

2015

Archives of Metallurgy and Materials

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In this study, recovered phosphor from end of life three-wavelength fluorescent lamp was selected for reuse rare earth elements in the phosphor. The effect of a type of acid, concentration, and time was investigated as solubility of rare earth elements. In addition, precipitate heat-treated was investigated as possibility of reusable phosphor. The results showed that the amount of the rare earth elements was different values depending on the type of acid, and it was investigated with concentration of acid and reaction time. After precipitation reaction, the precipitate was sintered in electric furnace in order to reuse rare earth elements as phosphor. It was confirmed that yttrium, europium, oxygen, and carbon through X-ray diffraction and inductively coupled plasma analysis. Following the results, it can assume that rare earth oxide reuse the phosphor as three-wavelength fluorescent lamp.

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Prediction of yttrium, lanthanum, cerium, and neodymium leaching recovery from apatite concentrate using artificial neural networks

Cited by 21 publications

References 19 publications

Rare Earths in Phosphate: Characterization & Extraction

Rare Earths in Phosphate: Characterization & Extraction

Hybrid Serving of DOE and RNN-Based Methods to Optimize and Simulate a Copper Flotation Circuit

Study On The Separation And Extraction Of Rare-Earth Elements From The Phosphor Recovered From End Of Life Fluorescent Lamps

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