Mobility of  232 Th and  210 Po in red mud

Hegedűs, Miklós; Tóth-Bodrogi, Edit; Jónás, Jácint; Somlai, J.; Kovács, Tibor

doi:10.1016/j.jenvrad.2018.01.012

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…The reported concentration of natural radionuclides in BR produced in different countries is shown in (Hegedűs et al, 2018). In a different study, the radiological properties of post-processed BRs by sintering, smelting and acid leaching (for the recovery of Al, Fe and REE, respectively) were also examined; the sintered and the leached sample reported a concentration of radionuclides < 700 Bq kg -1 , while the slag from the smelting process demonstrated a possibility of having concentration higher than 1000 Bq kg -1 .…”

Section: Radionuclide Concentration In Different Bauxite Residuesmentioning

confidence: 99%

Radiological assessment of bauxite residue processing to enable zero-waste valorisation and regulatory compliance

Goronovski

Rivera

Gerven

et al. 2021

Journal of Cleaner Production

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Section: Radionuclide Concentration In Different Bauxite Residuesmentioning

confidence: 99%

Radiological assessment of bauxite residue processing to enable zero-waste valorisation and regulatory compliance

Goronovski

Rivera

Gerven

et al. 2021

Journal of Cleaner Production

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“…The half-life of 228 Ac, the daughter of 228 Ra, is 6.13 h. According to the literature, Th and Ra both have affinity for silicates [37]. Moreover, some studies have shown that the activities of 232 Th and 228 Ra in solid wastes such as red mud, phosphogypsum, and fly ash are approximately balanced [38][39][40]. Therefore, the equilibrium between 232 Th and 228 Ra was assumed, and the 232 Th was determined using 208 Tl (583.2 keV (30.78%) and 2614.5 keV (35.7%)) and 228 Ac (911.2 keV (26.6%)).…”

Section: Radioactivity Measurementmentioning

confidence: 99%

Radioactivity of Five Typical General Industrial Solid Wastes and its Influence in Solid Waste Recycling

Zhang

Cheng

et al. 2019

Minerals

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The level of radionuclides is an important index for the preparation of building materials from industrial solid waste. In order to investigate the radiological hazard of five kinds of typical general industrial solid wastes in Guizhou, China, including fly ash (FA), red mud (RM), phosphorus slag (PS), phosphogypsum (PG), and electrolytic manganese residue (EMR), the radiation intensity and associated radiological impact were studied. The results show that concentrations of 238U, 235U, 232Th, 226Ra, 210Pb, and 40K for different samples vary widely. The concentration of 238U was both positively correlated with 235U and 226Ra, and the uranium contents in the measured samples were all of natural origin. The radiation levels of PG, EMR, EMR-Na (EMR activated by NaOH), and EMR-Ca (EMR activated by Ca(OH)2) were all lower than the Chinese and the world’s recommended highest levels for materials allowed to be directly used as building materials. The values of the internal and external illumination index (IRa and Iγ, respectively) for FA and RM were higher (IRa > 1.0 and Iγ > 1.3 for FA, IRa > 2.0 and Iγ > 2.0 for RM). The radium equivalent activity (Raeq), indoor and outdoor absorbed dose (Din and Dout, respectively), and corresponding annual effective dose rate (Ein and Eout) of RM, PS, and FA were higher than the recommended limit values (i.e., 370 Bq/kg, 84 nGy/h, 59 nGy/h, 0.4 mSv/y, and 0.07 mSv/y, respectively), resulting from the higher relative contribution of 226Ra and 232Th. The portion of RM, FA, and PS in building materials should be less than 75.44%, 29.72%, and 66.01%, respectively. This study provides quantitative analysis for the safe utilization of FA, RM, PS, PG, and EMR in Guizhou building materials.

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“…On average, 1–1.5 tons of RM are produced for every ton of alumina produced [ 1 , 2 ]. The abandoned RM not only occupies a large area of land but also contains thorium, potassium, and other radioactive elements [ 3 , 4 ], which are hazardous solid wastes. In addition, the RM is an alkaline material and the alkali dissolves in the rainwater and contaminates the surface water and groundwater, causing severe environmental pollution [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Formulation Optimization and Performance Prediction of Red Mud Particle Adsorbents Based on Neural Networks

Li,

Wang,

Wang

2024

Molecules

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Red mud (RM), a bauxite residue, contains hazardous radioactive wastes and alkaline material and poses severe surface water and groundwater contamination risks, necessitating recycling. Pretreated RM can be used to make adsorbents for water treatment. However, its performance is affected by many factors, resulting in a nonlinear correlation and coupling relationship. This study aimed to identify the best formula for an RM adsorbent using a mathematical model that examines the relationship between 11 formulation types (e.g., pore-assisting agent, component modifier, and external binder) and 9 properties (e.g., specific surface area, wetting angle, and Zeta potential). This model was built using a back-propagation neural network (BP) based on single-factor experimental data and orthogonal experimental data. The model trained and predicted the established network structure to obtain the optimal adsorbent formula. The RM particle adsorbents had a pH of 10.16, specific surface area (BET) of 48.92 m2·g−1, pore volume of 2.10 cm3·g−1, compressive strength (ST) of 1.12 KPa, and 24 h immersion pulverization rate (ηm) of 3.72%. In the removal of total phosphorus in flotation tailings backwater, it exhibited a good adsorption capacity (Q) and total phosphorous removal rate (η) of 48.63 mg·g−1 and 95.13%, respectively.

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Mobility of 232 Th and 210 Po in red mud

Cited by 11 publications

References 21 publications

Radiological assessment of bauxite residue processing to enable zero-waste valorisation and regulatory compliance

Radiological assessment of bauxite residue processing to enable zero-waste valorisation and regulatory compliance

Radioactivity of Five Typical General Industrial Solid Wastes and its Influence in Solid Waste Recycling

Formulation Optimization and Performance Prediction of Red Mud Particle Adsorbents Based on Neural Networks

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