Introduction

Red mud is a byproduct of aluminum production operations from bauxite mineral induced by Bayer process. Significant amounts of alkaline waste materials due to caustic use in the process are generated and pose a potential environmental threat. In addition to aluminum escaping from the production process to these wastes, precious metals such as titanium and REEs may have economic value (Şayan and Bayramoǧlu 2001). In literature, considerable studies related to REEs recovery have been carried out from red mud (Borra et al. 2016b; Davris et al. 2016; Li et al. 2022; Ochsenkühn-Petropulu et al. 1995, 1996; Smirnov and Molchanova 1997; Rivera et al. 2018; Wang et al. 2021a, b). REEs are a group of 17 elements with a similar geochemical behavior consisting of the 15 lanthanides, yttrium (Y) and scandium (Sc) (Reinhardt et al. 2018). REEs are frequently classified in the groups of light REE (LREE: La, Ce, Pr, Nd, Sm, and Eu, with lower atomic numbers and masses) and heavy REE (HREE: Gd, Tb, Dy, Ho, Er, Tm, Yb and Lu, with higher atomic numbers and masses), while Sc and Y are traditionally unclassified (Henderson 1984).

Sequential extraction procedures have been frequently performed for the recovery and speciation of metals, including rare earths (Hass and Fine 2010; Pan et al. 2019; Çelebi and Öncel 2021; Wang et al. 2021b). Particularly, due to abundance of Sc in red mud materials among rare earths, most of studies have emphasized on this element (Wang et al. 2013a; Zhou et al. 2018; Zhu et al. 2020; Rychkov et al. 2021; Agrawal and Dhawan 2021; Lei et al. 2021). In these studies, hydrometallurgical and pyrometallurgical procedures including aggressive acid leaching, solvent extraction, sulfation, and roasting procedures have taken place for the extraction (Wang et al. 2013a; Yang et al. 2015; Borra et al. 2016b; Davris et al. 2016; Zhu et al. 2020; Agrawal and Dhawan 2021). These procedures mainly focus on recovery of metals. In addition, studies on the speciation of metals from red mud have frequently been carried out with various modification approaches (Ghosh et al. 2011; Milačič et al. 2012; Rubinos and Barral 2013). However, REEs speciation in red mud has been of limited interest, not comprehensive, and only the red mud produced in China has been investigated (Gu et al. 2018).

For the speciation, sequential extractions have been first proposed by Tessier et al. (1979) for the removal of trace elements selectively bound to functionally defined residue fractions. However, these procedures are not exactly specific, they may help to assess geochemical alterations and metal mobility in soils with changing environmental conditions (Pueyo et al. 2008). The modified version of sequential extraction has been subsequently proposed by the European Community Bureau of Reference (BCR). The three-step sequential extraction procedure has been frequently used as reliable and validated method to characterize contaminated soils and sediments, and wastes, e.g., mining, and municipal (Mossop and Davidson 2003; Cappuyns et al. 2007; Pueyo et al. 2008; Larios et al. 2013; Fernández-Ondoño et al. 2017; Alan and Kara 2019; Qureshi et al. 2020). In this method, metals are fractionated as acid-soluble/exchangeable, reducible, and oxidizable, helping to understand metal leachability.

The primary goal of this study is to determine metal fractionation in red mud waste material including REEs in terms of assessing the leachability of the metals. The secondary goal is to support chemical data with mineralogical data for the better understanding of fractionation. The third goal is to detect metals statistically in terms of speciation similarity. Finally, determination of REE anomalies in red mud is aimed. For these purposes, beside quantitative mineralogy procedures consisting of X-ray diffraction (XRD) followed by Rietveld refinement, and determination of pseudo total metal contents by microwave digestion followed by metal analysis, the modified BCR three-step sequential extraction procedure was implemented for the determination of exchangeable (F1), reducible (F2), oxidizable (F3), and residual (F4) fractions of red mud waste material derived from Seydişehir bauxites.

Materials and methods

Mining and sampling

Bauxite deposits are abundant and majorly distributed in Seydişehir—Akseki region, Southern Türkiye. In the region, economically significant non-metamorphosed bauxite deposits of Mortaş and Doğankuzu are available as the most important aluminum sources operated by ETİ Aluminum company (Uyanik et al. 2016; Hanilçi 2019). The deposits are of karst unconformity-type, associated with Mesozoic limestones situated unconformably between Cenomanian and Santonian shallow marine limestones formed at the crest of the Taurides Mountains more than 1500 m above sea level as north south oriented, with thickness varying from 1 to 40 m. Bauxites are observed as brown to red, massive, oolitic–pisolitic textured. The site-specific schists may be the bedrock of bauxites and have revealed an acidic source (majorly granite) containing hornblende and plagioclase minerals. The schists were structurally mature with minimal alkali feldspar deposits (Uyanik et al. 2016).

The aluminum in bauxite has been extracting by Bayer process, resulting the deposition of approximately 50 thousand tons of waste material named red mud to the waste pile annually. The flowchart of the Bayer process is given in Fig. 1.

Fig. 1
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Flowchart of Bayer process operated in the facility

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Superficial red mud material was sampled from five different points of waste pile of ETİ Aluminum Seydisehir Facility, Turkey, and subsequently each sample of approximately 1 kg was gathered in equal amounts to form composite red mud due to the high homogeneity of materials as a result of the Bayer process. After dehumidification (24 h, at 105 °C), composite red mud was crushed by Retsch BB2 followed by Retsch BB100 Mangan jaw crushers to reduce the grain size below 75 µm. Finally, the red mud sample was obtained for the sequential extraction procedure, and mineralogical and chemical analysis.

Chemical and mineralogical analysis

The chemical compositions of red mud and the pseudo total concentration of a metal (\({Pseudo total}_{m}\)) were determined by X-ray fluorescence (XRF) technique and microwave digestion followed by metal analysis including rare earths carried out by inductively coupled plasma–optical emission spectroscopy (ICP–OES), respectively. ICP–OES procedure has been proposed as a suitable alternative for rare earths determination in geological samples (Amaral et al. 2017). The microwave digestion was carried out in Milestone Start D giving a maximum energy of 650 W for 24 min using a mix solvent media made up of analytical grade orthophosphoric acid and nitric acid (6:1, v:v) (Sigma-Aldrich). The XRF was performed by Rigaku Primus ZSX IV. In addition, the XRD technique with was conducted by Bruker D8 Advance at room temperature with 40-kV operation voltage and CuKα radiation to determine mineralogy of red mud. In addition, quantitative mineralogy was determined by Rietveld refinement using PROFEX software (Doebelin and Kleeberg 2015).

Sequential extraction procedure

The modified BCR sequential extraction procedure was used in the metal fractionation of red mud. The original BCR procedure was performed for the steps of 1 to 3, while the microwave digestion used for determination of residual metal fraction (F4) and the pseudo total metal content. The sequential extraction procedure is outlined in Table 1.

Table 1 Procedure of sequential extraction based on BCR applied on red mud
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Three replicates were carried out to determine reproducibility of data. The fractionated total concentration of a metal (\({\text{Fractionated total}}_{m})\) was calculated by the following formula (Eq. 1). In addition, the recovery check procedure was carried out by comparing the total amount of metal extracted with the results of the pseudo total extraction. The percentage recovery of the sequential extraction method was calculated using Eq. 2:

$${{\text{Fractionated total}}}_{m}={\text{step}} 1 \left({\text{F1}}_{m}\right)+{\text{step}}2 \left(\text{F2}_{m}\right)+{\text{step}}3 \left(\text{F3}_{m}\right)+{\text{residue}} \left(\text{F4}_{m}\right),$$
(1)
$${\text{Recovery}} \,\left(\%\right)=\frac{{\text{Fractionated total}}_{m}}{{\text{Pseudo total}}_{m}}\times 100.$$
(2)

Multivariate analysis

Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable. In this context, hierarchical cluster analysis (HCA) and principal component analysis (PCA) were performed for better understanding of the fractional grouping of metals. The variables are the fractional relative distribution of metals as percentage, while the observations are REEs and non-REE metals. The metals were grouped in terms of Euclidean distances. On the other hand, correlation matrix was analyzed by extracting two components in PCA.

REE anomaly

The chondrite normalized (CN) REE patterns were obtained from BLambdaR software developed by Anenburg and Williams (2022) using the normalization method reported by O’Neill (2016). For the obtaining quantitative anomaly data, the anomaly scores of these REEs were calculated using the following equation (O’Neill 2016):

$$\text{a REE anomaly}=\frac{{[{\text{REE}}]}_{N, {\text{obs}}}}{[{{\text{REE}}]}_{N, {\text{calc}}}}$$
(3)

where [REE]N,obs and [REE]N,calc are the observed CN value of REE and the abundance of REE that should on a smooth CN REE pattern, respectively. If the anomaly score is less than 1, negative anomaly; If it is more than 1, positive anomaly is detected. On the other hand, the parameter of LaN/YbN (where N refers to a chondrite normalized value, see O’Neill (2016) is a quantitative indicator whether LREE (> 1.0) or HREE (< 1.0) dominance.

Results and discussion

Mineralogy and major oxides

In the scope of mineralogical identification, the XRD pattern of red mud was obtained and presented in Fig. 2.

Fig. 2
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XRD pattern of the red mud and the Miller indices (hkl) of the peaks

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From the pattern, hematite, sodalite, muscovite, gibbsite, diaspore, titanite, orthoclase, and calcite minerals were detected as major minerals in red mud, while perovskite, sodium–alum, rutile, boehmite, and bayerite were minor and accessory minerals. Furthermore, quantitative mineralogy results obtained for red mud sample by Rietveld refinement using Profex software are given in Table 2 with the results of quantitative major oxides determined by XRF technique. In addition, the statistical data of refinement parameters are given in Table 3.

Table 2 Major oxides and quantitative mineralogy of red mud
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Table 3 Statistics of Rietveld refinement
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The relatively low RSD% scores between the XRF and quantitative mineralogy prove the reliable analysis. Similarly, acceptable statistical scores were obtained from the Rietveld refinement. As a result, the amount of iron was mostly composed of hematite. However, the sources of aluminum content varied widely. Gibbsite and diaspore were the major mineral containing Al, while boehmite, bayerite, sodalite, muscovite, orthoclase, and sodium alum were minor. The primary titanium minerals detected were titanite, perovskite, and rutile, respectively. Calcium was principally detected as calcite, and as titanite and perovskite together with titanium. Muscovite and orthoclase majorly represented the presence of potassium in red mud. Sodalite and sodium alum minerals were detected in XRD presumably due to hot caustic addition leading to secondary mineralization in Bayer process. The remarkable content of S, Mg, Zr, Cr (0.09–0.30%) was also measured by XRF. The REEs were determined by microwave digestion carried out with the orthophosphoric and nitric acid mixture followed by ICP–OES procedure, presenting in the next sections.

Sequential and pseudo total extractions of non-REE metals

Speciation of major and minor metals in red mud were determined by the modified sequential BCR test. In addition, the pseudo total major and minor metal concentrations were obtained. The results table, and bar graphs including percentage relative distribution of fractionated non-REE metals are presented in Table 4, and Fig. 3, respectively.

Table 4 Fractionated and pseudo total non-REE metal concentrations of red mud
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Fig. 3
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Bar graph of non-REE metal fractions of red mud determined by modified BCR extraction

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The satisfactory recovery scores between 92.1% and 113.3%, and the RSD% scores ranging from 0.01 to 35.36 indicate the method reliability and reproducibility, respectively. The alkaline metals of K, Na, Ca, and Li have the highest ion exchangeable and readily soluble fractions, respectively, most likely due to high solubility of calcite in acetic acid leachate, dissolution of orthoclase, and soluble alkaline metal salts (Bevan and Savage 1989; Blinkova and Eliseev 2005). On the contrary, almost no exchangeable and readily soluble fraction (F1) of Fe, Ti, Cr, Pb, and Co metals was determined. Mg, Zn, and Ag have low exchangeable fraction (F1) with relative distribution from 1.7% to 3.8%.

Loosely bound (reducible) fractions (F2) of Al, Zn, Cu, Li, K, Ca, Mn, Na metals were majorly determined with the relative distribution (RD) scores ranging from 2.5 to 34.6. The highest reducible fraction (F2) was obtained for aluminum (RD% = 34.6) due to the presence and dissociation of Al hydroxide and oxyhydroxide minerals, e.g., gibbsite, diaspore, boehmite, and bayerite detected by XRD. Muscovite contribution to the reducible K fraction is not considered due to its high resistance to weak acidic media (Lottermoser 2010). Hence, the reducible K fraction (F2) is possibly originated from orthoclase and potential potassium hydroxide dissolutions. The presence of accessory K and Li hydroxides in caustic solution used in Bayer process may lead to reducible K and Li fraction availability. Due to the use of caustic solution, the reducible Na fraction (F2) was also determined notably (RD% = 5.8). On the other hand, the remarkable reducible fractions (F2) of Zn, Cu, and Mn were obtained with the RD scores of 27.2%, 14.6%, and 4.7%, respectively.

Sulfides can be found in some bauxite rocks (Hu et al. 2011; Chai et al. 2018). In a study, the presence of pyrite and marcasite have been reported as accessory minerals in Seydişehir bauxites (Muzaffer Karadaǧ et al. 2009). In the red mud, sulfide content is also predicted to be scarce due to the detection of sulfate presence in the sodium alum mineral by mineralogical analysis as the major sulfur source. More comprehensive studies are needed for sulfide determination (Çelebi and Ribeiro 2023). Therefore, the scientific assessment for the oxidizable fraction data of Mg, Ni, Li, Cu, and Zn is quite difficult only from XRD results due to their undetectably low concentrations. On the other hand, acid ammonium acetate leaching is thought to be responsible for the presence of oxidizable calcium fraction (F3) (Wada and Furumura 1994).

The highest residual fractions (F4) were determined for Fe, Ti, Cr, Pb, and Co with the RD scores above 99.3%. As examining the XRF, pseudo total digestion, and mineralogical data, it is understood that almost all iron content refers to hematite. The resistance of hematite against the leachates used in BCR sequential extraction supports that almost all iron is in residual fraction (F4). Almost all titanium content in red mud was determined as residual fraction included in titanite, perovskite, and rutile minerals. The great amount of aluminum oxyhydroxide and hydroxide minerals were obtained by quantitative mineralogy as approximately 24.8% totally. Hence, the residual Al fraction could be underestimated due to possible inadequate leachant concentration of NH2OH.HCl 0.1 M + HNO3 0.15 M mixture used in the determination of reducible Al content (Golim et al. 2018). Nevertheless, it is understood that aluminum in the red mud is distributed in two main fractions, reducible (F2) and residual (F4). The residual fraction (F4) of Ca is most likely related to calcium-bearing titanium oxides of titanite and perovskite. Potassium was almost not detected in the residual fraction (F4) due potentially to its high cation exchange capacity in muscovite and orthoclase (Meng et al. 2015; Xiu-ting et al. 2021) The activity of sodalite and sodium alum minerals may be lower in BCR leachates potentially leading to remarkable residual Na fraction (F4) with the RD score of 34.2%. The reliable evaluations for the residual fraction (F4) data of Mg, Ni, Li, Cu, and Zn are also quite difficult due to their undetectably low concentrations by XRD technique.

Sequential and pseudo total extractions of REEs

Pseudo total concentrations and speciation of REEs in red mud were determined by the microwave digestion followed by metal analysis and the modified sequential BCR test, respectively. The results table, and bar graphs including percentage relative distribution of fractionated REEs are presented in Table 5, and Fig. 4, respectively.

Table 5 Fractionated and pseudo total REEs concentrations of red mud
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Fig. 4
figure 4

Bar graph of rare earth metal fractions of red mud determined by BCR extraction

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The recovery scores ranging from 97.8% to 106.8% and their low RSD% scores reveal the high method reliability and repeatability. In the red mud, most of the REEs are composed of LREEs with 73.3%, while the sum of Sc and Y, and HREEs are 18.0% and 8.7%, respectively. Hence, the LREE to HREE ratio was determined as 8.42 showing that the content of LREEs is significantly higher than that of HREEs. The oxidizable fraction (F3) of HREEs with 3.9% were determined higher than the LREEs, while the reducible fraction (F2) of LREEs with 18.3% were higher.

The speciation of REEs can also give an idea about their mobility. With this approach, the order of REEs with the highest potential mobility is predicted as Eu > La > Gd > Nd > Y > Sm > Pr > Ho > Dy > Ce > Yb > Er > Tb > Lu > Sc, sorted by sum of their non-residual fractions from smallest to largest. Except Eu, majorly detection of the REEs in the residual fraction with the minimum score of 67.7% proves that uses of aggressive leaching agents are needed for the recovery of REEs from red mud. Particularly, Sc and Lu were almost entirely detected as the residual fraction with 99.8% and 98.5%, respectively. On the other hand, the ion exchangeable fraction of Eu, Gd, and La were determined as the highest (3.91–1.35%). The highest non-residual fractions (sum of F1, F2, and F3) beside Eu were obtained for La, Gd, and Nd with the RD scores of 32.3%, 26.3%, and 24.9%, respectively. Thus, approximately 30% of these REEs were proven to be extracted from the red mud by relatively less strong leaching agents. The highest oxidizable fractions (F3) were obtained for Y, Gd, and Eu with scores of 8.5%, 7.0%, and 6.6%, respectively. Thulium (Tm) was not detected in the red mud by the procedure.

In the previous studies carried out for the leaching of REEs from red mud, with the use of nitric, sulfuric, hydrochloric, and organic acids leachants, e.g., oxalic, acetic, and citric acid, the high and satisfactory leaching efficiencies have not been achieved except for Ce and La (Abhilash et al. 2014; Borra et al. 2015, 2016a; Li et al. 2022; Liu and Li 2015; Rivera et al. 2018; Shoppert et al. 2022). The use of orthophosphoric acid has not been encountered in the literature to dissociate REEs. However, high recovery scores obtained in this study reveals that orthophosphoric acid usage together with nitric acid in the hydrometallurgical studies on extracting REEs could be a better leachant agent option.

When the economic potential of REEs in the red mud is examined, high-value REEs such as Eu, Tb, and Dy were detected at relatively low concentrations ranging from 1.46 to 28.64 ppm. The most abundant REEs in red mud were identified as Ce, La, Nd, Y, Pr, and Sc, among which the metals Nd and Pr, with concentrations of approximately 90–100 ppm, are considered as a potential mining target with current values of around 70,000 USD per metric ton (evaluated according to Shanghai Metals Market, September 2023).

Statistical assessment of metal fractionation

The visual outputs of HCA and PCA indicating the fractional grouping of non-REE elements and meaningful cumulative REE parameters (∑REE, ∑HREE, ∑LREE, ∑Sc, Y) are presented in Fig. 5.

Fig. 5
figure 5

HCA (left) and PCA (right) scores plot of the fractionated non-REE elements and meaningful cumulative REE parameters in red mud. The ellipses were predicted with the confidence level of 90%

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The PCA plot data and eigenvalues of the correlation matrix of fractionated non-REE metals and meaningful cumulative REE parameters are given in Tables S.1 and S.2, respectively. When the HCA and PCA analysis were examined together, a five-group clustering was obtained. The elements that are most abundant in the residual fraction (F4), in short, the elements most difficult to extract are determined inside the purple ellipse as Fe, Ti, Pb, Cr, Co, Ni, Cd, Ag, Mn, Mg, ∑HREE and ∑Sc, Y (1st group), while the most easily liberated elements are K, Ca, and Na (2nd group) contained mostly in ion-exchangeable and reducible fractions (F1, F2). The third most liberation potential were determined for the blue ellipse, including Li, Cu, ∑REE, ∑LREE, followed by the elements located in green ellipses, Al and Zn (5th group). The visual outputs of HCA and PCA evaluating the REEs among themselves are presented in Fig. 6.

Fig. 6
figure 6

HCA (left) and PCA (right) scores plot of the fractionated REEs with meaningful REE parameters in red mud. The ellipses were predicted with the confidence level of 90%

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The PCA plot data and eigenvalues of the correlation matrix of fractionated REEs are given in Tables S3 and S4, respectively. The REE fractionation in red mud was defined under a six-group clustering. The elements that are mostly available in the residual fraction (F4) are determined inside the green ellipse as Lu, Sc, and Tb (1st group), while the most easily liberated element is Eu containing mostly ion-exchangeable and reducible fractions (F1, F2), verifying the possible cause of negative Eu anomaly (2nd group), followed by La (3rd group), Nd and Gd (4th group). The 5th and 6th groups mostly included in residual, reducible and oxidizable fractions (F2, F3 and F4) are the purple ellipse, including Er, Yb, ∑HREE, ∑Sc, Y, Ho, and Dy, and the brownish ellipse, including Y, ∑LREE, ∑REE, Sm, Pr, and Ce, respectively. As the HCA graph goes from right to left on the x-axis, the presence of metals in the residual fraction (F4) increases.

Assessment of REE anomalies

The chondrite normalized (CN) REE patterns of Mortaş bauxites and the red mud are presented in Fig. 7 to demonstrate the changes in REE anomalies as a result of the Al production process.

Fig. 7
figure 7

Chondrite normalized REE patterns of Mortaş bauxite (left, quoted from Uyanik et al. (2016)) and its processed waste, red mud (right)

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Both CN patterns reveal the LREE enrichment and the negative Ce, Eu, and Gd anomalies. For the red mud, the negative anomaly for Ce, Eu and Gd were quantified by BLambdaR as 0.80, 0.04, and 0.11, respectively, while for the bauxite ore were 0.84, 0.61, and 0.81, respectively. Hence, REE anomaly scores of the red mud and its origin, Mortaş bauxite ore, were quite different, indicating the liberation of these REEs during the process. On the other hand, the negative slope geometry for both patterns (observed in Fig. 7) quantified by the LaN/YbN scores of 7.64 and 7.82 is another indication of the relative enrichment of LREE to HREE.

Most REEs exist in the trivalent state. However, Europium can exist as divalent state, particularly under alkaline conditions, potentially proxy for calcium in plagioclase feldspar during igneous fractionation (Bau 1991; Allaby 2020). Moreover, Eu may also be enriched in carbonates, e.g., calcite mineral (Stipp et al. 2006; Navarro and Cardellach 2009). However, no Eu detected in Mortaş limestones has been reported, revealing non substitution between Eu and Ca in carbonated rocks (Temur et al. 2009). Therefore, the caustic usage in Bayer process may have reduced Eu (III) to Eu (II) under optimum redox condition and subsequently bond only to feldspar group leading to mobilization of Eu (II) from the process and waste pile. Hence, it is predicted that the strong negative Eu anomaly (0.04) is obtained for red mud.

As a result of the sequential extraction, almost all Eu was able to liberate in the first three fractions, where relatively less aggressive leaching agents were used potentially due to the presence of Eu (II). In a site-specific study, the negative Eu anomaly (< 1.0) has also been reported and explained by the Eu anomaly scores of 0.62 and 0.70 for Mortas bauxites and the bedrock Seydişehir schists, respectively, indicating a source with plagioclase and hornblende (Uyanik et al. 2016). Although orthoclase (potassium feldspar) from the feldspar group was identified by XRD technique, the presence of Eu in non-residual fractions (F1, F2, and F3) may indicate the rarely presence of Ca-plagioclase. The remarkable difference between the Eu anomaly score of red mud (0.04) and its origin, Mortaş bauxite (0.62) may correlate the mobilization strength of Eu in the first three step of sequential extraction.

The only member of the REE group in a tetravalent oxidation state is cerium under relatively low-temperature, highly oxidizing, and alkaline pH conditions. Ce (IV) is more geochemically stable than Ce (III). Cerium acts disparately from other REEs during chemical weathering processes. Both positive and negative Ce anomalies may be encountered due to the influence of redox processes (Wang et al. 2013b). In red mud, the negative anomalies of Ce and Gd with the score of 0.80 and 0.11, respectively, indicate the depletion of Ce and Gd potentially due to the Al production processes or geochemical processes taken place in waste pile, as well as the weathering of bauxite ore entering the extraction process. In a study, Ce and Gd anomalies have only been reported for Mortaş bauxites with the scores of 0.84 and 0.81, respectively, not for Seydişehir schists (Uyanik et al. 2016). This may indicate that the red mud was majorly originated from the mining of Mortaş bauxites. Considering the negligible difference between the negative anomalies of Ce in red mud (0.80) and Ce in Mortaş bauxites (0.84), a small quantity of Ce may have been mobilized in reducing conditions due to the scarcity of atmospheric oxygen in deeper layers of waste pile. Furthermore, the detection of remarkable reducible fraction of Ce (11.4%) in red mud shows the potential Ce (IV) reduction to Ce (III), and subsequently its presumptive liberation. On the other hand, the geochemically stable Ce (IV) is predicted to be contained in the residual fraction (F4) as cerianite precipitates (CeO2), particularly in lateritic profiles. Briefly, the redox condition of Ce in the red mud determines the potential distribution of Ce precipitates mostly as either cerianite (CeO2) or Ce carbonates, effecting the Ce anomaly whether positive or negative (Wang et al. 2013b).

Conclusions

A three-step sequential extraction was carried out by modified BCR approach for fractionation of metals, including rare earths in red mud. Furthermore, rare earth anomalies in red mud were determined, and assessed considering its origin, bauxite ore. The results and suggestions may be summarized as follows:

  • The major mineralogy of red mud is constituted by hematite, sodalite, muscovite, gibbsite, diaspore, titanite, orthoclase, and calcite, while the minors are perovskite, sodium–alum, rutile, boehmite, and bayerite.

  • In the red mud, iron content of approximately 25% is almost all represented by hematite, while Al with the highest reducible fraction (34.6%) exists as oxyhydroxide and hydroxide forms, mostly as gibbsite (γ-Al(OH)3) and diaspore (α-AlO(OH)).

  • The higher distribution to ion-exchangeable fraction was obtained for Ca, Na, and K potentially due to the presence of readily soluble calcite, and Na–K hydroxides originated from Bayer process. Almost all Fe, Ti, Cr, Ni, Mn, Pb, Co, and Ag were determined in the residual fraction.

  • Significant liberation of reducible Cd and Cu fractions has the potential to cause toxicity.

  • For red mud, LREE to HREE and LaN/YbN scores of 8.42 and 7.82, respectively, indicating the negative slope geometry prove the enrichment of LREE to HREE.

  • When the natural weathering process was accelerated by sequential extraction, almost all of the Eu metal (92.7%) was mobilized in the ion exchangeable and reducible phase. Negative Eu anomalies detected in red mud and its origin, Mortaş bauxite ore (0.04 and 0.62, respectively) may correlate this mobilization potential. The significant Eu anomaly difference between these two materials also showed that Eu was mostly depleted in red mud, similarly for Gd (a decrease from 0.81 to 0.11).

  • Eu could have been mobilized as Eu2+ under reducing conditions, as well as Ce3+ mobilization leading to also negative Ce anomaly (0.80 for red mud). However, the Ce mobilization is thought to be limited understood by the negligible difference between the Ce anomaly score of red mud (0.80) and its origin, Mortaş bauxite (0.84).

  • The REEs except for Eu were mostly placed in the residual fraction with the scores above 67.7%. To extract the REEs, the necessity of aggressive leachant usage was proven againward. In this context, a mixture of orthophosphoric and nitric acid is thought to be a good alternative.

  • Six- and five-group clusters were obtained statistically for REEs and non-REE metals, respectively, in terms of fractional distribution. The easier release potential was predicted for Eu, La, Gd, K, Na, and Ca.

  • For future hydrometallurgical studies, it is recommended to determine the occurrence mode and geochemical behavior of REEs on a site-specific basis, with a particular focus on relatively abundant and high value Nd and Pr.