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Journal of Hazardous Materials 275 (2014) 136–145

Contents lists available at ScienceDirect

Journal of Hazardous Materials

jo ur nal ho me p ag e: www.elsev ier .com/ locate / jhazmat

xperimental design and optimization of leaching process forecovery of valuable chemical elements (U, La, V, Mo, Yb and Th)rom low-grade uranium ore

razyna Zakrzewska-Koltuniewicza,∗, Irena Herdzik-Konieckoa, Corneliu Cojocarub,welina Chajduka

Institute of Nuclear Chemistry and Technology, Dorodna 16, 03-195 Warsaw, PolandInstitute of Macromolecular Chemistry “Petru Poni”, Aleea Grigore Ghica Voda, nr. 41A, 700487 Iasi, Romania

i g h l i g h t s

The experimental design for optimization of leaching process of uranium from low-grade ores was applied.Multi-objective optimization method based on desirability approach was employed.The recovery of associated metals like vanadium, molybdenum and lanthanides was considered.The effects of factors were identified by 3-D surface plots.The optimum condition for valuable metals: P = 5 bar, T = 120 ◦C and t = 90 min has been determined.

r t i c l e i n f o

rticle history:eceived 21 February 2014eceived in revised form 29 April 2014ccepted 30 April 2014vailable online 9 May 2014

a b s t r a c t

The paper deals with experimental design and optimization of leaching process of uranium and associatedmetals from low-grade, Polish ores. The chemical elements of interest for extraction from the ore were U,La, V, Mo, Yb and Th. Sulphuric acid has been used as leaching reagent. Based on the design of experimentsthe second-order regression models have been constructed to approximate the leaching efficiency of

eywords:raniumeaching processranium ore

elements. The graphical illustrations using 3-D surface plots have been employed in order to identify themain, quadratic and interaction effects of the factors. The multi-objective optimization method based ondesirability approach has been applied in this study. The optimum condition have been determined asP = 5 bar, T = 120 ◦C and t = 90 min. Under these optimal conditions, the overall extraction performance is81.43% (for U), 64.24% (for La), 98.38% (for V), 43.69% (for Yb) and 76.89% (for Mo) and 97.00% (for Th).

esponse surface methodology

. Introduction

Uranium has the main use in the civilian sector as fuel for mostf currently operating nuclear power reactors. In nature, uraniumxists in three isotopic forms: 238U (about 99.3%), 234U (about 0,06%) and fissile 235U (about 0.7%). Either natural uranium or ura-ium enriched in 235U isotope is used as fuel in nuclear reactors.efore it becomes fuel for power plants, uranium ore has to gohrough several processing steps. These include crushing, grinding,

eaching, concentration and purification by solvent extraction andon exchange, to take the form of a pure U3O8 oxide. The leachingteps that can employ either alkaline or acidic reagents [1–3] yield

∗ Corresponding author. Tel.: +48 0225041214.E-mail addresses: g.zakrzewska@ichtj.waw.pl, graz.zakrzewska@gmail.com

G. Zakrzewska-Koltuniewicz).

ttp://dx.doi.org/10.1016/j.jhazmat.2014.04.066304-3894/© 2014 Elsevier B.V. All rights reserved.

© 2014 Elsevier B.V. All rights reserved.

uranium-pregnant liquors, which contain various metals that arepresent in the raw material. The recovery of associated rare metalsmay improve the profitability of the whole project of uranium pro-duction from the ores.

According to OECD-NEA Red Book (2012) total identified ura-nium resources are 5,327,200 tonnes of uranium metal (tU) in the<USD 130/kg U U3O8 category, and in the highest cost category(<USD 260/kg U U3O8) total identified resources are 7,096,600 tU.Total undiscovered resources (prognosticated resources and spec-ulative resources) as of 1 January 2011 amounted to 10,429,100 tU[4]. These resources are sufficient for roughly 230 years supply attoday’s consumption rate in total. It is predicted that further explo-ration and improvements in extraction technology could at least

double this estimate over time. The same Red Book introduces iden-tified uranium resources in Poland as about 7270 T and speculativeeven 100,000 T. Table 1 shows main uranium resources in Poland.Most of them are the black shale and sandstone type deposits. They

G. Zakrzewska-Koltuniewicz et al. / Journal of Hazardous Materials 275 (2014) 136–145 137

Table 1Polish uranium ore resources according to OECD NEA Red Book, 2012.

Region IdentifiedUnat [ton]

Prognostic and speculative Unat [ton] Uranium concentration in ore[ppm]

Deposit type

Rajsk (Podlasie Depression) 5320 88,850 250 Black shaleOkrzeszyn (Sudetes) 940 – 500–1100 OtherGrzmiaca (Sudetes) 790 – 500 Sandstone tabularWambierzyce (Sudetes) 220 2000 236 Black shale

at

omeisbOplauetmv

eroDnurc[aa

tmfnstad

mmsia

mtmlpoMoD

Peribaltic Syneclise – 20,000

Grand total 7270 tU 100,000 tU

re low-content resources and some of them are situated deeperhan 1000 m.

In Polish deposits uranium is usually associated with severalther metals such as Th, V, Mo, Cu, Co, Zn, Pb, and rare earth ele-ents that could be recovered at the same time to improve the

conomy of the processing low-grade ores. There are some casesn the world metallurgy of non-ferrous metals when valuable con-tituents are recovered as a by-product from the extraction of otherasic ore component. One of the worldwide largest copper mine,lympic Dam in Australia, recovers copper, gold and silver in theroduction of uranium. Resources of the mine include 146 mil-

ion tons of ore containing 1.98% Cu, 0.58 kg/t U3O8, 0.69 g/t Aund 4.01 g/t Ag. In 2011, 194,100 tons of mined copper, 4,045 t ofranium oxide, 111,368 oz of gold and 982,000 oz of silver werexcavated [5]. Extensive works are carried out in world labora-ories to elaborate the effective methods of recovery of various

etals from the ores and industrial waste, including molybdenum,anadium and rare earth elements [6–9].

Polish uranium ores have not been studied in terms of the recov-ry of associated metals yet. There are known research works on theecovery of non-ferrous metals from the copper industry. It is rec-gnized that in Polish copper deposits in Legnica - Glogow Copperistrict precious metals such as silver, zinc, nickel, cobalt, molybde-um, rhenium, vanadium, tungsten and other elements includingranium and rare earth metals occur. There are studies concerningecovery of Cu, Ni and Co from Lubin middlings (tailings from 1stleaning at Lubin Concentrator) and copper concentrates reported10,11]. An attempt to separate rare earth metals from uraniumnd other elements in leach solutions by ion exchange was takent Institute of Nuclear Chemistry and Technology (INCT) [12].

The production of rare earth elements is growing, even thoughhe quantities consumed are relatively small in comparison to other

etals. Lanthanide elements and their compounds have alreadyound many industrial applications. Lanthanum compounds haveumerous uses as catalysts, additives in glass, carbon lighting fortudio lighting and projection, ignition elements in lighters andorches, electron cathodes, and others. Ytterbium is used as andditive in stainless steels and optical materials; it is a prospectiveopant for efficient lasers, specifically solid state lasers.

Molybdenum is often employed in high-strength steel alloys foranufacturing armour, aircraft parts, electrical contacts, industrialotors and filaments. Vanadium is mainly used to produce special

teel alloys such as the high speed tool steels. The most importantndustrial vanadium compound, vanadium pentoxide, is applied as

catalyst for the production of sulphuric acid.Mostly, these metals are present in naturally occurring raw

aterials in small quantities and therefore, their extraction fromhe ores is rather difficult. To extract effectively the rare, valuable

etals together with uranium from raw materials, the research ofeaching process is important. Likewise, the design of experiments,rocess modelling and optimization are useful for further devel-

pment of uranium technology and design of processing schemes.athematical models play an important role for predicting and

ptimization of the behaviour of complex processes and systems.esign of experiments combined with multivariate statistical

up 15,000 Sandstone tabular

modelling, and optimization is a powerful tool of applied mathe-matics for exploring and studying the real problems in science andengineering. To date, empirical modelling has been successfullyapplied to solve various problems in environmental engineeringsuch as removal of heavy metals from waters [13–16], radionuclideadsorption [17,18] and mineral processing [19–22]. Some of thesereferences dealing with leaching of chemical elements are detailedin the following.

The purpose of this work is to optimize the recovery (leachingefficiency) of valuable chemical elements (U, La, Mo, V, Yb) fromlow-grade uranium ore from Polish deposits using experimentaldesign and desirability function approach. The work refers to thePolish Nuclear Power Program [23] and the necessity of evaluationof feasibility of the technology for obtaining uranium oxide for pro-duction of nuclear fuel from Polish uranium ores. Presented work isa continuation of studies on the obtaining uranium from resourcesin Poland. The leaching experiments with different Polish ores anddiverse leaching agents were described in reference [3] where orecharacteristics and applied process conditions were specified. Thepresent work is focused on process optimization, which allowsdeeper understanding of the influence of specific parameters on theleaching process and their mutual interactions. Results of previousworks were the basis for the planning of experiments, selection ofprocess variables and the scope of their variability.

2. Experimental: leaching in the autoclave

2.1. Materials

The sample of dictyonema shale collected from Polish Geologi-cal Institute – National Research Institute characterized chemicallyand geochemically was used in experiments. Sulphuric acid, H2SO4of analytical grade (Sigma–Aldrich) was applied as a leaching agent,and manganese dioxide MnO2, as an oxidizing agent. For prepara-tion of the ore samples for ICP-MS and XRF analyses analytical gradereagents: Na2O2, HNO3, Li2B4O7, LiBO2 were used.

2.2. Methods

Leaching of the uranium ore was done in the autoclave,with good efficiency. This apparatus gives the possibility ofadjustment of factors in a wide range, thus enables to carryexperiments with the aim of process optimization [24–27]. Thesample of uranium ore taken from Rajsk Deposit (dictyone-mic brown shale; sample notation – 3227 Rajsk Deposit JG-1)was used in leaching experiments. The average concentration ofmetals in the sample from this borehole was evaluated by ICP-MSmethod as: U (120.91 ppm), V (1486.08 ppm), Mo (296.20 ppm), Cu(263.11 ppm), Zn (5927.00 ppm), Ni (262.20 ppm), La (41.00 ppm),Th (16.22 ppm), Yb (3.90 ppm) and Co (53.35 ppm). The ICP-MSinstrument, ELAN DRC II PerkinElmerTM, with a cross-flow nebu-

lizer and with a Scott double-pass spray chamber and Ni cones wasused in measurements. Standard solutions (1 mg/mL) used in ICP-MS analyses were supplied by PerkinElmer. The following certifiedmaterials were applied in the analysis: Soil 5 (International Atomic

1 al of H

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toiuecnimTcne

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38 G. Zakrzewska-Koltuniewicz et al. / Journ

nergy Agency – IAEA, Vienna), Lake Sediment SL-1 (IAEA, Vienna),nd Apatite Concentrate CTA-AC1 (Institute of Nuclear Chemistrynd Technology – INCT, Warsaw). The uncertainty for elementaletermination by this method was evaluated to be 5–20% depend-

ng on the element.Representative ore sample collection by the quartering method

as done. For analysis, 0.5 g samples were weighed and placed in zirconium crucible; 2 g of Na2O2 was added and carefully mixed.he fusion was carried out in a muffle furnace at 550 ◦C. The sinteras dissolved in water, then 25 mL of 5 M HNO3 was added, and

he mixture was heated at temperature of about 80 ◦C to obtainlear solution. Then, the solution was transferred to the volumetricask and adjusted to 250 mL with water. The obtained solution wasiluted with 0.7% HNO3, and it was added as an internal standardrior to analysis. All samples for analysis were taken in triplicate.

The basic constituents of the ore sample were analysed by XRFethod (Philips WD-XRF PW 2004TM) after melting of the sampleith the flux (1 g sample + 5 g 66%Li2B4O7 + 34%LiBO2). The basic

omposition of dictyonema shale is reported in [3].Before extraction process, uranium ore was crushed and grinded

n the mill to a suitable range of particle size. Then, each fraction waseated in the muffle furnace, CZYLOK FCF 12SHMTM, at temperaturef 550 ◦C for 4 h in order to get rid of organic contaminants. Theffect of ore grinding and the influence of particle size was tested in.1–1.0 mm particle size range. For further experiments, the samplef 0.1 mm particle size was used.

The leaching experiments were performed in the laboratoryigh-pressure reactor (Berghof BTC-3000 V17TM), which is a stain-

ess steel autoclave with Teflon liners as reaction vessels, internalolume of 300 mL, with electric heating and magnetic stirreradjusted at frequency of 650 rpm for leaching experiments).

During leaching process the maintenance of appropriate oxida-ion conditions is very important factor for obtaining high recoveryf uranium, as well as for leaching other associate metals. Thiss essential when uranium occurs as U(IV), in the ores reach inraninite mineral, like in the case described in this paper. Differ-nt oxidants like manganese dioxide, hydrogen peroxide or sodiumhlorate are used to convert U(IV) to U(VI) form. However, U(IV) isot directly oxidized to U(VI) by these oxidants. They oxidize Fe2+

ons present in the solution and originated from associated ironinerals to Fe3+ form, which subsequently oxidizes U(IV) to U(VI).

o convert ferrous ions into ferric form, the Eh potential should bea. 500 mV. Moreover, to avoid the precipitation of insoluble ura-ium forms the pH value should be kept close to 1, and usually thexcess of acid is used in the leaching process.

The acid leaching conditions were as follow: weight of the sam-le of 1 g sufficient for leaching experiments; quantity of sulphuriccid of 18 ml and concentration of sulphuric acid (10% H2SO4) weredjusted on the basis of the results of the previous laboratory testsith the mechanically agitated vessel. The oxidizing agent, man-

anese dioxide (MnO2) to oxidize all uranium to U(VI) form, wasdded. The amount of MnO2 added was 2% of total mass of the oreample. The post-leaching solution was separated from the leachedre by filtering on the Buchner’s funnel and subsequently washedith distilled water. The pregnant liquor was analysed for uranium

nd other elements of interest by the ICP-MS method, to estimatehe leaching efficiency.

All the chemicals used in experiments were of analytical reagentrade. The aqueous phase, 10% H2SO4, was prepared by dissolvingulphuric acid (Sigma–Aldrich) in distilled water.

Percent of recovery or leaching efficiency (Y) of chemical ele-ents was determined according to the following relationship:

q =(

mq

moq

)· 100%, (1)

azardous Materials 275 (2014) 136–145

where Yq – efficiency of leaching, mq – the total mass of the metalrecovered in post-leaching solution, mo

q – the total mass of themetal in the ore sample, and q – the index for the chemical element(metal).

This Eq. (1) can be converted to the form expressed by concen-trations:

Yq = cq (mg/L) · 0.018 (L)cq (mg/kg) · 0.001 (kg)

· 100% (2)

After transformation:

Yq = 18 · cq

coq

· 100% (3)

where cq – concentration of metal in the sample collected in theexperiment, in ppm (mg/L) and co

q – concentration of the elementin the solid phase in ppm (mg/kg).

3. Results and discussions

The decision variables taken into account for modelling of leach-ing process involve the following factors: pressure in the autoclaveP (bar), temperature in the autoclave T (◦C) and time of leaching t(min). For modelling purpose, the actual values of decision variableshave been coded to dimensionless levels as presented in Table 2.As one can see from Table 1, the region of experimentation (exper-imental domain) is defined by the following boundary constraints:

2.57 ≤ P ≤ 7.43 (bar); 71.4 ≤ T ≤ 168.6 (◦C);

53.5 ≤ t ≤ 126.5 (min) (4)

After codification, all corresponding coded variables x1, x2 andx3 are normalized into the interval of variation ranging from −1.215up to 1.215. A central composite design (CCD) of experimentswas applied in this study to investigate the leaching of chemi-cal elements from low-grade uranium ore. The designed operatingconditions are summarized in Table 3 showing that all factorsare varied simultaneously. According to experimental plan, a totalnumber of 16 runs have been designed. These experiments aregrouped in Table 3 as follows: (a) trials 1–8 correspond to theorthogonal design; (b) trials 9–14 are the axial experiments with“star points” to form the central composite design and (c) trials15–16 are replicate points to estimate the experimental error.

The leaching experiments were performed in accordance withdesigned conditions shown in Table 3 and the final concentrationsof extracted chemical elements in leaching solution were deter-mined. These concentrations of chemical elements recovered inliquid phase (i.e. leaching solution) are reported in Table 4. Thus,eleven chemical elements were identified by leaching from ore, i.e.U, Th, Cu, Co, Zn, La, V, Yb, Mo, Ni and Sb.

Fig. 1 shows the logarithm in base 10 of concentrations ofchemical elements in liquid phase under the following conditions:P = 5 bar, T = 120 ◦C and t = 90 min (trial 15). As one can see from thisfigure, the natural ore is reach in Zn, V, Cu, Ni and Mo. Relatively lowconcentration of U has been found and even lower concentrationsof La, Co, Th and Yb have been detected. The concentration of Sbis almost insignificant. By arranging the chemical elements in thedescending order of concentrations the following set has resulted:

Zn > V > Cu > Ni > Mo > U > La > Co > Th > Yb > Sb (5)

As one can see from the ordered set (5), uranium is positioned inthe middle of the group of leached chemical elements. The concen-tration of U varies from 3.77 to 5.51 ppm, while the concentration ofthe head group element Zn varies between 231.86 and 329.39 ppm.

G. Zakrzewska-Koltuniewicz et al. / Journal of Hazardous Materials 275 (2014) 136–145 139

Table 2Decision variables and their coded and actual values used for experimental design of leaching process.

Decision variables (factors) Coded variables Actual values and coded levels

−a −1 0 +1 +a*

Pressure, P (bar) x1 2.57 3 5 7 7.43Temperature, T (◦C) x2 71.4 80 120 160 168.6Time of leaching, t (min) x3 53.5 60 90 120 126.5

* a – is an axial level known also as “star point” which is specific for central composite designs (a = 1.215).

Table 3Central composite design (CCD) of orthogonal type used for planning the leaching experiments.

Run Factors (controllable input variables)

Pressure Temperature Time of leaching

x1 P (bar) x2 T (◦C) x3 t (min)

1 +1 7 +1 160 +1 1202 −1 3 +1 160 +1 1203 +1 7 −1 80 +1 1204 −1 3 −1 80 +1 1205 +1 7 +1 160 −1 606 −1 3 +1 160 −1 607 +1 7 −1 80 −1 608 −1 3 −1 80 −1 609 +1.215 7.43 0 120 0 90

10 −1.215 2.57 0 120 0 9011 0 5 +1.215 168.6 0 9012 0 5 −1.215 71.4 0 9013 0 5 0 120 +1.215 126.514 0 5 0 120 −1.215 53.515 0 5 0 120 0 9016 0 5 0 120 0 90

Table 4Concentrations of metal species in leaching solutions determined experimentally according to the designed conditions (CCD).

N cU (ppm) cTh (ppb) cCu (ppm) cCo (ppm) cZn (ppm) cLa (ppm) cV (ppm) cYb (ppb) cMo (ppm) cNi (ppm) cSb (ppb)

1 5.51 705 9.10 1.37 319.50 1.44 78.70 106 12.71 14.88 542 5.21 562 14.79 1.20 322.66 1.31 79.99 74 12.58 15.37 203 4.62 541 14.00 1.02 282.25 1.49 28.74 48 10.51 14.59 234 4.58 547 12.20 1.01 291.51 1.45 24.21 47 10.03 14.39 225 4.01 484 16.77 1.11 266.79 1.07 63.59 62 9.81 11.76 296 4.83 555 11.02 1.33 310.61 1.28 75.53 73 11.90 14.05 357 4.56 551 7.87 1.26 289.79 1.50 32.85 50 10.93 14.49 198 4.63 555 6.49 1.18 278.90 1.56 29.63 51 10.54 12.92 389 5.09 555 16.82 1.06 311.95 1.52 58.48 56 12.25 14.84 31

10 5.06 621 14.31 1.36 329.39 1.48 67.68 72 12.57 15.75 4211 3.77 420 11.43 1.03 231.86 1.03 55.66 64 8.95 10.72 2512 4.60 533 8.90 2.01 271.00 1.52 22.88 44 9.41 12.50 2013 5.10 574 15.67 1.06 312.60 1.52 64.49 56 12.22 15.37 3514 4.25 511 9.82 1.14 284.51 1.315 5.47 618 18.42 1.09 319.87 1.416 5.44 636 18.00 1.12 315.61 1.4

Fig. 1. The logarithm (base 10) of chemical elements concentrations in solution afterleaching test; leaching conditions: P = 5 bar, T = 120 ◦C and t = 90 min.

1 50.48 52 10.45 12.35 296 81.22 94 12.65 15.36 405 80.39 93 12.56 15.16 41

The concentration of the last group element Sb varies from 20 to54 ppb.

In this study U, La, Mo, V and Yb have been considered aschemical elements of interest (i.e. valuable elements) because oftheir rareness and applications. For these elements of interest,the percent of recovery (i.e. leaching efficiencies) was determinedusing Eq. (1). These responses are summarized in Table 5. Theresults reveal that leaching efficiency for U vary between 56.09 and82.01%, for other elements the leaching efficiency changes as fol-lows, La (45.02–68.37%), V (27.71–98.38%), Yb (20.55–49.04%) andMo (54.40–77.27%).

Based on experimental design matrix and observed responses(see Table 3 and Table 5) the second-order regression models with

coded variables (see Appendix A) have been constructed by meansof ordinary least squares OLS method [28–32]. The significance ofall regression coefficients was estimated using the Student t-test. Inorder to evaluate the goodness-of-fit of second-order polynomial

140 G. Zakrzewska-Koltuniewicz et al. / Journal of Hazardous Materials 275 (2014) 136–145

Table 5Leaching efficiencies (responses) determined experimentally for the chemical species of interest according to the design of experiments (CCD).

N YU (%) YLa (%) YV (%) YYb (%) YMo (%) YTh (%)

1 82.01 63.36 95.33 49.04 77.27 98.002 77.58 57.31 96.89 34.31 76.42 92.783 68.84 65.30 34.82 22.37 63.85 89.324 68.24 63.56 29.33 21.66 60.98 90.335 59.69 46.92 77.03 29.01 59.60 79.926 71.92 56.10 91.49 33.76 72.32 91.657 67.83 65.69 39.80 23.18 66.40 91.008 68.93 68.37 35.89 23.26 64.08 91.659 75.72 66.95 70.83 26.00 74.46 92.46

10 75.33 64.77 81.98 33.08 76.39 96.0011 56.09 45.02 67.42 29.86 54.40 69.3212 68.48 66.70 27.71 20.55 57.16 87.9913 75.87 66.64 78.12 25.70 74.29 94.7614 63.29 57.45 61.15 24.18 63.48 84.2715 81.43 64.24 98.38 43.69 76.89 97.0016 80.92 63.75 97.38 43.10 76.31 96.43

Table 6Statistical test for evaluation of goodness-of-fit of the models.

Model Significant level Degrees of freedom F-ratio(calculated value)

F-ratio(tabulated value)

YU(x1 , x2 , x3) = 0.05 �1 = N − L = 9�2 = N0 − 1 =1

Fc = S2res.

S20

= 229.7 F1−�(�1,�2) = 240.5

YLa(x1 , x2 , x3) = 0.05 �1 = N − L = 8�2 = N0 − 1 =1

Fc = S2res.

S20

= 70.7 F1−�(�1,�2) = 238.9

YV(x1 , x2 , x3) = 0.05 �1 = N − L = 9�2 = N0 − 1 =1

Fc = S2res.

S20

= 239.9 F1−�(�1,�2) = 240.5

YYb(x1 , x2 , x3) = 0.05 �1 = N − L = 10�2 = N0 − 1 =1

Fc = S2res.

S20

= 236.9 F1−�(�1,�2) = 241.9

YMo(x1 , x2 , x3) = 0.05 �1 = N − L = 8�2 = N0 − 1 =1

Fc = S2res.

S20

= 140.1 F1−�(�1,�2) = 238.9

YTh(x1 , x2 , x3) = 0.05 �1 = N − L = 8�2 = N0 − 1 =1

Fc = S2res.

S20

= 223.9 F1−�(�1,�2) = 238.9

N ignific 2 2

s

msscab

– number of experimental runs; N0 – number of replicate runs; L – number of square.

odels, the statistical Fisher F-test was employed considering theignificance level of = 0.05 [29,32,33]. The results of F-ratio test areummarized in Table 6 for all fitting models. The table reveals that

alculated F-ratio is lower than the tabulated value in all cases. Inddition, the goodness-of-fits of multiple-regression models haveeen illustrated graphically as shown in Figs. 2–4. As one can see,

Fig. 2. Comparison between experimental observations and modeling data show

ant coefficients in regression model; Sres – residual mean square; S0 – error mean

the modelling data predict appropriately the experimental obser-vations and thus corroborating the statistical F-ratio test.

It is of practical interest to convert the fitting models with coded

variables into empirical models with actual variables. To this end,the empirical models with actual variables were obtained by sub-stitution technique. In our case, the final empirical models with

ing leaching efficiency for (a) Uranium (YU, %) and (b) Lanthanum (YLa, %).

G. Zakrzewska-Koltuniewicz et al. / Journal of Hazardous Materials 275 (2014) 136–145 141

ing le

a

Y

Y

Y

Y

Y

Y

Fig. 3. Comparison between experimental data and modeling data show

ctual variables (real values) may be presented as:

ˆU = 74.564 − 11.0P + 0.634T − 0.404t + 0.756P2

− 3.723 × 10−3T2 + 0.038Pt + 2.88 × 10−3Tt (6)

ˆLa = 93.992 − 9.715P + 0.292T − 0.418t + 0.603P2

− 2.733 × 10−3T2 + 0.041Pt + 2.379 × 10−3Tt (7)

ˆV = −151.046 + 4.765P + 3.202T − 0.341t − 0.011T2

− 0.04PT + 3.671Tt (8)

ˆMo = 21.534 − 8.47P + 1.235T − 0.254t + 1.167P2

− 5.406 × 10−3T2 − 0.027PT + 2.856 × 10−3Tt (9)

ˆYb = 50.338 − 3.8P − 0.063T − 0.437t + 0.042Pt

+ 2.394 × 10−3Tt (10)

ˆTh = 103.754 − 14.438P + 0.644T − 0.362t + 1.133P2

− 3.767 × 10−3T2 + 0.035Pt + 2.314 × 10−3Tt (11)

Fig. 4. Comparison between experimental data and modeling data showing

aching efficiency for (a) Molybdenum (YMo, %) and (b) Vanadium (YV, %).

In these models, the actual variables P, T and t are subjected tothe boundary constraints (4). The models (6)–(11) have been usedfor simulation and graphical representations of response surfacesas shown in Figs. 5–10.

Fig. 5 shows uranium leaching efficiency (YU) depending ondesign variables P, T and t. As one can see the increment of leachingtime (t) results in ascending values of response. Due to interactioneffect between T and t variables, the effect of time is more promi-nent at higher values of temperature (Fig. 5a). Likewise, Fig. 5bindicates that high values of response are achieved for a tempera-ture interval lying between 120 ◦C and 140 ◦C.

Fig. 6 illustrates the relationship between lanthanum leach-ing efficiency (YLa) and the decision variables. The computationresults revealed that the increment of leaching time (t) conductedto increasing of YLa response especially at high value of tempera-ture. For the lowest values of temperature, the effect of leachingtime becomes insignificant (Fig. 6a). The increment of temperatureresults in increasing of quantity YLa. The pressure for the case of Laleaching is the little worth factor, its variation does not change toomuch response value (Fig. 6b).

The effects of factors (variables) on leaching efficiency of molyb-denum (YMo) are shown in Fig. 7. As seen in this figure, the incrementof temperature leads to increasing of response up to a maximumplateau is approached. After this plateau, the trend is reversed andthe decreasing of response is observed for higher values of temper-

ature. The maximum plateau is laid into the temperature intervalof 120 ◦C and 140 ◦C. The increment of time does not influencetoo much the leaching efficiency of Mo for low values of temper-ature (80–100 ◦C). For higher temperature values than 100 ◦C the

leaching efficiency for (a) Ytterbium (YYb, %) and (b) Thorium (YTh, %).

142 G. Zakrzewska-Koltuniewicz et al. / Journal of Hazardous Materials 275 (2014) 136–145

Fig. 5. Response surface plot and contour-lines showing Uranium leaching efficiency (YU) (a) depending on t and T variables for a fixed level of P = 5 bar; (b) depending on Tand P variables for a fixed level of t = 90 min.

F cienco

iTc

tmariatY

Fo

ig. 6. Response surface plot and contour-lines showing the Lanthanum leaching effin T and P variables for a fixed level of t = 90 min.

ncreasing of time results in higher values of response (Fig. 7a).he pressure has the lowest effect on leaching efficiency (YMo) andonfers a saddle shape to the response surface (Fig. 7b).

Fig. 8 plots the leaching efficiency of vanadium (YV) as a func-ion of design variables. The computational data reveal that the

ost significant factor in case of vanadium leaching is the temper-ture in the autoclave. The increment of temperature up to 140 ◦Cesults in an obvious increase of the leaching efficiency. The leach-

ng time variable, t, does not affect the response too much especiallyt low temperatures (T < 110 ◦C) (Fig. 8a). Regarding the pressure,he effect of this variable is of minor importance on the responseˆV (Fig. 8b).

ig. 7. Response surface plot and contour-lines showing the Molybdenum leaching efficienn T and P variables for a fixed level of t = 90 min.

y (YLa) (a) depending on t and T variables for a fixed level of P = 5 bar; (b) depending

Fig. 9 shows the response surface plot of ytterbium leaching effi-ciency (YYb) depending on factors. According to the results shown inFig. 9a, the temperature is the principal factor affecting the leach-ing efficiency of ytterbium. The increment of temperature has asa consequence the increase of response. Owing to strong interac-tion effect between variables, T and t, the effect of temperaturebecomes stronger for higher values of leaching time (Fig. 9a). Like-wise, a strong interaction effect occurs between time and pressure

(Fig. 9b). Thus, the increment of pressure results in increasing ofresponse for t > 90 min. For the case t < 90 min, the increasing ofpressure conducts to decreasing of response. In a similar way, theeffect of leaching time on response depends on pressure values.

cy (YMo) (a) depending on t and T variables for a fixed level of P = 5 bar; (b) depending

G. Zakrzewska-Koltuniewicz et al. / Journal of Hazardous Materials 275 (2014) 136–145 143

Fig. 8. Response surface plot and contour-lines showing the Vanadium leaching efficiency (YV) (a) depending on t and T variables for a fixed level of P = 5 bar; (b) dependingon T and P variables for a fixed level of t = 90 min.

F ciencyo

IPlic

tfsts

Fo

ig. 9. Response surface plot and contour-lines showing the Ytterbium leaching effin t and P variables for a fixed level of T = 120 ◦C.

n accordance with computation results, for low pressure values < 3.5 bar, the increasing of time leads to minor decreasing ofeaching efficiency. As pressure becomes higher, i.e. P > 3.5 bar, thencrement of time results in gradual increasing of leaching effi-iency of ytterbium (Fig. 9b).

Fig. 10 illustrates thorium leaching efficiency (YTh) depending onhe input variables. As one can see, the saddle-shape response sur-

ace is observed in the design space T–P (Fig. 10a). Hence, Fig. 10auggests that high values of leaching efficiency are predicted for aemperature interval ranging from 110 ◦C to 130 ◦C. For the pres-ure approaching the midpoint value of 5 (bar) the lower values of

ig. 10. Response surface plot and contour-lines showing the Thorium leaching efficiency

n t and T variables for a fixed level of P = 5 bar.

(YYb) (a) depending on t and T variables for a fixed level of P = 5 bar; (b) depending

leaching efficiency are attained. The increment of time (t) leads tothe increasing of leaching efficiency, especially at higher values oftemperature (Fig. 10b).

For the optimization of leaching process, the desirability func-tion approach has been employed for optimum searching into themulti-response domain. This method is usually employed when anoptimal compromise between the considered responses has to be

identified. Desirability function [34], is the most important multi-objective searching technique employed in optimization problems.This methodology lies in the obtaining of individual desirabilityfunction d for each considered response y by converting the values

(YTh) (a) depending on T and P variables for a fixed level of t = 90 min; (b) depending

144 G. Zakrzewska-Koltuniewicz et al. / Journal of Hazardous Materials 275 (2014) 136–145

Table 7Individual desirability values and global desirability.

Run d1 (U) d2 (La) d3 (V) d4 (Yb) d5 (Mo) d6 (Th) D

1 0.755 0.542 0.942 0.363 0.716 0.975 0.6822 0.720 0.466 0.961 0.179 0.705 0.910 0.5773 0.611 0.566 0.185 0.030 0.548 0.866 0.3114 0.603 0.544 0.117 0.021 0.512 0.879 0.2665 0.496 0.337 0.713 0.113 0.512 0.749 0.4156 0.649 0.451 0.894 0.172 0.654 0.896 0.5467 0.598 0.571 0.247 0.04 0.580 0.887 0.3468 0.612 0.605 0.199 0.041 0.551 0.896 0.3379 0.697 0.587 0.635 0.075 0.681 0.906 0.479

10 0.692 0.560 0.775 0.163 0.705 0.95 0.56611 0.451 0.313 0.593 0.123 0.430 0.616 0.37412 0.606 0.584 0.096 0.007 0.464 0.85 0.21313 0.698 0.583 0.727 0.071 0.679 0.935 0.48714 0.541 0.468 0.514 0.052 0.679 0.803 0.39315 0.768 0.553 0.980 0.296 0.711 0.963 0.66216 0.762 0.547 0.967 0.289 0.704 0.955 0.654

ocdc

d

wrr(

D

irat

D

avTsParhid0tftattD

f responses on a non-dimensional scale ranging from 0 to 1. Inase of maximization of a response y the corresponding individualesirability function d is of type the-larger-the-best (LTB), and theonversion scheme can be written as follows:

=

⎧⎪⎪⎪⎨⎪⎪⎪⎩

0, if y ≤ ymin(y − ymin

ymax − ymin

)W

, if ymin ≤ y ≤ ymax

1, if y ≥ ymax

(12)

here y is the value of response, ymin is the lower-bound limit foresponse (in this case ymin = 20%), ymax is the upper-bound limit foresponse (in this case ymax = 100%) and W is the weight coefficientin this case W = 1). The global desirability can be determined as:

=(

m∏h=1

dh

)1/m

(13)

where m is the number of responses, in this case m = 6. For thenvestigated process, the global desirability combines six distinctesponses (i.e. YU, YLa, YV, YMo, YYb and YTh) in a single general desir-bility function D. In our peculiar case, Eq. (13) may be written ashe geometric mean of individual desirabilities as follows:

= 6√

d1 × d2 × d3 × d4 × d5 × d6 (14)

In accordance with data summarized in Table 4, the global desir-bility D was calculated for each experimental run. The computedalues of desirability D for each condition are reported in Table 7.hus, the lowest desirability value is about D = 0.213 and corre-ponds to the conditions of the experimental run number-12 (i.e.

= 5 bar, T = 71.4 ◦C and t = 90 min). In contrast, the highest desir-bility value is of D = 0.682 and is attributed to the experimentalun number-1 (i.e. P = 7 bar, T = 160 ◦C and t = 120 min). Likewise, aigh desirability value of D = 0.662 was obtained and for the exper-

mental run number-15 (i.e. P = 5 bar, T = 120 ◦C and t = 90 min). Theifference between the desirability values for runs 1 and 15 is about.02 (�D = 0.682–0.662), that is minor and therefore, the role of fac-ors levels is important for decision of the optimum. For example,rom the economic standpoint is more advantageous to performhe extraction at P = 5 bar, T = 120 ◦C and t = 90 min and to obtain

desirability of D = 0.662. By contrast, it is not beneficial to runhe process at the superior conditions of P = 7 bar, T = 160 ◦C and

= 120 min and to obtain a minor improvement of desirability to = 0.682. Therefore, taking into account both desirability value and

the levels of factors, the optimum condition in our decision corre-sponds to P = 5 bar, T = 120 ◦C and t = 90 min (run number-15) forwhich the desirability is of D = 0.662. For this optimum condition,the overall extraction performance is of 81.43% (for U), 64.24% (forLa), 98.38% (for V), 43.69% (for Yb), 76.89% (for Mo) and 97.00% (forTh).

4. Conclusions

In this work, the statistically designed experiments were carriedout to investigate the recovery of U, V, Mo chemical elements andrepresentative lanthanides like La and Yb from low-grade uraniumore using sulphuric acid as leaching reagent. The second-orderregression models were developed to predict the leaching efficien-cies of valuable chemical elements. These models were validatedfrom statistical standpoint using F-ratio test.

It was concluded that the most significant variable affectingthe leaching efficiency is the temperature. By contrast, the lit-tle worth factor is the pressure. The time of leaching is a controlvariable of average significance. Moreover, the interaction effectsbetween factors were identified and discussed. For the optimiza-tion of leaching process, the desirability function approach wasemployed. The optimum condition determined involves P = 5 bar,T = 120 ◦C and t = 90 min, for which the desirability was of D = 0.662.Under this optimum condition, the overall extraction performancewas of 81.43% (for U), 64.24% (for La), 98.38% (for V), 43.69% (forYb), 76.89% (for Mo) and 97.00% (for Th).

Modelling and optimization studies can deliver knowledgeabout the leaching process and the mutual interaction betweenthe factors affecting the efficiency of the method. The modelsallow to predict yields of individual metals from the mass unit ofprocessed ore, and to plan the successive stages of the technol-ogy scheme, at which examined metals can be separated from thepickling solutions sequentially. This is important for developmentof hydrometallurgical technologies responding to the growingdemand not only for uranium but also for other precious metalspresent in the uranium ore mined.

Acknowledgements

The studies were supported by POIG project No. 01.01.02-14-094-09-00 “Analysis of the possibility of uranium supply fromdomestic resources” financed by National Centre for Research andDevelopment (NCBiR) in Poland.

al of H

A

Y

Y

Y

Y

Y

Y

s

R

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G. Zakrzewska-Koltuniewicz et al. / Journ

ppendix A.

Multivariate regression models in terms of coded variables

ˆU = 72.893 + 3.98x3 + 3.023x21 − 5.956x2

2 + 2.296x1x3

+ 3.456x2x3 (A1)

ˆLa = 62.658 − 5.986x2 + 2.156x3 + 2.412x21

− 4.373x22 + 2.457x1x3 + 2.855x2x3 (A2)

ˆV = 78.98 + 24.574x2 + 2.992x3 − 18.127x22

− 3.177x1x2 + 4.405x2x3 (A3)

ˆMo = 70.773 + 2.46x2 + 2.673x3 + 4.669x21 − 8.65x2

2

− 2.135x1x2 + 3.427x2x3 (A4)

ˆYb = 29.31 + 6.113x2 + 1.829x3 + 2.533x1x3 + 2.873x2x3 (A5)

ˆTh = 90.855 − 2.069x2 + 2.643x3 + 4.531x21 − 6.027x2

2

+ 2.073x1x3 + 2.777x2x3 (A6)

ubjected to: −a ≤ xi ≤ a; ∀i = 1, 3; a = 1.215.

eferences

[1] A.E. Santos, A.C.Q. Ladeira, Recovery of uranium from mine waste by leachingwith carbonate-based reagents, Environ. Sci. Technol. 45 (8) (2011) 3591–3597.

[2] C.F.V. Mason, W.R.J.R. Turney, B.M. Thomson, N. Lu, P.A. Longmire, C.J. Chisholm-Brause, Carbonate leaching of uranium from contaminated soils, Environ. Sci.Technol. 31 (10) (1997) 2707–2711.

[3] K. Frackiewicz, K. Kiegiel, I. Herdzik-Koniecko, E. Chajduk, G. Zakrzewska-Trznadel, S. Wolkowicz, Analysis of uranium supply from domestic resources,Nukleonika 58 (4) (2012) 451–459.

[4] Uranium 2011: Resources, Production and Demand, A Joint Report by the OECDNuclear Energy Agency and International Atomic Energy Agency, OECD, Paris,2012.

[5] http://www.mining-technology.com/projects/olympic-dam/, available on2014.03.11.

[6] B. Gupta, P. Mailk, Z. Begum Irfan, Recovery of uranium, thorium and zirconiumfrom allanite by extraction chromatography using impregnated chromosorb,Water Resour. Ind. 4 (2013) 21–31.

[7] B. Gupta, P. Malik, A. Deep, Solvent extraction and separation of tetravalentlanthanides and yttrium using Cyanex 923, Solvent Extr. Ion Exch. 21 (2003)239–258.

[8] Li Zeng, Chu Yong Cheng, A literature review of the recovery of molybdenumand vanadium from spent hydrodesulphurisation catalysts: Part I: metallurgi-cal processes, Hydrometallurgy 98 (2009) 1–9.

[9] Li Zeng, Chu Yong Cheng, A literature review of the recovery of molybdenumand vanadium from spent hydrodesulphurisation catalysts: part II: separationand purification, Hydrometallurgy 98 (2009) 10–20.

10] T. Chmielewski, Atmosferic leaching of shale by-product from Lubin concen-trator, Physicochem. Probl. Miner. Process. 41 (2007) 337–348.

11] T. Chmielewski, K. Borowski, K. Gibas, K. Ochromowicz, B. Wozniak, Atmo-spheric leaching of copper flotation concentrate with oxygenated sulphuricacid solutions, Physicochem. Probl. Miner. Process. 47 (2011) 193–206.

[

azardous Materials 275 (2014) 136–145 145

12] G. Zakrzewska, D. Gajda, R. Dybczynski, Z. Samczynski, I. Herdzik, E. Chajduk,B. Danko, The study on the separation of uranium from associated metals inthe post leaching solution by ion-exchange process, Annual Report 2012, INCT,2013, pp. 46–50.

13] C. Cojocaru, G. Zakrzewska-Trznadel, Response surface modeling and opti-mization of copper removal from aqua solutions using polymer assistedultrafiltration, J. Membr. Sci. 298 (1–2) (2007) 56–70.

14] C. Cojocaru, G. Zakrzewska-Trznadel, A. Jaworska, Removal of cobalt ionsfrom aqueous solutions by polymer assisted ultrafiltration using experimentaldesign approach. part 1: Optimization of complexation conditions, J. Hazard.Mater. 169 (1–3) (2009) 599–609.

15] C. Cojocaru, G. Zakrzewska-Trznadel, A. Miskiewicz, Removal of cobalt ionsfrom aqueous solutions by polymer assisted ultrafiltration using experimen-tal design approach: part 2: optimization of hydrodynamic conditions for acrossflow ultrafiltration module with rotating part, J. Hazard. Mater. 169 (1–3)(2009) 610–620.

16] N. Uzal, A. Jaworska, A. Miskiewicz, G. Zakrzewska-Trznadel, C. Cojocaru, Opti-mization of Co2+ ions removal from water solutions via polymer enhancedultrafiltration with application of PVA and sulfonated PVA as complexingagents, J. Colloid Interface Sci. 362 (2) (2011) 615–624.

17] S . Sert, M. Eral, Uranium adsorption studies on aminopropyl modified meso-porous sorbent (NH2–MCM-41) using statistical design method, J. Nucl. Mater.406 (3) (2010) 285–292.

18] E. Cicek, C. Cojocaru, G. Zakrzewska-Trznadel, M. Harasimowicz, AgnieszkaMiskiewicz, Response surface methodology for the modelling of 85Sradsorption on zeolite 3A and pumice, Environ. Technol. 33 (1) (2012)51–59.

19] E. Jorjani, A.H. Bagherieh, Sh. Mesroghli, S. Chehreh Chelgani, Prediction ofyttrium, lanthanum, cerium, and neodymium leaching recovery from apatiteconcentrate using artificial neural networks, J. Univ. Sci. Technol. Beijing: Miner.Metallurgy Mater. 15 (4) (2008) 367–374.

20] E. Sayan, M. Bayramoglu, Statistical modelling of sulphuric acid leaching ofTiO2, Fe2O3 and A12O3 from red mud, Process Saf. Environ. Prot. 79 (5) (2001)291–296.

21] E. Sayan, M. Bayramoglu, Statistical modeling of sulfuric acid leaching of TiO2

from red mud, Hydrometallurgy 57 (2) (2000) 181–186.22] Zhao-Hui Guo, Feng-Kai Pan, Xi-Yuan Xiao, Long Zhang, Kai-Qi Jiang, Opti-

mization of brine leaching of metals from hydrometallurgical residue, Trans.Nonferrous Met. Soc. China 20 (10) (2010) 2000–2005.

23] Program Polskiej Energetyki Jadrowej (Polish Nuclear Power Program), Min-istry of Economy, Warsaw, January 2014.

24] V.V. Shatalov, S.A. Pirkovski, K.M. Smirnov, Oxidation of pyrite by oxygen andconcurrent leaching of uranium from ore under the conditions of an autogenousautoclave process, At. Energy 102 (2) (2007) 147–150.

25] M. Amme, T. Wiss, H. Thiele, P. Boulet, H. Lang, Uranium secondary phase for-mation during anoxic hydrothermal leaching processes of UO2 nuclear fuel, J.Nucl. Mater. 341 (2005) 209–223.

26] M. Ochsenkühn-Petropulu, Th. Lyberopulu, K.M. Ochsenkühn, G. Parissakis,Recovery of lanthanides and yttrium from red mud by selective leaching, Anal.Chim. Acta 319 (1996) 249–254.

27] F.K. Crundwell, N. du Preez, J.M. Lloyd, Dynamics of particle-size distributionsin continuous leaching reactors and autoclaves, Hydrometallurgy 133 (2013)44–50.

28] M.A. Bezerra, R.E. Santelli, E.P. Oliveira, L.S. Villar, L.A. Escaleira, Response sur-face methodology (RSM) as a tool for optimization in analytical chemistry,Talanta 76 (5) (2008) 965–977.

29] S. Akhnazarova, V. Kafarov, Experiment Optimization in Chemistry and Chem-ical Engineering, Mir Publishers, Moscow, 1982.

30] D.C. Montgomery, Design and Analysis of Experiments, 5th edition, John Wiley& Sons, New York, 2001.

31] G.E.P. Box, N.R. Draper, Response Surfaces, Mixtures, and Ridge Analyses, 2ndedition, John Wiley & Sons, New York, 2007.

32] M. Poroch-Seritan, S. Gutt, G. Gutt, I.R. Cretescu, C. Cojocaru, T. Severin, Design ofexperiments for statistical modeling and multi-response optimization of nickellectroplating process, Chem. Eng. Res. Des. 89 (2) (2011) 136–147.

33] M. Gavrilescu, R.Z. Tudose, Residence time distribution of the liquid

phase in a concentric-tube airlift reactor, Chem. Eng. Process 38 (1999)225–238.

34] N.R. Costa, J. Lourenco, Z.L. Pereira, Desirability function approach: a reviewand performance evaluation in adverse conditions, Chemom. Intell. Lab. Syst.107 (2011) 234–244.