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The critical factors influencing uranium precipitation by hydrogen peroxide: The use of experimental design techniques
Hydrometallurgy, 17 (1987) 315-334
315
Elsevier Science Publishers B.V., Amsterdam - - Printed in The Netherlands
The Critical Factors Influencing Uranium Precipitation by Hydrogen Peroxide: The Use of Experimental Design Techniques J.S. HOPKINS, J.A. GOLDING*
Chemical Engineering Department, University of Ottawa, Ottawa, Ontario KIN 9B4 (Canada) and G.M. RITCEY
HydrometaUurgical Section, CANMET-- Energy, Mines and Resources Canada, 555 Booth Street, Ottawa, Ontario KIA OG1 (Canada) (Received December 17, 1985; accepted in revised form August 27, 1986)
ABSTRACT Hopkins, J.S., Golding, J.A. and Ritcey, G.M., 1987. The critical factors influencing uranium precipitation by hydrogen peroxide: The use of experimental design techniques. Hydrometallurgy, 17: 315-334. The effects of process operating variables on properties of yellow cake produced by peroxide precipitation have been investigated using experimental design techniques. The variables studied were: the initial uranium concentration, added peroxide, sulphate concentration, pH and precipitation time. The properties evaluated were: the filter cake yield, uranium precipitated, recovery, barrens concentration, product purity and particle size distribution. The effects of operating variables were described by using a linear equation of the form: 5 i=1
5
5
i=lj=l
A two-level factorial design was used as the basis for two experimental plans to determine the effects of the processing variables. The plans were: (1) a central composite design, and ( 2 ) a twolevel factorial design with added centrepoints. The adequacy of any plausible model was evaluated using a quantitative lack of fit test. In general, due to the critical effect of the peroxide concentration, models describing the system responses did not pass this test for the central composite design. However, for the factorial design plan, the majority of the response models were adequately modeled. The results indicated that the most critical process variable would be the peroxide requirement, when the addition of lower than the stoichiometric amount of peroxide would not only reduce yields but affect cake properties. In the presence of excess peroxide the results indicated process control of a uranium mill could be readily carried out by a process operator on the basis of simple linear models. However, significant interaction effects were observed for sulphate, total peroxide concentration and pH on yields, recovery and yellow cake purity. The most critical factor influencingthe barrens concentration was found to be the precipitation pH. *Author to whom correspondence should be addressed.
316 INTRODUCTION
The production of yellow cake is an essential step in the preparation of fuels for use in nuclear reactors. In its production by precipitation methods, not only is a high purity product required, but good filtration, settling and material handling characteristics are needed as well as a product that can be readily dried. In North America, the most commonly used method [ 1 ] is ammonium hydroxide precipitation to form ammonium diuranate. Research in this area [ 2-5 ] has shown that the precipitation reaction will only take place at certain pH levels and ammonia concentrations; also for batch precipitations a sudden drop in pH takes place at the initiation of the reaction [ 2 ]. Studies to relate precipitation conditions to thickening, filtration and drying properties have shown that changes in precipitation conditions carried out to improve settling and filtration characteristics can adversely affect product purity. However, it has been found that sulphate levels in the yellow cake can be maintained within refiners' specifications by carrying out the precipitation reaction between 30 and 40 ° C [ 4 ]. Investigations of the structure of ammonium diuranate [ 5 ] show that (NH4)2U207 is only an approximate formula, indicating that the precipitation reaction is complex.
Peroxide precipitation Although at present the ammonium diuranate produced is of high quality, two factors are beginning to influence the industry. Firstly, product specifications are becoming more stringent, while, secondly, lower quality ore grades are being processed so that the impurity levels in the leach solutions may rise. As a result the use of hydrogen peroxide to precipitate uranium has received considerable attention [ 6-13 ]. In sulphate solution the basic reaction is taken as:
UO2" S04 + H2 02 + x H2 0-* UO4'xH2 0 + H2 SO4
(1)
After drying at 100 °C the dihydrate U04" 2H20 is assumed to be the most stable hydrate although higher hydrates are formed initially [6]. Shabbir and Tame [7] carried out batch precipitations to determine the effect of pH, sulphate level, molybdenum and vanadium impurities on product purity and barrens concentration. An optimum pH range between 4 and 5 was reported to be required for complete precipitation; however, a higher purity product was produced at a pH of 5. Increase in temperature increased the precipitation rate but the vanadium and molybdenum impurity levels in the cake were raised. Reaction rates were also found to decrease as the sulphate level was raised. It was also noted that a definite excess of peroxide was needed to prevent molybdenum precipitation. An optimum pH was also reported between 4.0 and 5 to minimize the uranium concentrations in the barrens. A lower optimum pH
317 value of 3.3 was reported by Yamire and Waters [8] in their studies involving an ion exchange eluate solution containing vanadium. A large excess of peroxide was recommended, 3:1 molar ratio H2 02 :U3 08, by these authors, while to minimize peroxide decomposition ambient precipitation temperatures were suggested. In another study by Zimmer [ 9 ] an effect of pH and temperature on crystal structure and filtration rate was noted. A small excess of peroxide and an operating pH of 2.0-2.5 at a temperature of 40-50 ° C were suggested as being the most suitable conditions. In a study by McFarlane and Rollwagen [10] hydrogen peroxide has also been found to reduce the silica content of yellow cake. The silica content causes a downstream production problem. Other work on the precipitation kinetics has been carried out by Bhattacharyya et al. [ 11,12 ]. They found that the reaction rate for the oxidation of U (IV) to U (VI) could be described by a bimolecular rate equation with respect to the uranium and peroxide concentrations in the presence of either sulphate or chloride. The rate constant was also found to depend on the particular acid medium, the following relationship being reported: kr (~ ( [ H + ] - 1.3) for hydrochloric acid k r c~
([H + ] -0.55) for sulphuric acid
Brown [ 13 ] studied the effect of process variables on the barrens concentrations, developing a model based on the chemical equilibria between U022+ , S042- and C1-. The proposed relationship for UaO8 concentration in g/1 was: [U3Os]
~- ( 1 0 - P H ) 2
{1 +710 [S042- ]2/96
+O.SS[C1- ]/35.5}/(3[H202]kn2o2/810×34}
(2)
The equilibrium constant kH~o2 for peroxide and the peroxide concentration were expressed as functions of time: kH2o2
=kt=o+ t 2 / ( 1 + 0 . 0 7 5 t 2) 3
[H,.,02] : [ H 2 0 2 l t = o ( 1 - k ~ ~
(3) )
([U3Oslt:o -- [U308]t]/[U308]t:o)
(4)
where kd = decomposition constant for peroxide. In comparing predicted results with experimental data it was found that the predicted barrens concentrations were lower than experimental values. The author then noted that an empirical model had been developed which more closely agreed with the experimental results; however, this relationship was not given.
318
Crystal growth Particle size and size distributions are of importance when considering ways to increase filtration and drying rates as they are both dependent on the specific surface area of the filter cake. Variables influencing the precipitation reaction affect crystal size [ 8,9 ]. However, these effects are not always beneficial, for as reported by Bryson [14 ] an improvement in filtration properties can also reduce filter cake quality. In precipitation reactions there is generally an instantaneous formation of small, uniformly sized particles which then grow by a process of crystal dissolution and re-precipitation or by agglomeration. Consequently, information on crystal growth, particle size and agglomeration can be expected to be extremely useful in selection of a practical range of process operating conditions. Thus if hydrogen peroxide is to be considered by a uranium mill without any major changes being made to the rest of the plant it is important to know the following: (1) which of the process variables have major influences on the precipitation reaction, and (2) their effect on product properties. In order to evaluate the effects of the process variables on the process responses, two experimental designs were used; one, a central composite design, the second, a two-level factorial design with added centrepoints. This method of experimentation involves the assumption of an empirical model to determine the critical factors affecting a reaction and allows for the screening out of unimportant variables. The method, though, does have limitations, as there is a basic inability to extrapolate beyond the experimental range covered and no mechanistic model is provided. LEAST SQUARES EQUATION A N D EXPERIMENTAL DESIGN
The assumption was made that the data could be fitted by a second-order polynomial. Five process variables were selected so that for a particular response, e.g. yield (see Table 3 ), the polynomial takes the form: 5
5
5
Y=flo + ~ flix, + ~ ~ ~ijxij i=1
(4)
i=lj=l
The difference between the predicted and the experimental response was then minimized using the least squares technique. To reduce correlation between the parameters the independent variables were redefined in coded form as follows:
xi = (~:i - Xi)/half the range of Xi, i = 1 to 5.
(5)
The mean value ()~i) and range of Xi were evaluated using the three internal values of the independent variables; these points were coded values of - 1, 0
319 and 1. Two experimental plans were considered. The basis of both plans was a 25-- 1 fractional factorial design with time xs, being confounded with the other four process variables (i.e. xs =xlx2x3x4). The first design considered was a central composite design which included the 16 runs of the 25-1 fractional factorial design, 10 centrepoint runs and 10 axial runs (xi = + 2, xi = 0) [11 ]. With this design it was anticipated that quadratic effects could be measured. The second plan was constructed from the 2°- 1design and the 10 centrepoints. The values of the independent variables for any of the runs involved in the designs were determined using standard procedures. Since the pH values of the experimental runs did not correspond to those prescribed by the experimental plans, the pH values were recorded employing the mean and range of all experimental values of the pH. In general parameters were evaluated at the 95% confidence level and if any parameter values were plausibly zero, i.e. the value of zero was contained within the confidence interval, the parameter was dropped from eqn. (4). This operation gave one or more plausible models for each of the process responses. If more than one model was found the need for additional parameters was determined by evaluating the Q ratio where Q was defined as: Q_I
--0.2
( S S R B - SSRA)/(NpB--NpA)
(6)
where SSR = sums of squares of residual, Np = number of parameters ( A and B refer to the models being compared), 0.2= S S R B / ( N - N B ) , N= number of runs, Model B is the model containing the most parameters. The Q ratio was then compared to Fo.os,Pi,v2, where p~= NpB-- NpA, P2 -~ N - NB. If Q was greater than Fo.os,Pl,P2, the extra parameter can be considered significant, i.e. additional parameters were required to adequately fit the data. The models were also tested for lack of fit, to determine whether plausible equations did indeed describe the data by using a quantitative lack of fit test. In this test the R ratio was calculated, where R is defined as:
R - ( SSR-pcp0.~p) / ( N - N p -Pep)
(7)
2 0"cp
where 0-c~is the estimated variance at the design plan centrepoint and Pep was the degree of freedom at the centrepoint. The R value was again compared to the distribution Fo.os,P,Pcp; when R was greater than Fo.os,P,Pcpthe model could be assumed not to fit the data, i.e. inadequate, where p = N - N p - P e p . The 0-~pvalues were only an estimate of the true experimental error due to problems with control of the pH. However, as the estimated values of 0.¢2pcan be considered to be greater than the true variance, any model which failed this test would certainly fail if, the true value of the variance were known (see Draper and Smith [15] for a discussion of the methods used).
320 EXPERIMENTAL The variables chosen for study were the initial uranium concentration, initial molar ratio of peroxide to uranium, the precipitation pH, excess sulphate and the precipitation or digestion time. The range of operating variables studied is given in Table 1 and the values chosen were based on data reported in the literature as representative of concentrations that would be used in an actual plant operation. The previous work by Shabbir and Tame [ 7 ] indicated that an excess of peroxide was required when molybdenum and vanadium Were present. It was decided, however, that for this work the amount of these particular impurities should be kept to a minimum by using a scrap yellow cake. This was so that the effectiveness of experimental design techniques to provide a suitable model for control of the process could be evaluated. In addition, if the process is to be optimized with respect to minimizing the peroxide cost, it was of interest to obtain information on the effect of hydrogen peroxide on the reaction in the presence of sulphate. As a result one run was carried out with insufficient peroxide available to precipitate all the uranium. It was also decided that for this particular study temperature and mixing characteristics would not be investigated. The process variables were also analyzed in terms of alternative methods of representing them, i.e. the amount of peroxide added rather than the molar ratio, the total sulphate rather than excess sulphate, and the hydrogen ion concentration rather than the pH. Data analysis was again carried out by expressing the variables in coded form. The response variables studied to determine the precipitation characteristics were; filter cake yield, i.e. filter cake weight, the uranium content, sulphur content, particle size and size distribution. Initially, the filtration rate had been chosen as a significant process reponse. However, except for two runs as discussed later, the precipitate filtered so rapidly that meaningful filtration data could not be obtained.
Procedures The uranium source was a blend of scrap yellow cake remaining from previous experimental investigations at CANMET; the blended yellow cake contianed 67.86% U by weight. The uranium solution was prepared as follows. The required amount of uranium to produce one litre of solution having the desired initial concentration was dissolved in sulphuric acid. The amount of acid added was the stoichiometric amount desired to form U02 (S04) 4- plus any excess required by the experimental design. A small amount of distilled water was also added at this point so that the heat of reaction would facilitate dissolution of the yellow cake. After complete dissolution the solution was transferred to a one-litre volumetric flask and made up to volume. The solution was then transferred to the four-litre batch precipitation reactor and the solu-
321 TABLE 1 The operating and coded values of process variables
Run
X.
X~,
X3,
X4,
Xs,
number
Initial uranium content, g/1
[ H2OJU ]
Experimental pH
Excess sulphate, g/1
Time, h
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
12.5 ( - - I ) a 17.5 ( i ) 12.5(-I) 17.5 ( 1 ) 12.5 (--1) 17.5 ( 1 ) 12.5 (--1) 17.5 ( 1 ) 12.5 (--1) 17.5 ( 1 ) 12.5 (--1) 17.5 ( 1 ) 12.5 (--1) 17.5 ( 1 ) 12.5 ( - I ) 17.5 ( 1 ) 10.0 (--2) 20.0 ( 2 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15,0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 )
1.50 (--I)" 1.50 ( - - I ) 3.00(i) 3.00 ( 1 ) 1.50 (--1) 1.50 (--1) 3.00 ( 1 ) 3.00 ( 1 ) 1.50 (--1) 1.50 (--1) 3.00 ( 1 ) 3.00 ( 1 ) 1.50 (--1) 1.50 (--1) 3.00 ( I ) 3.00 ( 1 ) 2.25 ( 0 ) 2.25 ( 0 ) 0.75 (--2) 3.75 ( 2 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2,25 ( 0 ) 2.25 ( 0 ) 2.25(0) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 )
2.83 (--0.4182) a 1.81 (--0.6958) 2.11 (--0.6141) 3,07 (--0.3529) 7.20 ( 0 . 7 7 0 9 ) 7.85 ( 0 . 9 4 7 8 ) 7.84 ( 0 . 9 4 5 0 ) 5.00 ( 0 . 1 7 2 2 ) 3.26 (--0.3012) 1.79 (--0.7012) 2.60 (--0.4808) 2.92 (--0.3937) 6.80 ( 0 . 6 6 2 0 ) 8.34 ( 1 . 0 8 1 1 ) 3.32 (-0.2849) 4.65 ( 0 . 0 7 7 0 ) 8.90 ( 1 . 2 3 3 5 ) 3.22 (--0.3125) 3.72 (--0.1765) 3.63 (--0.2010) 1.55 (-0.7670) 4.29 (-0.0214) 3.09 (-0.3479) 7.67 ( 0 . 8 9 8 3 ) 3.65 (-0.1955) 3.50 (-0.2364) 2.94 (-0.3887) 4.74 ( 0 . 1 0 1 1 ) 4.22 (-0.0404) 4.28 (-0.0241) 6.60 ( 0 . 6 0 7 2 ) 5.40 ( 0 . 2 8 0 7 ) 3.29 (-0.2935) 3.80 (-0.1547) 3.09 ( 0 . 3 4 7 9 ) 4.25 (-0.0323)
4.4 (--1) a 4.4 ( - I ) 4.4(--I) 4.4 (--1) 4.4 ( - 1 ) 4.4 (--1) 4.4 (--1) 4.4 (--1) 13.2 ( 1 ) 13.2 ( 1 ) 13.2 ( 1 ) 13.2 ( 1 ) 13.2 ( 1 ) 13.2 ( 1 ) 13.2 ( I ) 13.2 ( 1 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8,8 ( 0 ) 0 (-2) 17.6 ( 2 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 )
4 ( i)" 2 (--I) 2 (-I) 4 (1) 2 (--1) 4 (1) 4 (1) 2 (--1) 2 (-1) 4 (1) 4 (1) 2 (--1) 4 (1) 2 (--1) 2 (-I) 4 (1) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 1 (-2) 5 (2) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0)
"Coded values for statistical analysis.
tion stirred. The stirrer Reynolds number was maintained at 1500 for all experimental runs. The impeller was a propeller type having a diameter of 2.54 cm. The p H was then raised to 3.0 by addition of magnesium oxide powder and the
322 required amount of hydrogen peroxide added. After this the pH was measured and re-adjusted by addition of more MgO. Due to the rapidity of the precipitation reaction it proved to be difficult to obtain the actual design pH so that values differed from those initially selected. After t h e required precipitation time the suspension was filtered. The filter cake was then dried at room temperature for two or three days until a constant weight was reached. This technique was used so as to avoid loss of any water of hydration that might have taken place if elevated temperatures had been used to remove the superficial water. The cake was analyzed for uranium, magnesium and sulphate content, while the filtrate was analyzed for uranium to determine the barrens concentration. The chemical analyses were carried out by the analytical chemistry division of CANMET using standard procedures. The particle sizes were measured using an image analysis technique. A small sample from each run was mounted on a microscope slide and the crystals suspended in an emulsion oil. The image of the field was transmitted directly to a computer for size measurement and estimation of size distributions. Eight to ten fields were examined on each slide and differential and cumulative distributions were obtained. The range of operating variables and the actual values are shown in Table 1. RESULTS AND DISCUSSION Runs (Table 1 ) were carried out to permit analysis of the data for quadratic effects, full central composite design plan, and a two-level factorial design plan which would evaluate main effects and two variable interactions. Due to the difficulties experienced in controlling the precipitation pH, pH values did not correspond to design plans chosen; this could be expected to lead to some parameter correlation. Consequently parameters were evaluated not only for their significance at the 95% confidence level but also at the 80% and 90% levels. Analysis of the response data was based on the following procedures: Case 1. Data were analyzed using the original variables, i.e. x~ to xs. Case 2. Data were analyzed with the hydrogen ion concentration rather than the pH, i.e. the effects of x,, x2, x*3, x4 and xs. Case 3. Data were analyzed on the basis of the total peroxide and total sulphate concentration rather than the molar ratio H202/U or the excess sulphate, i.e. the effect ofxi, x*2, x3, x*4 and xs. Case 4. Data were analyzed on the basis of the total peroxide, total sulphate and the hydrogen ion concentration, i.e. the effects of xl, x*2, x*3, x*4 and x5. The means and variances of the centrepoint values of response variables are given in Table 2. The full results obtained are given in Table 3 and were analyzed according to the two experimental plans, i.e. Plan 1, the central composite design, and Plan 2, the two-level factorial design.
323 TABLE 2 Means and variances of the response variables at the design centrepoints Variable
Mean
a2
Filter cake yield (g) Uranium precipitated (g) Uranium content of the cake (wt. %) Barrens (rag/l) Recovery ( %) Sulphur (wt.%) Mean particle size (/lm) Particle size distribution
23.90 14.44 60.41 2.3 96.87 0.216 8.88 0.388
0.32 0.15 1.45 0.015 6.60 0.007 7.09 0.006
Filter cake yield, uranium precipitated and recovery These three responses were closely related and therefore are discussed together. The filter cake yield was the weight of filter cake after air drying and can be taken as an indication of the completeness of the precipitation reaction. It was not possible to base the actual yield on the inlet concentration and barrens concentration due to errors arising from transfer of solution to the precipitation reactor, filter cake losses and the chemical analysis. The amount of uranium was the filter cake yield multiplied by the uranium content while the recovery was the latter figure divided by initial weight of uranium charged to the precipitator. The filter cake yield was found to be a simple function of the initial uranium charged; however, there was an effect of p H on yield, as shown in Fig.l, the yield increasing slightly as the p H was raised. Models were developed to identify significant process variables and to predict yields. Plausible models found from design Plan 1 all failed the lack of fit test, i.e. the R test. The reason for this lack of fit was found to be run number 19 where the peroxide level was below the stoichiometric amount required to precipitate the uranium, see Fig. 2. This indicated the critical nature of the precipitation reaction to the amount of hydrogen peroxide added. The amount of uranium recovered for this run was much lower than would be expected from the stoichiometry of the reaction, i.e. 58.8% compared with a theoretical value of 75%. Consequently, even for the relatively pure solution used in this investigation, the amount of peroxide must equal or exceed the stoichiometric amount required to precipitate all the uranium in solution. It also meant that the choice of the range for Plan 1 was too large and any future modelling to determine quadratic effects, particularly to determine any optimum with respect to the pH, would be subject to the restriction that the molar ratio H202/U be greater than or equal to one. In Plan 2, this run was automatically eliminated from the analysis and it was found that the amount of uranium precipitated could be
324 TABLE 3 Experimental results
Run number
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Filter cake yield, g
Uranium content of cake,
Sulphur content of cake,
%
%
18.86 26.81 19.71 27.67 19.93 28.57 20.17 28.25 19.87 25.08 19.44 27.11 19.81 28.11 19.81 28.45 16.06 31.83 12.62 24.63 20.70 24.63 24.06 24.43 24.78 21.98 22.88 24.42 23.87 23.42 24.19 23.00 24.19 24.81 23.74 24.42
62.56 61.34 61.08 61.00 59.14 59.26 58.00 58.89 58.82 60.29 60.86 61.56 58.58 57.03 60.33 59.34 55.41 60.25 58.26 59.18 65.44 59.29 60.27 58.66 57.43 61.86 61.34 58.78 60.41 59.33 59.90 62.59 59.88 61.92 60.44 59.50
0.25 0.14 0.13 0.29 0.10 0.10 0.35 0.20 0.63 0.53 0.15 0.18 0.26 0.37 0.15 0.14 0.07 0.29 0.44 0.22 0.10 0.18 0.17 0.12 0.72 0.21 0.16 0.22 0.40 0.22 0.08 0.20 0.24 0.26 0.15 0.23
Barrens Uranium concentration, recovered g U/I in cake, 0.061 0.160 0.045 0.0022 0.0001 0.0002 0.0002 0.0001 0.0065 1.57 0.029 0.010 0.0003 0.0005 0.0074 0.0005 0.026 0.023 0.24 0.060 0.640 0.0032 0.0049 0.0004 0.0033 0.0008 0.013 0.0004 0.002 0.0019 0.0001 0.0001 0.0023 0.0006 0.0015 0.0008
Mean particle size,
%
tim
94.39 93.97 96.31 96.45 94.30 96.75 93.58 95.06 93.50 86.41 94.65 95.37 92.80 91.61 95.61 96.47 88.99 95.89 58.82 97.17 90.31 97.35 96.67 95.54 94.07 90.65 93.57 95.65 96.13 92.63 96.00 96.22 96.67 102.41 95.65 96.87
5.59 7.03 8.82 5.88 4.86 7.36 6.90 12.05 14.29 28.59 8.22 8.70 14.31 11.42 13.63 11.61 18.37 9.07 20.46 10.82 8.36 19.23 11.21 10.21 9.11 13.24 10.85 -12.61 -6.09 -6.27 -9.20 --
Particle size distribution (dispersity) 0.43 0.74 0.58 0.91 0.52 0.33 0.80 0.65 0.45 0.02 0.35 0.51 0.62 0.66 0.35 0.37 0.35 0.42 0.23 0.34 0.42 0.28 0.39 0.47 0.45 0.29 0.43 -0.27 -0.45 -0.36 -0.43 --
aSee Table 2.
well represented as a simple linear function of the initial uranium concentration. There was negligible effect of time on the amount precipitated; however, the results did show that pH was significant with the yield increasing slightly with increase in pH as noted previously. Plausible models found are given in Table 4. With regards to the uranium precipitated, plausible models found from design
325 3O
,,=,24 --
~
# £~I~77,, .o Uo=lSg/L ,,
X
X
22
.J )-
18
I 3
l 5
I 7
I 9
I II
pH
Fig. 1. Effect of final pH on filter cake yield. Uo: initial uranium concentration, g/l; - - : predicted yields.
32
30
28 26
~24
w" o~ 22 E 2o .J
uJ 18 >-
16 14 12 .75
1.5
2.25 3.0 MOLAR RATIO(HtOt/U)o
3.75
Fig. 2. The effect of molar ratio of peroxide to uranium on filter cake yield. Uo: initial uranium concentration, g/l; - - : predicted yields.
326 TABLE 4 Plausible models for the filter cake yield - - Experimental Plan 2 Case/ model
Initial conf. int., %
Significant parameters
Parameter values
SSR
a2
1/1
90
0.61
95
10.66
0.46
3/1
90
8.77
0.40
3/2
95
23.72±0.32 3.90 _ 0.40 23.73±0.28 3.92 _+0.35 0.78_+0.53 23.71_+0.26 3.91 _+0.34 0.35 + 0.34 0.87 _+0.50 23.72±0.32 3.90 ± 0.40
14.74
1/2
fl0 fl 1 flo fll 173 flo fl, f12* f13 flo fll
14.74
0.61
Q-test results
R-test results
model A
model B
Q
F
case/model
R
F
1/1 3/2
1/2 3/1
8.81 7.47
4.28 3.40
1/2 3/1
1.71 1.39
3.02 3.04
Plan 1 again failed the lack of fit test. In the case of Plan 2, the amount of uranium precipitated was mainly proportional to the initial uranium concentration. The results showed that there were interaction effects of the sulphate and hydrogen peroxide concentrations such that with an increase in the total sulphate concentration the amount of peroxide added should be raised to ensure that all the uranium in solution is precipitated. The pH had no significant effect on the actual amount of uranium precipitated. Plausible models are shown in Table 5. Recoveries were normally high; however, no plausible models were found from either Plan 1 or Plan 2, all failing the lack of fit test. The data did show the following: (i) recoveries in excess of 95% could be expected as long as sufficient peroxide was present; (ii) if sulphate levels were high and peroxide levels low, recoveries could fall (see run number 10); (iii) there is a possible significant effect of pH on recovery, as when the pH was raised recoveries did increase, see runs 10 and 14. As a result, an increase in the operating pH may be necessary when sulphate levels are high. This observed interaction indicates further study is needed in this area to investigate the effect of sulphate and pH with respect to both the actual precipitation reaction and the decomposition of hydrogen peroxide when sulphates are present.
327 TABLE 5 Plausible models for the uranium precipitated - - Experimental Plan 2 Case/ model
Initial conf. int., %
Significant parameters
Parameter values
SSR
(7 2
1/1
all
0.20
all
14.25+_.0.17 2.33 ___0.22 14.32 ± 0.19 2.33 ± 0.22 -0.53 ±0.44 0.76 ± 0.71
4.70
3/1
flo fll flo fll
3.26
0.15
•14" ]~24$
Q-test results
R-test results
model A
model B
Q
F
case/model
R
F
1/1
3/1
9.77
4.20
1/1 3/1
1.50 0.99
3.01 3.06
Barrens concentration T h e results i n d i c a t e d t h a t p e r o x i d e c o n c e n t r a t i o n , s u l p h a t e c o n c e n t r a t i o n a n d p r e c i p i t a t i o n p H i n f l u e n c e d t h e b a r r e n s c o n c e n t r a t i o n . D e s p i t e t h e scatter, t h e p r e c i p i t a t i o n p H seems to be t h e m o s t i m p o r t a n t variable t h a t m u s t be controlled, see T a b l e 6 a n d Fig. 3. Qualitatively, models i n d i c a t e d t h a t all the above variables would be significant, t o g e t h e r w i t h a possible effect o f r e a c t i o n time. T h e s e effects s h o u l d be i n v e s t i g a t e d in m o r e detail. As t h e p r e c i p i t a t i o n r e a c t i o n was rapid, r e a c t i o n t i m e would h a v e to be studied o v e r m u c h s h o r t e r periods t h a n were c o n s i d e r e d in this s t u d y to o b t a i n a kinetic model. In addition, t h e d a t a were s p r e a d over several orders of m a g n i t u d e a n d indiTABLE 6 Effect of precipitation pH on barrens concentration Precipitation pH range 2.0 2.0-2.99 3.0-3.99 b 4.0-4.99
t>5.0
Barrens concentration range a, g/1 1.57 -0.64 0.064-0.013 0.06 -0.0006 0.0032-0.0001
0.0005-0.0001
aIncludes the effect of precipitation time which varied within the pH ranges, see Table 2. bExcluding the results of run number 19.
328 I.OxlO "=
_.1
_
,
i.
r
5
Z -0
I'<~ nI--Z LU 0 Z 0
l.OxlO, e
5
• ",,, ,
(/) l.Oxl0 "I Z LLJ rr 5 rr <~ nn
I.OxK)- 4
5
1.0
I
2.0
I
3.0
I
I
4.0
5.0
I
6.0
7.0
pH Fig. 3. Effect of pH on barrens concentration.
cated an exponential variation of barrens concentration with pH. Consequently, linear modeling of the response was not possible, i.e. none of the plausible models passed the lack of fit test. In any future work the effects of process variables on the barrens concentration would require the application of a non-linear modelling technique.
Product purity The uranium and sulphur content determine the product purity. The uranium content varied from 55% to 65% while the sulphur content varied from 0.07% to 0.44%. All processing variables seem to influence product purity and several plausible models can be developed to predict the sulphur content of the filter cake. All the models indicated that a decrease in the peroxide concentration would increase the sulphur content of the cake. However, the results showed that even at the highest sulphate levels the product would meet refiners' standards [ 17 ].
329 TABLE 7 Plausible models for the sulphur content of the filter cake - - Experimental Plan 2 Case/ model
Initial conf. int., %
Significant parameter
Parameter values
SSR
a2
1/1
90
0.005
95
0.02
0.009
3/1
all
0.236_+0.03 - 0.049 ___0.04 0.053 _+0.04 0.097 _+0.04 - 0.145 + 0.04 0.236_+ 0.03 - 0.049 + 0.04 0.053 _+0.04 0.097 _ 0.04 0.299_-L-0.05 --0.118+0.10 0.095 _+0.09 0.149_+ 0.13 - 0.294_+ 0.19
0.01
1/2
flo f12 f14 fl24 fl45 flo f12 f14 fl24 fl0 f12* f14* fl12" fl24"
0.23
0.011
Q-test results
R-test results
model A
model B
Q
F
case/model
R
F
1/2
1/1
19.4
4.32
1/1 3/1
0.42 1.99
3.07 3.07
The mode of representing data was found to be important and the best model for predicting the sulphate concentration was expressed in terms of total peroxide and total sulphate rather than the molar ratio of peroxide to u r a n i u m or the excess sulphate, i.e. model 3/1 where the data were fitted at all confidence intervals ( see Table 7).
Crystal size and size distribution Typical size analyses are shown in Fig. 4. In general distributions were found to be adequately represented by a log normal distribution. The mean particle diameter was then calculated on this assumption, as well as the coefficient of variation between the mean particle diameters and the process variables. The models obtained indicated that the most significant variable would be the sulphate concentration. Models developed to predict the coefficient of variation again indicated an effect of sulphate concentration with possible interaction effects of the uranium concentration with total peroxide, pH and time. Microscopic examination of the crystal indicated that a large amount of
330
APPROXIMATE
S I Z E DISTRIBUTIONS
50--
4o
-
Run No. 15
z
o h.~) (n m
E ~
Run No.lO 3o
2O
Run No.6
I0
O 0
tL
, 30 , I0 20 ~0
°I
102O3O40
°I
I0 20 30
Dp /..Lm
Fig. 4. Approximate size distributions air-dried filter cake.
agglomeration had occurred. The image analysis did not distinguish between individual crystals and crystal agglomerates, measuring the agglomerate as a single particle. Time was not a particularly significant variable affecting the particle size, as would be expected from either crystal growth or agglomeration theory. It is likely, therefore, that agglomerates were formed due to the washing and drying of the filter cake before the sample was mounted on the slide. One variable that was not measured was the filtration rate; the slurries filtered readily, taking no more than two to three minutes. However, for runs number 10 and number 19 filtration rates were approximately 40 minutes, indicating that the nature of these precipitates was quite different from the
331 remaining runs. In these runs mean diameters tended to be larger rather than smaller. Run 10 also showed a significantly different size distribution than was found for the other runs (see Fig. 4). Samples taken directly from the precipitator indicated that crystals were of uniform size and less than 1.0/zm in length. As a result the effects of sulphate and peroxide concentration on particle size observed appear to represent the caking tendencies of the precipitate taking place during washing and drying rather than on crystal growth in the precipitator. Consequently, future studies should be aimed at determining the filtration rate, to more accurately assess slurry handling characteristics. CONCLUSIONS This study confirmed results previously reported in the literature that a high quality yellow cake is readily obtained from uranyl sulphate solutions. The kinetics of the reaction are fast; however, an excess of peroxide must always be present. Under this condition yields were high and the crystal slurries exhibited good handling characteristics. A linear model analysis was used to examine the process. This analysis showed that as the reaction was rapid, the amount of peroxide added can be considered the most significant process variable. Thus, in the presence of the stoichiometric amount of peroxide or greater, the amount of uranium precipitated and the filter cake yield can be adequately represented by linear modelling techniques which would permit simple control of the processin a uranium mill. Interaction effects were noted with respect to sulphate and pH. At high sulphate levels insufficient peroxide resulted not only in a decrease of yield but also decreased product quality and adversely affected filtration characteristics. Several plausible models were found for determining the sulphur content of the filter cake. The precipitation pH, although not explicitly present in the models, did affect the reaction, resulting in an increase in yield and a lowering of the barrens concentration. Due to exponential variation of the barrens concentration, the barrens concentrations could not be adequately modelled. The results did indicate that an effluent concentration of less than 0.001 g uranium/1 can readily be obtained. Reaction time was not found to be a significant variable with respect to yield or recovery. The barrens concentration did decrease with increase in time, while reports in the literature indicate time can influence crystal growth. From the results obtained it would appear that a digestion time of two hours should be sufficient to produce a slurry with good handling characteristics. The effect of impurities such as molybdenum and vanadium was not considered. It would be expected that they would produce significant interaction effects and further work should be carried out to see if suitable linear models can be developed for control of the process, with respect to uranium recovery, product purity, barrens concentrations and filtration rates.
332 ACKNOWLEDGMENTS One of us (JSH) would like to thank Energy, Mines and Resources for financial support in form of an unsolicited research grant. LIST OF SYMBOLS
C.V° Dp
eu F, Pl~V2 kr
kd kH202 N
g. Q R SSR t Xi Xiu Xi
Y
Coefficient of variation Particle diameter, Hm Residual value for run u Value of F-distribution for vl and P2 degrees of freedom Observed reaction rate constant, mol-1 s - i Decomposition rate of H202, moles per unit time, eqn. (3) Equilibrium constant, eqns. (1) and (2) Number of data points Number of parameters in the linear equation Ratio of extra sum of squares to the estimated variance Ratio of error due to lack of fit to the pure error variance Sum of squares of the residuals Time, s Coded value of independent variable i in the linear equations Coded value of independent variable i for run u Actual value of the independent variable i Measured response for experimental run u Dependent response variable in the linear equation
Greek symbols
E P P2
Parameter associated with main effect i in the linear equation i = 0,1,2,3,4,5 Parameter associated with a quadratic effect i, i-- 1,2,....,5k Parameter associated with an interaction effect between variable i and j; i,j= 1,2 ..... ,5, i ¢ j Parameter value estimated from linear least squares Random error associated with a measured response Degrees of freedom for a t-distribution Degrees of freedom in the numerator of an F-distribution Degrees of freedom in the denominator of an Fdistribution
333 G2
Estimated variance
Superscripts *
Alternative set of independent variables in the linear equations, Cases 2 and 4
Subscripts 0 1 2 3 4 5 cp
Initial value Initial uranium content Amount of H202 pH Sulphate level Time Centrepoint
REFERENCES 1 Ritcey, G.M. and Ashbrook, A.W., Solvent Extraction, Principles and Application to Process Metallurgy, Part 2, Elsevier, Amsterdam, 1979, pp. 452-522. 2 Tomazic, B., Smarzija, M. and Branica, M., Characteristics of ammonium uranate suspensions, J. Inorg. Nucl. Chem., 31 (1969) 1771. 3 Merino, J.L., Production of yeUowcake and uranium fluorides, Proceedings of an Advisory Group Meeting, Paris, 5-8 June 1979, IAEA, Vienna, 1980, p. 63. 4 Rodriquez, B., Obtaining Purlex uranium concentrates with low sulphate content production of yellowcake and uranium fluorides, Proceedings of an Advisory Group Meeting, Paris, 5-8 June 1979, IAEA, Vienna, 1980, p. 83. 5 Stuart, W.L. and Whateley, T.L., Composition and structure of ammonium uranates, J. Inorg. Nucl. Chem., 31 (1969) 1639. 6 Merrit, R.C. The Extractive Metallurgy of Uranium, Colorado School of Mines, Golden, CO, 1971, p. 247. 7 Shabbir, M. and Tame, J.E., Hydrogen peroxide precipitation of uranium, U.S. Bur. Mines, Rep. Invest., R1-7931, 1974. 8 Yamire, M. and Waters, J., An improved precipitation technique for recovery of uranium from its liquors, Chemeco 80, Process Industries in the 80's, 8th Australian Chemical Engineering Conference, August 1980. 9 Zimmer, E.L., Preparation and separation of uranium peroxide, as a stage in the chemical purification of crude uraniferous products, Proceedings International Conference on Peaceful Use of Atomic Energy Vol. 8, 1955, 120. 10 McFarlane, L. and Rollwagen, D., Hydrogen peroxide precipitation of uranium at Madawaka Mines Ltd, Proceedings Canadian Mineral Processors, Ottawa, Jan. 19-21, Paper No. 8, 1982, 139 p. 11 Bhattacharyya, P.K., Saini, R.D. and Ruikar, P.B., Reaction between U(IV) and H~02 in hydrochloric acid medium, Int. J. Chem. Kin., 14 (1982) 1219.
334
12 Bhattacharyya, P.K., Saini, R.D. and Ruikar, P.B., Kinetics of the oxidation of U (IV) by hydrogen peroxide in sulfuricacid medium, Int.J. Chem. Kin., 13 (1981) 385. 13 Brown, R.A., The Precipitationof Uranium with Hydrogen Peroxide, F M C Corp.,Chemical Research and Development Centre, Princeton, NJ, 1980. 14 Bryson, A.W., Electrolyticprecipitationof ammonium diuranate,J.S.Aft.Inst.Min. Metall., 76 (Sept. 1975) 13. 15 Draper, N. and Smith, H., Applied Regression Analysis, 2nd edn., J. Wiley & Sons, New York, NY, 1981, p. 33. 16 Stinson,W.J., Possiblechanges in concentrate specificationsfor UF6 conversion,Canadian Uranium Producers MetallurgicalCommittee Meeting, Ottawa, May 19, 1977. 17 List,J.E. and Coleman, R.B., Current U.S. methods of yellowcake precipitation,Eng. Min. J., (Dec. 1979) 78. 18 Randolf,A.P. and Larson, M.A., Theory of ParticulateProcesses,Academic Press,New York, NY, 1971, p. 38.
315
Elsevier Science Publishers B.V., Amsterdam - - Printed in The Netherlands
The Critical Factors Influencing Uranium Precipitation by Hydrogen Peroxide: The Use of Experimental Design Techniques J.S. HOPKINS, J.A. GOLDING*
Chemical Engineering Department, University of Ottawa, Ottawa, Ontario KIN 9B4 (Canada) and G.M. RITCEY
HydrometaUurgical Section, CANMET-- Energy, Mines and Resources Canada, 555 Booth Street, Ottawa, Ontario KIA OG1 (Canada) (Received December 17, 1985; accepted in revised form August 27, 1986)
ABSTRACT Hopkins, J.S., Golding, J.A. and Ritcey, G.M., 1987. The critical factors influencing uranium precipitation by hydrogen peroxide: The use of experimental design techniques. Hydrometallurgy, 17: 315-334. The effects of process operating variables on properties of yellow cake produced by peroxide precipitation have been investigated using experimental design techniques. The variables studied were: the initial uranium concentration, added peroxide, sulphate concentration, pH and precipitation time. The properties evaluated were: the filter cake yield, uranium precipitated, recovery, barrens concentration, product purity and particle size distribution. The effects of operating variables were described by using a linear equation of the form: 5 i=1
5
5
i=lj=l
A two-level factorial design was used as the basis for two experimental plans to determine the effects of the processing variables. The plans were: (1) a central composite design, and ( 2 ) a twolevel factorial design with added centrepoints. The adequacy of any plausible model was evaluated using a quantitative lack of fit test. In general, due to the critical effect of the peroxide concentration, models describing the system responses did not pass this test for the central composite design. However, for the factorial design plan, the majority of the response models were adequately modeled. The results indicated that the most critical process variable would be the peroxide requirement, when the addition of lower than the stoichiometric amount of peroxide would not only reduce yields but affect cake properties. In the presence of excess peroxide the results indicated process control of a uranium mill could be readily carried out by a process operator on the basis of simple linear models. However, significant interaction effects were observed for sulphate, total peroxide concentration and pH on yields, recovery and yellow cake purity. The most critical factor influencingthe barrens concentration was found to be the precipitation pH. *Author to whom correspondence should be addressed.
316 INTRODUCTION
The production of yellow cake is an essential step in the preparation of fuels for use in nuclear reactors. In its production by precipitation methods, not only is a high purity product required, but good filtration, settling and material handling characteristics are needed as well as a product that can be readily dried. In North America, the most commonly used method [ 1 ] is ammonium hydroxide precipitation to form ammonium diuranate. Research in this area [ 2-5 ] has shown that the precipitation reaction will only take place at certain pH levels and ammonia concentrations; also for batch precipitations a sudden drop in pH takes place at the initiation of the reaction [ 2 ]. Studies to relate precipitation conditions to thickening, filtration and drying properties have shown that changes in precipitation conditions carried out to improve settling and filtration characteristics can adversely affect product purity. However, it has been found that sulphate levels in the yellow cake can be maintained within refiners' specifications by carrying out the precipitation reaction between 30 and 40 ° C [ 4 ]. Investigations of the structure of ammonium diuranate [ 5 ] show that (NH4)2U207 is only an approximate formula, indicating that the precipitation reaction is complex.
Peroxide precipitation Although at present the ammonium diuranate produced is of high quality, two factors are beginning to influence the industry. Firstly, product specifications are becoming more stringent, while, secondly, lower quality ore grades are being processed so that the impurity levels in the leach solutions may rise. As a result the use of hydrogen peroxide to precipitate uranium has received considerable attention [ 6-13 ]. In sulphate solution the basic reaction is taken as:
UO2" S04 + H2 02 + x H2 0-* UO4'xH2 0 + H2 SO4
(1)
After drying at 100 °C the dihydrate U04" 2H20 is assumed to be the most stable hydrate although higher hydrates are formed initially [6]. Shabbir and Tame [7] carried out batch precipitations to determine the effect of pH, sulphate level, molybdenum and vanadium impurities on product purity and barrens concentration. An optimum pH range between 4 and 5 was reported to be required for complete precipitation; however, a higher purity product was produced at a pH of 5. Increase in temperature increased the precipitation rate but the vanadium and molybdenum impurity levels in the cake were raised. Reaction rates were also found to decrease as the sulphate level was raised. It was also noted that a definite excess of peroxide was needed to prevent molybdenum precipitation. An optimum pH was also reported between 4.0 and 5 to minimize the uranium concentrations in the barrens. A lower optimum pH
317 value of 3.3 was reported by Yamire and Waters [8] in their studies involving an ion exchange eluate solution containing vanadium. A large excess of peroxide was recommended, 3:1 molar ratio H2 02 :U3 08, by these authors, while to minimize peroxide decomposition ambient precipitation temperatures were suggested. In another study by Zimmer [ 9 ] an effect of pH and temperature on crystal structure and filtration rate was noted. A small excess of peroxide and an operating pH of 2.0-2.5 at a temperature of 40-50 ° C were suggested as being the most suitable conditions. In a study by McFarlane and Rollwagen [10] hydrogen peroxide has also been found to reduce the silica content of yellow cake. The silica content causes a downstream production problem. Other work on the precipitation kinetics has been carried out by Bhattacharyya et al. [ 11,12 ]. They found that the reaction rate for the oxidation of U (IV) to U (VI) could be described by a bimolecular rate equation with respect to the uranium and peroxide concentrations in the presence of either sulphate or chloride. The rate constant was also found to depend on the particular acid medium, the following relationship being reported: kr (~ ( [ H + ] - 1.3) for hydrochloric acid k r c~
([H + ] -0.55) for sulphuric acid
Brown [ 13 ] studied the effect of process variables on the barrens concentrations, developing a model based on the chemical equilibria between U022+ , S042- and C1-. The proposed relationship for UaO8 concentration in g/1 was: [U3Os]
~- ( 1 0 - P H ) 2
{1 +710 [S042- ]2/96
+O.SS[C1- ]/35.5}/(3[H202]kn2o2/810×34}
(2)
The equilibrium constant kH~o2 for peroxide and the peroxide concentration were expressed as functions of time: kH2o2
=kt=o+ t 2 / ( 1 + 0 . 0 7 5 t 2) 3
[H,.,02] : [ H 2 0 2 l t = o ( 1 - k ~ ~
(3) )
([U3Oslt:o -- [U308]t]/[U308]t:o)
(4)
where kd = decomposition constant for peroxide. In comparing predicted results with experimental data it was found that the predicted barrens concentrations were lower than experimental values. The author then noted that an empirical model had been developed which more closely agreed with the experimental results; however, this relationship was not given.
318
Crystal growth Particle size and size distributions are of importance when considering ways to increase filtration and drying rates as they are both dependent on the specific surface area of the filter cake. Variables influencing the precipitation reaction affect crystal size [ 8,9 ]. However, these effects are not always beneficial, for as reported by Bryson [14 ] an improvement in filtration properties can also reduce filter cake quality. In precipitation reactions there is generally an instantaneous formation of small, uniformly sized particles which then grow by a process of crystal dissolution and re-precipitation or by agglomeration. Consequently, information on crystal growth, particle size and agglomeration can be expected to be extremely useful in selection of a practical range of process operating conditions. Thus if hydrogen peroxide is to be considered by a uranium mill without any major changes being made to the rest of the plant it is important to know the following: (1) which of the process variables have major influences on the precipitation reaction, and (2) their effect on product properties. In order to evaluate the effects of the process variables on the process responses, two experimental designs were used; one, a central composite design, the second, a two-level factorial design with added centrepoints. This method of experimentation involves the assumption of an empirical model to determine the critical factors affecting a reaction and allows for the screening out of unimportant variables. The method, though, does have limitations, as there is a basic inability to extrapolate beyond the experimental range covered and no mechanistic model is provided. LEAST SQUARES EQUATION A N D EXPERIMENTAL DESIGN
The assumption was made that the data could be fitted by a second-order polynomial. Five process variables were selected so that for a particular response, e.g. yield (see Table 3 ), the polynomial takes the form: 5
5
5
Y=flo + ~ flix, + ~ ~ ~ijxij i=1
(4)
i=lj=l
The difference between the predicted and the experimental response was then minimized using the least squares technique. To reduce correlation between the parameters the independent variables were redefined in coded form as follows:
xi = (~:i - Xi)/half the range of Xi, i = 1 to 5.
(5)
The mean value ()~i) and range of Xi were evaluated using the three internal values of the independent variables; these points were coded values of - 1, 0
319 and 1. Two experimental plans were considered. The basis of both plans was a 25-- 1 fractional factorial design with time xs, being confounded with the other four process variables (i.e. xs =xlx2x3x4). The first design considered was a central composite design which included the 16 runs of the 25-1 fractional factorial design, 10 centrepoint runs and 10 axial runs (xi = + 2, xi = 0) [11 ]. With this design it was anticipated that quadratic effects could be measured. The second plan was constructed from the 2°- 1design and the 10 centrepoints. The values of the independent variables for any of the runs involved in the designs were determined using standard procedures. Since the pH values of the experimental runs did not correspond to those prescribed by the experimental plans, the pH values were recorded employing the mean and range of all experimental values of the pH. In general parameters were evaluated at the 95% confidence level and if any parameter values were plausibly zero, i.e. the value of zero was contained within the confidence interval, the parameter was dropped from eqn. (4). This operation gave one or more plausible models for each of the process responses. If more than one model was found the need for additional parameters was determined by evaluating the Q ratio where Q was defined as: Q_I
--0.2
( S S R B - SSRA)/(NpB--NpA)
(6)
where SSR = sums of squares of residual, Np = number of parameters ( A and B refer to the models being compared), 0.2= S S R B / ( N - N B ) , N= number of runs, Model B is the model containing the most parameters. The Q ratio was then compared to Fo.os,Pi,v2, where p~= NpB-- NpA, P2 -~ N - NB. If Q was greater than Fo.os,Pl,P2, the extra parameter can be considered significant, i.e. additional parameters were required to adequately fit the data. The models were also tested for lack of fit, to determine whether plausible equations did indeed describe the data by using a quantitative lack of fit test. In this test the R ratio was calculated, where R is defined as:
R - ( SSR-pcp0.~p) / ( N - N p -Pep)
(7)
2 0"cp
where 0-c~is the estimated variance at the design plan centrepoint and Pep was the degree of freedom at the centrepoint. The R value was again compared to the distribution Fo.os,P,Pcp; when R was greater than Fo.os,P,Pcpthe model could be assumed not to fit the data, i.e. inadequate, where p = N - N p - P e p . The 0-~pvalues were only an estimate of the true experimental error due to problems with control of the pH. However, as the estimated values of 0.¢2pcan be considered to be greater than the true variance, any model which failed this test would certainly fail if, the true value of the variance were known (see Draper and Smith [15] for a discussion of the methods used).
320 EXPERIMENTAL The variables chosen for study were the initial uranium concentration, initial molar ratio of peroxide to uranium, the precipitation pH, excess sulphate and the precipitation or digestion time. The range of operating variables studied is given in Table 1 and the values chosen were based on data reported in the literature as representative of concentrations that would be used in an actual plant operation. The previous work by Shabbir and Tame [ 7 ] indicated that an excess of peroxide was required when molybdenum and vanadium Were present. It was decided, however, that for this work the amount of these particular impurities should be kept to a minimum by using a scrap yellow cake. This was so that the effectiveness of experimental design techniques to provide a suitable model for control of the process could be evaluated. In addition, if the process is to be optimized with respect to minimizing the peroxide cost, it was of interest to obtain information on the effect of hydrogen peroxide on the reaction in the presence of sulphate. As a result one run was carried out with insufficient peroxide available to precipitate all the uranium. It was also decided that for this particular study temperature and mixing characteristics would not be investigated. The process variables were also analyzed in terms of alternative methods of representing them, i.e. the amount of peroxide added rather than the molar ratio, the total sulphate rather than excess sulphate, and the hydrogen ion concentration rather than the pH. Data analysis was again carried out by expressing the variables in coded form. The response variables studied to determine the precipitation characteristics were; filter cake yield, i.e. filter cake weight, the uranium content, sulphur content, particle size and size distribution. Initially, the filtration rate had been chosen as a significant process reponse. However, except for two runs as discussed later, the precipitate filtered so rapidly that meaningful filtration data could not be obtained.
Procedures The uranium source was a blend of scrap yellow cake remaining from previous experimental investigations at CANMET; the blended yellow cake contianed 67.86% U by weight. The uranium solution was prepared as follows. The required amount of uranium to produce one litre of solution having the desired initial concentration was dissolved in sulphuric acid. The amount of acid added was the stoichiometric amount desired to form U02 (S04) 4- plus any excess required by the experimental design. A small amount of distilled water was also added at this point so that the heat of reaction would facilitate dissolution of the yellow cake. After complete dissolution the solution was transferred to a one-litre volumetric flask and made up to volume. The solution was then transferred to the four-litre batch precipitation reactor and the solu-
321 TABLE 1 The operating and coded values of process variables
Run
X.
X~,
X3,
X4,
Xs,
number
Initial uranium content, g/1
[ H2OJU ]
Experimental pH
Excess sulphate, g/1
Time, h
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
12.5 ( - - I ) a 17.5 ( i ) 12.5(-I) 17.5 ( 1 ) 12.5 (--1) 17.5 ( 1 ) 12.5 (--1) 17.5 ( 1 ) 12.5 (--1) 17.5 ( 1 ) 12.5 (--1) 17.5 ( 1 ) 12.5 (--1) 17.5 ( 1 ) 12.5 ( - I ) 17.5 ( 1 ) 10.0 (--2) 20.0 ( 2 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15,0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 ) 15.0 ( 0 )
1.50 (--I)" 1.50 ( - - I ) 3.00(i) 3.00 ( 1 ) 1.50 (--1) 1.50 (--1) 3.00 ( 1 ) 3.00 ( 1 ) 1.50 (--1) 1.50 (--1) 3.00 ( 1 ) 3.00 ( 1 ) 1.50 (--1) 1.50 (--1) 3.00 ( I ) 3.00 ( 1 ) 2.25 ( 0 ) 2.25 ( 0 ) 0.75 (--2) 3.75 ( 2 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 ) 2,25 ( 0 ) 2.25 ( 0 ) 2.25(0) 2.25 ( 0 ) 2.25 ( 0 ) 2.25 ( 0 )
2.83 (--0.4182) a 1.81 (--0.6958) 2.11 (--0.6141) 3,07 (--0.3529) 7.20 ( 0 . 7 7 0 9 ) 7.85 ( 0 . 9 4 7 8 ) 7.84 ( 0 . 9 4 5 0 ) 5.00 ( 0 . 1 7 2 2 ) 3.26 (--0.3012) 1.79 (--0.7012) 2.60 (--0.4808) 2.92 (--0.3937) 6.80 ( 0 . 6 6 2 0 ) 8.34 ( 1 . 0 8 1 1 ) 3.32 (-0.2849) 4.65 ( 0 . 0 7 7 0 ) 8.90 ( 1 . 2 3 3 5 ) 3.22 (--0.3125) 3.72 (--0.1765) 3.63 (--0.2010) 1.55 (-0.7670) 4.29 (-0.0214) 3.09 (-0.3479) 7.67 ( 0 . 8 9 8 3 ) 3.65 (-0.1955) 3.50 (-0.2364) 2.94 (-0.3887) 4.74 ( 0 . 1 0 1 1 ) 4.22 (-0.0404) 4.28 (-0.0241) 6.60 ( 0 . 6 0 7 2 ) 5.40 ( 0 . 2 8 0 7 ) 3.29 (-0.2935) 3.80 (-0.1547) 3.09 ( 0 . 3 4 7 9 ) 4.25 (-0.0323)
4.4 (--1) a 4.4 ( - I ) 4.4(--I) 4.4 (--1) 4.4 ( - 1 ) 4.4 (--1) 4.4 (--1) 4.4 (--1) 13.2 ( 1 ) 13.2 ( 1 ) 13.2 ( 1 ) 13.2 ( 1 ) 13.2 ( 1 ) 13.2 ( 1 ) 13.2 ( I ) 13.2 ( 1 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8,8 ( 0 ) 0 (-2) 17.6 ( 2 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 ) 8.8 ( 0 )
4 ( i)" 2 (--I) 2 (-I) 4 (1) 2 (--1) 4 (1) 4 (1) 2 (--1) 2 (-1) 4 (1) 4 (1) 2 (--1) 4 (1) 2 (--1) 2 (-I) 4 (1) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 1 (-2) 5 (2) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0)
"Coded values for statistical analysis.
tion stirred. The stirrer Reynolds number was maintained at 1500 for all experimental runs. The impeller was a propeller type having a diameter of 2.54 cm. The p H was then raised to 3.0 by addition of magnesium oxide powder and the
322 required amount of hydrogen peroxide added. After this the pH was measured and re-adjusted by addition of more MgO. Due to the rapidity of the precipitation reaction it proved to be difficult to obtain the actual design pH so that values differed from those initially selected. After t h e required precipitation time the suspension was filtered. The filter cake was then dried at room temperature for two or three days until a constant weight was reached. This technique was used so as to avoid loss of any water of hydration that might have taken place if elevated temperatures had been used to remove the superficial water. The cake was analyzed for uranium, magnesium and sulphate content, while the filtrate was analyzed for uranium to determine the barrens concentration. The chemical analyses were carried out by the analytical chemistry division of CANMET using standard procedures. The particle sizes were measured using an image analysis technique. A small sample from each run was mounted on a microscope slide and the crystals suspended in an emulsion oil. The image of the field was transmitted directly to a computer for size measurement and estimation of size distributions. Eight to ten fields were examined on each slide and differential and cumulative distributions were obtained. The range of operating variables and the actual values are shown in Table 1. RESULTS AND DISCUSSION Runs (Table 1 ) were carried out to permit analysis of the data for quadratic effects, full central composite design plan, and a two-level factorial design plan which would evaluate main effects and two variable interactions. Due to the difficulties experienced in controlling the precipitation pH, pH values did not correspond to design plans chosen; this could be expected to lead to some parameter correlation. Consequently parameters were evaluated not only for their significance at the 95% confidence level but also at the 80% and 90% levels. Analysis of the response data was based on the following procedures: Case 1. Data were analyzed using the original variables, i.e. x~ to xs. Case 2. Data were analyzed with the hydrogen ion concentration rather than the pH, i.e. the effects of x,, x2, x*3, x4 and xs. Case 3. Data were analyzed on the basis of the total peroxide and total sulphate concentration rather than the molar ratio H202/U or the excess sulphate, i.e. the effect ofxi, x*2, x3, x*4 and xs. Case 4. Data were analyzed on the basis of the total peroxide, total sulphate and the hydrogen ion concentration, i.e. the effects of xl, x*2, x*3, x*4 and x5. The means and variances of the centrepoint values of response variables are given in Table 2. The full results obtained are given in Table 3 and were analyzed according to the two experimental plans, i.e. Plan 1, the central composite design, and Plan 2, the two-level factorial design.
323 TABLE 2 Means and variances of the response variables at the design centrepoints Variable
Mean
a2
Filter cake yield (g) Uranium precipitated (g) Uranium content of the cake (wt. %) Barrens (rag/l) Recovery ( %) Sulphur (wt.%) Mean particle size (/lm) Particle size distribution
23.90 14.44 60.41 2.3 96.87 0.216 8.88 0.388
0.32 0.15 1.45 0.015 6.60 0.007 7.09 0.006
Filter cake yield, uranium precipitated and recovery These three responses were closely related and therefore are discussed together. The filter cake yield was the weight of filter cake after air drying and can be taken as an indication of the completeness of the precipitation reaction. It was not possible to base the actual yield on the inlet concentration and barrens concentration due to errors arising from transfer of solution to the precipitation reactor, filter cake losses and the chemical analysis. The amount of uranium was the filter cake yield multiplied by the uranium content while the recovery was the latter figure divided by initial weight of uranium charged to the precipitator. The filter cake yield was found to be a simple function of the initial uranium charged; however, there was an effect of p H on yield, as shown in Fig.l, the yield increasing slightly as the p H was raised. Models were developed to identify significant process variables and to predict yields. Plausible models found from design Plan 1 all failed the lack of fit test, i.e. the R test. The reason for this lack of fit was found to be run number 19 where the peroxide level was below the stoichiometric amount required to precipitate the uranium, see Fig. 2. This indicated the critical nature of the precipitation reaction to the amount of hydrogen peroxide added. The amount of uranium recovered for this run was much lower than would be expected from the stoichiometry of the reaction, i.e. 58.8% compared with a theoretical value of 75%. Consequently, even for the relatively pure solution used in this investigation, the amount of peroxide must equal or exceed the stoichiometric amount required to precipitate all the uranium in solution. It also meant that the choice of the range for Plan 1 was too large and any future modelling to determine quadratic effects, particularly to determine any optimum with respect to the pH, would be subject to the restriction that the molar ratio H202/U be greater than or equal to one. In Plan 2, this run was automatically eliminated from the analysis and it was found that the amount of uranium precipitated could be
324 TABLE 3 Experimental results
Run number
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Filter cake yield, g
Uranium content of cake,
Sulphur content of cake,
%
%
18.86 26.81 19.71 27.67 19.93 28.57 20.17 28.25 19.87 25.08 19.44 27.11 19.81 28.11 19.81 28.45 16.06 31.83 12.62 24.63 20.70 24.63 24.06 24.43 24.78 21.98 22.88 24.42 23.87 23.42 24.19 23.00 24.19 24.81 23.74 24.42
62.56 61.34 61.08 61.00 59.14 59.26 58.00 58.89 58.82 60.29 60.86 61.56 58.58 57.03 60.33 59.34 55.41 60.25 58.26 59.18 65.44 59.29 60.27 58.66 57.43 61.86 61.34 58.78 60.41 59.33 59.90 62.59 59.88 61.92 60.44 59.50
0.25 0.14 0.13 0.29 0.10 0.10 0.35 0.20 0.63 0.53 0.15 0.18 0.26 0.37 0.15 0.14 0.07 0.29 0.44 0.22 0.10 0.18 0.17 0.12 0.72 0.21 0.16 0.22 0.40 0.22 0.08 0.20 0.24 0.26 0.15 0.23
Barrens Uranium concentration, recovered g U/I in cake, 0.061 0.160 0.045 0.0022 0.0001 0.0002 0.0002 0.0001 0.0065 1.57 0.029 0.010 0.0003 0.0005 0.0074 0.0005 0.026 0.023 0.24 0.060 0.640 0.0032 0.0049 0.0004 0.0033 0.0008 0.013 0.0004 0.002 0.0019 0.0001 0.0001 0.0023 0.0006 0.0015 0.0008
Mean particle size,
%
tim
94.39 93.97 96.31 96.45 94.30 96.75 93.58 95.06 93.50 86.41 94.65 95.37 92.80 91.61 95.61 96.47 88.99 95.89 58.82 97.17 90.31 97.35 96.67 95.54 94.07 90.65 93.57 95.65 96.13 92.63 96.00 96.22 96.67 102.41 95.65 96.87
5.59 7.03 8.82 5.88 4.86 7.36 6.90 12.05 14.29 28.59 8.22 8.70 14.31 11.42 13.63 11.61 18.37 9.07 20.46 10.82 8.36 19.23 11.21 10.21 9.11 13.24 10.85 -12.61 -6.09 -6.27 -9.20 --
Particle size distribution (dispersity) 0.43 0.74 0.58 0.91 0.52 0.33 0.80 0.65 0.45 0.02 0.35 0.51 0.62 0.66 0.35 0.37 0.35 0.42 0.23 0.34 0.42 0.28 0.39 0.47 0.45 0.29 0.43 -0.27 -0.45 -0.36 -0.43 --
aSee Table 2.
well represented as a simple linear function of the initial uranium concentration. There was negligible effect of time on the amount precipitated; however, the results did show that pH was significant with the yield increasing slightly with increase in pH as noted previously. Plausible models found are given in Table 4. With regards to the uranium precipitated, plausible models found from design
325 3O
,,=,24 --
~
# £~I~77,, .o Uo=lSg/L ,,
X
X
22
.J )-
18
I 3
l 5
I 7
I 9
I II
pH
Fig. 1. Effect of final pH on filter cake yield. Uo: initial uranium concentration, g/l; - - : predicted yields.
32
30
28 26
~24
w" o~ 22 E 2o .J
uJ 18 >-
16 14 12 .75
1.5
2.25 3.0 MOLAR RATIO(HtOt/U)o
3.75
Fig. 2. The effect of molar ratio of peroxide to uranium on filter cake yield. Uo: initial uranium concentration, g/l; - - : predicted yields.
326 TABLE 4 Plausible models for the filter cake yield - - Experimental Plan 2 Case/ model
Initial conf. int., %
Significant parameters
Parameter values
SSR
a2
1/1
90
0.61
95
10.66
0.46
3/1
90
8.77
0.40
3/2
95
23.72±0.32 3.90 _ 0.40 23.73±0.28 3.92 _+0.35 0.78_+0.53 23.71_+0.26 3.91 _+0.34 0.35 + 0.34 0.87 _+0.50 23.72±0.32 3.90 ± 0.40
14.74
1/2
fl0 fl 1 flo fll 173 flo fl, f12* f13 flo fll
14.74
0.61
Q-test results
R-test results
model A
model B
Q
F
case/model
R
F
1/1 3/2
1/2 3/1
8.81 7.47
4.28 3.40
1/2 3/1
1.71 1.39
3.02 3.04
Plan 1 again failed the lack of fit test. In the case of Plan 2, the amount of uranium precipitated was mainly proportional to the initial uranium concentration. The results showed that there were interaction effects of the sulphate and hydrogen peroxide concentrations such that with an increase in the total sulphate concentration the amount of peroxide added should be raised to ensure that all the uranium in solution is precipitated. The pH had no significant effect on the actual amount of uranium precipitated. Plausible models are shown in Table 5. Recoveries were normally high; however, no plausible models were found from either Plan 1 or Plan 2, all failing the lack of fit test. The data did show the following: (i) recoveries in excess of 95% could be expected as long as sufficient peroxide was present; (ii) if sulphate levels were high and peroxide levels low, recoveries could fall (see run number 10); (iii) there is a possible significant effect of pH on recovery, as when the pH was raised recoveries did increase, see runs 10 and 14. As a result, an increase in the operating pH may be necessary when sulphate levels are high. This observed interaction indicates further study is needed in this area to investigate the effect of sulphate and pH with respect to both the actual precipitation reaction and the decomposition of hydrogen peroxide when sulphates are present.
327 TABLE 5 Plausible models for the uranium precipitated - - Experimental Plan 2 Case/ model
Initial conf. int., %
Significant parameters
Parameter values
SSR
(7 2
1/1
all
0.20
all
14.25+_.0.17 2.33 ___0.22 14.32 ± 0.19 2.33 ± 0.22 -0.53 ±0.44 0.76 ± 0.71
4.70
3/1
flo fll flo fll
3.26
0.15
•14" ]~24$
Q-test results
R-test results
model A
model B
Q
F
case/model
R
F
1/1
3/1
9.77
4.20
1/1 3/1
1.50 0.99
3.01 3.06
Barrens concentration T h e results i n d i c a t e d t h a t p e r o x i d e c o n c e n t r a t i o n , s u l p h a t e c o n c e n t r a t i o n a n d p r e c i p i t a t i o n p H i n f l u e n c e d t h e b a r r e n s c o n c e n t r a t i o n . D e s p i t e t h e scatter, t h e p r e c i p i t a t i o n p H seems to be t h e m o s t i m p o r t a n t variable t h a t m u s t be controlled, see T a b l e 6 a n d Fig. 3. Qualitatively, models i n d i c a t e d t h a t all the above variables would be significant, t o g e t h e r w i t h a possible effect o f r e a c t i o n time. T h e s e effects s h o u l d be i n v e s t i g a t e d in m o r e detail. As t h e p r e c i p i t a t i o n r e a c t i o n was rapid, r e a c t i o n t i m e would h a v e to be studied o v e r m u c h s h o r t e r periods t h a n were c o n s i d e r e d in this s t u d y to o b t a i n a kinetic model. In addition, t h e d a t a were s p r e a d over several orders of m a g n i t u d e a n d indiTABLE 6 Effect of precipitation pH on barrens concentration Precipitation pH range 2.0 2.0-2.99 3.0-3.99 b 4.0-4.99
t>5.0
Barrens concentration range a, g/1 1.57 -0.64 0.064-0.013 0.06 -0.0006 0.0032-0.0001
0.0005-0.0001
aIncludes the effect of precipitation time which varied within the pH ranges, see Table 2. bExcluding the results of run number 19.
328 I.OxlO "=
_.1
_
,
i.
r
5
Z -0
I'<~ nI--Z LU 0 Z 0
l.OxlO, e
5
• ",,, ,
(/) l.Oxl0 "I Z LLJ rr 5 rr <~ nn
I.OxK)- 4
5
1.0
I
2.0
I
3.0
I
I
4.0
5.0
I
6.0
7.0
pH Fig. 3. Effect of pH on barrens concentration.
cated an exponential variation of barrens concentration with pH. Consequently, linear modeling of the response was not possible, i.e. none of the plausible models passed the lack of fit test. In any future work the effects of process variables on the barrens concentration would require the application of a non-linear modelling technique.
Product purity The uranium and sulphur content determine the product purity. The uranium content varied from 55% to 65% while the sulphur content varied from 0.07% to 0.44%. All processing variables seem to influence product purity and several plausible models can be developed to predict the sulphur content of the filter cake. All the models indicated that a decrease in the peroxide concentration would increase the sulphur content of the cake. However, the results showed that even at the highest sulphate levels the product would meet refiners' standards [ 17 ].
329 TABLE 7 Plausible models for the sulphur content of the filter cake - - Experimental Plan 2 Case/ model
Initial conf. int., %
Significant parameter
Parameter values
SSR
a2
1/1
90
0.005
95
0.02
0.009
3/1
all
0.236_+0.03 - 0.049 ___0.04 0.053 _+0.04 0.097 _+0.04 - 0.145 + 0.04 0.236_+ 0.03 - 0.049 + 0.04 0.053 _+0.04 0.097 _ 0.04 0.299_-L-0.05 --0.118+0.10 0.095 _+0.09 0.149_+ 0.13 - 0.294_+ 0.19
0.01
1/2
flo f12 f14 fl24 fl45 flo f12 f14 fl24 fl0 f12* f14* fl12" fl24"
0.23
0.011
Q-test results
R-test results
model A
model B
Q
F
case/model
R
F
1/2
1/1
19.4
4.32
1/1 3/1
0.42 1.99
3.07 3.07
The mode of representing data was found to be important and the best model for predicting the sulphate concentration was expressed in terms of total peroxide and total sulphate rather than the molar ratio of peroxide to u r a n i u m or the excess sulphate, i.e. model 3/1 where the data were fitted at all confidence intervals ( see Table 7).
Crystal size and size distribution Typical size analyses are shown in Fig. 4. In general distributions were found to be adequately represented by a log normal distribution. The mean particle diameter was then calculated on this assumption, as well as the coefficient of variation between the mean particle diameters and the process variables. The models obtained indicated that the most significant variable would be the sulphate concentration. Models developed to predict the coefficient of variation again indicated an effect of sulphate concentration with possible interaction effects of the uranium concentration with total peroxide, pH and time. Microscopic examination of the crystal indicated that a large amount of
330
APPROXIMATE
S I Z E DISTRIBUTIONS
50--
4o
-
Run No. 15
z
o h.~) (n m
E ~
Run No.lO 3o
2O
Run No.6
I0
O 0
tL
, 30 , I0 20 ~0
°I
102O3O40
°I
I0 20 30
Dp /..Lm
Fig. 4. Approximate size distributions air-dried filter cake.
agglomeration had occurred. The image analysis did not distinguish between individual crystals and crystal agglomerates, measuring the agglomerate as a single particle. Time was not a particularly significant variable affecting the particle size, as would be expected from either crystal growth or agglomeration theory. It is likely, therefore, that agglomerates were formed due to the washing and drying of the filter cake before the sample was mounted on the slide. One variable that was not measured was the filtration rate; the slurries filtered readily, taking no more than two to three minutes. However, for runs number 10 and number 19 filtration rates were approximately 40 minutes, indicating that the nature of these precipitates was quite different from the
331 remaining runs. In these runs mean diameters tended to be larger rather than smaller. Run 10 also showed a significantly different size distribution than was found for the other runs (see Fig. 4). Samples taken directly from the precipitator indicated that crystals were of uniform size and less than 1.0/zm in length. As a result the effects of sulphate and peroxide concentration on particle size observed appear to represent the caking tendencies of the precipitate taking place during washing and drying rather than on crystal growth in the precipitator. Consequently, future studies should be aimed at determining the filtration rate, to more accurately assess slurry handling characteristics. CONCLUSIONS This study confirmed results previously reported in the literature that a high quality yellow cake is readily obtained from uranyl sulphate solutions. The kinetics of the reaction are fast; however, an excess of peroxide must always be present. Under this condition yields were high and the crystal slurries exhibited good handling characteristics. A linear model analysis was used to examine the process. This analysis showed that as the reaction was rapid, the amount of peroxide added can be considered the most significant process variable. Thus, in the presence of the stoichiometric amount of peroxide or greater, the amount of uranium precipitated and the filter cake yield can be adequately represented by linear modelling techniques which would permit simple control of the processin a uranium mill. Interaction effects were noted with respect to sulphate and pH. At high sulphate levels insufficient peroxide resulted not only in a decrease of yield but also decreased product quality and adversely affected filtration characteristics. Several plausible models were found for determining the sulphur content of the filter cake. The precipitation pH, although not explicitly present in the models, did affect the reaction, resulting in an increase in yield and a lowering of the barrens concentration. Due to exponential variation of the barrens concentration, the barrens concentrations could not be adequately modelled. The results did indicate that an effluent concentration of less than 0.001 g uranium/1 can readily be obtained. Reaction time was not found to be a significant variable with respect to yield or recovery. The barrens concentration did decrease with increase in time, while reports in the literature indicate time can influence crystal growth. From the results obtained it would appear that a digestion time of two hours should be sufficient to produce a slurry with good handling characteristics. The effect of impurities such as molybdenum and vanadium was not considered. It would be expected that they would produce significant interaction effects and further work should be carried out to see if suitable linear models can be developed for control of the process, with respect to uranium recovery, product purity, barrens concentrations and filtration rates.
332 ACKNOWLEDGMENTS One of us (JSH) would like to thank Energy, Mines and Resources for financial support in form of an unsolicited research grant. LIST OF SYMBOLS
C.V° Dp
eu F, Pl~V2 kr
kd kH202 N
g. Q R SSR t Xi Xiu Xi
Y
Coefficient of variation Particle diameter, Hm Residual value for run u Value of F-distribution for vl and P2 degrees of freedom Observed reaction rate constant, mol-1 s - i Decomposition rate of H202, moles per unit time, eqn. (3) Equilibrium constant, eqns. (1) and (2) Number of data points Number of parameters in the linear equation Ratio of extra sum of squares to the estimated variance Ratio of error due to lack of fit to the pure error variance Sum of squares of the residuals Time, s Coded value of independent variable i in the linear equations Coded value of independent variable i for run u Actual value of the independent variable i Measured response for experimental run u Dependent response variable in the linear equation
Greek symbols
E P P2
Parameter associated with main effect i in the linear equation i = 0,1,2,3,4,5 Parameter associated with a quadratic effect i, i-- 1,2,....,5k Parameter associated with an interaction effect between variable i and j; i,j= 1,2 ..... ,5, i ¢ j Parameter value estimated from linear least squares Random error associated with a measured response Degrees of freedom for a t-distribution Degrees of freedom in the numerator of an F-distribution Degrees of freedom in the denominator of an Fdistribution
333 G2
Estimated variance
Superscripts *
Alternative set of independent variables in the linear equations, Cases 2 and 4
Subscripts 0 1 2 3 4 5 cp
Initial value Initial uranium content Amount of H202 pH Sulphate level Time Centrepoint
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334
12 Bhattacharyya, P.K., Saini, R.D. and Ruikar, P.B., Kinetics of the oxidation of U (IV) by hydrogen peroxide in sulfuricacid medium, Int.J. Chem. Kin., 13 (1981) 385. 13 Brown, R.A., The Precipitationof Uranium with Hydrogen Peroxide, F M C Corp.,Chemical Research and Development Centre, Princeton, NJ, 1980. 14 Bryson, A.W., Electrolyticprecipitationof ammonium diuranate,J.S.Aft.Inst.Min. Metall., 76 (Sept. 1975) 13. 15 Draper, N. and Smith, H., Applied Regression Analysis, 2nd edn., J. Wiley & Sons, New York, NY, 1981, p. 33. 16 Stinson,W.J., Possiblechanges in concentrate specificationsfor UF6 conversion,Canadian Uranium Producers MetallurgicalCommittee Meeting, Ottawa, May 19, 1977. 17 List,J.E. and Coleman, R.B., Current U.S. methods of yellowcake precipitation,Eng. Min. J., (Dec. 1979) 78. 18 Randolf,A.P. and Larson, M.A., Theory of ParticulateProcesses,Academic Press,New York, NY, 1971, p. 38.