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Optimization of Nickel Hydroxycarbonate Precipitation Using a Laboratory Pellet Reactor Damien Guillard and Alison E. Lewis* Precipitation and Crystallisation Research Facility, Chemical Engineering Department, University of Cape Town, Cape Town, South Africa 7701
Hydroxycarbonate precipitation of nickel in a pellet reactor was investigated in a previous study (Guillard, D.; Lewis, A. E. Ind. Eng. Chem. Res. 2001, 40 (23), 5564-5569)1 in order to gain an understanding of the precipitation processes occurring inside the reactor. This paper presents the results of an attempt to establish conditions for optimum nickel removal and to map the response surface representing the response of the total nickel concentration to changes in a number of operating parameters. It was established that, of the five parameters investigated (carbonate/nickel ratio, pH, nickel feed concentration, number of feed points, and recirculation ratio), the first three had the most significant influence on the efficiency of nickel removal in the reactor for the range of conditions investigated. At optimum conditions, 98% removal of nickel was achieved, with the concentration of total nickel in the effluent being reduced from 69.5 to 1.38 ppm. Significant variability in the results prevented a global optimum from being found, and only a stationary point could be established. Introduction As environmental release standards and economic constraints become tighter, industry is placing greater emphasis on treating metal-containing effluents. Several techniques have been developed to recover metals, such as evaporation, ion exchange, and electrolysis. The most commonly used process is precipitation2 because it offers a cost-effective solution applicable to large operating units. The development of heavy-metal precipitation in pellet reactors is one of a few proposed methods to improve metal recovery. Pellet reactor technology has been developed for the softening of drinking water,3 for the removal of phosphates from wastewater,4 and for the removal of heavy metals,5,6 using carbonate as a precipitating agent. In the literature,5,6 treated heavy-metal concentrations range from 10 to 100 000 ppm of metal from various waste streams such as acid mine drainage, electroplating, and base metal refining. The pellet reactor provides an ideal environment for controlled crystallization in a stable and easily operated process. The large crystal surface area provided by the pellets favors heterogeneous nucleation and allows operation with a slightly supersaturated solution that avoids spontaneous nucleation of fines that are not easily separated from the stream. The relatively high fluid velocity in the reactor, typically in the range of 10-35 cm/s,7 ensures both fluidization and good mixing of the reactants. Using this technology, a dense metal salt precipitate is obtained directly on the pellets. Sludge formation and related problems are avoided, and the metal salt covering the pellets can then be recovered in a concentrated solution by dissolving in strong acid. The pellets can also be reused in the reactor. This paper focuses on the optimization of precipitation of nickel hydroxycarbonate from a synthetic nickel * To whom all correspondence should be addressed. Email:
[email protected]. Phone: +27 21 650 4091. Fax: +27 21 689 7579.
sulfate stream, using a laboratory-scale pellet reactor. A response surface method of experimental design was used in an attempt to establish the operating conditions for optimal nickel removal and to estimate the response surface for a number of operating conditions. Experimental Setup Reactor Design. The pellet reactor consisted of a cylindrical vessel of 1 m height and 0.025 m i.d. sealed from the atmosphere. See Figure 1. The bottom of the column consisted of a conical glass fitting incorporating a plastic nozzle with large holes. This fitting was filled with glass beads of decreasing diameters that allowed uniform distribution of the upward flow as well as provided a support for the pellets. The reactor was filled with a narrow particle size distribution (50-70 mesh) white quartz sand mixed with calcite and aragonite (Xray diffraction analysis). The height of the bed at rest was 60 ( 5 cm, and the fluidization was achieved mainly through the recirculation flow (maximum recirculation ratio of 1.66 to the inlet stream). See Table 1. There were three possible inlets for the carbonate solution on the side of the column. Each inlet was controlled with a separate valve and consisted of a steel tube of 1 mm i.d. The inlets were spaced every 10 cm starting 15 cm from the bottom of the bed. There were seven sampling points (10 mm o.d.), spaced every 10 cm from the bottom of the column. The outlet points allowed the withdrawal of solution as well as pellets from the reactor at different heights. At the top of the column, there were two similar outlets (glass tube of 10 mm o.d.), which were used for the recirculation flow and the treated water flow. The recirculated flow was mixed with the fresh inlet stream just before the reactor inlet using a T mixer. The distance between the T mixer and the entrance to the reactor was minimized to prevent an excess of fines precipitating outside the reactor. The recirculation flow was used to increase the number of passes of the nickel through the reactor as well as to provide a dilution of the inlet stream at the bottom of the reactor.
10.1021/ie010873h CCC: $22.00 © 2002 American Chemical Society Published on Web 05/22/2002
Ind. Eng. Chem. Res., Vol. 41, No. 13, 2002 3111 Table 2. Encoding of the Five Variables Investigated variable pH ratio of CO3 to Ni RR, ratio (rpm) MF Nifeed, ppm
Figure 1. Experimental apparatus. Table 1. Reactor and Bed Properties column bed at no flow
quartz pellet at steady state
height (m) diameter (m) surface XS (m2) height (m) volume (m3) mass (kg) bed bulk density (kg/m3) absolute density (kg/m3) average particle size (µm) absolute density (kg/m3) average particle size (µm)
1 2.50 × 10-2 4.91 × 10-4 0.6 2.95 × 10-4 4.22 × 10-1 1434 2643 250 2300 500
The nickel and carbonate solution flow rates were kept constant, and the carbonate/nickel ratio was adjusted by changing the carbonate mother solution concentration. The reactor was operated at a pH ranging from 9 to 11, corresponding to the region of lowest solubility of the product. The pH was maintained by addition of hydrochloric acid or sodium hydroxide in the recirculation loop in order to allow an instantaneous mixing of the pH liquors with the bulk of the solution. The temperature during operation was monitored but not controlled, and the operating range was 25 ( 4 °C. Response Surface Methodology (RSM) RSM has been developed as a method of process and product optimization using designed experiments.8 The methodology is mostly used in industry when several input variables influence the system response.9 RSM was used to map the surface representing the response of the total nickel concentration (Nitot,out) to changes in the following five variables: the pH, the carbonate-tonickel ratio (RATIO), the recirculation rate of the peristaltic pump in rpm (RR), the number of carbonate inlet points (MF), and the nickel concentration of the feed flow in ppm (Nifeed). Because Nitot,out consists of both fines and dissolved nickel, it is an indicator of the total efficiency of the process. Two levels straddling the estimated optimum values were selected for each of the five variables in order to reduce the number of experiments (Table 2). The replication of a number of center points (see Table 2) during the 25 design was used to provide an estimate of the experimental error. A total of 51 experiments, including 9 center points and 10 axial points, were
low value (-1) mid-value (0) high value (+1) 9.5 1 0.67 (30) 1 50
10 2.5 1.165 (50) 2 100
10.5 4 1.67 (70) 3 150
carried out to map the response surface in the region of interest. A face-centered central composite design was used, with the axial points being the same as the existing high and low levels of the factors. It was necessary to impose restrictions on two of the variables. The MF variable represented the physical number of carbonate inlets and therefore had to be an integer value of 1, 2, or 3. The RR variable could take any value between 30 and 70 rpm (55-129 mL/min or a recycle/fresh feed ratio of 0.67-1.67). Below 30 rpm, scaling in the reactor prevented optimum operation, and above 70 rpm, the pellets were swept upward out of the column. The SAS statistical package10 was used to analyze the experimental data. First-order models and subsequently second-order models were established. Analysis of Variance. The first step in the RSM was the fitting of a second-order model using analysis of variance. Canonical Analysis. The aim of the second-order model was to locate the point of minimum response within the limits of the investigated surface. This point is called a stationary point. Depending on the system studied, several geometries are possible for the surface structure. Elliptic or hyperbolic systems are commonly encountered. In the particular case of the hyperbolic system, the stationary point is neither an absolute maximum nor a minimum and is called a saddle point (or local optimum). In the case of a local optimum, further searching for the absolute optimum has to be carried out on another portion of the surface. In contrast, on elliptic surfaces, the stationary point is an absolute optimum and the coordinates of the stationary point give the optimal conditions of operation for the process. The characterization of the nature of the system and the location of the stationary point are important parts of the second-order model analysis and are called “canonical analysis”.8 The nature of the stationary point is given by the signs of the eigenvalues (λi) of the second-order model matrix. If all of the λ values are negative, the stationary point is a maximal response; all λ positive values give a minimal response, and finally λ values of different signs are typical of a saddle point. Ridge Analysis. The goal of this analysis is to determine the nature and shape of the surface response within the experimental region or on its perimeter. The ridge analysis (although in this case a “valley” rather than a ridge was considered) consists of a constrained optimization algorithm, which evaluates the response of points on a sphere of a certain radius, centered on the center of the design. The radii are studied in steps of 0.1 coded data units. The aim of the study was either to anchor the stationary point inside the region or to obtain some guidelines on the direction of future experiments in order to achieve more desirable conditions.8 In the case of the saddle surface, the point on the “path” at the design perimeter is the point of the minimum estimated response. It may be viewed as a reasonable
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Table 3. Fitted Second Order for the Model for Nitot,out Equation: y ) b0 + b1x1 + b2x2 + b3x3 + b4x4 + b5x5 + b11x12 + b12x1x2 + b13x1x3 + b14x1x4 + b15x1x5 + b22x22 + b23x2x3 + b24x2x4 + b25x2x5 + b33x32 + b34x3x4 + b35x3x5 + b44x42 + b45x4x5 + b55x52 Terms: y ) Nitot,out; x1 ) pH; x2 ) RATIO; x3 ) RR; x4 ) MF; x5 ) Nifeed parameter b0 b1 b2 b3 b4 b5 b11 b12 b13 b14 b15 b22 b23 b2 b25 b33 b34 b35 b44 b45 b55
term
value
standard error
base pH RATIO RR MF Nifeed pH2 pH × RATIO pH × RR pH × MF pH × Nifeed RATIO2 RATIO × RR RATIO × MF RATIO × Nifeed RR2 RR × MF RR × Nifeed MF2 MF × Nifeed Nifeed2
5.00 3.58 -6.39 -0.11 0.09 4.38 0.63 -2.92 0.11 -0.22 2.27 2.60 0.52 -0.71 -4.94 0.74 1.68 0.51 1.15 -0.32 0.48
1.37 0.92 0.92 0.92 0.92 0.92 3.41 0.99 0.99 0.99 0.99 3.41 0.99 0.99 0.99 3.41 0.99 0.99 3.41 0.99 3.41
Table 6. Estimated Ridge of Minimum Response for Nitot,out estimated coded response standard radius of Nitot,out error 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
4.98 4.18 3.48 2.87 2.37 1.97 1.66 1.44 1.2 1.01 0.75
1.15 1.15 1.15 1.17 1.19 1.24 1.31 1.41 1.79 2.30 2.85
uncoded factor values pH
RATIO
RR
MF
10.00 9.98 9.96 9.93 9.91 9.88 9.85 9.83 9.83 9.83 9.84
2.50 2.61 2.72 2.83 2.94 3.04 3.13 3.06 2.88 2.75 2.64
50.0 50.0 50.1 50.1 50.1 50.2 50.3 51.1 52.3 53.1 53.8
2.00 100.0 2.00 97.4 2.00 94.9 2.00 92.3 2.00 89.7 2.00 87.1 2.00 84.3 1.97 76.2 1.92 66.8 1.89 60.2 1.86 54.5
Nifeed
Table 4. Coordinates of the Stationary Point for Nitot,out variable
stationary point
pH RATIO RR, ratio (rpm) MF
9.97 3.5 1.01 (43.9) 2.3
variable
stationary point
Nifeed (ppm) Nitot,out (ppm) efficiency (%)
69.5 1.37 98.0
Table 5. Eigenvalues and Eigenvectors for Canonical Analysis eigenvector eigenvalue
pH
RATIO
RR
MF
Nifeed
5.01 1.81 0.34 -0.23 -1.32
0.39 -0.06 0.37 0.84 -0.06
-0.77 -0.00 0.29 0.26 0.51
-0.00 0.61 0.70 -0.28 -0.25
0.04 0.79 -0.51 0.27 0.21
0.51 -0.02 0.19 -0.26 0.79
candidate for optimal operating conditions. The method uses only the predicted response surface and selects the minimum value for the intersection of the sphere and the response surface. The fitted second-order model for total nickel out (Nitot,out) is given in Table 3. The R2 value is 82.8%, indicating that the five variables accounted for 82.8% of the variation in Nitot,out. Discussion Second-Order Model Analysis. (i) Canonical Analysis. The canonical analysis for the second-order model indicated that there was no unique minimum value for Nitot,out in the region investigated. The coordinates of the stationary point are shown in Table 4. This stationary point on the Nitot,out response surface corresponded to a concentration of 1.37 ppm of total nickel in the reactor outlet stream, i.e., a removal efficiency of 98.0%. For Nitot,out, two out of five eigenvalues were negative (see Table 5) and the eigenvector coefficients associated with these eigenvalues were interpreted in order to determine the relative influence of the parameters in exploring the saddle surface. The factors with the
Figure 2. Estimated Nitot,out and standard error as a function of the coded radius.
largest coefficients were pH, RATIO, and Nifeed. This suggests that these were the important factors in exploring the saddle surface. The factors RR and MF were not significant at all. This can be seen by the standard error values for the three factors in Table 3. (ii) Ridge or “Valley” Analysis. In the valley analysis, all of the variables corresponding to the minimum of the response varied on the surface as a function of the radius length, but the Nifeed variable changed the most (see Table 6). Several minima appeared at different locations on the surface. The analyses of the surface were complicated because of the large standard error of the prediction, with the reliability of the predictions decreasing with the distance from the center of the design. Consequently, the large variability of the response did not allow accurate predictions of the surface optima nor of the exact direction in which to proceed to optimize the process. Figure 2 shows the estimated Nitot,out response with its standard error, as a function of the distance from the center of the design. The predicted response values at radii beyond 0.6 encoded units had large standard errors (from 27% to 404% of the predicted value), indicating that the prediction is unreliable when far away from the center of the design. MF and RR did not have a large influence: MF fluctuated around 2 and so can be considered fixed at this integer value. The RR factor remained stable across the range of radii, slowly increasing from 50 to 54 rpm. The pH factor appeared to stabilize at a pH of 9.85 around the region of the local optimum. The RATIO variable reached a maximum value of 3.12 at a radius of 0.6 and then decreased again to 2.64 for a radius of 1. The Nifeed factor showed the greatest change in value at each radius, falling from 100 ppm at the center of the design to 54 ppm at a radius of 1.
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Error in the Measurements. The error in the measurements of Nitot,out was monitored during the runs. It was found that the standard deviation on the Nitot,out (from run replicates) corresponded to 46% of the data mean and the standard error was 0.67 ppm. The relative error was large because the variation in Nitot,out included not only the variation in the dissolved nickel concentration but also the variation in the fines concentration. Fines formation depended on many parameters such as the attrition rate of the pellets, the supersaturation level, and the agglomeration rate. These parameters were largely dependent on the experimental conditions. The influence of the precipitation reaction temperature was investigated by including it as a covariate in a repeat second-order analysis. According to the analysis, the temperature did not contribute to the uncontrolled variation in the data and, when included in the model, actually increased the standard errors on the predictions. Parameters of Influence and Practical Application. For the surface response investigated, the three parameters found to be significant from the canonical analysis, pH, RATIO, and Nifeed, are known to influence the process through determination of the supersaturation, the speciation, and the composition of the precipitate.1 Thus, their influence is compatible with theoretical predictions. It is also expected that the relative significance of these parameters in relation to the others tested will hold independently of the scale of the process. The RR and MF parameters had minimal influence on the process efficiency, and these variables stabilized around their coordinates at the center of the design: 50 rpm for the recirculation and 2 for the multiple inlet feed. RR did not have a significant influence on the process efficiency and pellet attrition, but this could have been because its operating range was limited. It varied between 30 and 70 rpm and would not have significantly influenced the hydrodynamics in the reactor because the Reynolds number increased only from 2.29 to 3.54 and the flow remained laminar. Because of the small scale of the reactor, RR could not be increased significantly to allow a change in the state of the flow, and therefore its influence on the agglomeration and attrition mechanisms was limited. The operating height of the reactor would have to be increased to allow a more extensive study of the influence of RR on the process efficiency, and particularly on the attrition of the pellets. MF had to take an integer value, therefore 2, because it represented the physical number of inlet points of the carbonate stream. When MF was increased, two simultaneous phenomena occurred: first, the supersaturation was lowered at each inlet point and at the bottom of the column; second, the supersaturation was more efficiently dispersed over the height of the reactor. The finding that the variable MF did not have a large influence on the process could be interpreted in two ways: First, the supersaturation at the bottom of the reactor did not need to be dispersed to avoid the development of high local concentration zones. This might have been because the concentration of the incoming nickel stream was not sufficiently high to induce a very high local supersaturation. Second, because agglomeration of fines has been shown to occur up the length of the reactor,1 the number of inlets would
not have a significant influence (at low Nifeed concentration) so long as there was sufficient residence time in the reactor for agglomeration to occur. The Nitot,out response was directly related to the efficiency of the process because it takes into account both dissolved nickel and fines formation. The pH of the stationary point for Nitot,out stabilized at around 9.85 during the valley analysis, indicating a potential reduction in postneutralization costs. Around this zone of pH, most of the nickel that was not recovered in the reactor exited the column in dissolved form, fines formation was not important, and filtration could be avoided. If the process had to be run industrially, it might be necessary to add a postfiltration stage in the case of an uncontrolled fines bloom. An interesting study on the maximum load of fines possible on the filter could be carried out because the addition of a filtration stage could further reduce the concentration of nickel in the effluent and thus increase the efficiency of the process. Conclusions (a) For the response variable Nitot,out, the most influential variables were pH, carbonate/nickel ratio, and nickel feed concentration. The values of these variables, which gave a local minimum and a Nitot,out value of 1.37 ppm, were established as 9.97, 3.5, and 69.5 ppm. (b) The significance of these three variables (pH, carbonate/nickel ratio, and nickel feed concentration) is compatible with theoretical predictions, and it is expected that the relative significance of these parameters in relation to the others tested will hold independently of the scale of the process. (c) Based on the total nickel concentration leaving the column, a removal efficiency of 98.0% was achieved. (d) The variables recirculation ratio and number of feed points had minimal influence on the process efficiency. The minor influence of the recirculation ratio was most probably due to the narrow range over which it was possible to explore the variable. The minor influence of the number of feed points was most probably due to the low concentration of the incoming nickel stream as well as the fact that agglomeration of fines occurs up the length of the reactor.1 Thus, the number of inlets would not have a significant influence (at low concentration) as long as there was sufficient residence time in the reactor for agglomeration to occur. Acknowledgment The authors gratefully acknowledge Professor Derek Chalton for his assistance with the statistical analysis. Literature Cited (1) Guillard, D.; Lewis, A. E. Nickel hydroxy-carbonate precipitation in a pellet reactor. Ind. Eng. Chem. Res. 2001, 40 (23), 5564-5569. (2) Mishra, S. K. Resource recovery in waste treatment increasingly used. Min. Eng. 1999, April, 29-34. (3) Graveland, A.; van Dijk, J. C.; de Moel, P. J.; Oomen, J. H. C. M. Developments in water softening by means of pellet reactors. J. Am. Water Works Assoc. 75 (12), 619-662. (4) Seckler, M. M. Calcium phosphate precipitation in a fluidized bed. Ph.D. Thesis, Delft University of Technology, Delft, The Netherlands, 1994.
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(5) Wilms, D. Recovery of nickel by crystallization of nickel carbonates in a fluidised bed reactor. VTT Symposium on Nonwaste Technology, Espoo, Finland, 1988. (6) Zhou, P.; Huang, J.-C.; Wei, S. Heavy metal removal from wastewater in fluidised bed reactor. Water Res. 1999, 33 (8), 19181924. (7) Scholler, M.; Van Dijk, J. C.; Wilms, D. Recovery of metals by crystallisation in the pellet reactor. S, VD, W, Environ. Technol., Prod. Env. 2nd Conf. 1987, 294-303. (8) Myers, H.; Montgomery, D. C. Response surface methodology. Process and product optimization using designed experiments; Wiley: New York, 1995.
(9) McAnally, S.; Benefield, L.; Reed, R. B. Nickel removal from synthetic and actual nickel plating wastewater using sulfide and carbonate for precipitation and coprecipitation. 39th Perdue Ind. Waste Conf. 1984, 81-98. (10) SAS Institute Inc., Cary, NC, 1999.
Received for review October 24, 2001 Revised manuscript received April 18, 2002 Accepted April 19, 2002 IE010873H
