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High pure and ultrapure Aluminum is increasingly used in highly corrosion resistant applications or to replace copper in conductive parts such as high voltage cable and transformers due to their higher electrical conductivity properties. Ultrapure Aluminum is also used in semiconductors industry. Hence, the market demands innovative refining technologies, which consume less energy, take less time and lead to higher resource efficiencies. The most common methodology to produce ultrapure Aluminum is zone refining, based on the principle of fractional crystallization. Zone refining is however a very time-consuming process. In order to achieve a purity of 6N, several passes along the crucible with each full day treatment must be carried out. Also this method cannot be conducted continuously. This paper presents first time a new and eco-efficient method, which offers high potential to meet the mentioned multi-dimensional demands. Firstly, distribution coefficients of main impurities in Aluminum—a deciding factor to forecast and assess the removal behavior of impurities-were studied theoretically as a function of temperature using thermo-chemical calculation in FactSage
^{TM}. Secondly, the innovative method using a rotating and gas cooled crystallizer (“cooled finger”) was developed to validate the principle. The influencing process parameters such as temperature gradient, cooling gas flow rate, etc. were experimentally investigated and the theoretical results could be verified.

Fractional crystallization principle: fractional crystallization is the crucial pro- cess for ultra-purity refining of metals and metalloids. Crystallization from the melt has the great advantage of high selectivity in comparison to other pyro- metallurgical melting processes. Thereby, distribution of impurities during the phase transition liquid/solid is used for refining of the initial metal as well as for doping of accompanying elements (e.g. to produce extrinsic semiconductors). An important parameter for impurity distribution in the target metal is the distribution coefficient (see _{S}) divided by the concentration in the liquid phase (C_{L}).

k = C S C L (1)

k―Distribution coefficient; C_{S}―concentration in solid; C_{L}―concentration in liquid.

Here, the distribution coefficient of any chemical composition is depending on thermo-dynamical properties of components, kinetics on the interface liquid/ solid, diffusion and convection on the phase boundary [

The crystallization is, among other factors, greatly influenced by the undercooling effect on the growth layer at the liquid-solid interface, which can be represented by the equation 2 below [

G Δ T ≤ D ⋅ k m ⋅ C 0 ∗ ( 1 − k ) (2)

where ΔT is the temperature gradient in the liquid interface, G is growth rate, m is the slope of liquidus line in the phase diagram, k is the distribution coefficient [C_{s}/C_{L}], D is the impurity diffusivity in the melt and C_{0} is the initial concentration.

For a better segregation, one should keep the growth rate at low levels, allowing this Kinetic-driven process to occur. Process parameter like furnace temperature, mixing degree and cooling gas flow rate can indirectly control the temperature gradient in the process and as well as the growth rate. Those will eventually be indirectly correlated to the segregation effectiveness of the process. As the growth rate increases to a level higher than the rate of solute rejection, an increase in the k-value should be observed.

Innovative methodology “rotational immersing cooled finger”: The process to be studied in this paper is based on an internally cooled crystallization unit (so- called “cooled finger”), which is rotationally immersed into the molten Aluminum. Although this idea was patented 1982 in Japan (see

This is more remarkable, because all purification methods currently available are distinguished by complex technical systems and low efficiency. For example, while zone refining requires several passes with each taking a full day in order to improve the metal just to one purity-grade (reduction of foreign metals of a factor of 10) higher. According to

The distribution coefficient of four common impurities in Aluminum, i.e. Iron, Silicon, Nickel and Lead have been preliminary modeled via the thermo -chemical software FactSage™, (using the database Ftlite). The data of the solid and liquid lines, gathered from each binary phase diagram Al-X, were converted to a polynomial equation of C_{X,S} = f(T), respectively C_{X,L} = F(T), in which C_{X} is the concentration of Element X in solidus or liquidus line and T the temperature. Based on these equations, the concentration of each impurity element in liquidus resp. solidus line and at different temperatures were calculated. Using Equation (1) the distribution coefficient of Element X was then calculated as a function of temperature as well.

Parallel to the theoretical studies a series of experimental trials were applied to investigate practical feasibility of the concept, namely the temperature gradient, crystallization temperature as well as growth rate. For this procedure an innovative setup including a rotational cooled hollow rod made of steel covered with high purity graphite was designed and installed, illustrated in

The cooled finger is able to rotate inside the melt via an electrical motor with variable rpms. This allowed to adjust an appropriate mixing inside the bath and

ensure small diffusion layers. In order to avoid the driving system becoming too hot, this is cooled indirectly with water (see

Before calculating the theoretical or experimental distribution coefficient, the two important terms controlling crystallization, i.e. temperature gradient and crystallization temperature were determined.

Experimentally measured temperature gradient: As illustrated in

We assume in this preliminary study, that the temperature of the crystallization front is more or less the same as the temperature inside the “cooled finger” (due to the fact of very good heat conductivity of Graphite and Al and particularly because it is very difficult to measure the temperature while growing the front). Hence, in this experimental setup, the thermo-elements were placed inside the graphite shell (T_{2}) and adjacent to the crucible wall (T_{1}) at the same depth in the molten bath. The temperature profile was then recorded during the trials. Consequently, these trials were conducted without rotation due to the thermocouple wires attached to the equipment.

With the two parameters of ∆T and δx the temperature gradient (∆T/δx) will be approximated as well.

By comparison, the trial with e.g. 50 L/min proved to have optimum conditions to achieve a stable growth rate as well as induce the solute segregation and enable the adhesion of a solid layer on the “cooled finger” surface (see the collected products for each curve in

Crystallization temperature:

the crystallization temperature range, where from this point, latent heat is extracted and through an undercooling effect the growth is induced on the coldest region e.g. “cooled finger” surface.

The duration to achieve this stable level is depending on the cooling effect of the finger, influencing by cooling gas flow rates, cooling surface and finger dimensions, diameter of the crucible/molten bath, rotation, etc. In these series of experiments, the initial temperature of the molten bath as well as the cooling gas flow rate were taken as the variable parameters. However, in all these different operational situations, the stable temperature (crystallization temp.) remains with ±2˚C the same (see

As the process starts, the temperature of the melt (T_{1}) is constantly dropped by the inlet gas flow inside the Cooled Finger until it reaches a “plateau” (see the gray area), where the extracted heat from the system induces the crystallization of the melt over the colder surface of the Cooled Finger. From now on, the T_{1} remains relatively constant until the end of the experiment or until there is no more molten metal to be crystallized. Hence, for any initial conditions or cooling rate, the crystallization temperature will be the same but also depends on the liquidus temperature of applied metallic system. It should be though mentioned that crystallization rate is influenced by convection and agitation/mixing through rotation mechanism as well.

For the current system (Al), the Green line (starting T_{1} = 700˚C, cooling gas flow rate = 50 L/min), demonstrates a better stability to achieve the gray plateau without generating too much cooling effect while keeping a satisfactory growth rate. That all will result a good solute segregation.

Chemical analysis through spark spectrometry showed e.g. in two cases of green (699˚C, 50 L/min) and red (700˚C, 70 L/min) lines, significant differences about the removal percentages of iron and silicon impurities. The process parameters of the green line caused about 60% Si- as well as 50% Fe-removal in comparison with only 11% Si- and 28% Fe-removal through the parameters of red line. It should though be noticed?as mentioned before?that the results of this figure were gathered without using any rotation in the “cooled finger”. The effect of rotation (presented in

The control of growth rate, either by the cooling gas flow rate itself or in conjunction with rotation, will promote a stable growth front, in which the impurities will be segregated from the base metal towards the liquid phase. In the case of multi-crystalline growth during the process, those impurities, which cannot migrate to the solid/liquid interface and therefore not be expelled to the adjacent liquid phase, will be precipitated on the grain boundary of the crystallized material.

(showed in region A) compared to the grain surface (Region B) as a SEM?EDX analyses pattern. The lighter gray region has the same results showed in region B, only with different grain orientation. The existence of carbon and oxygen traces in these patterns could be because of the oxidization of Aluminum while processing as well as external contaminations through graphite crucible.

Calculation of distribution coefficient: The distribution coefficient as a function of temperature was calculated in Fact Sage, using the individual binary phase diagram of each system (Al-X) as well as using the Equation (1). The X_{S} and X_{L} in the binary phase diagrams developed in Fact Sage were defined as a polynomial function of temperature as X = a T 3 + b T 2 + c T + d with a R^{2}~0.99999. Using the individual constant factors for each equation at a specific temperature, X_{S} and X_{L} were calculated for values of temperature until the eutec-

tic point. Using Equation (1) the distribution coefficient is calculated as k = X S X L

and represented in

in the liquid is assumed (ideal system). Here the different tendencies of distribution coefficient in dependence of temperature for different impurities have been illustrated. This figure shows that in contrast to iron, silicon, whose coefficients decrease due to the temperature rising, some impurities such as lead and nickel show an increasing pace with temperature. That means finding an appropriate crystallization temperature to remove all these impurities simultaneously would be a challenge. Though, according to

Experimental-based distribution coefficient and comparison with FactSage-calculation: Generally for a non-ideal system with partial mixing in the liquid, the effective distribution coefficient based on BPS (Burton-Prim-Slichter) should be considered (see Equation (3)) [

k e f f = C S C L = k 0 k 0 + ( 1 − k 0 ) e G δ D → ln ( 1 k e f f − 1 ) = ln ( 1 k 0 − 1 ) − G δ D (3)

In which C_{S} and C_{L} are the experimental values for the impurity concentration in solid as well as in liquid (e.g. here chemically analyzed through spark spectrometric method), k 0 is the equilibrium distribution coefficient, G the growth/solidification rate of the crystallization front, D the diffusion coefficient of each impurity in Al and δ is the diffusion layer thickness.

Using the experimental results and the chemical analysis of the products, k_{eff} can be calculated as k e f f = C oncoldfinger C crucibleresidue . That means, C_{S} is in this case the con-

centration of the impurity in already crystallized material and C_{L} is the concentration of the same impurity remained in the molten phase (crucible residue). On the other hand, the solidification velocity (G) for different trials can be calculated through measuring the thickness of the crystallized material over the known period of dwelling time (as micron/sec). With the help of these two known variables (k_{eff} and G), the trend line of y = A x + B can be drown, where y is the k_{eff}, x is the G, A (slope) is − δ / D and B (intercept) is ln ( 1 / k ′ 0 − 1 ) . From this trend line, the k ′ 0 -the empirical values of distribution coefficient-can be evaluated for each impurity and compared to the theoretical value from FactSage.

The solidified material of these “proof of principle experiments” was analyzed chemically via spark spectrometry measurement device, whose results have been illustrated in

Application of the Burton-Prim-Slichter model (BPS): _{S} and C_{L} (via chemical analysis) as well as the empirical keff into the BPS model to calculate the experimental k ′ 0 . The preliminary results obtained showed a correlation of the δ / D coefficient in relation to impurities that has similar distribution coefficients. While the k 0 for iron and Silicon are 0.03 and 0.13 respectively, the obtained coefficients and k ′ 0 were also close to each other. Same effect can be observed for the Ni

and Pb system. Despite the requirement of higher numbers of data in BPS model and despite the fact that in this series of experiments just few results exist for every measuring point, the graphs in

The difference between the thermodynamic distribution coefficients obtained from the binary phase diagram ( k 0 ) and the k ′ 0 obtained by BPS method is related to the efficiency of the system, which must be improved for example by reducing the growth rate. That in turn will led to an increase in the overall purification efficiency, since more time is given to allow the segregation of impurities on the growth front. Rotation speed will also play an important role on promoting the mixing of the segregated impurities from the growth boundary layer to the bulk melt and hence on increasing the purification effectiveness. In the case of Ni and Pb, the obtained k ′ 0 was expected to be similar, as seen for Fe and Si. This difference could indicate a correlation between the impurities present in the system with the Pb and/or Ni.

First time an internal cooled rotor was used for fractional crystallization. The study shows a successful proof of principle study in theory and experiments. Among the investigated cooling gas flow rates, the rate of 50 L/min was shown to be the most suitable one both to avoid the mushy area in the melt as well as to avoid a rapid solidification of the material over the “cooled finger”. Crystallization temperature was quite constant with ±2˚C independent from the gas flow rate or the initial temperature of the melt. However, the chemical analysis’ results of the different process parameters revealed to be significantly different.

The removal efficiencies of the impurities from aluminum via experimental investigation do not show always accordance to the calculated values of distribution coefficient in FactSage. That is due to the lack of taking the interaction of the accompanying elements into account. FactSage theoretical calculation applied in this study was based only on binary (e.g. Al-X), although the empirical investigations took place considering multi-component systems.

The graphs of BPS model were confirmed to be valid for binary systems Al-Si, Al-Ni, Al-Pb and Al-Fe. An improved precision and better correlation between the BPS model and the theoretical values could be achieved through an increase in data points across a wide growth rate range, decreasing the influence of experimental uncertainties. Additionally, implementation of this model into a binary system would be beneficial to avoid the influence of accompanying elements in the overall purification results.

The solid-liquid interface shape plays an important role in the impurity distribution in the melt. It is expected that the boundary layer thickness can be further decreased by increasing the stirring (by rotation), and as consequence, decreases the k_{eff} value and thereby maximizing the range of purification. The effect of rotation into the solute segregation shall be investigated in the future.

The Authors would like to appreciate Dr. Alexander Schwedt from GFE Central Facility for Electron Microscopy, RWTH Aachen University for the friendly supply of SEM/EDX analyses.

Also, many thanks to the CNPQ-Brazilian National Council for Scientific and Technological Development for the financial support of the Brazilian scholarship holder and guest researcher D. Curtolo.

Dr.-Ing. Semiramis Friedrich: https://orcid.org/0000-0002-4078-0799; Danilo Coladetti Curtolo: https://orcid.org/0000-0002-7043-7094; Prof. Dr.-Ing. Dr. h.c. Bernd Friedrich: https://orcid.org/0000-0002-2934-2034.

Friedrich, S., Curtolo, D.C. and Friedrich, B. (2017) Effect of Process Parameter Variation on Purity during Rotary Fractional Crystallization of Aluminum. Open Journal of Metal, 7, 25- 38. https://doi.org/10.4236/ojmetal.2017.72003