This article is also available online at:www.elsevier.com/locate/mineng

Minerals Engineering 19 (2006) 299–308

Modelling of aluminium scrap melting in a rotary furnace

B. Zhou, Y. Yang *, M.A. Reuter, U.M.J. Boin

Department of Geotechnology, Delft University of Technology, Mijnbouwstraat 120, 2628 RX Delft, The Netherlands

Received 7 April 2005; accepted 21 July 2005Available online 19 September 2005

Abstract

In a typical secondary aluminium process, the scrap feed is charged into a rotary furnace, melting and mixing under a salt layer inthe furnace. The complexity in such a pyrometallurgical process is due not only to the high temperature effect and the complex chem-ical reactions, but also to the highly complex scrap feed with a distributed nature of aluminium types, compositions, sizes, shapes,paintings and other contaminations. In this study, user sub-models, which represent the distributed nature of the scrap feed, weredeveloped and integrated into a computational fluid dynamics (CFD) based process model of a rotary furnace. Aluminium scrapwas classified into several groups depending on their properties, e.g., size, establishing a discretized population balance model(PBM). The melting behaviour of aluminium scrap was simulated with the exchange of information between the melting sub-modeland the CFD calculations. In addition, the sub-model for scrap burn-off was also developed and integrated in the CFD frameworkproviding distributed burn-off rates. Simulations of the melting process were made to model the flow and thermal phenomena insuch a furnace, and the influence of the scrap size, shape and quality, as well as burn-off rate were studied.� 2005 Elsevier Ltd. All rights reserved.

Keywords: Modelling; Simulation; Computational fluid dynamics; Population balance; Recycling

1. Introduction

Secondary aluminium production has been used in anumber of areas, such as transportation, building andpackaging (EAA, 2004). Its production is increasingvery rapidly in recent years and it will keep a steadygrowing in the future. Recycling is a critical componentof the aluminium industry based on its favourable eco-nomic impact on production and its contribution toenvironment. Compared to the processing of primaryaluminium, recycling of aluminium is highly beneficial,saving approximately 95% of energy consumption re-quired for primary aluminium production. Productionof secondary aluminium also results in less gaseousemissions, water consumption and solid residues.

In a typical secondary aluminium process, the rotaryfurnace functions simultaneously as a smelter and a

0892-6875/$ - see front matter � 2005 Elsevier Ltd. All rights reserved.doi:10.1016/j.mineng.2005.07.017

* Corresponding author.E-mail address: Y.Yang@CiTG.TUDelft.NL (Y. Yang).

phase separator. It is capable to deal with heavily con-taminated scrap (Aluminium Handbook, 2003). Fig. 1illustrates the complex phenomena in such a furnace.The scrap feed is charged into a rotary furnace, passingthrough a salt layer, melting, mixing and being cleanedin the furnace. The rotary furnace is normally operatedat a temperature around 800 �C. Burning of natural gaswith oxygen is used as the heat source. The produced li-quid aluminium is tapped into a holding furnace, furtherrefined and then directly transported to the industrialpartners or cast into ingots. The salt slag with variouscontaminations should be further processed and reused.The complexity in the process is due not only to the hightemperature effect and the complex chemical reactions,but also to the highly complex scrap feed with a distrib-uted nature of aluminium types, sizes, shapes, composi-tions, paintings and other contaminations.

Though the process for reclamation of aluminiumscrap has been developed for many years, little publicknowledge is available. In this study, a computational

Fig. 1. Illustration of the rotary melting furnace and the complex phenomena inside the furnace.

300 B. Zhou et al. / Minerals Engineering 19 (2006) 299–308

fluid dynamics (CFD) based process model of scrapmelting in a rotary furnace was developed to predictingthe melting rate and energy distribution in relation tothe scrap types and properties, and to make improve-ments of the process. Turbulent fluid flow, gas combus-tion, radiation, and conjugated heat transfer weresimulated. To represent the distributed nature of the alu-minium scrap feed with different types, sizes, shapes etc.,a scrap melting sub-model with population balance mod-elling (PBM) was developed and integrated into the CFDbased process model. The scrap melting sub-model wassimplified from the previous developed numerical modelfor a single aluminium particle melting in molten melts(Zhou et al., 2003). Furthermore an aluminium burn-off sub-model was developed and integrated to take intoaccount the heat generated due to the burn-off (oxida-tion) effect during the melting process. Finally a numberof case studies were conducted to understand the influ-ence of particle size, shape and quality of the scrap.

2. CFD framework of the process model

2.1. General information of the CFD based process

model

Metallurgical processes involve complex phenomenaof momentum, heat and mass transport, which playimportant roles in reaction kinetics and reactor perfor-mance. CFD as a research tool was found useful instudying various metallurgical processes. In this study,a commercial CFD package, ANSYS-CFX 5.6 (2003),was used as a framework of the process model, coupledwith user-developed sub-models.

The industrial scale rotary furnace is 3.0 m in innerdiameter, 3.65 m in outer diameter including the liningstructure and 6.9 m in length. The model consists of agas region with turbulent flow and combustion as wellas radiative heat transfer in the upper part of the fur-nace, a solid region of the furnace lining, and a solid–liquid region of salt and aluminium metal in the lower

part of the furnace. The rotation of the furnace, about1.33 rpm, and the agitation of the paddles built in thefurnace wall were not included in this model.

An unstructured mesh was applied in this study, thetotal number of the meshes is 88,566 and finer mesheswere used in some sensitive areas, e.g., burner and flameareas, while coarser meshes were applied in the end partof the furnace and in the furnace lining. The CPU timefor a whole simulation on a Pentium IV, 2.66 GHz PC isabout 35 h with the main time step set at 30 s.

2.2. Treatment of the scrap-salt zone

The solid–liquid region was regarded as a conductingsolid, a mixture of scrap and salt solids. The phasechange of scrap melting was handled by the user-developed melting sub-model. Thus the fluid flow in thiszone was not considered, while the effect on heat trans-fer in this zone was represented by a number of thermalparameters. It was assumed that scrap and salt are wellmixed. The thermal properties of the mixed materialwere calculated based on the mass fraction and thephase state of the materials in the mixture. The effectivedensity of this region was calculated based on densitiesof solid scrap and salt, and it was assumed not to changeduring the process, despite the effect of thermal expan-sion. Heat capacity was also defined in a similar way.Some augmentation coefficients for the thermal conduc-tivity of the mixture were applied here to take into ac-count the influence of the voidage in the scrap-saltzone, as well as the effect on heat transfer due to the fluidflow in the scrap-salt zone, agitated by furnace rotation.

2.3. Initial and boundary conditions

The initial temperature in the gas zone and the scrap-salt zone was set as 303 K. The initial temperature in thelining structure was imported from a previous steadysimulation of heating the empty furnace. An initial sizedistribution of the scrap and salt particles waspredefined.

B. Zhou et al. / Minerals Engineering 19 (2006) 299–308 301

The inlets of the burner for natural gas and oxygenare really small compared to the furnace body. In thiscase the inlet was simplified to reduce the computingtime. The profiles of velocity, temperature and massflows of gases at the inlet were defined using CFXExpression Language (CEL), based on a previous simu-lation with a full burner structure and finer meshes.Pressure boundary condition was set for the outlet. Heattransfer coefficient was applied for outside wall. In thiscase, it is set as 15 W/m2 K and the environmental tem-perature is set as 303 K.

2.4. Physical models

For the simulation of the turbulent flow in the fur-nace gas zone, the widely used standard k–e modelwas applied. Eddy dissipation model (ANSYS-CFX,2003) was used for the combustion of the natural gaswith oxygen. This model is based on the concept thatchemical reaction is fast relative to the transport pro-cesses in the flow. It is applicable in many industrialcombustion problems where reaction rates are fast com-pared to reactant mixing rates. Radiation has a largecontribution of the heat transfer to the scrap-salt zoneand the furnace wall. Discrete transfer model (DTM)(ANSYS-CFX, 2003) was applied. The emissivity ofthe inner furnace wall and the interface between thegas zone and scrap-salt zone was set as 0.8. The buoy-ancy flow of the gas was simulated with the full buoy-ancy model implemented in ANSYS-CFX (2003).

3. User developed sub-models

3.1. Population balance model for aluminium scrap

melting

Population balance model (PBM) (Sohn et al., 1979)is a very useful tool to represent the dynamic particlesize variations as function of time, distributed physicalproperties and other process parameters. There aretwo forms of the population balance: a microscopicform and a macroscopic form. In this study, the simpli-fied form of the microscopic population balance modelwas applied.

The microscopic population balance model accountsfor changes in a particle population in an infinitesimalvolume at any geometrical position, as function of time.The general form of the microscopic population balanceis

owot

þ o

oxðvxwÞ þ

o

oyðvywÞ þ

o

ozðvzwÞ

þXJ

j¼1

o

ofjðvjwÞ þ D� B ¼ 0 ð1Þ

where w = w(x,y,z,f1,f2, . . . ,fj, t) is the distribution of aproperty, as function of x, y, z, f1,f2, . . . ,fJ, t; vx, vy, vzare geometric velocities, vx = dx/dt, vy = dy/dt, vz = dz/dt; fj({f1,f2, . . . ,fJ}) are a specified set of properties ofthe particles, vj = dfj/dt is the time rate of change ofthe property fj; D is the death function of the propertyand B is the birth function of the property.

For the application of the population balance modelfor aluminium scrap melting, the form of PBM can begreatly simplified. There is no input or output duringthe melting process. The birth and death of the scrappieces due to break-up of the bigger ones, is difficultto obtain and has been neglected, and there is no coales-cence of the solid particles. Then the factor left is theshrinking (melting) of the scrap particles. For example,considering the scrap property of size, for a certain scrapparticle, it shrinks during the process, transfers from abigger-size group to a smaller-size group, and finally dis-appears. Furthermore, in the current process model, thescrap-salt zone was regarded as a conducting solid. Theeffect of fluid flow, furnace rotation and agitation wasnot taken into account directly, but represented by somemodel parameters, such as the Nusselt number for thescrap melting sub-model, the coefficients of effectivethermal conductivity in the process model. The effectof geometric velocities can be omitted, thus, Eq. (1)can be simplified as

owot

þXJ

j¼1

o

ofjðvjwÞ ¼ 0 ð2Þ

where fj can be the properties of the scrap such as size,shape and thermal properties. In the case of aluminiumscrap melting, the population balance modelling of alu-minium scrap size distribution is in fact the calculationof the melting rate of the aluminium scrap. The meltingrate of a certain scrap particle is dependent on the posi-tion, time, local temperature, local Nusselt number,scrap properties etc., and it can be calculated by theuser-developed melting sub-model, which was intro-duced in the next section.

3.2. Scrap melting model for a single aluminium

particle

To calculate the melting rate of the scrap in such atwo-melt (salt and aluminium metal) system in a rotaryfurnace, experimental study (Zhou et al., 2002) andnumerical modelling (Zhou et al., 2003) was firstly con-ducted for a single aluminium particle melting in themolten metal and salt melts. The melting process of ascrap particle is dependent on the properties of the par-ticle (such as the size, shape, composition, and initialtemperature), the properties of the bulk melt (such asthe temperature, liquid flow and agitation of the melt),and the situations when the particle was charged passing

Fig. 2. Experimental and numerical results of salt shell formation and

302 B. Zhou et al. / Minerals Engineering 19 (2006) 299–308

through the salt layer (such as the thermal propertiesof the salt melt, residence time in the salt layer andspeed passing through the salt layer). If the scrap parti-cle is regarded as a sphere with an original radius ofR0, energy conservation equations due to heat transferin the system can be described as follows (Zhangand Oeters, 1998). For heating of the sphere, when0 < r 6 R0:

oTot

¼ apo2Tor2

þ 2

roTor

� �ð3Þ

I.C. : T ¼ T 0ðr < R0Þ; T ¼ Tm;sðr ¼ R0Þ at t ¼ 0

B.C.1: oT=or ¼ 0 at r ¼ 0

B.C.2 : kpoTor

� �r¼R0�

¼ kshelloTor

� �r¼R0þ

where r is the particle radius, T is the particle tempera-ture, t is the time, Tm,s is the melting point of the shell,kp and kshell are the thermal conductivities of the sphereand the shell, respectively, and ap is the thermal diffusiv-ity of the metallic sphere. Heat balance at r = R0, theinterface between the sphere and the shell, is expressedas B.C.2 in Eq. (3).

A shell is formed when a cold metal particle chargedinto a hot melt. For the shell development and re-melting, when R0 < r 6 Rshell:

oTot

¼ ashello2Tor2

þ2

roTor

� �ð4Þ

I.C.: Rshell¼R0; T ¼Tm;s at t¼ 0

B.C.1: T ¼Tm;s at r¼Rshell

B.C.2: kshelloTor

� �r¼Rshell

¼qshellDH shell

dRshell

dtþhðT f �Tm;sÞ

where Rshell is the radius of the shell, qshell is the densityof the shell, Tf is the temperature of the bulk salt melt,DHshell is the latent heat of the phase change, h is theheat transfer coefficient from the bulk melt to the solid-ifying shell, and ashell is the thermal diffusivity of theshell. Heat balance at r = Rshell, the interface betweenthe shell and the melt, is expressed as B.C.2 in Eq. (4).If the bulk melt is changed during the process, e.g., analuminium particle passing through the salt layer andentering the metal bath, there may be two shell layersformed.

If the melting point of the sphere is higher than thatof the shell, there will be no pre-melting of the sphere in-side the shell. After the shell is re-melted, the metal solidsphere is further heated up and begins to melt. In thissituation, the governing equation is the same as Eq.(3) but with different boundary conditions:

B.C.1: oT=or ¼ 0 at r ¼ 0

B.C.2 : kpoTor

� �¼ qpDHp

dRsolid

dtþ hðT f � Tm;pÞ

r¼Rsolid

where Rsolid is the radius of the solid core, qp is the den-sity of the particle, Tm,p is the melting point of the metal-lic sphere, and DHp is the latent heat of fusion of thesphere. Similarly, if the melting point of the sphere islower than that of the shell, pre-melting of the sphere in-side the salt shell may happen, then the boundary condi-tions of the governing equation, Eq. (3), can beexpressed as follows, assuming that the temperature ofthe metal liquid inside the shell is unique and it equalsthe melting point of metal.

B.C.1: oT=or¼ 0 at r¼ 0

B.C.2: kpoTor

� �r¼Rsolid

¼ qpDHp

dRsolid

dtþ R2

0

R2solid

kshelloTor

� �r¼R0

By solving these equations with a finite differencemethod (Zhou et al., 2003), the melting process of ascrap particle under certain conditions can be simulatedand the size change of the particle against heating timecan be obtained. The melting behaviour of a metal par-ticle as well as the salt shell formation and re-meltingwas studied. The results were compared with the previ-ous experimental study (Zhou et al., 2002), and reason-able agreement was obtained.

As an example shown in Fig. 2, an aluminium parti-cle, its equivalent radius is about 1.0 cm, was screwed ona steel rod, pre-heated to 280 �C, dipped into the saltmelt (NaCl–KCl 70%–30%wt base salt system plusCryolite 5%wt, stagnant, 800 �C) and then taken outafter a certain immersion time. The weight and thicknessof the salt shell formed on the aluminium solid was mea-sured. The triangles in Fig. 2 are the measured thicknessof the salt shell, compared with the model prediction ofthe first 80 s. In this case, pre-melting of the aluminiumcore inside the salt shell happens, as shown in the figure.It should be noted that in this case, there is no metalmelt in the system, because it is difficult to conductand control the experiment when two melts exist.

More details of the experimental study (Zhou et al.,2002) and the numerical modelling (Zhou et al., 2003)for aluminium melting as well as salt shell formationand re-melting can be found in the previous publications.

re-melting on Al particles in molten salt bath.

B. Zhou et al. / Minerals Engineering 19 (2006) 299–308 303

3.3. Implementation of the scrap melting sub-mode

For a multi-size particle system, the scrap is classifiedinto certain groups depending on the scrap size. It canalso be classified by other criteria, such as shape, compo-sition, and scrap thermal properties, as well as the com-binations of those criteria. For each cell in the scrap-saltzone, it is assumed that it has the same initial size distri-bution. A scrap melting sub-model was developed andintegrated in the CFD based process model, it handleseach size group the same as a single solid particle, calcu-lates the melting rate based on the conditions in each cellin the scrap-salt zone at any time, based on the informa-tion exchange with the CFD framework. The meltingsub-model provides the CFD framework with the infor-mation due to the melting of solid scrap, e.g., the heatsink due to melting, the amount of liquid metal and so-lid scrap, and the size distribution of scrap. At the sametime, the CFD framework provides the informationneeded for the phase change calculations, e.g., the localtemperatures.

The scrap melting sub-model was simplified from theprevious work of modelling a single particle melting inmolten melts (Zhou et al., 2003), in order to reducethe computing time. The fluid flow in the scrap-salt zoneand the agitation due to furnace rotation were not fullyincluded in the CFD based process model, and the re-solidification process was not simulated in the simplifiedmelting sub-model and its effect on heat transfer weretaken into account by some model parameters, such asthe Nusselt number for the scrap melting model, thecoefficients of effective thermal conductivity of thescrap-salt zone in the process model. Since the thermalconductivity of the aluminium metal is very high, thetemperature difference within the particle can be ig-nored: oT/or = 0. When the temperature of the solidparticle reaches its melting point, the heat transferredfrom the environment to the particle is totally used formelting of the solid metal. The heat balance at the inter-face between the bulk melt and the solid, (r = Rsolid) canbe expressed as follows:

qpDHp

dRdt

¼ �hðT f � Tm;pÞ ð5Þ

where the heat transfer coefficient, h, can be calculatedbased on the particle size, fluid flow condition and bulkmelt properties, and the bulk melt temperature, Tf, canbe obtained from the CFD simulations. Thus the sizechange of the particle can be calculated as follows:

dR ¼ � hðT f � Tm;pÞqpDHp

dt ð6Þ

New particle size for each group in each cell and thetotal amount of melted particle in each cell at the cur-rent time step can be obtained. The heat sink for eachcell due to phase change can be calculated and returned

to the CFD framework as an energy source term, whichinfluences the temperature distribution in the scrap-saltzone and heat transfer from the combustion gas or fur-nace wall to the scrap-salt zone. In this way, the meltingrate of the scrap at any position and time can be ob-tained, and therefore the population balance of alumin-ium scrap melting can be established, based on thecoupling of the scrap melting modelling and the CFDsimulation of the furnace.

3.4. Development of the scrap burn-off sub-model

Aluminium is a very reactive metal, thus oxidation isalways occurring during its life. In secondary aluminiumprocess, the oxidized aluminium can never be reclaimedin secondary aluminium process and contributes to thelosses. During the melting process, aluminium scrap issometimes exposed in a high temperature and oxidationatmosphere, despite the presence of a protecting saltlayer. The burn-off rate is dependent on the operation,salt amount, scrap quality and many other factors.The generated heat amounts to between 1/3 and 1/2 ofthe total energy input generated by burning of the natu-ral gas (Boin et al., 2004). This indicates that to build avalid process model, scrap burn-off must be taken intoconsideration.

The scrap burn-off is composed of several differentsources and reactions (Boin et al., 2004). These reactionscan be summarised as follows:

2Al+ 3/2O2 =Al2O3 ð7Þ

2Al+ 3H2O=Al2O3 + 3H2 ð8Þ

2Al+ 3CO2 =Al2O3 + 3CO ð9Þ

4Al+ 3CH2 =Al4C3 + 3H2 ð10Þ

2CH2 + 3O2 =2CO2 + 2H2O ð11Þ

It includes the direct oxidation reaction of aluminiummetal, as expressed in Eq. (7), and the reaction betweenthe aluminium metal and the moisture, as expressed inEq. (8), or carbon dioxide, as expressed in Eq. (9). Thealuminium metal also reacts with the contaminationmaterials, e.g., plastic materials, and these reactionscan also be regarded as the scrap burn-off, which gener-ates a large quantity of heat and increase the metal loss,as expressed in Eq. (10), if the structure formula of thecontamination materials can be approximately ex-pressed as CH2. Burning of the contamination materialsand organic components attached on the scrap also hasa similar effect on the melting process, as expressed inEq. (11).

Related to the distributed nature of the scrap, thescrap size distribution, surface to volume ratio, and con-tent of contamination have a big influence of the scrap

Fig. 3. Pre-defined burn-off heat source (solid line), and the industri-ally recorded off-gas temperature.

304 B. Zhou et al. / Minerals Engineering 19 (2006) 299–308

burn-off. For the scrap of ‘‘good’’ quality, which has arelatively small surface to volume ratio, less contamina-tions and/or higher metal content, the burn-off is nor-mally less. And the total amount of scrap burn-offduring the melting process is also influenced by a num-ber of other variables in the system, e.g., temperature inthe furnace, free-oxygen in the furnace, moisture contentof scrap, melting status (liquid and solid ratio of metal).It makes very difficult to obtain the total amount ofburn-off directly from the burn-off reactions and reac-tion kinetics. In this study, it was estimated by a massand energy balance model with data reconciliation (Boinet al., 2004).

Data reconciliation is a technique by which the massand energy balances can be closed by adjusting the mea-sured data, while the measurements should be adjustedas little as possible. The adjusted data should give amore consistent representation of the actual process,which then forms the basis for any subsequent model-ling that covers energy balances, statistics, kinetic mod-elling, neural nets and CFD modelling. For twenty-sixfurnace cycles, the burn-off rate for each furnace cyclewas obtained through data reconciliation by closingthe mass and energy balance with minimum of standarddeviation of errors. These cycles were then split intothree groups based on the properties of the scrap, andthe calculated results are listed in Table 1 (Boin et al.,2004). It indicates that for a certain type of the scrapfeed, e.g., the scrap with a metal recovery rate of 80%,the scrap burn-off is 2.69%, and the heat generateddue to scrap burn-off is about 657 MJ per ton of thescrap feed.

The scrap burn-off sub-model translates this part ofheat, which contributes in both of the gas zone andthe scrap-salt zone, into heat sources. It is too complexto consider all the influencing factors by far and hereonly a simplified definition of the burn-off is present:

• Total amount of heat generated by scrap burn-off ispre-defined based on the mass and energy balancecalculations (Boin et al., 2004), in relation to thescrap type and quality.

• Kinetics involved in burn-off was not taken intoaccount yet, however this can be assumed to be rapid.The overall heat generation rate was defined based onthe general industrial observations and the measured

Table 1Different burn-off rates for different scrap groups

Groupa Average expected metal yield (%) Burn-off rate (%)

1 65 4.422 80 2.693 90 2.14

a Group 1: dross; Group 2: granules, shredder residue, turnings;Group 3: packages (bottle caps, cooling elements, turnings), shredderresidue.

off-gas temperature, which can be used to indicate theextra heat generation in the furnace. Therefore ascrap burn-off function was defined with the functionparameters roughly estimated based on a statistic cal-culation of several typical cycles. Fig. 3 shows thepre-defined curve of the burn-off heat source togetherwith the measured off-gas temperature.

• The scrap burn-off reactions and their following reac-tions generate heat both in the gas zone and thescrap-salt zone, while the ratio of the two parts is dif-ficult to determine. It was thus studied as a modelparameter.

• It is assumed that the burn-off reactions are positionindependent, and it is evenly distributed in the gaszone or in the scrap-salt zone.

• The effect of burn-off on the mass balance wasignored, which is very small.

4. Results

4.1. General results

Turbulent fluid flow, gas combustion, radiation, andconjugated heat transfer in the rotary furnace were sim-ulated in the CFD framework. The detailed informationof the fluid flow in the gas zone, the temperature distri-bution in the furnace and energy flows of the process canbe obtained. As an example, Fig. 4 shows the gas com-bustion and flow in the gas zone and Fig. 5 shows thetemperature field in the furnace, at the 9600th secondof the process when about 80% of the scrap has beenmelted.

One of the main purposes of the simulation is to ob-tain the total melting time under a certain condition.Fig. 6 shows the changing history of the total solidsremaining, including the scrap and salt, against the heat-ing time. It should be noted that the plotted remainingratio are calculated by the total weight of the feed, whichincludes both scrap and salt. The melting curve can beregarded as the main criterion for the melting process,

Fig. 4. Gas combustion and flow in the gas zone at t = 9600 s.

Fig. 5. Temperature field in the furnace at t = 9600 s.

Fig. 6. Melting curve of the solid (scrap and salt) in the scrap-salt zone(ratio by total weight of feed).

B. Zhou et al. / Minerals Engineering 19 (2006) 299–308 305

which indicates the time the scrap starts to melt and thetime it has been melted completely. For melting of about13 tons of scrap and 4 tons of salt flux, the total meltingtime is about 4.0 h in this case, assuming that the burn-off rate is 2.7% and 50% of the burn-off heat (about8540 MJ in total) contributes in the gas zone.

4.2. Influence of scrap size and shape

On the one hand, the experimental and numerical re-sults (Zhou et al., 2003) suggest that an aluminium par-ticle with a larger size needs longer time to be melted. Onthe other hand, if the total weight of the feed is fixed, acombination with smaller sizes of scrap has a larger total

Fig. 10. Simulated changing history of size distribution D.

306 B. Zhou et al. / Minerals Engineering 19 (2006) 299–308

surface area, which means more salt flux is needed inoperation. Here, the melting sub-model has been devel-oped and coupled with the CFD based process model.Population balance modelling of scrap with different sizedistributions can be established and the influence of thesize distribution on scrap melting can be studied.

The initial size distributions as well as their changeduring the melting process are shown in Figs. 7–11.The distribution is plotted as the weight percentage tothe total weight of scrap, against particle size, and thenumber of scrap groups is 25. In reality, the size distri-bution may be more complicated, dependent on thescrap type and the feed recipe. Here the initial size distri-butions studied are defined as follows:

Fig. 7. Simulated changing history of size distribution A.

Fig. 8. Simulated changing history of size distribution B.

Fig. 9. Simulated changing history of size distribution C.

Fig. 11. Simulated changing history of size distribution E.

• Size distribution A: 0.0–0.5 m in diameter, with a lar-ger portion of smaller size of scrap.

• Size distribution B: 0.0–0.5 m in diameter, with a lar-ger portion of bigger size of scrap.

• Size distribution C: uniform size, 0.1 m in diameter.• Size distribution D: uniform size, 0.4 m in diameter.• Size distribution E: 0.0–0.5 m in diameter, randomlydistributed.

Fig. 12 shows the melting curve of the aluminiumscrap with different size distributions. For the scrap feedwith larger portion of smaller particles (size distribu-tions C and A, which can be regarded as a ‘‘bad’’ quality

Fig. 12. Melting curve of the solids in scrap-salt zone, with differentsize distributions.

Fig. 13. Influence of the scrap burn-off rate, the total amount.

Fig. 14. Influence of the scrap burn-off rate, the ratio in the gas zoneand the scrap-salt zone.

B. Zhou et al. / Minerals Engineering 19 (2006) 299–308 307

scrap), the melting is faster than that with larger portionof bigger particles (size distributions D and B, which canbe regarded as a ‘‘good’’ quality scrap) in the earlierstage. While the total melting time for all these cases isalmost the same, about 14,400 s (4.0 h). It indicates thatthe melting process is mainly dependent on the heattransfer but scrap size. Here it should be noted that, inreality, scrap with smaller sizes normally has a lowermetal recovery, causes a higher burn-off rate and requiresmore salt flux. These consequent effects have not beentaken into account in the cases presented here.

The aluminium scrap also has a large variety ofshapes. The shape factor was applied and defined asthe ratio of the surface area of the particle to the surfacearea of a sphere with the same volume, e.g., it is 1.0 forthe spheres and it is 1.24 for the cubes. The results indi-cated that there is little influence of the shape, the shapefactor ranges from 1.0 to 20.0, if other parameters arenot changed. While in reality, larger surface area to vol-ume ratio of scrap would result in a larger requirement ofsalt flux, which may influence the scrap melting process.

4.3. Influence of scrap quality

Metal burn-off during the melting process is one of themain reasons of metal loss in secondary aluminium,while it also generates a large amount of heat. Accordingto the observations and data measurements in the plant,as well as the mass and energy balance calculations, thescrap burn-off rate has an obvious relationship to thescrap type or scrap quality (Boin et al., 2004). Normally,for the ‘‘good’’ quality of scrap, which has a relativelysmall surface to volume ratio, less contamination and/or higher metal content, the metal recovery rate is higherand the metal loss is normally less. The burn-off model isonly implemented preliminarily, chemical reactions andtheir mechanisms are not taken into account, and herethe study is focused on the energy aspect.

For a total input of 13 tons of scrap feed, the scrapmelting behaviour was simulated with different scrapburn-off rates, 0.0% (A, no burn-off), 1.5% (B), 2.0%(C), 2.7% (D) and 3.5% (E), and the heat generated in to-tal is 0 MJ, 4750 MJ, 6330 MJ, 8540 MJ and 11,070 MJ,respectively. It assumes that the heat generated in the gaszone and that in the scrap zone are the same, 50% to 50%.Fig. 13 shows the melting curves for these cases. Thetotal melting time is about 6.25 h, 5.25 h, 5.0 h, 4.35 h,and 3.5 h, respectively, which indicates that the totalamount of burn-off has a big influence on scrap melting.A higher burn-off rate results in a shorter melting time,but it is at the expense of metal loss.

The generated heat due to scrap burn-off contributesboth in the gas zone and the scrap-salt zone, as discussedin the previous paragraphs, while the ratio of these twoparts is difficult to decide. It was studied as a parameterin the model. Fig. 14 shows the melting curves with dif-

ferent ratios of the heat contributed in the gas zone andthe scrap-salt zone, 0%, 20%, 50%, 80% and 100% in thegas zone, respectively. For the same scrap burn-off rate,2.7% here, larger ratio in scrap-salt zone results in a fas-ter melting of scrap. For these cases, the total meltingtime ranges from 4.0 h to 4.5 h.

5. Concluding remarks

A CFD based process model was developed, in whichfluid flow, heat transfer, natural gas combustion, andradiative heat transfer were simulated for the scrap melt-ing process. Data measured in industry were applied inthe model as initial and boundary conditions, as wellas for model validation. User sub-models for scrap melt-ing and scrap burn-off were developed and integratedinto the CFD framework. A simplified population bal-ance model for aluminium scrap melting was establishedby classifying the scrap feed into a number of scrapgroups. The melting rate for each group in each cell ateach time step was calculated with the exchange of infor-mation between the melting sub-model and the CFDbased framework. Thus the distributed nature of thecomplex scrap feed can be taken into account. Thedistributed properties of scrap also result in distrib-uted burn-off rates. The scrap burn-off sub-model was

308 B. Zhou et al. / Minerals Engineering 19 (2006) 299–308

developed to take into account the influence of the burn-off heat on the scrap melting process.

Various case studies were carried out focussing on therelationship between the melting process and the scrapproperties, e.g., scrap size, shape, burn-off and quality.It shows that the scrap size and shape only have a smallinfluence on melting, if ruling out the other consequenteffects due to size and shape difference. The distributedburn-off rate, which represents the scrap quality here,is one of the important factors for the melting process.For the scrap with a higher burn-off rate, which nor-mally is the scrap with a ‘‘poor’’ quality, the total melt-ing time and the gas consumption can be reduced, whileit is at the expense of more metal loss.

The melting sub-model can be further improved, e.g.,taking into account the re-solidification, and refining themodel parameters. The scrap burn-off sub-model canalso be improved by refining the burn-off function, tak-ing into account the chemical composition, distributedkinetics, etc. Moreover, the sub-models are not com-pletely coupled with each other yet, e.g., the scrapburn-off rate has not been directly related to the distrib-uted scrap properties but pre-defined, which should beimproved in the future.

Acknowledgements

This work is part of the E.E.T. (Economy, Ecologyand Technology) project (http://www.batchcentre.

tudelft.nl) supported by the Dutch government. Thefinancial support from the E.E.T. program is gratefullyacknowledged. Special thanks to Karl KonzelmannMetallschmelzwerke GmbH, Hanover, for the sharingof the information, knowledge and data, and for theirsupport, cooperation and hospitality.

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