Materials Transactions, Vol. 48, No. 9 (2007) pp. 2518 to 2524
#2007 The Japan Institute of Metals
Dynamic Substance Flow Analysis of Aluminum and Its Alloying Elements*1
Hiroki Hatayama*2 , Hiroyuki Yamada*3 , Ichiro Daigo, Yasunari Matsuno and Yoshihiro Adachi
Department of Materials Engineering, Graduate school of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
Aluminum demand in Japan has grown significantly during the last few decades. For most uses, small amounts of other metals are added to
the primary aluminum to make harder alloys, which are classified by the nature and concentrations of their alloying elements. Aluminum scraps
from end-of-life products, which are used as raw materials for secondary aluminum, are often mixtures of several alloys. Therefore, not only the
amount of scrap but also the concentrations of their alloying elements must be taken into account when assessing the maximum recycle rate of
aluminum scraps.
This paper reports a dynamic substance flow analysis of aluminum and its alloying elements in Japan, focusing on the alloying elements Si,
Fe, Cu and Mn. We devised eight categories of aluminum end uses and 16 types of aluminum alloys. The amount of each alloy in each end-use
category was estimated from statistical data. We then estimated future quantities of discarded aluminum in each of the eight categories using the
population balance model. At the same time, we calculated the concentrations of the alloying elements in each of the end uses.
It was estimated that the amount of aluminum recovered in Japan would be about 1800 kt in 2050, which is 2.12 times that recovered in
1990. Calculated concentrations of alloying elements in aluminum scraps showed good correlation with those of the measured data.
[doi:10.2320/matertrans.MRA2007102]
(Received May 1, 2007; Accepted July 3, 2007; Published August 22, 2007)
Keywords: material flow analysis, substance flow analysis, aluminum alloy, alloy content, aluminum scraps, population balance model
1.
Introduction
Increasing public concern for environmental protection
and resource conservation has generated interest in the
recycling of materials. In Japan, technological development
and legislation that protects the environment have increased
the recycling of various materials such as aluminum, steel,
concrete and wood from end-of-life products. Therefore,
materials in the products accumulated in a society will
become raw materials for recycling in the future. Kleijn et
al.1) and Van der Voet et al.2) developed a dynamic approach
for estimating the material outflow (as end-of-life products)
from in-use stock. Using similar approaches, previous
research has estimated the amount of discarded material
likely to be recovered in the future, in particular focusing on
steel (Kakudate et al., Daigo et al.),3,4) construction minerals
(Hashimoto et al.),5) consumer durables (Tasaki et al.)6) and
TVs (Yamada et al.).7)
This paper focuses on the recyclability of aluminum,
which is one of the most widely used metals in Japan.
Aluminum is a versatile metal, owing to various properties
such as its light weight, high corrosion resistance and good
formability. These properties are fostered by adding alloying
elements; Si, Fe, Cu, Mn, Mg, Zn, and so on. R ecycling of
aluminum is important as the energy consumed in producing
secondary aluminum is much less than that used in producing
primary aluminum. In producing secondary aluminum from
scraps, the alloying elements in the scraps are usually reused
into the recycled alloys and at the same time some alloying
elements are newly added in order to satisfy standards on
chemical composition. On the other hand, excess presence of
the alloying elements would require additional primary
aluminum for dilution because some alloying elements
*1This
Paper was Originally Published in Japanese in J. Japan Inst. Metals
70 (2006) 975–980.
*2Graduate Student, The University of Tokyo
*3Graduate Student, The University of Tokyo, Present address: New
Energy and Industrial Technology Development Organization
cannot be removed at secondary smelters. Therefore the
chemical composition of scraps should be taken into account
in assessing the recyclability of aluminum. These features of
aluminum recycling require MFA/SFA (material flow analysis/substance flow analysis) to be conducted for each of the
respective alloys. A material flow analysis studying the
limitations caused by the presence of contaminants in
recycling has been conducted for Cu in steel (Kakudate et
al., Daigo et al.)3,4) and for Ni and Cr in stainless steel
(Igarashi et al.).8)
A static material flow analysis of aluminum, which shows
annual aluminum flow in Japan, has already been conducted
by Umezawa et al.9) However, there has been no dynamic
analysis conducted because of the lack of data on such factors
as product lifetime and demand for each end use. The
objective of this paper was to analyze dynamically the
substance flow of aluminum and its alloying elements. In
order to do this, statistical data were analyzed in advance, as
there were insufficient data for the analysis.
2.
Data Preparation
2.1 Consumption by end use
Published statistics10) provide information about aluminum demand in Japan classified into about 30 end uses. In
this paper, end uses were aggregated into eight categories,
and demand data on these eight end uses from 1960 to 2003
were prepared (Table 1).
These data took into account the indirect trade of
aluminum and industrial scraps, after allocation (described
in 2.3). The indirect trade is the difference between statistical
domestic demand and actual consumption in Japan, as goods
are manufactured in Japan and consumed in other countries
and vice versa. In this study, automobile export was taken
into account because it is supposed to have much larger
impact than other indirect trades. We calculated the amount
of aluminum included in exported automobiles as follows.
First, the average consumption of aluminum in an automobile
Dynamic Substance Flow Analysis of Aluminum and Its Alloying Elements
Table 1
End-use classifications for aluminum.
End use
Table 2
2519
Chemical compositions of aluminum alloys (2003 fiscal year).
Examples
Concentration (%)
Foil
foil
Si
Fe
Cu
Mn
Fabricated metal
household utensils, furniture
1000 series
0.325
0.381
0.086
0.048
Beverage can
beverage cans
2000 series
0.779
0.726
4.935
0.810
General machinery
Electric communication
machinery
machinery for agriculture and fishery
home electronics appliances, audio
machinery
3003
0.600
0.700
0.200
1.500
3004
0.300
0.700
0.250
1.500
Other 3000 series
0.600
0.748
0.275
1.112
Automobile
automobiles (engines, heat exchangers
etc.)
4000 series
5052
13.500
0.250
1.000
0.400
1.300
0.100
0.000
0.100
Construction
window frames, doors
Other products
precision machinery, ships, aircraft
was calculated by dividing the statistical demand for
aluminum in the automobile category by the annual automobile production.11) Actual domestic consumption was then
calculated by multiplying the average consumption of
aluminum by the annual registration of new automobiles.11)
And this study did not take into account other indirect trades.
In other end uses (e.g. electric communication machinery),
there are many kinds of products (e.g. TV, refrigerator,
washing machine), therefore it is difficult to estimate the
average aluminum consumption in each end uses.
Industrial scraps were also taken into account for mill
products, which are manufactured from wrought alloy.
Fabricating mill products generates industrial scraps as either
mill ends or irregular products. The amount of industrial
scrap was calculated by multiplying the demand for mill
products by the yield rate of scraps from milling processes.
We assumed that the industrial scraps were generated in the
processing year. The yield rates for industrial scraps from
various end-use manufacturing processes were obtained from
the literature.12)
It should be also noted that all the statistical data in this
study related to the fiscal year.
2.2 Production by alloy
Aluminum alloys include many kinds of alloying elements. Four of these alloying elements, Si, Fe, Cu, and Mn,
were considered in this study because they each have the
potential to restrict aluminum recycling. The production of
42 types of aluminum alloys in mill products, castings and
die-castings can be obtained from published statistics.10) We
prepared data on production according to alloy type from
1974 to 2003 by aggregating some alloy types that have
similar chemical compositions or that have relatively small
production (Table 2). It was assumed that the concentration
of every alloying element was equal to the upper limit
described in the industrial standards.13) The components of
aggregated alloy types were calculated as the weighted
average according to production volume. The compositions
of wrought alloys calculated from data obtained in 2003 are
shown in Table 2. Alloy compositions were calculated from
data obtained in 1974 to 2002 using the same method, and
little fluctuation was found compared with those of 2003.
Therefore, the compositions of wrought alloys from 1960
onwards were represented by those of 2003. As for aluminum
castings, their composition was assumed to be equal to that of
AC2B alloy because it is the most widely used casting alloy.
Mill
products
5182
0.200
0.350
0.150
0.500
Other 5000 series
0.288
0.311
0.130
0.562
6061
0.800
0.700
0.400
0.150
6063
0.600
0.350
0.100
0.100
Other 6000 series
0.958
0.496
0.229
0.628
7000 series
0.182
0.215
2.076
0.157
8000 series
0.225
1.500
0.050
0.000
Castings
7.000
1.000
4.000
0.500
Die–castings
12.000
1.300
3.500
0.500
Table 3
Matrix of demand by end use and alloy.
End use 1
...
End use m
Total
Alloy 1
..
.
a11
..
.
...
a1m
..
.
X1
..
.
Alloy n
an1
...
anm
Xn
Total
Y1
...
Ym
The composition of die-castings was assumed to be equal to
that of ADC12 alloy for the same reason.14)
2.3
Data development — demand by both end use and
alloy
Data on demand according to end use and on production
according to alloy were obtained from the available statistics.
Data on demand by both end use and alloy were determined
from these two sets of data using the following procedure.
Table 3 is a matrix representing the relationship between
various end uses and the alloys used for them. The X (row
sum) and Y (column sum) values in the matrix can be
obtained from statistics, where X is the demand by alloy and
Y is the demand by end use. The value of each matrix
element was determined by demand by end use (column) and
demand by alloy (row), estimated by the variables X and Y.
As the first step in this estimation, the alloy types used for
each end use were determined from the literature.15) The
matrix elements were then classified into two types,
‘‘identifiable elements’’ and ‘‘inferable elements’’. An identifiable element was an element whose value could be
identified because there was a reliable relationship between
end use and alloy. For example, only 1000 series alloys were
used in products in the ‘‘Foil’’ end-use category. In this case,
the element a1000 series Foil was an identifiable element and the
value was defined as being equal to YFoil , while the other
elements in that column were set to 0. An inferable element
was an element whose value could not be identified, as the
alloy used in the end use was only assumed. Values of the
2520
H. Hatayama, H. Yamada, I. Daigo, Y. Matsuno and Y. Adachi
Table 4
Foil
Fabricated
Beverage can
metal
++
Electric communication
General
End/Tab
+
+
Automobile
machinery
Construction
machinery
Body
1000 series
Identifiable and inferable elements defined by end use and alloy.
Heat
Fin
Other
+
+
+
+
+
+
exchanger
Engine
Other
Exports
products
Other
+
2000 series
3003
3004
++
Other 3000 series
++
+
4000 series
5052
Mill
products
++
+
5182
++
+
+
+
+
+
+
+
++
Other 5000 series
+
6061
+
6063
+
+
+
+
+
Other 6000 series
7000 series
+
+
+
+
+
8000 series
Castings
++
++
++
++
Die–castings
++
++
++
++
++; Identifiable element
+; Inferable element
*; Calculated element
Blank; Null
inferable elements were defined according to the allocation
method described later. Table 4 shows the identification of
various elements based on the literature: ++ denotes an
identifiable element, + denotes an inferable element, and
blank elements are 0. For the assumption of which alloys
were used in the ‘‘Beverage can’’ category, alloy 3004 was
assumed to be used in the can body and alloy 5182 was
assumed to be used in the can end. The excess of demand
over production for alloys 3004 and 5182 was made up for by
other 3000 series alloys and the 5052 alloy.
The allocated values for the various elements should show
no discrepancy between row and column totals. In this paper,
demand by end use (Yi ) was allocated to each of its
component elements according to the proportion of alloy
production, Xi . However, there was difficulty in making a
valid allocation in ‘‘Exports’’ and ‘‘Other products’’ categories because the alloy types used in these end-use categories
could not be identified from any literature. Therefore, the
values of elements belonging to these two end uses were
determined from the row sum, Xi , after the allocation in the
other columns was complete. In the second step, every value
of the identifiable element was defined. At the same time, the
value of alloy production for the inferable elements (represented as Xi 0 ) was calculated by subtracting the values of each
identifiable element from the total alloy production, Xi . The
value of end-use demand for the inferable elements (represented as Yi 0 ) was calculated in the same way. In the third
step, Yi 0 was allocated into respective inferable elements in
the column, according to the proportion of Xi 0 . The second
and the third steps were applied to all end-use categories,
except ‘‘Exports’’ and ‘‘Other products’’, defining all values
in these columns. Then in the final step, the elements of the
‘‘Exports’’ and ‘‘Other products’’ end-use columns were
defined. In each row, the amount of alloy used for each of the
two end uses was derived by subtracting all the fixed values
from the total alloy production, Xi . The derived value was
then divided into the two end uses according to the ratio of
their end-use demand.
Statistics are kept of wrought alloy production, recording
its shape, sheet and extrusion.10) The allocation method was
applied individually to two of the shapes, and the total was
calculated. Table 5 shows the estimated demand by both end
use and alloy from the 2003 data to which the method was
applied. It should be noted that there was a difference of 2%
between the grand total of alloy production (sum of Xi ) and
the grand total of end-use demand (sum of Yi ) in Table 5.
This difference was adjusted in the ‘‘Exports’’ and ‘‘Other
products’’ categories.
3.
Quantitative and Qualitative Analysis on Scraps
The amounts of aluminum discarded from various end uses
were estimated using dynamic analysis (Population Balance
Model: PBM).3)
We defined some of the parameters of a product’s lifetime,
as this was necessary for the dynamic analysis. For the ‘‘Foil’’
category, a product lifetime was not considered because little
aluminum is recovered from foil products. For ‘‘Fabricated
metal’’, ‘‘General machinery’’, ‘‘Electric communication
machinery’’ and ‘‘Other products’’ categories, the mean
lifetimes were obtained from the literature.12) Other parameters of product lifetime were based on past research on
consumer durables (Tasaki et al.).6) Beverage cans were
assumed to be discarded in the production year because their
mean lifetime was less than one year. For the ‘‘Automobile’’
category, a lifetime distribution was defined from the
Dynamic Substance Flow Analysis of Aluminum and Its Alloying Elements
2521
Table 5 Demand for mill products by end use and alloy (2003 fiscal year).
unit: (t)
Beverage can
Fabricated
Foil
Electric communication
General
metal
Automobile
machinery
Construction
machinery
Body
End/Tab
Fin
Heat
Other
exchanger
Exports
Total
Other
1000 series
158207
73847
0
0
7656
36145
42700
85317
0
28269
0
3520
2000 series
0
0
0
0
0
0
0
0
0
0
0
10538
12086
22624
3003
0
0
0
0
0
2507
3323
15238
0
0
0
5535
6347
32950
4036 439697
3004
0
0 267502
0
0
0
0
0
0
0
0
1545
1771 270818
Other 3000 series
0
0
21254
0
0
0
0
0
0
0
26388
67915
77886 193443
4000 series
0
0
0
0
0
0
0
28492
0
0
0
0
5052
0
36932
0
18059
4479
0
20867
0
0
14037
19835
16877
Mill
products
0
28492
19355 150440
5182
0
0
0
117406
0
0
0
0
0
0
0
26
29 117461
Other 5000 series
0
0
0
0
4871
0
0
0
0
10198
14409
39339
45114 113931
6061
0
873
0
0
0
0
652
0
0
0
18260
4828
6063
0
28364
0
0
73075
0
21170
0
0
87805
593320
17
5537
30150
19 803770
Other 6000 series
0
0
0
0
0
0
1144
0
0
0
32071
10384
11908
55507
7000 series
0
5817
0
0
0
0
0
0
0
4111
0
12660
14519
37107
0
0
0
0
0
0
0
0
0
0
53026
145833 288756
135465
90080
42300
89856
129047
0 144421
704283
220918
8000 series
Total
0
158207
Table 6
End use
Foil
Fabricated
metal
Beverage can
60811 113837
253353
Lifetime distribution, yield rate and collection rate in each end-use category.
Lifetime distribution function
Engine
Other
products
Distribution
function
Mean
lifetime
(years)
Variance
—
—
—
weibull
—
10
—
3.5
—
Yield rate
of
industrial
scraps (%)
Collection
rate (%)
10
0
10
40.3
15
—
General
machinery
weibull
10
3.5
10
10
Electric
communication
machinery
weibull
10
3.5
10
30.1
Automobile
—
—
—
10
90
Construction
log-normal
38.7
0.401
30
80
Other products
weibull
10
3.5
10
30
Shape parameter (in weibull distribution function)
empirical data. The estimated figures were verified from new
registrations and disposals.11,16) Window frames were the
main product in the ‘‘Construction’’ category, therefore the
lifetime of products in this category was represented by that
of dwellings.17)
Considering the aluminum input into society in a given
year, the amount of aluminum likely to be discarded in later
years can be calculated by multiplying the input by the
disposal probability, where disposal probability was defined
according to the assumed lifetime distribution. By calculating
the total amount of discarded aluminum that originated from
aluminum input in the past, the amount of aluminum
discarded in a certain year in the future can be estimated.
This study estimated the amount of discarded aluminum
likely to be discarded each year until 2050.
After the estimation of discarded aluminum, the amount of
recovered aluminum was estimated based on the collection
rate of each end-use category. The numerator of the
collection rate is the amount of aluminum collected and
consumed as aluminum scraps in Japan, and the denominator
of the rate is the amount of discarded aluminum. Aluminum
which is merged into general waste or exported as used goods
is not included in the collection rate. For beverage cans,
published data18) on consumption and recovery were used in
this study. The collection rates of other end uses were
obtained from the literature.12) The parameters assumed for
this study are shown in Table 6. In the dynamic analysis,
demand for each end use from 2004 onwards was assumed to
remain the same as the demand in 2003. Other parameters,
such as the lifetime distribution function, the yield rate, the
indirect trade and the collection rate were also assumed to be
constant in the future, as shown in Table 6.
Aluminum is collected from end-of-life products for
recycling. However, collected aluminum cannot be separated
into its alloy types. Furthermore, once alloyed with aluminum, some of the alloying elements cannot be removed from
2522
H. Hatayama, H. Yamada, I. Daigo, Y. Matsuno and Y. Adachi
2000
Amount of scrap recovery / kt
1800
Industrial scrap
1600
Other products
1400
Construction
Automobile
1200
Electric
communication
machinery
1000
800
Table 7 Chemical compositions of scraps discarded from (a) Fabricated
metal, (b) Beverage can, (c) General machinery, (d) Electric communication machinery, (e) Automobile (mill products), (f) Automobile
(engine), (g) Construction and (h) Other products end-use categories.
(a)
Fiscal year
2000
2010
2020
2030
2040
2050
0.764
0.773
0.771
0.770
0.770
0.770
0.410
0.411
0.410
0.410
0.410
0.410
0.342
0.344
0.343
0.342
0.342
0.342
Mn 0.096
0.096
0.096
0.096
0.096
0.096
2000
2010
2020
2030
2040
2050
0.285
0.285
0.285
0.285
0.285
0.285
Concentration Fe 0.593
(%)
Cu 0.217
Mn 1.144
0.593
0.593
0.593
0.593
0.593
0.217
1.144
0.217
1.144
0.217
1.144
0.217
1.144
0.217
1.144
2000
2010
2020
2030
2040
2050
6.400
5.562
5.299
5.297
5.297
5.297
0.856
2.178
0.778
1.762
0.755
1.661
0.754
1.661
0.754
1.661
0.754
1.661
Mn 0.343
0.301
0.291
0.290
0.290
0.290
2000
2010
2020
2030
2040
2050
3.264
0.617
3.046
0.598
2.649
0.566
2.646
0.566
2.646
0.566
2.646
0.566
1.026
0.936
0.805
0.804
0.804
0.804
Mn 0.231
0.222
0.208
0.208
0.208
0.208
Si
General machinery
Beverage can
600
Fabricated metal
400
Concentration Fe
(%)
Cu
Statistics
200
0
1990
2000
2010
2020
2030
2040
2050
(b)
Fiscal year
Fiscal year
Fig. 1
Estimation of scrap recovery.
their alloys (because of technical and cost limitations). These
facts need to be considered when analyzing the substance
flow of aluminum alloying elements. Therefore, we assumed
that aluminum from end-of-life products was collected
without alloy-by-alloy separation, for all end uses except
the ‘‘Automobile’’ category. We assumed that aluminum
contained in end-of-life automobiles was collected using a
rough separation between mill products used in the heat
exchanger and the body, and castings used in the engine.
Using this assumption, the chemical composition of scrap
generated from every end use was calculated from the results
of the PBM as a weighted average of the alloy compositions
shown in Table 2. It was also assumed that the proportion of
each wrought alloy in the total production of mill products for
each end use would be constant over time. This means that
the weighted average of wrought alloys calculated for each
end use was assumed to be the same as that in 2003.
4.
Results
Si
(c)
Fiscal year
Si
Concentration Fe
(%)
Cu
(d)
Fiscal year
Si
Concentration Fe
(%)
Cu
(e)
Fiscal year
2000
2010
2020
2030
2040
2050
1.794
1.794
1.794
1.794
1.794
1.794
0.449
0.449
0.449
0.449
0.449
0.449
0.256
0.256
0.256
0.256
0.256
0.256
Mn 0.164
0.164
0.164
0.164
0.164
0.164
2000
2010
2020
2030
2040
2050
9.953 10.05
10.24
10.26
10.26
10.26
Si
Figure 1 shows the estimated amount of aluminum
predicted to be recovered in Japan until 2050. According to
the analysis, the amount of aluminum recovered in 2000
would be 1.53 times as large as that in 1990, and in 2050, 2.12
times. The increase in recovered aluminum was due to the
increase in discarded aluminum, especially from ‘‘Automobile’’ and ‘‘Construction’’ end uses. The amounts of statistical
scrap recovery from 1990 to 2000 are also shown in Fig. 1.19)
This estimation based on parameters in Table 6 fits well with
the amount of statistical scrap recovery.
The compositions of scrap aluminum were estimated for
every year until 2050, as shown in Table 7. For the
‘‘Beverage can’’ (Table 7(b)) and ‘‘Construction’’
(Table 7(g)) categories, no fluctuation in composition was
observed because only mill products were used for these two
end uses. For the remaining five end uses (also disregarding
the ‘‘Automobile (engine)’’ category), concentrations of the
alloying elements would decrease over time because of an
increase in the proportion of mill products used in finished
products.
Estimated compositions of scrap were compared with
measured data, in order to validate the allocation method
adopted in this paper. The measured data were obtained from
the literature and from an investigation of a secondary
smelter.14,20) The results of this comparison are shown in
Concentration Fe
(%)
Cu
(f)
Fiscal year
Si
Concentration Fe
(%)
Cu
1.177
1.183
1.194
1.195
1.195
1.195
3.705
3.695
3.676
3.674
3.674
3.674
Mn 0.500
0.500
0.500
0.500
0.500
0.500
(g)
Fiscal year
2000
2010
2020
2030
2040
2050
0.605
0.605
0.605
0.605
0.605
0.605
0.381
0.381
0.381
0.381
0.381
0.381
0.121
0.121
0.121
0.121
0.121
0.121
Mn 0.173
0.173
0.173
0.173
0.173
0.173
Si
Concentration Fe
(%)
Cu
(h)
Fiscal year
2000
2010
2020
2030
2040
2050
3.106
2.776
2.313
2.312
2.312
2.312
0.848
0.832
0.809
0.809
0.809
0.809
1.329
1.217
1.077
1.076
1.076
1.076
Mn 0.553
0.555
0.558
0.558
0.558
0.558
Si
Concentration Fe
(%)
Cu
Dynamic Substance Flow Analysis of Aluminum and Its Alloying Elements
Concentration (%)
1.2
1
0.8
Measurement
0.6
Estimation
0.4
0.2
Concentration (%)
0
Si Fe Cu Mn
Si Fe Cu Mn
(a)
(b)
Si Fe Cu Mn
(c)
12
10
8
Measurement
Estimation: General machinery
Estimation: Automobile (engine)
6
4
2
0
Si
Fe Cu Mn
(d)
Si
Fe Cu Mn
(e)
Fig. 2 Comparison between the measurements and the estimation of the
chemical compositions of scraps discarded from (a) Fabricated metal, (b)
Beverage can, (c) Construction, (d) General machinery including
automobile (engine) and (e) Automobile (engine).
Fig. 2. For some kinds of scraps, there were differences
between the estimation and the measurement. These differences were attributable to the following reasons. (1) The
types of products included in the scraps were different, (2)
alloy composition was assumed incorrectly resulting in
inaccurate estimation of scrap compositions, (3) contamination by impurities occurred in the separation and collection
processes when aluminum was collected from end-of-life
products and (4) the allocation method was invalid. An
incorrect assumption of alloy composition means that the
alloying elements did not add up to their upper limit as
described in industrial standards. In this case, the estimated
concentration of the alloying element in the scrap would be
higher than the actual concentration. In the case of
contamination with impurities, the estimated concentration
of the incorporated elements would be lower than the actual
concentration. With this in mind, we discuss below the causes
of the differences between the estimated and measured scrap
compositions.
Figure 2(a) shows the comparison between the measured
composition of kitchen utensil scraps and the estimated
composition of the ‘‘Fabricated metal’’ category. The
estimation indicates a higher Si concentration and a lower
Fe concentration than the measurement. The overestimation
of Si was considered to be because of differences in the kinds
of products included in the scrap: the estimated composition
was based on the weighted average of mill products, castings
and die-castings used for the ‘‘Fabricated metal’’ end use,
whereas kitchen utensils do not use castings and die-castings
that include much Si. The underestimation of Fe was
attributable to the incorporation of impurities, such as
handgrips of pans, into the scrap. Figure 2(b) shows the
comparison between the measured composition of used
beverage cans and the estimated composition of the ‘‘Beverage can’’ category. These two show good correlation. Since
the alloys used in beverage cans are identifiable and few
impurities could have become incorporated into the beverage
cans, the estimation was more reliable. Figure 2(c) shows the
comparison between the measured composition of aluminum
2523
window frame scrap and the estimated composition of the
‘‘Construction’’ category. Although the incorporation of iron
into aluminum window frame scrap has been an issue, the
measured data showed lower concentrations of all four of the
alloying elements compared with the estimated data. This
result suggests that concentrations of alloying elements in
produced alloys were assumed to be higher than the actual
concentrations. Figure 2(d) shows the comparison for machinery scrap. The machinery scrap consisted of general
machinery and automobile engine scrap; therefore, composition of these two products was estimated separately and
then compared with the measured data. The measured
composition lay midway between the two estimates, which
correlated well with the scrap content. Figure 2(e) shows the
comparison between the measured composition of automobile engines and the estimated composition of the ‘‘Automobile (engine)’’ category. These two showed good correlation,
although the estimated concentrations of alloying elements
were slightly lower than the measured ones. The compositions of actual scrap were measured in 1995 (Fig. 2(a), (b)
and (d)) and 2006 (Fig. 2(c) and (e)). The estimated data for
the corresponding year were used for the comparison;
therefore, a small difference was observed between ‘‘Automobile (engine)’’ compositions in Fig. 2(d) and (e).
As shown in Fig. 2, the allocation method leads to a valid
estimation of the chemical compositions in aluminum scrap.
On the other hand, external factors such as incorrect
assumptions about the alloy composition and the presence
of impurities in the scrap can result in some differences
between the estimated and measured compositions. Further
investigation into these factors will be required to improve
the estimation of scrap composition.
5.
Conclusions
A dynamic substance flow analysis of aluminum was
conducted. In the analysis, both the quantitative information
and the chemical composition information were obtained in
parallel. The allocation method was developed to obtain data
on demand by both end use and alloy. Time-series data on the
amount and composition of aluminum scrap discarded by
society were estimated from the dynamic substance flow
analysis. Estimated compositions were compared with the
measured data, which verified the allocation method. However, further investigation will be required to study the effect
of external factors such as incorrect assumptions and
contamination with impurities.
Acknowledgements
This research was supported by a Grant-in-Aid for
Scientific Research (No. 17760658) from the Ministry of
Education, Culture, Sports, Science and Technology of
Japan.
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