Resources, Conservation and Recycling 60 (2012) 78–88
Contents lists available at SciVerse ScienceDirect
Resources, Conservation and Recycling
journal homepage: www.elsevier.com/locate/resconrec
Production planning for vehicle recycling factories in the EU legislative and
global business environments
Vladimir Simic ∗ , Branka Dimitrijevic
University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11000 Belgrade, Serbia
a r t i c l e
i n f o
Article history:
Received 19 April 2011
Received in revised form
28 November 2011
Accepted 29 November 2011
Keywords:
End-of-life vehicles
ELV Directive
Vehicle recycling factory
Automobile shredder residue
Reverse production planning
Environment
a b s t r a c t
End-of-life vehicles (ELVs) are a priority in the EU waste flow, and data show that as many as 6.34 million vehicles were processed in 2008. This paper focuses on the production process in a vehicle recycling
factory. It presents a tactical production planning problem for vehicle recycling factories in the EU legislative and global business environments. The problem is formulated as a linear program, which provides
optimal storage, processing and recovery, recycling and landfill disposal route decisions. The proposed
model can not only help vehicle recycling factories improve their eco-efficiency and profitability but also
answer many important questions. The present paper deals with questions regarding which costs should
be set in EU member states for landfill disposal, combustion in municipal solid waste incinerators and
processing in advanced thermal treatment plants so that the ELV Directive can have the most positive
eco-effect on the vehicle recycling factory business. The cost increase for landfill disposal will not always
reduce the quantity of disposed automobile shredder residue (ASR). The influence of the ELV Directive
on the vehicle recycling factory business is analysed. Future quotas will not endanger their profitability.
Comprehensive testing of the proposed model showed that the control of the recycling system efficiency
should be done at the system level because it will in no way jeopardise the ELV Directive objectives.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
The treatment of end-of-life vehicles (ELVs) and the environmental impact of discarding the resulting residues are subjects
of worldwide concern (Simic and Dimitrijevic, 2010). ELVs are a
priority in the EU waste flow. The latest data show that 6.34 million ELVs, with an average weight of 949.38 kg, were processed in
2008 (Eurostat, 2010). In an attempt to particularly reduce waste
originating from ELVs, in 2000, the EU enforced the ELV Directive (2000/53/EC). It aims to prevent waste generated by ELVs
and protect the environment by promoting the collection, reuse
and recycling of ELV components (EU, 2000). According to the ELV
Directive, which first took effect January 1, 2006, vehicle recovery must reach a minimum of 85% by weight per vehicle (with a
maximum energy recovery of 5%), of which a minimum of 80% will
have to be reusable and recyclable material. By the January 1, 2015,
recovery requirements will rise to a minimum of 95% (with the
maximum energy recovery raised to 10%), of which a minimum of
85% will have to be reusable and recyclable material.
The efficient processing of automobile shredder residue (ASR),
or auto fluff, represents a major concern for vehicle recycling factories. ASR is the waste generated during the shredding process,
and it consists of three parts (Joung et al., 2007): light ASR, heavy
∗ Corresponding author. Tel.: +381 11309132; fax: +381 113096704.
E-mail address: [email protected] (V. Simic).
0921-3449/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.resconrec.2011.11.012
ASR and soil/sand. ASR is an agglomerate of plastic (19–31%), rubber (20%), textiles and fibrous materials (10–42%) and wood (2–5%),
which are contaminated with metals (8%), oils (5%), and other substances, some of which may be hazardous (about 10%) (Srogi, 2008;
Vidovic et al., 2011). It is a by-product of the recycling procedure
and makes up 20–25% of the average ELV weight, that is, approximately 200 kg. Vigano et al. (2010) estimated that the total ASR
production in the EU may be in the range of 1.93–2.34 million
tonne per year. Moreover, this type of waste represents up to 10%
of the whole amount of hazardous wastes produced per year in
the EU and about 60% of the EU’s total shredding wastes (Rossetti
et al., 2006). The development of technology for recycling ASR is
complicated because it is a very heterogeneous waste material
(Santini et al., 2010); its composition, density, and moisture content change depending on the location and time of day (Boughton,
2007). In addition, factors that prevent the total recovery of ASR
include its physical nature, frequent contamination, poor development of certain secondary markets and substantial processing
costs.
This paper focuses on the vehicle recycling factory production
process. It presents a tactical production planning problem for vehicle recycling factories in the EU legislative environment and the
global business environment. The problem is formulated as a linear
program, which provides optimal storage, processing and recovery,
recycling and landfill disposal route decisions.
The remaining part of the paper is organised as follows: Section
2 provides a comprehensive literature review. Section 3 presents
V. Simic, B. Dimitrijevic / Resources, Conservation and Recycling 60 (2012) 78–88
the vehicle recycling factory model. Section 4 presents a case study,
and Section 5 presents the paper’s main conclusions.
2. Literature review
The literature provides a significant number of different mathematical models. Their detailed analysis is necessary to identify the
key directions for the further development of this very important
and dynamic research area.
Isaacs and Gupta (1997) were the first researchers to model
vehicle recycling infrastructure using the Goal Programming (GP)
method. They analysed the profitability of dismantlers and recyclers in the following cases of processing polymer-intensive (PI)
vehicles: increase in the polymer’s share of the vehicle’s material
composition, mandatory plastics dismantling and increase in the
landfill disposal cost for plastics. Boon et al. (2003) expanded Isaacs
and Gupta’s (1997) mathematical formulation for the recycling
infrastructure to assess the materials streams and process profitability for several clean vehicles cases. Gupta and Isaacs (1997)
solved the ELV recovery planning problem using GP. Individual
models were created for PI and aluminium-intensive (AI) vehicles.
They reached the conclusion that a polymer share increase in the
vehicle material composition would not jeopardise the existence
of the ELV processing industry but would deteriorate its business
results. Boon et al. (2001) used GP to model the vehicle recycling
infrastructure and investigated material streams and process profitability for the following AI vehicle processing scenarios: price
increase in isolated non-ferrous (NF) metals, more detailed dismantling being carried out, increase in processing costs and change in
AI vehicle design. They emphasised that the existing infrastructure,
in most cases, is able to process these vehicles while making profits. Sodhi et al. (1999) investigated cases examining the sequencing
79
problem for sorting individual and all target material(s), and they
presented a solution based on dynamic programming. Johnson and
Wang (2002) created two types of optimisation models: an US
model, which focused on profits, and an EU model in which optimisation depends on the defined vehicle recovery rate. According
to the US processing model, recovery rates for premature and true
ELV were 89.4% and 75.1%, respectively. In the EU model of processing without energy recovery, 85.0% recovery was possible only
if recycling tires and remanufacturing more valuable parts. In the
EU model of processing with energy recovery, recovery rates for
premature and true ELV were 96.1% and 85.0%, respectively. Consequently, they were able to conclude that it was not possible
to recover 95% of the weight of the average ELV with the existing sorting equipment. Coates and Rahimifard (2006) presented a
holistic end-of-life cost model for the vehicle recovery sector and
focused on the potential applications of this model to support both
high- and low-level decisions. Coates and Rahimifard (2009) developed a post-fragmentation separation model capable of simulating
the value-added processing that a piece of automated separation
equipment can have on a fragmented ELV waste stream. The model
takes the input composition of the ELV waste stream and determines the most likely route of each material flow. Williams et al.
(2007) proposed a recycling planning model for vehicle shredders
to make short-term tactical decisions regarding to what extent
materials should be processed and reprocessed through multiple
passes. In addition, the mixed integer programming model determines whether to combine materials for shipment. Qu and Williams
(2008) formulated the vehicle reverse production planning and
pricing problem in a nonlinear programming model, developed
an approximate supply function for hulks ordering when adjacent shredders price independently, and compared market with
an optimised pricing strategy in three trends for ferrous metal
Mixer
(i=9)
Magnetic
separation 2
(i=3)
Procurement
NF
mix
(j=7)
Ferrous
metals 2
(j=6)
Eddy
current 2
(i=5)
ASR
mix
(j=18)
Non
metals
2 (j=11)
ATT
processes
(i=10)
MSWI
(i=11)
NF metals 2
(j=10)
Storage
(i=0)
Landfill
(i=12)
Hulks
(j=1)
Shredding/
air suction
(i=1)
Heavy materials
(j=2)
Magnetic
separation 1
(i=2)
Copper
production
(i=13)
Light ASR
(j=3)
Non-metals 1
(j=9)
Heavy ASR
(j=5)
Ferrous metals 1
(j=4)
Eddy
current 1
(i=4)
Cu-rich fraction
(j=13)
NF metals 1
(j=8)
Heavy media Al-rich
fraction
sorter
(j=12)
(i=6)
ORPR fraction
(j=15)
Eddy
current 3
(i=7)
Al
fraction
(j=14)
Aluminium
production
(i=14)
Manual
separation
(i=8)
Final Fe
metals
(j=16)
Steel
production
(i=15)
Copper wires (j=17)
Export
(i=16)
Fig. 1. Flow sheet of the vehicle recycling factory in the EU legislative and global business environments.
80
V. Simic, B. Dimitrijevic / Resources, Conservation and Recycling 60 (2012) 78–88
and hulk costs: constant, increasing and decreasing. Kumar and
Sutherland (2008) provided an overview of studies on vehicle
recovery infrastructure and identified the following limitations of
available models: inadequate description of the complex material
flows and economic transactions within the infrastructure, minimal consideration of market factors (such as scrap metal prices),
lack of consideration for government policies and a limited variety of examined future scenarios. Chen et al. (2010) thoroughly
described the principles and characteristics of the ELV recycling
system in Taiwan and concluded that improving and optimising the
process of tactical and operational planning is necessary to make
recycled materials more competitive.
The isolated ORPR fraction can be either incinerated in a MSWI or
landfilled. More detailed descriptions of processing and cost features of sorting equipment, MSWI and ATT processes can be found
in Section 4, which presents a case study.
3.1. Notation
The following notation is used.
Indices and sets:
i
index of entity (i.e., sorting equipment, manual processes,
storage, mixer or predestination, and destinations);
i ∈ {0, . . ., Ir }
index of material flow; j ∈ {1, . . ., J}
index of time period; t ∈ {1, . . ., T}
set of material flows isolated with entity (i.e., sorting
equipment and manual processes) i; i ∈ {1, . . ., I − I − 2}
set of entities on which material flow j is forwarded;
j ∈ {2, . . ., J}
set of entities that route materials to entity i;
i ∈ {1, . . . ,I − 1}
set of entities on which materials are routed from entity i;
i ∈ {1, . . ., I − I − 1}
set of destinations where material recycling take place
set of various metal producers in EU member state or other
country
set of destinations where energy recovery takes place
j
t
Ai
3. Vehicle recycling factory planning model
j
Modelling separation processes can provide a foundation for
facility optimisation, where waste flows are assessed for their value
and recoverability (Coates and Rahimifard, 2009). The optimal processing route is selected based on environmental and economic
drivers. Obtaining a detailed flow sheet is first needed to formulate a vehicle recycling factory planning model. The flow sheet,
presented in Fig. 1, contains a network of various unit operations
necessary for processing numerous material flows, ranging from
shredding to metal producing processes. Therefore, the flow sheet
provides the configuration for the contemporary vehicle recycling
factory. In addition, any possible route can be viewed and assessed
as a potential solution of the analysed planning problem.
When the procured hulks arrive, they are unloaded from transportation vehicles and forwarded to storage. Hulks planned for
recycling are successively transported to the shredder, which is the
core of the vehicle recycling factory. It is a giant 3000- to 8000-hp
hammer mill that shreds vehicle hulks into mostly fist-size chunks
to liberate the metals from everything else (Jody and Daniels, 2006).
A heavy-duty cyclone is usually installed on top of the shredder to
vacuum the light ASR fraction. This fraction can be further sorted or
shipped to an advanced thermal treatment (ATT) plant. If the first
option is chosen, then the second magnetic sorter separates this
material flow to ferrous metals 2 and NF mix fractions. The NF mix
can also be further sorted to extract NF metals, be sent to an ATT
plant, or be disposed of as landfill. If the first option is chosen, then
the second eddy current sorter separates this material flow into
NF metals 2 and a second fraction of non-metals, which will then
be routed to the optimal destination. The heavy materials fraction
passes through the first magnetic sorter, which diverts the ferrous
metals 1 from the heavy ASR fraction. Market requirements dictate that both fractions of ferrous metals are first manually treated
along a conveyor for possible impurities (especially for insulated
Cu wires), and only then can they be sold to the steel industry.
As for the fraction of insulated Cu wires, two routes are possible:
export and (manual) recycling in countries with low labour costs
or landfill disposal. Incineration of this fraction in municipal solid
waste incinerators (MSWIs) has not been taken into consideration
because of financial unfeasibility (Bellmann and Khare, 1999) and
ecological unacceptability. The heavy ASR fraction is forwarded to
the first eddy current sorter, which separates it into NF metals 1
and the first fraction of non-metals. As shown in Fig. 1, the first
and the second NF metals fractions are then routed to a heavy
media sorter (HMS). The HMS is filled with a medium that has a
specific gravity equal to 3.5 tonne/m3 and separates Al-rich and
Cu-rich fractions. Alternatively, it is common for an HMS to utilise
heavy liquids such as magnetite and ferro-silicate solutions with
specific densities of 1.5 tonne/m3 and 3.5 tonne/m3 , respectively
(Coates and Rahimifard, 2009). The Al-rich fraction can be sold as is
or routed on a third eddy current sorter for further refinement from
the organics, rubber, plastics and the remaining fraction (ORPR).
˝i
˚i
F
M
D
Parameters:
I
J
T
I
S0
Smin
Ci
Eij
number of entities
number of material flows
number of analysed time periods
number of destinations
initial hulk inventory weight
safety inventory level
processing capacity in the case of entity i per time period
efficiency of sorting entity i in the case of material flow j in
percentages
recycling efficiency of destination i in percentages
energy efficiency of destination i in percentages
recycling quota
recovery quota
energy quota
revenue from each unit weight of metal fraction sorted on
entity i and sold to destination i in time period
(advanced) thermal treatment cost in destination plant i
per weight unit
landfill disposal cost of ASR fraction sorted on entity i per
weight unit
procurement cost per weight unit in period t
inventory holding cost rate per time period
sorting cost on entity i per unit weight
transportation cost from entity i to i per weight unit
EiR
EiE
QR
QR QE
Ri i t
CiA
CiL
CtP
CI
CiS
CiT i
Variables:
St
Pt
Xi i t
weight of hulks in storage at the end of time period t
weight of incoming procurement in period t
weight of material flow routed from entity i to i in time
period t
3.2. Model formulation
Based on previous notation, the production planning problem
for a vehicle recycling factory can be formulated as a linear programming model
Max
I−1
T Xi i t Ri i t −
T Xi 12 t CiL −
t=1 i ∈ ˝12
−
T I−I −2
t=1
i=1
CiS
T
t=1
i ∈ ˝i
CiA
t=1 i=I−I t=1 i=13 i ∈ ˝i
−
11
T Xi i t −
CtP Pt − C I
Xi i t
i ∈ ˝i
T
St
t=1
I
T
t=1 i=I−I i ∈ ˝i
Xi i t CiT i
(1)
V. Simic, B. Dimitrijevic / Resources, Conservation and Recycling 60 (2012) 78–88
Table 1
Efficiency of sorting entity i in the case of material flow j.
subject to:
St =
Pt + S0 − X0 1 t , if t = 1
Pt + St−1 − X0 1 t , if t = 2, . . . , T
St ≥ Smin ,
(2)
t = 1, .., T
(3)
i = 1, . . . , I − I − 2; t = 1, .., T
Xi i t ≤ Ci ,
(4)
i ∈ ˝i
Xi j t = Ei j
j ∈ j
i = 1, . . . , I − I − 2;
j ∈ Ai ; t = 1, . . . , T
(5)
Xi j t =
Xi i t ,
Xi i t +
i ∈ M i ∈ ˝i
+
Xi i t +
i∈D
EiE
EiE
i∈D
i = 9; j = 10; t = 1, . . . , T
EiR
i∈F
Xi i t ≥ QR X0 1 t ,
t = 1, . . . , T
(6)
(7)
i ∈ ˝i
i∈F
i ∈ M i ∈ ˝i
EiR
Xi i t
i ∈ ˝i
Xi i t ≥ QR X0 1 t ,
t = 1, . . . , T
(8)
i ∈ ˝i
Xi i t ≤ QE X0 1 t ,
t = 1, . . . , T
(9)
i ∈ ˝i
Eij (%)
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
81.47
18.53
89.89
10.11
5.1
94.9
67.6
32.4
9.66
90.34
85.53
14.47
89.38
10.62
99.67
0.33
2
3
5
6
8
recovery quotas) should be calculated according to ISO 22628 standards, a point especially emphasised by Santini et al. (2011). In the
above-mentioned standard, recycling and recovery rates are simply defined as the ratio between the sum of the mass of materials
reused/recycled/recovered during dismantling, metal separation
and non-metallic residue treatment and the complete “vehicle
kerb” mass (ISO 22628:2002). Finally, constraints (10) and (11)
define the value domain (i.e., non-negativity) of decision variables.
4. Case study
4.1. Data collection
Pt ≥ 0, St ≥ 0,
Xi j t ≥ 0,
j
1
7
i ∈ ˝i
i
4
Xi i t ,
i ∈ ˝i
81
t = 1, . . . , T
i = 0, . . . , I − I − 1; j ∈ ˚i ; t = 1, . . . , T
(10)
(11)
The objective function (1) seeks to maximise the vehicle recycling facility’s profit over the planning horizon. In the objective
function, the first term represents income from sale of the isolated
metals, the second term represents cost for (advanced) thermal
treatment of ASR, the third term relates to ASR landfill disposal cost,
the fourth term calculates the procurement cost, the fifth term represents the storage cost for hulks that have not been assigned for
recycling, the sixth term represents the material fragmentation and
sorting costs (i.e., processing costs), and the last term represents
costs associated with transportation to the final destinations.
Constraint (2) enforces the inventory balances; that is, the
weight of the stored hulks at the end of period t is determined
when the total quantity of procured and available hulks is reduced
by the weight of hulks planned for recycling in the analysed period.
Since the shipping of procured vehicle hulks depends on transportation congestion and weather (Qu and Williams, 2008), the
vehicle recycling factory seeks to keep enough hulks to maintain
the minimum processing rate. Therefore, constraints (3) ensure
the safety stock level of hulks. Constraints (4) represent the processing capacity of available sorting entities, and constraints (5)
maintain their material flow balances. Constraints (6) describe the
mixing operation. The mixer has been defined in the model to
combine the various residual fractions to the ASR mix fraction,
which can be transported to an ATT plant. Constraints (7)–(9) represent specific eco-efficiency requirements imposed by the ELV
Directive. More specifically, these constraints enforce that the percentage of recycling cannot be less than the prescribed recycling
quota (constraints (7)), the percentage of recovery cannot be less
than the prescribed recovery quota (constraints (8)), and the percentage of energy recovery cannot be larger than the prescribed
energy quota (constraints (9)). In addition, the vehicle recycling
facility efficiency (i.e., the attained recycling, recovery, and energy
The application of planning models is usually obstructed by
the lack of available data, and their gathering often represents
a very demanding process. To test the proposed model, we collected the necessary data from a great number of peer-reviewed
papers and published scientific studies. To provide a good survey
of the acquired data, they have been presented in the corresponding categories. In addition, so that data from different sources can
be compared, all prices are converted into euros based on Reuters
quotes from March 1, 2011 (i.e., D 0.7240 = US$1) (Reuters, 2011),
and tonne is adopted as the weight unit.
For modelling purposes, it is important to identify a typical
European ELV’s characteristics and, as best as possible, establish
a single “generic equivalent” that can serve as a convenient reference. Recent data suggest that 6.34 million ELVs were processed
in the EU in 2008 and that their average weight was 949.38 kg
(Eurostat, 2010). Assuming that further analysis excludes incomplete data about the number and purpose of the exported vehicles,
the calculated data suggest that 20.579% (195.37 kg) of a vehicle or
of its parts and components were stripped on average by European
dismantling companies in 2008 (Eurostat, 2010). From the same
data, 41.760%, 51.078%, 5.751% and 1.411% were reused, recycled,
energy recovered and landfilled, respectively. As a result, in 2008,
the average weight of the flattened hulk sold to a vehicle recycling
factory was 754.01 kg.
4.1.1. Efficiency of sorting entities
The efficiency of the equipment used for sorting material flows
is simulated in this paper using the post-fragmentation separation
model (SMART, 2006; Coates and Rahimifard, 2009), and the calculated values are presented in Table 1. The material compositions of
light and heavy ASR fractions are in accordance with the EPA (2010).
The portion of ferrous metals in the light ASR fraction is 5.1%, and
the fraction can be isolated using the second magnetic sorter. The
portion of the second fraction of NF metals in the NF mix fraction
82
V. Simic, B. Dimitrijevic / Resources, Conservation and Recycling 60 (2012) 78–88
Table 2
Processing rates, capacities and costs.
Index of sorting
entity
Processing rate
(tonne/h)
Processing capacity
(tonne/week)
Processing cost
(D /tonne)
1
2
3
4
5
6
7
8
80
65.2
65.2
30
30
30
30
59.4
4000
3260
3260
1500
1500
1500
1500
2970
26.25
4.33
4.33
9.42
9.42
70.60
9.42
1.67
is 6%, and the second eddy current sorter is applied for its isolation.
The portion of the first fraction of NF metals in the heavy ASR fraction is 66.4%, and this fraction can be isolated with the first eddy
current sorter. The HMS sorts the fraction of non-ferrous metals on
Al-rich and Cu-rich fractions. Because the aluminium share in the
Al-rich fraction is around 89%, it can be either routed on the third
eddy current sorter for further refinement (purity >99.5%) or sold
at a lower price. In the first case, the ORPR fraction is separated
from the aluminium. The copper portion in the flow of isolated ferrous metals must be less than 0.25%. Because manual sorting from
the flow of ferrous metals allows the isolation of 0.21–0.45% PVCcoated copper wires (SEES, 2006), an assumed value used in this
paper is 0.33% isolation.
4.1.2. Processing capacity and cost of sorting entities
Typically, a tactical plan is determined for the planning horizon,
which ranges from one month to a year (Bostel et al., 2005). In this
paper, a one-month production plan was created and comprises
four one-week plan periods or 50 working hours.
When conducting capacity planning for the vehicle recycling
factory, special attention should be paid to the processing workflow. For instance, Sakkas and Manios (2003) noted that at least
85,000 hulks must be processed per year to achieve a return on
investment. Currently, the 2000-hp shredders are very popular in
the EU. In addition, shredding rates are subject to material content, materials’ pre-shred densities, feed rates and shredding rates
directly affect capacity planning. According to specifications of one
of the leading manufacturers of scrap processing, recycling and
waste handling equipment, shredding capacity fluctuates between
25.4–40.6 tonne/h and 61.0–81.3 tonne/h for the 2000-hp model
HS6090 and model HS80115, respectively (Harris shredders, 2009).
On the other hand, typical processing rates for eddy current separators are between 18.3 and 30.5 tonne/h (Bandivadekar et al., 2004;
Williams, 2006; Williams et al., 2007; Kumar and Sutherland, 2008).
Because the analysed sorting process is continuous (Fig. 1), the processing rates of the magnetic sorters, the manual separation and the
HMS can be calculated based on the assumed material composition
of the procured hulks and on the adopted processing rates for the
shredding/air suction and the eddy current sorters (Table 2).
The estimated processing costs are presented in Table 2. Shredding operating costs include investment, labour, energy, material
and maintenance cost components. A detailed review of the referential material identifies the following three cost categories of hulk
shredding:
• Low cost – 6.4 D /tonne (Williams et al., 2007) and
9.6–10.7 D /tonne (Qu and Williams, 2008).
• Medium cost – 20–25 D /tonne (Liljenroth, 2004) and 30 D /tonne
(Sakkas and Manios, 2003).
• High cost – 47.6–72.1 D /tonne (GHK/BioIS, 2006).
For the operational shredding cost, it has been decided that the
average value of the medium cost category, 26.25 D /tonne, will be
adopted.
The fixed costs of installing the eddy current sorting equipment (i.e., the costs for buying, building, engineering and overhead)
were estimated to be D 370,000. If the operational costs for
energy/maintenance/labour and interest are D 72,400, then a return
on investment can be achieved in 3.8 years (Nijhof and Rem,
1999), whereas the average service life lasts between 12 and 15
years (Bandivadekar et al., 2004). Therefore, the planned capacity leads to a cost of 9.42 D /tonne. Processing costs for magnetic
separation are set according to quotes provided by Mas magnetics
(2011) and are presented in Table 2. The cost for processing materials in the HMS is between 66.6 and 74.6 D /tonne (Manouchehri,
2007), which is in line with the recommendation that appropriating funds for advanced separation should not exceed 100 D /tonne
(CEC, 2007). The manual sorting of ferrous metal fractions is subject to a fraction’s material composition. It can vary between 1.27
and 1.35 D /tonne for an average processing capacity of 2325 tonne
per planning period (Williams et al., 2007). Keeping in mind that
a larger manual processing capacity requires the deployment of a
larger number of operators, the adopted value is 1.675 D /tonne.
Storage is defined in 3 parameters. Presuming that initial inventory weight of hulks and safety inventory level are equal and have
the same value as the maximal daily capacity of the shredder, we
estimate the storage to be 800 tonne. On the other hand, if the percentage of capital cost for inventory is 0.48%/week (Williams et al.,
2007), the value reached is 0.82 D /tonne.
4.1.3. Cost of ASR landfill disposal
The cost of ASR landfill disposal is influenced by landfill costs
and the value of the prescribed general tax on polluting activities (GTPA). In France, landfill costs and GTPA are 40–45 D /tonne
and 9.15 D /tonne (GHK/BioIS, 2006), respectively. Denmark has
announced the introduction of a tax on ASR landfill disposal in
two phases: the first phase will begin January 1, 2012, when the
basic cost of 26.67 D /tonne will be raised to 50 D /tonne (tax is
21.33 D /tonne), and the final phase will begin January 1, 2015,
when the cost will be 90 D /tonne (tax is 63.33 D /tonne) (Moakley
et al., 2010). Additionally, a continuous increase in the cost of ASR
landfill disposal has been noted in the US, with prices increasing from 23.5 D /tonne (Isaacs and Gupta, 1997; Boon et al., 2001,
2003) and 36.2–43.4 D /tonne (Kanari et al., 2003) to 53.4 D /tonne
(Bandivadekar et al., 2004). When adopting a reference cost for
landfill disposal, one should bear in mind that it depends on
its specific density. For example, for a density of 300–350 kg/m3
(Bandivadekar et al., 2004), the cost can be 36.2–53.8 D /tonne.
Accordingly, if the ratio of the general specific density (specific density multiplied by material share) of the first non-metals fraction,
second non-metals fraction, NF mix, ORPR fraction and insulated
copper wires is 1:0.7111:1.0118:0.7349:5.771, then the study can
introduce the following 2 cases:
• Case 1 – low cost of ASR disposal. If we assume the landfilling cost
of the first non-metals fraction is 35 D /tonne (GHK/BioIS, 2006;
CEC, 2007; Hjelmar et al., 2009), then costs for the second nonmetals fraction, NF mix, ORPR fraction and insulated copper wires
will be 24.89, 35.41, 25.72 and 201.98 D /tonne, respectively.
• Case 2 – high cost of ASR disposal. If we assume that landfilling
cost of the first non-metals fraction is 115 D /tonne (GHK/BioIS,
2006; CEC, 2007; Hjelmar et al., 2009), then costs for the second non-metals fraction, NF mix, ORPR fraction and insulated
copper wires will be 81.78, 116.36, 84.51 and 663.66 D /tonne,
respectively.
4.1.4. Procurement costs and prices of sorted metals
The hulk cost in the planning period should not be a deterministic value, although some authors have observed it as such:
Isaacs and Gupta (1997) and Boon et al. (2001, 2003) used the
V. Simic, B. Dimitrijevic / Resources, Conservation and Recycling 60 (2012) 78–88
83
Table 3
Vehicle hulk costs and sorted metal prices in D /tonne (February–March 2011).
Date
Hulk
Ferrous metal
Al-rich fraction
Aluminium fraction
Cu-rich fraction
11.02.11
18.02.11
25.02.11
04.03.11
171.02
171.02
171.02
171.02
327.78
327.78
327.78
327.78
1428.57
1452.51
1452.51
1452.51
1714.28
1743.01
1743.01
1743.01
2292.61
2292.61
2292.61
2314.56
amount (35.6 D /tonne), whereas Bandivadekar et al. (2004) used
amounts in the range of 35.6–71.2 D /tonne. The procurement cost
that a vehicle recycling factory pays for a decontaminated vehicle
hulk should exclusively depend on the metal market. Moreover, Qu
and Williams (2008) established that the procurement cost represents approximately 50% of the scrap ferrous metal price. In this
paper, hulk cost is determined as the minimum price for a “vehicle auto body” obtained from Boston, Los Angeles, San Francisco,
Philadelphia and New York export yards (Table 3).
The sale process for certain metals is determined in the following way (Table 3):
• The sorted Fe metal price is determined as the selling price for
“ferrous shredded auto scrap” from the Birmingham metal market (AMM, 2011).
• The Al-rich fraction price is determined as the price for “nonferrous auto shred (90% aluminium) for secondary smelters” (AMM,
2011).
• The aluminium fraction is priced as 120% of the Al-rich fraction
price because it comprises more than 99% scrap aluminium.
• The Cu-rich fraction price is determined as the average price for
“mixed yellow brass turnings, borings copper scrap” from 14 US
and 2 Canadian dealers (AMM, 2011).
• The fraction of insulated copper wires is labelled as “Druid”
according to ISRI classification, and its price is based on individual
buy–sell agreements (ISRI, 2009). With that in mind, an analysis
of the offers given for the analysed planning period (Recycler’s
World, 2011) determined that the offer from Kuwait was the best.
4.1.5. Parameters of advanced thermal treatment plants
Useful overviews of ASR thermal treatment processes can be
found in Nourreddine (2007) and Srogi (2008). However, at present,
there are not many well-established processes for ASR thermal
treatment. An analysis of the characteristics of multiple ATT processes (GHK/BioIS, 2006) indicates that TwinRec technology is a
possible solution. Moreover, Vigano et al. (2010) confirmed that
this sequential gasification and combustion technology can demonstrate appealing energy and environmental performances.
TwinRec is an advanced thermal technology developed by
the Japanese company Ebara. It is designed to combine material recycling with energy recovery. This technology operates at
atmospheric conditions, without the consumption of fossil fuels
(except at start-up) or oxygen. In the TwinRec process, the waste is
introduced into the circulating fluidised bed gasifier at 500–600 ◦ C
(Ignatenko et al., 2008). The gasifier, besides detoxification of the
organic material, separates the remaining metals and large inert
particles from the combustibles and fine ash, maximising total
metal recovery from ELVs. The synthesis gas and organic particles
produced in the gasifier are burnt in the cyclonic combustion chamber at 1350–1450 ◦ C, while fine inorganic particles are melted into
the slag. With high net efficiency, the energy content of the waste
is converted into electricity and/or district heat (Selinger et al.,
2003a). TwinRec is capable of processing a variety of waste materials, such as shredder residues, waste plastics, electronics waste,
industrial residues, municipal waste and sewage sludge (Selinger
et al., 2003b). For instance, ASR can be fed to the gasifier as delivered from the vehicle recycling factory without any additional
Insulated copper wires
13,812.0
13,812.0
13,812.0
13,812.0
preparation. Ebara Corporation built the world’s largest waste gasification and ash melting plant in Kuala Lumpur, Malaysia, with
a total capacity of 1500 tonne/day (or 62.5 tonne/h) in 5 process
lines. However, at present, there are no TwinRec plants in Europe
(GHK/BioIS, 2006).
With regard to the necessary model parameters, the following
assumptions have been adopted: recycling efficiency is 33% and
energy efficiency is 52% (GHK/BioIS, 2006). The corresponding gate
fee primarily depends on the agreed quantity, and this fee can range
between 90 and 250 D /tonne (Selinger et al., 2003a) or 120 and
200 D /tonne (GHK/BioIS, 2006). As such, the following cases were
considered:
• Case 1 – low ATT cost of 90 D /tonne.
• Case 2 – high ATT cost of 200 D /tonne.
4.1.6. Parameters of municipal solid waste incinerators
The MSWI cost can depend on the ASR composition, development of incinerator network, cost of generated ash disposal, price
of produced electricity, etc. The cost for incineration of plastics with
11% PVC is 20–49 D /tonne (Bellmann and Khare, 1999), while in the
case of light ASR fraction, it is 140–190 D /tonne (EPA, 2010). Waste
incineration in Switzerland and Germany costs 149–259 D /tonne
(CEC, 2007) and 200–300 D /tonne, respectively. In Denmark, the
cost is rather low (7–55 D /tonne) because a well-developed incinerator network exists there (Moakley et al., 2010). Gesing (2004)
emphasised that incinerating 1 tonne of ASR could produce approximately 30 GJ of electrical energy and generate 0.3 tonne of ash.
Alternatively, when analysing incineration feasibility in a MSWI,
some studies use the unique cost of 100 D /tonne (CEC, 2007), some
use the cost interval 30–250 D /tonne (Lundqvist et al., 2004), and
some use cost categories (Liljenroth, 2004). This paper analyses the
following two cases:
• Case 1 – low MSWI cost of 80 D /tonne.
• Case 2 – high MSWI cost of 250 D /tonne.
According to the new directive on waste (2008/98/EC), a MSWI
can be classified as a processing plant only if its energy efficiency
is not less than 60% or 65%, if it was built before January 1, 2009 or
after December 31, 2008, respectively (EU, 2008). Therefore, only
when ASR is incinerated in a certified MSWI can the process be
characterised as energy recovery. Because Ciacci et al. (2010) estimated the energy efficiency of a MSWI in the case of incinerating
the mixture of ASR/MSW to be 75.96%, this paper uses that value
when calculating recovery efficiency and energy recovery.
4.1.7. Transportation cost
The transportation cost represents an important element in the
vehicle recycling factory’s cost structure. Williams et al. (2007)
and Qu and Williams (2008) presumed the following respective
amounts: light non-ferrous metals (21.4 and 24.9 D /tonne), heavy
non-ferrous metals (17.8 and 21.4 D /tonne), ferrous metals (20 and
21.5 D /tonne), and ASR (10.7 and 14.3 D /tonne). Some studies use
the unique cost of 15 D /tonne (CEC, 2007), whereas others use a
proposed value interval such as 10–15 D /tonne for the ASR case
(GHK/BioIS, 2006). In this paper, transportation cost is estimated by
84
V. Simic, B. Dimitrijevic / Resources, Conservation and Recycling 60 (2012) 78–88
considering the general specific densities of transported materials,
the maximum weight and volume per truckload, and the corresponding transportation distances. If it is assumed that maximum
weight and volume per truckload are 30 tonne and 15 m3 , respectively, that all materials are transported to the same distances, and
that the cost per truckload is D 500, then transportation costs of ferrous, light non-ferrous metals, aluminium, and heavy non-ferrous
metals are 16.67 D /tonne. Conversely, if it is assumed that the closest landfill and MSWI site are twice as close (i.e., that the truckload
cost is approximately D 250), then transportation costs of the NF
mix, the first and the second non-metals fraction, the ORPR fraction, and the insulated copper wires are 16.13, 16.32, 22.96, 22.21
and 8.33 D /tonne, respectively.
Ciacci et al. (2010) assumed that the TwinRec plant is placed at
a distance of 5 km from the vehicle recycling factory to minimise
transportation-related environmental impacts. In that case, there
should be no hesitation in adopting the recommended ASR mix
fraction transportation cost of 10 D /tonne (GHK/BioIS, 2006).
The export of the insulated copper wires fraction for (manual) recycling purposes can provide certain profits for a vehicle
recycling factory, especially in India, China, Russia, Kuwait, among
others (Recycler’s World, 2011). However, while deciding which
offer to accept, transportation costs should be carefully considered. Keeping in mind that these kinds of contracts are usually
signed on a monthly basis (at a minimum) and that intercontinental transportation is multimodal, transportation cost can, therefore,
be assumed to be 100 D /tonne.
4.2. Scenario description
The ELV Directive sets the eco-efficiency quotas with which
recycling systems should comply. At the same time, no consideration was given to the fact that dismantlers, on the one hand, and
vehicle recycling factories, on the other, are completely independent of one another. Therefore, the question arises as to whether
the control of efficiency should be implemented at the subsystem
level or the system level. To answer this question, the following
scenarios were created:
• Scenario a – analysis of the processing efficiency at the subsystem
level. This approach additionally reduces the already low flexibility of the recycling system, because it introduces 6 constraints in
relation to individual processing efficiency rates (i.e., 3 each for
dismantlers and the vehicle recycling factory).
- Sub-scenario 1 – valid quotas (valid since January 1, 2006). The
vehicle recycling factory has to guarantee that recycling rate
and recovery rate do not fall under 80% and 85%, as well as that
the energy recovery rate does not exceed 5%.
- Sub-scenario 2 – future quotas (valid beginning January 1, 2015).
The vehicle recycling factory has to guarantee that recycling
rate and recovery rate do not fall under 85% and 95%, as well as
that the energy recovery rate does not exceed 10%.
• Scenario b – system approach (i.e., integration of recycling system). This approach changes (except in a marginal case) the legal
conditions of the vehicle recycling factory business, as quotas are
modified depending on the dismantlers’ accomplished efficiency.
Nonetheless, a marginal case occurs only when the dismantlers’
energy recovery rate and disposal rate are exactly 5% and 15%,
respectively. In that case, scenarios a-1 and b-1 will not differ.
When the predetermined rate of the dismantling subsystem is
above or below the prescribed value, then the appropriate quota
can be decreased or increased, respectively.
Sub-scenario 1 – valid quotas. Dismantlers mostly incinerate
stripped tires, large plastic parts and dispensed fluids (excluding fuel). Given the absolute rate of energy recovery of the
dismantling subsystem in 2008 was 1.18% (Eurostat, 2010), for
the vehicle recycling facility’s future energy recovery quota, we
can adopt QE = 4.81%. If it is known that the absolute rate of
disposal at the dismantling subsystem in 2008 was only 0.29%
(Eurostat, 2010), we can adopt QR = 81.48% as the vehicle recycling facility’s “new”, fairly relaxed recovery quota. Introducing
a more intense energy quota is inevitable because dismantlers
achieved a relative energy recovery quota of 5.75% and, therefore, exceeded the 5% limit. Ultimately, the value of the new
recycling quota is gained by subtracting QE from QR ; this results
in the revised value QR = 76.67%.
Sub-scenario 2 – future quotas. Taking into account the abovementioned absolute rates of both landfill disposal and energy
recovery in the dismantling subsystem in 2008, the attained
recovery quota, energy recovery quota and recycling quota are
94.07%, 11.10% and 82.97%, respectively.
5. Results and discussion
This section presents the results of the proposed model testing in scenarios from a-1 to b-2 and in business conditions that
can occur in certain EU member states (i.e., at low or high costs
of landfill disposal, combustion in an MSWI and processing in ATT
plant). Optimal decisions for the selected test problems were solved
using the CPLEX 12.2 solver on a Toshiba Qosmio with an Intel Core
i5-430 M mobile technology processor.
Testing of the proposed model showed that during every planning period the quantity of procured hulks is exactly the same as the
maximum capacity of shredding (4000 tonne per week) and that
the vehicle recycling factory aims at attaining the maximum possible quantity and quality of sorted metal flows. For instance, the
Al-rich fraction is always additionally purified because the additional income always exceeds the costs of its sorting and further
manipulation of the ORPR fraction. On the other hand, copper wire’s
import possibilities have primacy over landfilling, as expected.
The profits and recycling, recovery, and energy recovery efficiencies of the optimal decisions for the 32 test problems are
summarised in Table 4. Testing the proposed model answered the
question of which financial conditions are the most favourable for
the vehicle recycling factory business from an ecological perspective. Considering valid quotas, a vehicle recycling factory will have
the best ecological results at a high cost for ASR landfill disposal (i.e.,
the landfill disposal cost of the first non-metals fraction is not less
than 115.0 D /tonne) and with a cost for processing in an ATT plant
not greater than 91.6 D /tonne. In both approaches, the recycling
rate will be 83.96%, while energy recovery in the system approach
will decrease 0.19% as this quota is reduced. In reference to future
quotas, the best ecological results will be attained when the cost
for landfilling is not less than 115.0 D /tonne, ATT cost is low (not
greater than 91.6 D /tonne), and MSWI cost is low (not greater than
84.5 D /tonne). In both analysed approaches, the recycling rate will
be 86.91%, while energy recovery in the system approach will be
somewhat higher because the quota has been relaxed. As for profits,
they range from 134.18 D /tonne of processed hulks (test problems
1 and 3) to 162.23 D /tonne of processed hulks (test problem 16).
The next objective of testing the proposed model was to inspect
the individual influence of available financial instruments (i.e.,
costs of landfill disposal, combustion in MSWI and advanced thermal treatment) (Fig. 2):
• Scenario a-1: The testing generally confirmed the standpoint
that increases in cost for landfill disposal reduce the quantity
of discarded ASR. However, we identified situations when this
important financial instrument would not change vehicle recycling factory plans: when the ATT cost was greater than or
equal to 94.8 D /tonne or MSWI cost was greater than or equal
V. Simic, B. Dimitrijevic / Resources, Conservation and Recycling 60 (2012) 78–88
85
Table 4
Case study profits and eco-efficiencies.
Test problem
Scenario
Sub-scenario
ATT cost
MSWI cost
Landfill
disposal cost
Profit (D )
Recycling (%)
Recovery (%)
Energy
recovery (%)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
a
1
Low
Low
Low
High
Low
High
Low
High
Low
High
Low
High
Low
High
Low
High
Low
High
Low
High
Low
High
Low
High
Low
High
Low
high
Low
High
Low
High
Low
High
2,562,219.75
2,435,785.20
2,562,219.75
2,435,785.20
2,553,273.60
2,419,306.58
2,474,944.93
2,344,978.81
2,464,055.05
2,442,898.80
2,464,055.05
2,442,564.94
2,233,326.84
2,214,928.30
2,169,721.40
2,146,842.34
2,595,746.03
2,435,508.09
2,595,746.03
2,435,508.09
2,594,134.52
2,419,235.34
2,581,355.91
2,406,850.73
2,473,184.36
2,443,041.24
2,473,184.36
2,442,564.94
2,343,020.49
2,320,141.42
2,198,107.18
2,165,269.03
82.42
83.96
82.42
83.96
80.79
80.79
82.42
82.42
86.30
86.91
86.30
86.91
85.0
85.0
86.30
86.30
81.05
83.84
81.05
83.84
80.79
80.79
81.05
81.05
85.94
86.91
85.94
86.91
82.97
82.97
85.94
85.94
85.0
88.96
85.0
88.96
85.0
85.79
85.0
85.0
95.0
96.91
95.0
96.56
95.0
95.0
95.0
95.0
81.48
88.65
81.48
88.65
81.48
85.60
81.48
81.48
94.07
97.06
94.07
96.56
94.07
94.07
94.07
94.07
2.58
5.0
2.58
5.0
4.21
5.0
2.58
2.58
8.70
10.0
8.70
9.65
10.0
10.0
8.70
8.70
0.43
4.81
0.43
4.81
0.69
4.81
0.43
0.43
8.13
10.15
8.13
9.65
11.10
11.10
8.13
8.13
High
High
Low
High
2
Low
Low
High
High
Low
High
b
1
Low
Low
High
High
Low
High
2
Low
Low
High
High
Low
High
to 81.8 D /tonne. In those cases, throwing away the maximumallowed quantity of ASR was always a financially optimal decision
(test problems 7 and 8). The change of MSWI cost illustrated an
effect only when the ATT cost was not less than 98.6 D /tonne (test
problems 5–8), because incineration would not be executed at
lower ATT costs (test problems 1–4). The change of ATT cost had
no effect when landfill disposal costs were less than or equal to
108.3 D /tonne and combustion costs were greater than or equal
to 178.0 D /tonne because ATT cannot compete with cheap landfilling (test problems 3 and 7).
• Scenario b-1: Implementing the system approach will relax the
prescribed recycling and recovery quotas to 3.33% and 3.52%,
respectively, but will not expand the influence domain of the
first two financial instruments. On the contrary, it is clear that
MSWI (a-1)
ATT (a-1)
LD (a-1)
MSWI (a-2)
ATT (a-2)
LD (a-2)
MSWI (b-1)
ATT (b-1)
LD (b-1)
MSWI (b-2)
ATT (b-2)
LD (b-2)
3,000
Quantity (tonne)
2,500
2,000
Current
quotas
Future
quotas
1,500
1,000
500
0
LD cost
MSWI cost
ATT cost
low
high
low
low
high
high
low
low
high
low
low
high
high
high
low
high
low
low
high
high
low
Fig. 2. Comparison of optimal decisions.
low
high
low
low
high
high
high
86
V. Simic, B. Dimitrijevic / Resources, Conservation and Recycling 60 (2012) 78–88
Fig. 3. Breakdown of vehicle recycling factory costs in percentages.
reducing the energy recovery quota to 0.19% will not improve
efficiency of the change in MSWI cost, whose influence is evident
only in test problems 21–24.
• Scenario a-2: Test problems 9–16 show the present approach for
the second implementation phase of ELV Directive requests. The
most important conclusion is that only in half of the analysed situations will the increase in cost for the landfill disposal lead to
the preferred effect (test problems 9–12); that is, it will reduce
the total quantity of ASR that is being disposed. Such a resolution
is not unforeseen, as the reduced possibility of landfill disposal
(i.e., from ≤15% to ≤5% of average vehicle weight) decreases this
financial instrument’s influence. Moreover, in all test problems,
a larger quantity of ASR is being disposed in landfills indirectly
(i.e., via MSWIs and ATT) rather than directly. ATT and MSWI costs
become more influential as the allowed energy recovery is substantially increased (i.e., from ≤5% to ≤10% of average vehicle
weight);
• Scenario b-2: Implementing a system approach will increase the
recycling quota to 2.97%, whereas it will relax the future quotas
for recovery and energy recovery to 0.97% and 1.1%, respectively.
Alternatively, influence domains of individual costs are identical
to the ones in scenario a-2.
The breakdown of vehicle recycling factory costs for valid and
future quotas is presented in Fig. 3. Observing the average shares of
analysed cost parameters for test problems 1–8 and 17–24, which
represent the two potential approaches for the first implementation phase of ELV Directive requests, one can detect that the most
important parameters are hulk procurement (70.97%), processing
(16.59%), and transportation to optimal destinations (7.27%). The
significance of other parameters frequently varies, except in the
case of the storage cost, which is always approximately 0.7%. The
share of landfill disposal cost varies from 1.47% (test problem 7)
to 6.37% (test problem 24), when the maximum 2943.58 tonne are
discarded at a high cost. The share of ATT cost is the highest in the
case of test problem 7 (4.10%), as the 793.41 tonne are processed
at a high cost. Meanwhile, the share of MSWI cost is the highest
in the case of test problem 6 (2.14%), when the maximum quantity of 1053.19 tonne are incinerated. Introducing future quotas for
vehicle processing (test problems 9–16 and 25–32) will disrupt the
existing parameters’ ranking and set up the following order of the
dominant costs segments: hulk procurement (68.34%), processing
(15.97%), ATT cost (8.21%) and transportation to optimal destinations (6.44%). The share of landfill disposal is the lowest for test
problem 26, when there is no direct disposal, and is the greatest for
test problem 32 (1.13%), when the maximum 573.69 tonne are discarded. On the other hand, the share of ATT cost ranges from 5.26%
(test problem 30), when the minimal 1059.38 tonne are processed
at a low cost, to 12.81% (test problem 15), when 2675.76 tonne are
processed at a high cost. Finally, the share of MSWI cost is the highest for test problem 29 (3.22%), when the maximum 1612.85 tonne
are incinerated in that case.
The previous analysis clearly identified that a vehicle recycling
facility’s cost structure is influenced by legal provisions (through
quota definition for recycling, recovery and energy recovery) and
official business conditions. However, the costs and shares of
recycling parameters can also be influenced by technology development. This influence can be analysed from different aspects,
among which the most important are the development of novel
ASR applications and the development of new physical separation technologies. In reference to novel applications, ASR can be
used as a binder and/or aggregate in asphalt for roadways, highways and airfields (Forton et al., 2006; Vermeulen et al., 2011),
applied as a mineral supplement in cement manufacturing (Rossetti
et al., 2006; Forton et al., 2006; Boughton, 2007; Mancini et al.,
2010; Vermeulen et al., 2011), for the production of low-strength
components such as housings, casings and covers (Nourreddine,
2007; Vermeulen et al., 2011), used as an alternative fuel in the
iron metallurgical industry (Srogi, 2008; Vermeulen et al., 2011)
and utilised as a secondary fuel in the cement industry (Boughton
and Horvath, 2006; Forton et al., 2006; Gomes, 2006; Boughton,
2007; Srogi, 2008; Mancini et al., 2010; Vermeulen et al., 2011).
The introduction of previously mentioned ASR applications might
have a very positive influence on the vehicle recycling factory
business, as it provides an opportunity to reduce landfill disposal
costs. However, we should not expect significant changes in the
most important cost parameters’ (i.e., procurement and processing
costs) shares. On the other hand, share and amount of transportation cost will increase, while that growth would mostly depend
on a new destination’s distance. In general, the development of
novel ASR applications leads to more attainable eco-efficiency quotas, resulting in the mitigation of ATT, MSWI, and LD cost shares.
However, particular attention should be paid to waste-to-energy
applications, as the ELV Directive quota related to energy recovery
is very rigorous. Regarding the development of new physical separation equipment, among many technological solutions, we should
emphasise fluidised bed sink float technology, which can separate
different density scraps without transferring them to different liquid baths (Gaustad et al., 2012), froth flotation (Forton et al., 2006;
Gent et al., 2009; Mancini et al., 2010; Vermeulen et al., 2011;
Grause et al., 2011) and laser-induced breakdown spectroscopy,
which has many advantages over current separation technologies
for automotive applications (Grause et al., 2011; Gaustad et al.,
2012). The installation of additional sorting equipment in a vehicle
recycling factory might increase the share and amount of processing costs but may also improve the recycling rate. Additionally, the
decrease in the remaining ASR can mitigate transportation, ATT,
MSWI, and LD cost shares.
6. Conclusions
The world economy has already overcome the recent economic crisis, and the expected recovery of the metals market
has already begun. Vehicle recycling factories benefit greatly from
V. Simic, B. Dimitrijevic / Resources, Conservation and Recycling 60 (2012) 78–88
this recovery, as high prices for secondary metals make its business exceptionally profitable (134.18–162.23 D /tonne of processed
hulks), even in strictly controlled and legally rigorous production
conditions. Testing the proposed model proved that, in such conditions, a vehicle recycling factory will continuously procure the
maximum quantity of hulks that it is able to process according to
the planning period; also, testing showed that the factory will aim
to achieve the highest quantity and the best quality of sorted metal
flows.
The country is expected to create optimal business conditions
for its legal entities and represents an important actor in the
vehicle supply chain. That is why this paper investigates the financial conditions that are required in an EU member state for the
ELV Directive to have the most positive eco-effect on the vehicle
recycling factory industry. Considering valid quotas, the best ecological result will be attained at a cost for landfilling not less than
115.0 D /tonne and cost for processing in an ATT plant not greater
than 91.6 D /tonne. On the other hand, after January 1, 2015, EU
member states will have to raise their costs for landfill disposal to
at least 115.0 D /tonne and do what is in their power to lower ATT
costs (not greater than 91.6 D /tonne) and MSWI costs (not greater
than 84.5 D /tonne) if they want their vehicle recycling factories to
be considered “green”.
The ELV Directive regulates the quotas with which the recycling system must comply. However, dismantlers, on the one hand,
and vehicle recycling factories, on the other, are completely independent in conducting their business. Comprehensive testing of
the proposed model showed that the control of the recycling
system efficiency should be done at the system level because that
will in no way jeopardise the ELV Directive’s objectives. Additionally, a vehicle recycling factory’s profitability will increase to 2.15
and 2.19 D /tonne of processed hulks considering valid and future
quotas, respectively.
The most important conclusion reached while researching available financial instruments’ individual influences is that the increase
in landfill disposal cost will not always reduce the quantity of disposed ASR. Moreover, until January 1, 2015, it will have no effect
on ATT costs greater than or equal to 94.8 D /tonne or MSWI costs
greater than or equal to 81.8 D /tonne. After this date, the increase
in the landfill disposal cost will be justified only if the ATT cost is
not greater than 94.8 D /tonne.
The breakdown of vehicle recycling factory costs considering
valid and future ELV Directive quotas is presented here for the
first time, and it confirms that the cost structure is influenced
by legislation and financial business conditions. It was concluded
that during the first implementation phase of the ELV Directive, the most important cost parameters were hulk procurement
(70.97%), processing (16.59%) and transportation (7.27%). Furthermore, it was determined that raising all quotas would set a new
order for the most important cost parameters: hulks procurement
(68.34%), processing (15.97%), advanced thermal treatment (8.21%)
and transportation (6.44%).
The proposed model of production planning can be of assistance not only to European vehicle recycling factories that aim to
improve their eco-efficiency and profitability but also, for instance,
to Japanese and Chinese factories if the model also implements
eco-efficiency requirements imposed by Japan’s ELV Recycling
Law and China’s Automobile Industry Development Policy,
respectively.
Additional research in two areas is needed. The first area should
focus on a detailed analysis of the influence that technological
development might have on vehicle recycling factory eco-efficiency
and profitability. The second research field should focus on including different types of M1 (passenger vehicles with less than 8 seats)
and N1 (vans not exceeding 3.5 tonne) hulks in the presented production planning problem.
87
Acknowledgements
The authors would like to thank the anonymous referees for
their valuable comments.
This work was partially supported by Ministry of Science and
Technological Development of the Republic of Serbia through the
project TR 36006 for the period 2011–2014.
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