Environmental systems analysis of meals : model description and data used for two different meals

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SIK-rapport Nr 735 2005

Environmental Systems Analysis of

Meals

– Model Description and Data Used for Two

Different Meals

Ulf Sonesson Jennifer Davis

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2 SIK-rapport

Nr 735 2005

Environmental Systems Analysis of Meals

– Model Description and Data Used for Two Different Meals

Ulf Sonesson Jennifer Davis

SR 735

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Summary

This report is part of FOOD 21, a research program working within the area of sustainable food production. One part of the FOOD 21 program is environmental systems analysis, in which both the agricultural as well as the post-farm system are studied. The aim with the present study was to investigate the environmental impact and resource use for the entire food chain from farm to fork for integrated food chains, i.e. not single food products. In the first study the objectives were to compare three different ways of producing a single meal: home made, semi-finished and a ready-to-eat meal. A meatball meal was chosen for this purpose, partly because it is a common meal in Swedish diets and also because the meal is available as a semi-finished and ready-to-eat meal. Results from this first study, and discussions with stakeholders in the food chain determined the main direction in the second study. Here, two different meals based on chicken were chosen for analysis: one home made and one semi-finished meal. The objectives were to explore how different improvement measures in the meals’ supply chain would affect the environmental impact of each meal. Overall, the objective of the project was also to develop a simulation model suitable for analyses of this type.

This report gives detailed information on the model and data used in the two studies. The results from the studies and a more thorough background, including references to other literature, as well as analyses and discussions are presented in Sonesson et al. (2005a), and an impending article (Davis & Sonesson, 2005).

Results from both case studies show that raw material utilisation in the post-farm system is very important. Since the largest environmental impact occurs early in the meal’s life cycle, i.e. in agriculture, any food wasted in industry and households means that significant

environmental burden has taken place to no use. By reducing wastage after the farm, the impact from the farm is also reduced, as less material from the farm is then needed to produce the meal. The chicken study shows that small measures in terms of reducing wastage do have an impact, so the potential for improvements is large.

The holistic approach used in the simulation model is important as it allows for analysis of the impact of changes to the system. When researching possible ways to sustainable production and consumption of food, it is crucial to have a system’s perspective to ensure that

improvements in one part of the system do not lead to negative impacts in other parts. Furthermore, the approach also enables you to pinpoint where in the system improvement efforts are best focused.

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Introduction

This report is part of FOOD 21, a research program working within the area of sustainable food production. The overall long term goal of the FOOD 21 program is to define optimal conditions for sustainable food production that generate high quality food products (FOOD 21, 2004). One part of the FOOD 21 program is environmental systems analysis, in which both the agricultural as well as the post-farm system are studied. The overarching aim with the systems analysis projects is to analyse the environmental impact of the food chain with a holistic approach, using research results from other parts of the FOOD 21 program. The focus of the present study is how different processing in the post-farm system affects the overall environmental impact of the food chain (including impacts from the farm). The working process has been to first analyse three different ways to produce a single meal (a meatball meal). The results from this study were then disseminated through meetings with stakeholders in the supply chain: food industries (Findus, Procordia/Orcla and Swedish Meats), a

representative for the retail sector (COOP), and a consumer organisation

(Konsumentföreningen Stockholm). This was with the aim to distribute the results, but also to discuss the next steps in the study: which areas did they pinpoint as relevant to analyse? With these discussions as a starting point, the aims and scenarios of the second study were

determined. This procedure was beneficial as we received better understanding of the needs of stakeholders in the food chain, but will also be valuable in future research projects (the

discussions with the stakeholders resulted in many interesting research questions, more than could be covered within this project).

This report gives detailed information on the model and data used in the two studies. The results from the studies and a more thorough background, including references to other literature, as well as analyses and discussions are presented in Sonesson et al. (2005a), and an impending article (Davis & Sonesson, 2005).

We would like to express our thanks to Anna Flysjö, SIK, for contributing to the data collection, and other colleagues at SIK for discussions on food processing technology. The contact persons from companies and organisations are also greatly acknowledged for their help and information.

Aim and Objectives

The aim with the present study was to investigate the environmental impact and resource use for the entire food chain from farm to fork for integrated food chains, i.e. not single food products. In the first study the objectives were to compare three different ways of producing a single meal: home made, semi-finished and a ready-to-eat meal. A meatball meal was chosen for this purpose, partly because it is a common meal in Swedish diets and also because the meal is available as a semi-finished and ready-to-eat meal. Results from this study, and discussions with stakeholders in the food chain determined the main direction in the second study. Here, two different meals based on chicken were chosen for analysis: one home made and one semi-finished meal. The motivation for exploring chicken based meals was partly to give the study another dimension (otherwise we could have continued to explore the meatball meals further), and partly because consumption of chicken has increased significantly in the past ten years in Sweden. The objectives were to explore how different improvement

measures in the meals’ supply chain would affect the environmental impact of each meal. The scenarios of each study are described further in the Scenario chapter (page 51).

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Overall, the objective of the project was also to develop a simulation model suitable for analyses of this type. The aim of this report is to present all data used and the model

employed for performing the analysis. This is very important since it is crucial to supply all information used for large systems analyses like this, in order to make the study credible.

Structure of the Presentation of the Model and Input Data

The amount of data used in the presented study is large; hence the data presentation is structured according to how data is used and where in the study and model.

1. The model is described so the reader can understand how it uses the input data to calculate the emissions and resource use.

2. Input data that are general for all scenarios in both studies are presented for each part of the model, as energy transports, cooking etc.

3. The three scenarios for the meatball meal study are described in terms of how the food chain is designed and what assumptions have been made.

4. Input data that are specific to each meatball meal scenario is described

5. The four scenarios for the chicken meal study are described in terms of how the food chain is designed and what assumptions have been made.

6. Input data that are specific to each chicken meal scenario is described 7. Results for the two studies are presented

Model description

General description

The model developed is basically a material and substance flow model, where energy flows are also accounted for. The simulation model SAFT (Systems Analysis of Food processing and Transport) is constructed to facilitate simulation experiments for different food supply chains from farm gate to consumption. The results are consumption of resources and

emissions from the system. Methods from Life Cycle Assessment (LCA) are used regarding characterisation of emissions and resource use (for more information about LCA see e.g. Lindfors et al., 1995 and CEN, 1997). The systems perspective is crucial when defining systems boundaries, also in this respect the modelling resembles LCA. Using LCA vocabulary, the function of the system is to transport and process food raw material from farms to the point of consumption. In order to take into account all effects of the food flow both upstream systems (e.g. agriculture, energy production, packaging production) and downstream systems (e.g. waste management) are included. In Figure 1 the principal system boundaries are described. Within the “core system” the models are more detailed and

facilitate simulations if different technologies and organisations. For the systems in the “background system”, static LCA input data is used to calculate energy use and emissions per kg of input needed or outflow treated.

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Agriculture Industry Retail Consumption

T T T Sewage treatment Core system Background system Water production Packaging production Energy System Fertiliser production

Residue and waste treatment

Figure 1. Principal system boundaries for the SAFT model, “T” denotes transports.

Since one important function of food (however not the only) is to supply the human population with nutrients, it is important to be able to track the main nutrients through the system. Different system will be more or less efficient in this respect. Moreover, unwanted substances in foods, e.g. cadmium, can also be of interest to trace. This makes a

material/substance flow modelling (MFA/SFA) approach appropriate. Conclusively, the SAFT model is an MFA/SFA model that uses LCA methodology to evaluate both the sustainability issues relating to resource use and emissions as well as mapping the substance flows through the system.

The SAFT model has a modular approach, each sub system is modelled individually and connected to the rest of the model by its in- and outflow of energy and materials. The energy- and material flows are described by vectors (Table 3, Table 4 and Table 5). This means that new processes can be included in the existing model if new systems are studied

In Figure 2 the top level of the SAFT model is depicted, arrows indicate material- and energy flows. Each box is further divided in sub-systems, often on several levels of detail, where the connections and flows are described graphically.

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Figure 2. Top level of the SA 8

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al- or energy flows, note that th

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are opposite the

actual flow, since it is infor

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ation of am

ount and type of energy needed in ot

her parts of the m

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General modelling approach

The model is intended to be modular, i.e. consist of independent sub-models that may be combined in many ways. The model is constructed in the software MATLAB/Simulink. All individual sub-models are connected with a data file (m-file in MATLAB) where all data for that process is inserted. This approach facilitates easy documentation of data used in a study. This means that the SIMULINK model describes the structure of the models and the principal causal connections between in- and outflows, and the m-files contain all data used. All sub-models are listed in Table 1 along with the corresponding init-file.

These individual sub-models can be used solitarily, but can also be used for building

“Clustered models”. Such clustered models are used to organise product flow, and individual sub-models are parts where energy consumption, emissions etc are calculated. These clustered models are constructed for each study performed, depending on what food that are studied and how the flows are organised in the system. The clustered models used in this set-up of SAFT are shown in Table 2.

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Table 1. List of all individual sub-models in SAFT and their corresponding init-file

Sub-model Corresponding init-file

Models used in both studies

Truck and trailer, heavy truck, light truck, Pickup, Car TransportInit

Retail RetailInit

Private households HhInit

Cooking CoInint

Residue treatment ResidueInit

Energy Production EnergyInit

Packaging production PackInit

Air emissions Weighingfactors

Water emissions Weighingfactors

Drinking milk dairy DairyInit

Mill MillInit Bakery MillInit Models used in the Meatball meal study

Agricultural production (milk, cattle, pigs, wheat, potato, carrot)

Abattoir AbbatiorInit

Carrot Packer CarrotInit

Potato Packer PotatoInit

Meat ball production AbbatiorInit

Mashed potato production PotatoInit

Ready-to-eat meal production ReadyInit Models used in the chicken meal scenarios

Agricultural production (milk, chicken, wheat, potato,

onion, lettuce, carrot, apple) FarmInit

Chicken abattoir AbattoirInit

Hash manufacture ReadyInit

Carrot Packer CarrotInit

Potato Packer PotatoInit

Lettuce packer LettuceInit

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Table 2. Clustered sub-models used in the study.

Clustered model

Individual sub-model included

Init file for data in the clustered model (except the ones used for the included individual sub-model) Distribution Heavy Truck

Truck and Trailer

HhInit, RetailInit Retail and home transport Retail Car Pick-up - Households Cooking Storing CoInit HhInit

SAFT is basically a material flow model, i.e. a certain amount of food raw material enters the model at one end, the product flows through the system and all use of energy and emissions that occur as a result of the flow is calculated. Finally, consumed products leave the system at the “other end”. The time scale is yearly averages due to the purposes of the model, to study future systems overall environmental performance. Throughout the SAFT model vectors are used to describe all physical flows between sub-models, as material and energy. There are three different vectors;

1. The material flow vector, which describes the flow of material and has 60 single positions (Table 3)

2. The second one describes the use of net energy and has 45 positions (Table 4)

3. Primary energy carriers, this vector describes the consumption of primary energy carriers (e.g. amount of oil and coal extracted from the earth’s crust and amount of biofuel used), and has 15 positions (Table 5).

Obviously, all positions in the vectors are not relevant for all flows, often there are only a few positions used for a certain flow. The fact that both single substances and materials are

included means that both chemical composition and type of material for e.g. packaging can be described by one vector, which simplifies the modelling work. The rationale to use such extensive vectors is that it offers flexibility regarding what questions that can be analysed with the use of the SAFT model.

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Table 3. The vector that describes all physical flows between sub-models in the SAFT model

Position Substance etc. Position Substance etc.

1 C-tot 31 Pb

2 C-”slow” (lignin, humus) 32 Cd

3 C-medium (cellulose, hemicellulose) 33 Hg

4 C-Fast (sugar, starch) 34 Cu

5 C-Fat 35 Cr

6 C-Protein 36 Ni

7 COD 37 Zn

8 VS (DM-Ash) 38 Particles/susp.

9 DM 39 Volume, packaging included

10 CO2-fossil origin 40 Plastic, HDPE (Primary package)

11 CO2-biological origin 41 Plastic, LDPE (Primary package)

12 CO 42 Plastic, PP (Primary package)

13 CH4 43 Plastic PS (Primary package)

14 VOC (volatile hydrocarbons) 44 Plastic PET (Primary package)

15 PAH 45 Cardboard (Primary package)

16 Phenols 46 Laminate PE/Cardb. (Primary package)

17 PCB’s 47 Laminate PE/Cardb./al (Primary package)

18 Dioxins 48 Aluminium (Primary package)

19 H2O 49 Glass (Primary package)

20 N-tot 50 Plastic HDPE (Secondary package)

21 N-NH3/NH4+ 51 Plastic LDPE (Secondary package)

22 N-NOX 52 Plastic PP (Secondary package)

23 N-NO3- 53 Corr. cardboard (Secondary package)

24 N-N2O 54 Wood (Secondary package)

25 S-tot 55 Steel (Secondary package)

26 S-SOX 56 Aluminium (Secondary package)

27 P-tot 57 Sewage

28 Cl-tot 58 Solid org. Waste

29 K-tot 59 Burnable solid waste

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Table 4. The vector that describes the energy flows in SAFT

Position Content Comment

1 District heating, mix 1 Use of heat

2 District heating, mix 2 d:o

3 District heating,mix 3 d:o

4 Fossil oil d:o

5 Coal d:o

6 Fossil gas d:o

7 Solid biofuel d:o

8 Electricity d:o

9 Biogas d:o

10 Heat pump d:o

11 Extra d:o

12 Extra d:o

13 Extra d:o

14 Extra d:o

15 Extra d:o

16 Base load, grid 1 (Swedish average) Electricity

17 Base margin load, grid 1 d:o

18 Top load grid 1 d:o

19 Base load, grid 2 (EU-average) d:o

22 Base load, grid 3 (OECD average) d:o

25 Extra d:o 26 Extra d:o 27 Extra d:o 28 Extra d:o 29 Extra d:o 30 Extra d:o

31 Diesel oil Fuel used locally

32 Petrol d:o

33 Fossil gas d:o

34 Biogas d:o

35 RME (Rape methyl esther) d:o

36 Methanol d:o

37 Ethanol d:o

38 Base load, grid 1 (Swedish average) d:o

41 Extra d:o

42 Extra d:o

43 Extra d:o

44 Extra d:o

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Table 5. The vector that describes the use of primary energy carriers, it is only used within the sub-model ”Energy Production”

Position Energy carrier Comment

1 Oil 2 Coal 3 Fossil gas 4 Biofuel 5 Biogas 6 geotermic energy 7 Uranium

8 Hydropower In this study amount of MJ hydropower is as used primary energy carrier

9 Extra 10 Extra 11 Extra 12 Extra 13 Extra 14 Extra 15 Extra

Description of the models in the foreground system

Transports

All transport models have the same basic structure, first the fuel consumption is calculated as a function of amount of goods, load size and distances, thereafter the emissions are calculated as a function of amount and type of fuel. It is possible to adjust the energy consumption for certain traffic situations or transport modes, as freeze transports. All transport models are connected to the m-file ”transportInit.m” where data are set.

Truck and Trailer

In Figure 3 the truck and trailer sub-model is shown. Below a description of the model

follows, step by step, mainly from left to right. Shaded blocks in Figure 3 are input data set in “transportInit.m”:

At ”product” the product to be transported enters the model as the 60-position vector. ”Max load, m3” gives maximum load volume on the vehicle, this together with data on total volume of the load (calculated in ”Volume, m3 ”) gives number of loads if volume is limiting. ”Max load, tonnes” gives maximum load weight on the vehicle, which together with total weight of the load (calculated in ”weight ton”) gives number of loads if weight is limiting. At ”N:o loads” the number of loads are compared and the highest is chosen for further

calculations.

Number of loads (”n:o loads”) and distance per load (”Distance driven per load”) gives the total distance driven per year for the goods.

At ”Average load, ton/load” the average load in kg is calculated. This figure is used to create the quotient between maximum load and real average load, this is done in ”average load/max load”.

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Data on fuel consumption per km at maximum load (”Fuel cons. full load”) and empty vehicle (”Fuel cons. empty”) are set in ”transportInit”. At working points in between, the fuel

consumption is assumed to change linearly. The average load as part of maximum load together with this interpolated fuel consumption results in fuel consumption per km for the actual transport.

The total distance driven (”Total distance km/year”) and the average fuel consumption (”Aver. Fuel cons.”) together with two correction factors (”Allocation factor”, ” Traffic factor”) gives the total fuel consumption per year (”MJ fuel used”). The ”Allocation factor” is a dimensionless unit that is used if the transported goods is co-transported with some other goods and only a part of the transport should be allocated to the studied goods (if this is not the case, the Allocation factor is one). ”Traffic factor” is a figure that facilitates compensation for extraordinary traffic situations, as distribution transports with a lot of stops or driving mainly in city areas where traffic congestion occur frequently. If the transport is a refrigerated or freeze transport this can also be accounted for using this parameter.

It is possible to use seven different fuels and combinations thereof (diesel, petrol, fossil gas, methanol, Rape methyl ester (RME), ethanol, biogas). In “transportInit” a figure 0-1 (part of fuel mix) for each fuel type is set and the sum for all fuels must equal 1. It is not an actual fuel mix that is given, but parts of the truck fleet that are fuelled with the different fuels. The amount of fuel (in MJ) calculated in “MJ fuel used” is multiplied with this vector describing the fuel mix and the resulting amount of each fuel calculated in “MJ of each fuel type” is delivered to the surrounding system via the outport “Energy”.

The vector of amount of fuels used is also used to calculate the emissions. In “Air emission1” the amount of diesel used (from the vector described above) is multiplied with the emission vector “emissions for diesel trucks”. The same operation is performed for the other fuels, and the sum of all emissions is delivered to the surrounding system via the outport “Air

emissions”.

The product that is transported is not affected by the transport and leaves the transport model via outport “Product out”

Data on where the transports are performed (“Part of transport in urban areas” and “ Part of transport in rural areas”) together with “ Total distance km/year” gives distance driven in urban and rural areas (“vehicle*km in urban areas” and “ vehicle*km in rural areas”). This information may be used for a simple assessment of the impacts of transport that is not covered by energy consumption and emissions.

Heavy Truck, Light Truck

These models have identical structures as ”Truck and Trailer”, it is only data on fuel consumption, maximum load volume and maximum load weight that differs.

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16 3 E ner g y 2 A ir em is si ons 1 P rod uc t ou t f( u ) w e ight t o n vehi cl e* km in ur ba n ar ea s vehi cl e *k m in ru ra l a re a s T ran sA ir E m T ruc kT ra il er (: ,2 ) e m issi o n s f o r p e tr o l tr u ck s T ran sA ir E m T ruc kT ra il er (: ,6 ) e m issi o n s f o r m e thanol t ruc ks T ra n sA irE m T ru ck T ra il e r( :, 3 ) em is si ons f o r fo ss il g a s tr u ck s Tr a n sA ir E m Tr u ck T ra il e r( :, 7 ) e m issi o n s f o r et hanol t ruc ks Tr a n sA ir E m Tr u ck T ra il e r( :, 1 ) e m issi o n s f o r d ies el t ruc ks T ra n sA irE m T ru ck T ra il e r(: ,4 ) em is si ons f o r bi og as t ru ck s T ran sA ir E m T ruc kT ra il er (: ,5 ) e m issi o n s f o r RM E t ru ck s a ver ag e l oad / m a x l oad u( 44) /1000 Vo lu m e , m 3 T rans F uel U sedT ru ck T ra il e r1 T ype of fuel u sed T ra n sT ra ff ic F a ct T ru ck T ra il e r1 T ra ffi c fa ct o r T o ta l d is tanc e km /y ear T ra n sk m R u ra lT ru ck T ra ile r1 T ra n sk m U rbanT ru ck T rai le r1 Su m 1 P roduc t2 P rod uc t1 T rans P a rt U rbanT ru ck T ra il e r1 P a rt of t rans p o rt i n ur b an ar e a s T ra n sP ar tR ur al T ruc kT ra il e r1 P a rt of t ran spor t in ru ra l a re a s MA T L A B F u nc ti on N :o l oads Mu x Mu x T ran sM ax L oadT ru ck T ra il e r M a x l oad, t o nnes T rans M a xV ol um eT ru ck T rai le r1 M a x l oad, m 3 u( 3 2 ) M J p e tr o l us ed M J of eac h fu e l ty p e u (36) M J m e than ol us ed M J f uel u sed u( 3 3 ) M J fo ss il g a s us ed u(37) M J et han ol us ed u( 3 1 ) M J d ies el us ed u( 34 ) M J bi og as us ed u(35) MJ R M E us e d T ra n sLoa dF ac to rT ru ck T rai le r1 Lo ad f a ct or ( 0 -1 ) T ra n sM JP e rkm F u ll T ru ck T ra il e r F u e l c o n s. fu ll load T rans M JP e rk m E m p ty T ruc kT ra il er F uel c ons . e m pt y T rans D is tP e rL oadT ru ck T rai le r1 D is tanc e d ri ven per l o ad A ver a ge l o a d , to n /l oad A ve r. fu e l cons . T rans A ll o ca ti o n F a ct or T ruc kT ra il er 1 A ll o ca ti on f a ct or A ir e m issi o n s7 A ir em is si ons 6 A ir em is si ons 5 A ir e m issi o n s4 A ir e m issi o n s3 A ir e m issi o n s2 A ir e m issi o n s1 1 P roduc t

Figure 3. The Truck and trailer sub-model. Shaded

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Drinking milk dairy

In this model energy consumption, local air emissions, use of water, wastage and waste generation is calculated as a static function of the flow of milk to the dairy. By changing input data in the init-file, different fuels for local heat production can be changed.

The inflow to the “Drinking milk dairy is used to calculate consumption of water, energy use and direct emissions as a function of amount of milk processed. The amount of milk wasted and the proportion of the wasted milk going to sewage and feed respectively, is also

calculated using percentages.

Amount of package used, both primary and secondary, is calculated as a function of amount of milk delivered from the dairy. The amount of different package material is sent to the “packaging production” model where use of energy and emissions are calculated. The package material is also added to the vector that describes the product flow.

Abattoir

The abattoir model consists of three parallel sub-models; cattle, pig and poultry slaughter. They have the same structure but the input data differs. The inflow of animals, described with the 60 position vector, is used to calculate the use of water and energy and also direct

emissions for the process. The total inflow is then partitioned by percentages to residual products, waste and meat. The residual products (“slaughterhouse waste”) are sent to the model “Residues treatment”, the waste is sent to the “Waste management” sub-model. The amount of package used is calculated as a function of the total amount of meat leaving the abattoir.

Mill

The mill model uses the incoming vector describing the grain, to calculated wastage, energy- and water using static parameters, based on performance per unit grain milled. The percentage of incoming grain that is sold as flour and bran is also calculated. The amount of packaging material used and waste generated is calculated as a function of the amount of flour produced.

Potato Packer

The model for Potato packer is constructed the same way as the mill model, the difference is the input data used in the data files.

Carrot Packer

The model for Carrot packer is constructed the same way as the mill model, the difference is the input data used in the data files.

Bakery

Meat ball production

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Mashed potato production

Ready-to-eat meal production

Chicken hash production

The model for chicken hash production is constructed the same way as the mill model, the difference is the input data used in the data files.

Distribution

The sub model for distribution is a combination of some other sub-models that partly use the same data, hence together they make up an individual sub model. The reason for modelling distribution in this way is that the mode of transport is coupled to where the food is to be delivered. The structure of this part of the food chain is also different depending on what food that is studied. In this setup of the SAFT- model there are different distribution systems for the three different receivers of food, External retailers, Neighbourhood retailers and e-shopping (se the respective heading for a detailed explanation of the individual sub-models). This leads to the structure described in Figure 4 and Figure 5, where the former describes the top level of the distribution model, and the latter distribution of each product. In Figure 5 the distribution model for dairy is shown, the corresponding sub models for other products are identical in structure. The inflow is divided according to how large part of the product that is sold in the different retail types (external, neighbourhood and e-shopping). For each transport the fuel consumption and emissions are calculated as described above under heading

“Transports”. The product flow is not affected by the transport but is passed on to the retail sub model.

In the examples in the figure, the distribution to neighbourhood retail is done with a heavy truck and to the other two retail types with truck and trailer. This can be changed, and also combinations of different transport modes can be modelled by inserting other transport models or combination of transport models.

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Figure 5. The model for distribution of dairy products to retailers

Retail and Home transport

Background

The retail- and home transport systems are so closely connected so they are put together to form a clustered model. It seems likely that one type of retailing structure is connected with certain types of home transport. For example the part of the customers that use car are lower in small, local shops than in large, more externally situated (Sonesson et al., 2005b).

Moreover, e-shopping is naturally followed by some kind of delivery, either to homes or some “spreading points”. However, the retail and home transport parts are mainly modelled in a structure that separates them as much as possible.

The clustered model consists of two main parts, “Retail” and “Home transport”. The connection between them is that the flow of food that enters “Retail 1” automatically continues to “Car 1”. This means that everything bought in Retail 1 is transported home by Car 1 (except the part that is transported home by walking etc., which is omitted from the study, see below). This structure is chosen since both transport distances and shopping frequency are assumed to be depending on type of shop, as described above.

Retail

There are three types of retailers included in the model, the dividend between them are where they are situated.

1. “External retail”, this type is a large shop situated away from dwelling areas and relies on the customers having cars, only a small part of the customers use public transports.

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2. “Neighbourhood retail”, this type is situated close to dwelling areas, the home transport may be performed either by car or walking, biking and public transport

3. “E-shopping”, this retailing type is characterised by the fact that the food is not collected by the customer at all, it is delivered by the retailer. How this delivery is performed may differ, that is decided in the transport models following the retail model.

This model structure facilitates differences in energy use etc. between different stores. In reality “External retailers” are probably more energy efficient than “Neighbourhood retail” since the former are larger. Since we have not found any reliable data on the differences, both these models of retail use the same data. The differences are probably small considering the total impact from a products entire life cycle (Carlsson & Sonesson, 2000).

The inflow of each food under study is divided in parts that are sold in each type of retail. This is done with parameters set in “RetailInit” and states between 0-1 for each type, where the sum must equal 1. This partitioning is done in the “Distribution block”

Before the flows of foods are entered to the three retail models, they are sorted in types, in block called “Sorting of products in types”. This is a block that must be tailored for each product flow, since there is no information in the vector that can be used for automatic sorting. The foods are put in one of six “type foods” (types):

1. Frozen, long storage 2. Frozen, short storage 3. Cold, long storage 4. Cold, short storage 5. Normal, long storage 6. Normal, short storage

The reasons for this are presented below.

In the next step the food enters the retail sub-models, the model for “External Retail” is presented in Figure 6. In this example it is shown that there are six flows of food through the model. The energy consumption is mainly depending on whether the food is frozen, cold or kept in room temperature and the retention time in the shelf. To manage this, the incoming flow of food must be connected to the right inport (“frozen”, “cold” or “room temp”). In Figure 6 the model for “External retail2” is presented. The flow from each inport is then further divided in two flows, “long” and “short” retention time, the partitioning is done by 0-1 parameters set in “retailInit”. Thereafter the energy consumption and amount of bags are calculated for each of the six alternatives (“frozen/long”, frozen/short”, “cold/long”,

“cold/short”. “Room temp/long” and “Room temp/short”). Finally all energy consumption are summed at “Total energy” and delivered to the surrounding system as are the amount of bags used and the food flow, now completed with bags.

In this way all foods studied can be handled with the same model and still take differences between types of food into account. The model is a simplification, but according to Carlsson & Sonesson (2000) the main differences are included using this structure. The alternative would be to set average retention time for each food, but that is data that can not be attached to the flow of food, since it differs between retail types. Then one has to have a specific model for each type of food, but that bring about more work that can be justified from the

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22 5 Wastege returned to supplier 4 To waste man. 3 Added packages (to pack.prod.) 2 Energy turnover 1 Food out, 6 vectors Frozen products in Product out Energy turnov er out To waste management Added packages Waste to other use

Other products, short shelf time

Frozen products in

Product out Energy turnov er out To waste management Added packages Waste to other use

Other products, long shelf time

Mux

Frozen products in

Frozen products, short shelf time

Frozen products in

Frozen products, long shelf time

Frozen products in

Cold products, short shelf time

Frozen products in

Cold products, long shelf time

6 Other products short shelf time

5 Other products long shelf time

4 Cold products short

shelf time 3 Cold products long

shelf time 2 Frozen products short

shelf time 1 Frozen products long

shelf time

Figure 6. The model for ”External retail”

The next level of the retail models is shown in Figure 7. The food enters the sub-model at “Product in”. At “MJ used” the energy consumption is calculated as a function of kg food passing the model), as well as amount of bags (paper and/or plastic). The bag material is both delivered to the “Packaging production” model for calculations of manufacturing the material (“Packaging material”) and added to the product (“Add bags”). The latter is to reflect what actual happens; bags are brought home. The food together with bags leaves the model at “Product out”.

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23 5 Waste to other use 4 Added packages 3 To waste management 2 Energy turnover out 1 Product out u(9)+u(19) waste kg Waste, kg Terminator Sum Return kg Package added Mux Mux1 Mux Mux MJ Ground3 Ground2 Ground1 Ground u(9)+u(19) Fcn Demu Demux Retail1ReturnColdShortTime Retail1WastageColdShortTime Retail1PackageAdded Retail1EnergyColdShortTime 1 Frozen products in

Figure 7. The second level of the”External retail” model Home transport, car

The model for transport by car is somewhat different in structure than truck transports. The difference is that the fuel consumption is not at all correlated to the amount of goods transported. The rationale for this is that the effect of kg goods on fuel consumption is negligible, transporting 10 or 40 kg in a vehicle weighing 1000 kg uses practically the same volume of fuel. After the fuel consumption is calculated the model is similar to “Truck and Trailer”

The average distance, shopping frequency and the number of cars used in the area under study is used to calculate the total distance driven. Thereafter the fuel consumption is calculated by data on MJ/km. In Figure 8 the structure of the Car model is presented.

“No of Cars” is calculated as a function of “Total number of Households”, “Part of Customers in retail1 that use car” and “Part of households that shop in Retail 1”. “No of Cars” is the number of households that use car for the specific transport.

The total distance driven is calculated as a function of “No of Cars”, “Shopping frequency” “Distance per Trip” and “Weeks per year”. The resulting amount of kilometres is multiplied with “Allocation factor” and “Traffic factor” (se heading “Truck and Trailer) and “Fuel Cons.”. This results in “MJ fuel used”. After that point the model for Car is similar to the “Truck and Trailer” model.

At the inport “product in” the goods is entering the model. The food is not affected by the transport and leaves the model by the outport “Product out”. The flow of food through the

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24

model is used to detect whether the model is used at all. In “weight kg” the weight is

calculated. This figure is then compared to a zero in “Zerocheck1” and “Zerocheck2”. If the flow is zero the results from the model will be vectors of zeros both for “Energy” and “Air emissions”. This check is necessary since the Car sub-model is included in the model block “Retail and home transport” and depending on the choices for structure of retail one of the two Car models may not be used. Since the energy consumption is calculated independently of the product flow the model will generate energy consumption and emissions even if no products are transported.

Home transport, delivery with pick-up

This model is used for e-shopping followed by home delivery of groceries. The model is almost similar to the “Car” model; the only difference is that the total distance driven is calculated in a somewhat different way (Figure 9). “Distance per Trip” is the number of kilometres driven each delivery round, “Shopping frequency” is number of deliveries per week and “Number of Hh per round”. These parameters are used to calculate the total driving distance.

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25 3 Ai r em is si ons 2 Ener gy 1 Pr oduc t out u( 8) + u (22) w e ight k g vehi cl e *k m in ur ban ar eas vehi cl e* km in r u ra l ar eas T rans Ai rE m C ar (: ,2 ) em is si ons f o r p e tro l c a rs T rans Ai rE m C ar (: ,6 ) em is si ons f o r m e thanol c a rs T rans Ai rE m C ar (: ,3 ) em is si ons f o r fo ss il gas c a rs T rans Ai rE m C ar (: ,7 ) em is si ons f o r et hanol c a rs T rans Ai rE m C ar (: ,1 ) em is si ons f o r di es el c a rs T rans Ai rE m C ar (: ,4 ) em is si ons f o r b iogas c a rs T rans Ai rE m C ar (: ,5 ) em is si ons f o r RM E c a rs 52 W eek s per y ear T ran sF uel U sedC ar 1 T ype o f fuel us ed T rans T raf fi cF ac to rC ar 1 T ra ffi c f a ct o r T rans N u m ber O fH o us ehol ds T o ta l num ber of hous ehol ds T o ta l di st anc e km /y ear and ca r T rans km R u ra lC ar 1 T rans km U rbanC ar 1 Sw it ch 3 Sw it ch 2 Sw it ch 1 Sw it ch Sum 1 T rans T ri p sP er W eek C a r1 Shoppi ng f requenc y T rans P a rt U rbanC ar 1 Par t of t rans por t i n u rban ar eas T rans Par tR u ra lC ar 1 Par t of t rans por t i n ru ra l ar eas R e ta il Par tLar geM il k P a rt of hous ehol d s t hat shop i n r e ta il 1 R e ta il 1Par tC ar P a rt of C u st om er s i n re ta il 1 that us e c a r No o f ca rs u( 32) M J pet ro l us ed M J of eac h fuel t ype u (36) M J m e thanol us ed M J f u e l us ed u( 33) M J f o ssi l g a s us ed u(37) M J et hanol us ed u( 31) M J di es el us ed u( 34) M J bi og as us ed u(35) MJ R M E us ed T ra n sMJ P e rk mC a r F uel c ons . T ra n sD is tP er LoadC ar 1 D is tanc e per t ri p 0 C ons tant 2 ze ro s( si ze (1 :4 5 )) ze ro s( si ze (1 :6 0 )) T ra n sA ll oc at ionF ac to rC ar 1 Al lo ca ti on f a ct or Ai r em is si ons 9 A ir e m is si o n s8 Ai r em is si ons 7 A ir e m is si o n s6 Ai r em is si ons 1 3 Ai r em is si ons 1 1 Ai r em is si ons 10 1 Pr oduc t i n

Figure 8. The ”Car” modell. Shaded blocks

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26 3 Air em is si ons 2 Ener gy 1 Pr o duc t o u t u( 8 )+ u (2 2) we ig h t k g vehi c le *k m in ur ban ar ea s ve h icl e *k m in ru ra l a re a s T ra n sN um ber O fH o us eh olds num ber of h ous e hol ds T ra n sA ir E m P ic ku p (:,2 ) em is si ons f o r pe tr ol p ic k-u p s T rans Ai rE m P ic ku p (: ,6 ) e m issi o n s f o r m e than ol p ic k-ups T ra n sA ir E m P ic ku p (:,3 ) em is si ons f o r fo ssi l g a s p ic k-u p s T rans Ai rE m P ic ku p (: ,7 ) e m issi o n s f o r et ha nol pic k-u p s T ra n sA ir Em Pic kup( :, 1) e m issi o n s f o r di es el pic k-ups T rans Air E m P ic kup( :, 4 ) e m issi o n s f o r biog as pi c k-u ps T rans Ai rE m P ic ku p (: ,5 ) e m issi o n s f o r R M E p ic k-ups 52 W e ek s pe r y ear T ran sF ue lU se dP ic ku p 1 Ty p e o f fuel us ed T ran sT ra ff ic F a c to rP ic kup1 T ra ffi c fa ct o r T o ta l d is tanc e km /y e a r T ra n skm R u ra lP ic ku p 1 T ra n skm U rb a n P ic ku p 1 Sw it c h 3 Sw it ch 2 Sw it ch 1 Sw it c h Su m 1 T ran sD eliv er yF re qPic kup1 Sho ppin g f requ enc y T rans Pa rt U rb anPic ku p 1 Pa rt of t ran spor t in ur b an a rea s T ra n sP ar tR ur al Pic ku p 1 Pa rt of t ran spor t in ru ra l a reas R e ta ilPa rt H om eD el iv M ilk Par t of pr od uc t t h at is s o ld b y h o m e de liv er y T ran sH h P er T ri p P ic kup 1 N u m b er o f H h p e r r oun d No o f hou seho lds u( 3 2 ) M J pe tr ol us e d MJ o f e a c h fuel t y p e u( 36 ) MJ me th a n o l us e d M J fu e l u se d u( 3 3 ) M J f o ss il ga s us e d u( 37 ) M J et h ano l us ed u( 3 1 ) M J di es el us ed u( 34) M J bio gas us e d u(3 5 ) MJ R M E us e d T ra n sM JP e rk m P ick u p F uel c o ns . pi c kup T rans D is tPer L o adPi c ku p 1 D is tanc e p e r t rip 0 Di sp la y1 0 Co n sta n t2 z e ro s( si ze (1 :4 5 )) z e ro s( si ze (1 :6 0 )) T ran sA ll oc at io nF a c to rP ic kup1 Alloc a ti on f a ct or A ir e m issi o n s9 Ai r em is si on s8 Air em is si ons 7 Ai r em is si on s6 Air em is si ons 1 3 Air em is si ons 1 1 Air em is si ons 1 0 1 Pr od uc t in

Figure 9. The ”Pickup” modell. Shaded blo

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27 Households

The model for household consists of three parts: storing, sorting/preparing and cooking. They are connected in that sequence, and the model following requires input from the former.

Storing

The storing model receives input from the retail model, sorted according to storage condition; freezer, refrigerator or room temperature. For freezers and refrigerators the energy use is calculated as a function of storage time and volume of the stored item, and there are different models for different equipment, both types and sizes. For a detailed description of the storage models see Sonesson et al. (2003) The wastage during storing is also set, specific for each product stored. In the storing model the packaging included, both primary food packaging and bags from the grocer’s in the flow is identified and sent to the “waste management model”

Sorting/preparing before cooking

This model facilitates the calculation of inputs and changes in the raw material before cooking. In the study presented it was mixing water and flour to produce dough, peeling of potatoes and carrots, mixing water and mashed potato powder and mixing minced meat to make meatballs. The amount of food wasted in these processes, as when peeling, is calculated and delivered to the “waste management model”.

Cooking, including dishwashing

The cooking sub model consists of four technologies for domestic cooking: Boiling in water, frying in pan, baking in oven and microwave oven. These models are thoroughly described in Sonesson et al (2003). In short the input data includes preparation time, physical data on foods, batch size, type of equipment, amount of water evaporated and specific energy use for the different modes of cooking.

Description of the models in the background system

Residues treatment

The model for residue treatment uses the inflow from the different industries to calculate the direct emissions and energy- and water used to process the residual products. In the case that the residual product can be used, for example whey as feed, it also calculates the amount of alternative products saved by the use of the residual product. The environmental impact and resource use for that alternative product is subtracted from the direct emissions and resource use resulting from the processing. LCA data for alternative products are used as input data for this latter part. This sub model is specific for each type of residual product and alternative product; it is not a general model that can be used in any system.

Energy system

Background

In the SAFT model all sub-models (e.g. transports, dairy) calculates the amount of energy needed for the flow of food through the model. Three forms of energy is used, “Heat”, “Electricity” and “Fuel”. “Heat” means processes etc. that use heat but does not burn fuel locally to produce it, an example is district heating used. “Electricity” is of course use of electricity bought from the grid. With “Fuel” we mean energy that is “produced” locally, i.e. fuel for trucks or gas used for drying in an industry. The amount of energy used in each sub-model is delivered to the “Energy Production” sub-sub-model. In the sub-model we have tried to mirror the reality regarding where emissions are calculated, so emissions from production of

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“Heat” is calculated in “Energy Production” as are emissions from production of “Electricity”. The amount of energy used in each sub-model is delivered to the “Energy Production” sub-model. Emissions from “Fuel” on the other hand, are calculated where it is combusted, i.e. in the sub-model itself. The amount of fuel is however also delivered to “Energy System” where calculations of emissions for producing and distributing the fuel are calculated (“pre-combustion”). In Figure 12 a principal description of how energy is managed in the SAFT model is shown.

Model description

This sub-model calculates the consumption of primary energy carriers (PEC) and emissions from the energy system. The ”inflow” to the model is information on amount of energy needed in all other sub-models, in vector form (see Table 4). In cases where some part of the system under study delivers a negative energy consumption (e.g. “Waste Management” if packaging is incinerated) it calculates saved PEC and emissions.

In Figure 10 the top level of the model is presented. All vectors entering the model are added to a vector describing the whole systems net energy turnover (“sum”). This vector is divided (“Demux”) in three 15-vectors, one for each energy type (see Table 4). These 15-vectors are indata for one sub-model each, where emissions and use of PEC are calculated. In Figure 11 an example of this level is presented, it is the sub-model for “Heat production” that is shown. The inflow, the 15-vector, is divided in scalars, each representing the amount of one type of energy used. The first position in the vector is “district heating”, and since district heat is produced with a mix of fuels that might change, an extra step is needed. In the function “District heating” the amount of MJ used is multiplied with a 15-vector describing the mix, thus producing a 15-vector with the same content as the one that entered the model (except for the first position, “district heating”, which is empty). Thereafter the amount of heat produced from different fuels directly in the system can be added to the amount of fuels used in the district heating system and delivered to the third level. These scalars enter the third level of the model (“calc. for waste”, “calc. for oil” etc.) Within these sub-model the emissions and use of PEC is calculated as a static function of the amount needed in the system. In these calculations both direct emissions from combustion as well as indirect emissions

(“pre-combustion”) is included. The amount of PEC for example, include both the energy content in the fuel itself and the energy needed to supply the fuel to the system, and the same for the emissions.

From the sub-models “Heat” and “Fuel”, information on amount of electricity used within these sub-models are sent to the sub-model “Electricity”. This is done in order to take into account electricity used in heat- and fuel production.

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29 8 W a te r em is si on s fuel ( onl y pr od. ) 7 W a te r em is si ons el ec tr . (u se + p rod. ) 6 W a te r em is si ons heat ( u se + p rod. ) 5 A ir em is si ons fuel ( onl y pr od. ) 4 A ir em is si ons el ec tr . ( u se + p rod. ) 3 A ir em is si ons heat ( u se + p rod. ) 2 N e t ene rg y co n sum pt ion (O ne 45 v e ct or /s ub-m odel ) 1 Pr im a ry E C Su m Mu x Mu x1 Mu x Mu x In Pr im ary EC Air em is s ions W at er em is s ions El ec tr ic ity us e d (15 v ec .) H eat In ele ct ric it y u sed (1 5 v ec ) Pri m ary EC Air em is si ons W a te r em is si ons F uel F rom he at prod . F rom s u b-m od els F rom f uel p rod. Prim a ry E C Ai r em is si o ns Wa te r e m is si o ns E le ct ric it y D em u x De m u x 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

Figure 10. The top level of the”

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30 4 E le c tr ic it y u se d (1 5 ve c. ) 3 W a te r em is si on s 2 Ai r e m is si o n s 1 Pr im a ry EC e le c tr ic it y 2 el e c tr ic it y 1 e le ct ric it y In 1 P ri ma ry EC A ir em is s ions W a te r em is s ions E lec tr ic it y us ed ca lc . fo r wa st e In 1 Pr im a ry E C A ir em is s ions W a te r em is s ions E lec tr ic it y us ed ca lc . fo r co a l In 1 Pr im a ry E C A ir em is s ions W a te r em is s ions E lec tr ic it y us ed ca lc . fo r Fo ss il o il In 1 Pr im a ry E C A ir em is s ions W a te r em is s ions E lec tr ic it y us ed ca lc . fo r Fo ss il g a s In 1 Pr im a ry E C A ir em is s ions W a te r em is s ions E lec tr ic it y us ed ca lc . f o r B iog as In 1 P ri ma ry EC A ir em is s ions W a te r em is s ions E lec tr ic it y us ed ca lc . f o r Bi o fu e l Su m 9 Su m 8 Su m 7 Su m 6 Su m 5 Su m 4 Su m 3 Su m 2 Su m 1 Su m M u x Mu x 1 M u x Mu x In 1 O u t1 D is tr ic t he at in g De m u x De m u x1 De m u x De m u x 1 In

Figure 11. The second level of ”Energy production”, the example is

”Heat” (from the previous figure)

. The other two (”Electrici

ty” and

”Fuel”)

are structu

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31

Conclusively on how the energy-vectors are used

The principle use of the energy vectors is described in Figure 12. The energy turnover for every sub-system included in the system is calculated on sub-model level. This information is delivered to the “Energy Production” sub-model using the “Net Energy vector”

(45-positions), see Table 4. These vectors are directly delivered without treatment to the “Result management” sub-model, since information on net energy turnover is an interesting result from a simulation. In “Energy Production” the direct emissions that occurs when producing electricity and district heating are calculated. Moreover, for all energy forms (electricity, heat and fuel) the consumption of primary energy carriers (PEC) and indirect emissions are calculated. These results are delivered to the “Result Management” model.

A comment on waste as fuel:

If waste is used as fuel in the district heat mix, one has to set the part that is considered to be fossil (mainly plastics) and biological (food and paper) respectively. In mixed household waste a rule of thumb is that 80% is biological and 20% fossil fuel.

A comment on electricity used in the model

The consumption of electricity in each model is defined as either “base load”, “base margin load” or “top load”, from one of three electricity grids (Swedish average, EU-average, the third grid is not defined yet, it might be used for different types depending on the system studied). Conclusively, one can choose for example “ Base load from Swedish grid” or “Base margin load” from the EU-grid etc.. The way electricity is produced, e.g. choice of fuel, depends on how the process under study can be said to affect the electricity system, or, more precisely, how a change in electricity demand in the system under study affects the electricity production as a whole. A process like a dairy, for example, can be considered as a “base load” user since it uses as much electricity practically all year round. This means that if the dairy industry increases its use of electricity, the fuel mix in the grid will not change. This goes back to how an electricity grid functions; it can be said to consist of two types of production: Base production and top production. Typical for the base production is that it has low running costs and higher capital costs, it is available all year around and it is rather slow to change. Examples of base production is nuclear power and to some extent hydropower. The top load fills another function, it should be quickly adjustable to the demand on the grid, and it balances the demand and supply. Characteristically are low capital costs and high running costs and examples are gas turbines.

The “Base margin production” is a bit more difficult to explain. It is the type of electricity production that will be decreased / increased if the need for “base load” changes permanently. Examples in Sweden could be co-production of heat and electricity using oil or coal

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Sub-model 1 Sub-model 2 Sub-model 3

Energy system Data on energy prod. (PEC/MJ NE, Emiss./MJ NE Pre-comb.) F(x) F(x) F(x) PEC +Emiss. For sub-model 1 PEC +Emiss. For sub-model 2 PEC +Emiss. For sub-model 3 Net Energy Vector (NE) NE Vector NE Vector

PEC Vector PEC Vector PEC Vector

Net energy, sub-model 3 Net energy, sub-model 2 Net energy, sub-model 1

Figure 12. The principal structure of how the Energy Production Sub-model works

Packaging Production

In sub-models where some kind of package is added to the product, as industry and retail, the amount of each type of packaging material is calculated. In the vector for physical flows (Table 3) 13 different packaging material is listed. They are also divided in primary and secondary packaging but that does not affect the packaging production, it is used in the retail sub-model to sort out the secondary packaging from the product flow. Also new secondary packaging is added in the retail sub-model; bags for the home transport.

The sub-model ”packaging Production” is built up of static connections between amount of each packaging material used in other sub-models and emissions and use of energy for producing them. The structure of “Packaging Production” is presented in Figure 13. Incoming flows from all models are summed to one single vector, describing the entire systems use of packaging material (“sum”). Subsequently the flow is divided in relevant scalars for each packaging material. Every material (scalar) is sent to next sub-model (e.g. “calc. for HDPE manufacture”, “calc. for LDPE manufacture”), where the use of energy and direct emissions for producing the packaging material is calculated as a static function of amount produced, data on these static functions are set in “PackInit.m”.

One important note is that the model calculates results for producing new, virgin material. In case of recycling of packaging this is dealt with in the “Waste Managment” sub-model.

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33 3 Water emissions 2 Air emissions 1 Energy

In1 Energy turnov erAir emissions Water emissions

calc. for wood manufacture

calc. for steel manufacture

calc. for laminate PE-Al manufacture

calc. for laminate PE manufacture

calc. for glass manufacture

calc. for corrugated cardboard manufacture

calc. for cardboard manufacture1

calc. for aluminium manufacture

calc. for PS manufacture

calc. for PP manufacture

calc. for PET manufacture

calc. for LDPE manufacture

calc. for HDPE manufacture

Terminator1 Terminator Sum7 Sum6 Sum5 Sum4 Sum3 Sum2 Sum1 Sum Ground3 Ground1 Ground Demu Demux 2 1

Figure 13. The top level of the sub-model ”Packaging Production”

Drinking Water Production

This model simply calculates the use of energy and emissions to water and air as a static function of the amount of water needed in the system, which in turn is calculated in the other sub-models as industries and households.

Waste Management

Comment on recycling of packaging and use of waste as fertiliser in agriculture

In the model there are no connections between “Waste management” and “Packaging production” and no connection to agriculture either, despite the fact that such connections exist in reality. Instead virgin material is assumed and the energy consumption and emissions due to manufacture of these materials are calculated in “Packaging production” and presented. In the model “Waste Management”, saved energy and emissions due to recycling is calculated and presented as negative figures. In the same manner, saved energy and emissions due to saved fertiliser if waste is used in agriculture are calculated in “Waste Management”. Since all models have to be included to produce relevant results this cause no problems, the net effect will be correct. However, there might be a pedagogical problem. If recycled material is used for packaging, the emissions and energy use for “Packaging production” would be lower and for waste management higher, albeit the net result would still be the same.

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The Waste Management Model

The waste generated in other sub-models are delivered to “Waste management”, the top level of this model is presented in Figure 14. The waste enters the model at the inports at left (1,2,3) and all flows are added. After that the total flow of waste is divided in its different fractions (in Table 3 the fractions handled are presented).

In the following blocks (“Sewage”, “Org. waste”, “Glass” etc.) the choice of treatment for each fraction is done. The parameters that are used for sending the waste to the chosen treatment (a mix of treatments is also an option) are set in “WasteInit” as 0-1 figures.

In each treatment system the emissions to air and water as well as the net energy turnover are calculated as a static function of the amount treated. In these static functions (found in

“WasteInit”) the following is included; Collection, Treatment and avoided emissions from saved material due to recycling. For the two treatments that can manage several fractions (incineration and landfilling) there are several different static factors. The reason is that energy turnover and also emissions differ dramatically for these fractions.

For the landfill the waste is divided in three fractions; organic waste and paper, plastic, and finally inert waste. The first type cause emissions relatively quick, before the end of the “methane producing phase” (see Sundquist et al, 2000), plastic is degraded slowly, thus causes the main part of its emissions after the methane producing phase. Inert materials finally, are considered not to emit anything, i.e. the landfill is considered as a sink. When studying food systems, inert waste consists of glass, ashes from incineration and small amounts of metal.

These treatment blocks deliver three vectors each, emissions to air, emissions to water and energy turnover. All these waste treatment specific vectors are added and delivered to the surrounding system, the emission vectors to the “Result management blocks” and the energy vector to the “energy system”. Moreover, the amount of inert materials left on the landfill is also calculated.

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35 3 Wa te r e m . 2 Ai r e m 1 E n er gy 1 c o rr u gat ed boar d W ood W a te r em is si ons W a st eM anI ner ts O nLandf il l T e rm inat o r1 T e rm inat or In 1 E ner g y A ir em is s ions w a te r em is s ions S teel re co ve ry S teel In En e rg y A ir em is s ions w a te r em is s ions S e w age pl ant w it hout N -pur .1 In E ner gy A ir em is s ions w a te r em is s ions S e w age pl a n t wi th N-p u r. S e w age In 1 E ner gy A ir em is s ions w a te r em is s ions Pl a st ic re co ve ry Pl a st ic Or g . w a st e Mu x Mu x In 1 E ner gy A ir em is s ions w a te r em is s ions "I n e rt" m a te ri a ls Land fi ll In 1 En e rg y A ir em is s ions w a te r em is s ions nt of as h t o l a ndf il l In c iner at io n G round3 G round1 In 1 En e rg y A ir em is s ions w a te r em is s ions Gl a ss re co ve ry Gl a ss E n er gy D em ux In 1 En e rg y A ir em is s ions w a te r em is s ions C o m pos t C a rdboa rd / la m inat e In 1 E ner g y A ir em is s ions w a te r em is s ions C a rdboar d re co ve ry B u rnabl e In 1 E ner gy A ir e m issi o n s w a te r e m issi o n s A naer . di ges ti on In 1 E ner g y A ir em is s ions w a te r em is s ions Al u m in iu m re co ve ry Al u m in iu m A ir em is si ons 3 3 2 2 1 1

Figure 14. The highest level of

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36 Agricultural production

The production of agricultural raw material is modelled in a rather simple way; the modelling does not allow for changes in how the agriculture is done. The model only delivers the

amount of product that is set in the input data file and uses LCA data on agricultural

production to calculate the emissions and resource use for producing that amount of products. If changes in agriculture need to be included in a study, other models must be used to create the input data needed that can be inserted in SAFT.

Result Management

In SIMULINK simulation data on substance level and single energy carriers/usage is sorted and treated to be manageable in later parts of the analysis. The sorted and managed data leaves SIMULINK and is further refined in MATLAB using m-files. This is an important part of the interface towards Microsoft Excel, it is possible to use some other software for result presentation but in this study Ms Excel is used. In Figure 15 the principal structure is presented. SAFT - SIMULINK model MATLAB workspace INIT-files = m-files 1) Indata to simulation 2) Indata to simulation 3) Simulation results Send-files = m-files 4) Simulation results 5) Arranged simulation results Excel, creating diagram, presenting results

Figure 15. The principal structure of how MATLAB – SIMULINK – Excel are connected when using SAFT. The figures indicate the order in which actions are performed.

In this section first the structure of the SIMULINK models is described and thereafter the m-files for sending data to excel. Finally the Excel m-files are briefly presented.

Air Emissions

In Figure 16 the highest level of the ”air emission” sub-model is presented. At left the results from all sub-models enters the “Air emissions” block as 60-vectors. In the first large rectangle in the left parameters containing raw simulation data are generated, they are created and given unique and logical names, these parameter are then sent to the MATLAB workspace, where they are accessible from MATLAB (see Figure 15). This raw simulation data is saved just as documentation, it is not used in the following data handling. The simulation result vectors are not affected but leaves the block at right.

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Next step is the “ Characterisation” block. Here the emission vector is used to calculate two new vectors. One is a 23-vector with the data for each substance weighted using LCA methodology (Lindfors et al., 1995), the vector is presented in Table 6. The other vector is a 7-vector with total amount in each effect category on each position. This vector is presented in Table 7. Weighing factors are inserted in the m-file “weighingfactors.m”

Table 6. The vector containing weighted results on substance level, sent to MATLAB workspace. Position Substance (weighted impact) Effect category 1 CO2 Global warming 2 CH4 d:o 3 VOC d:o 4 N2O d:o

5 BOD Eutrophication, min. scenario

6 P d:o

7 BOD Eutrophication, max. scenario

8 N-Org. d:o 9 NH4+ d:o 10 NOX d:o 11 NO3- d:o 12 N2O d:o 13 P d:o

14 SOX Acidification, min. scenario

15 HCl d:o

16 NH3 Acidification, max. scenario

17 NOX d:o

18 SOX d:o

19 HCl d:o

20 CO Photochemical oxidants, organic species

21 CH4 d:o

22 VOC d:o

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Table 7. The vector containing weighted results, totals, sent to MATLAB workspace.

Position Effect category

1 Global warming potential (GWP) 2 Eutrophication, min. scenario 3 Eutrophication, max. scenario 4 Acidification, min. scenario 5 Acidification, max. scenario

6 Photochemical oxidants, organic species 7 Photochemical oxidants, NOX

Environmental systems analysis of meals : model description and data used for two different meals

Environmental Systems Analysis of

Meals

– Model Description and Data Used for Two

Different Meals

Environmental Systems Analysis of Meals

– Model Description and Data Used for Two Different Meals

Summary

Table of Contents

Introduction

Model description