LCI data for the calculation
tool Feedprint for greenhouse
gas emissions of feed
production and utilization
Animal products
W.J. van Zeist1
M. Marinussen1
R. Broekema1
E. Groen1
A. Kool1
M. Dolman2
H. Blonk1
1
2
Blonk Consultants
Wageningen University and Research Centre
November, 2012
Blonk Consultants
Gravin Beatrixstraat 34
2805 PJ Gouda
the Netherlands
Telephone: 0031 (0)182 579970
Email: [email protected]
Internet: www.blonkconsultants.nl
Blonk Consultants helps companies, governments and civil society organisations put sustainability into practice. Our team of dedicated
consultants works closely with our clients to deliver clear and practical advice based on sound, independent research. To ensure optimal
outcomes we take an integrated approach that encompasses the whole production chain.
LCI data for the calculation
tool Feedprint for greenhouse
gas emissions of feed
production and utilization
Animal products
W.J. van Zeist1
M. Marinussen1
R. Broekema1
E. Groen1
A. Kool1
M. Dolman2
H. Blonk1
1
Blonk Consultants
2
Wageningen University and Research Centre
November, 2012
Table of contents
6.1
Introduction
1
6.1.1
Context of this document & reading guide
1
6.1.2
Overview of products and allocation principles
1
6.1.3
Structure of data
1
6.1.4
Glossary of terms
2
6.1.5
References
2
Fish meal and oil
3
6.2
6.2.1
By-products from fish meal and fish oil production
3
6.2.2
Note on allocation
3
6.2.3
Sourcing
3
6.2.4
Mass balance
4
6.2.5
Fisheries: Inputs
6
6.2.6
Processing fish: Inputs
7
6.2.7
Allocation
8
6.2.8
References
8
6.3
By-products from dairy products
11
6.3.1
By-products
11
6.3.2
Note on allocation
11
6.3.3
Sourcing
11
6.3.4
Flowcharts
12
6.3.5
Input and output
13
6.3.6
Production system: Milk powder.
14
6.3.7
Production system: Casein/Cheese production factory (up until liquid whey)
15
6.3.8
Production system: Liquid whey processing:
16
6.3.9
Production system: Casein production
19
6.3.10
Drying to 30/60% dry matter content of whey
19
6.3.11
Literature review
20
6.3.12
References
21
6.4
By-products from slaughtering animals
22
6.4.1
Slaughter by-products
22
6.4.2
Note on allocation principles
23
6.4.3
Sourcing
23
6.4.4
Flowcharts
23
6.4.5
Mass balance
24
6.4.6
Inputs
24
6.4.7
Allocation
25
6.4.8
References
26
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
6.1
Introduction
6.1.1
Context of this document & reading guide
This document is part of the background documentation for the FeedPrint program and database.
Background information of this project, underlying methodology and justification thereof, can be found
in the ‘FeedPrint methodology’ document. These chapters focus only on the processing step of crops into
the feed materials. Information on origin of crops is given, but details on cultivation and transportation
(to and from the processing facility) are described in separate documents: the cultivation of each crop is
described in the cultivation background reports similar to this one (Marinussen et al, 2012), whereas
transportation is described in the Feedprint methodology report (Vellinga et al, 2012).
Each chapter can be read and interpreted as a standalone set of LCI data, which covers the country of
crop cultivation, the country of processing, mass balances, energy inputs (and outputs, if applicable), as
well as data needed for the allocation of the by-products. In some cases, multiple processes can follow
one another with multiple allocation steps. In these cases, the data is entered into the database by
following these specific processing steps consecutively. Usually (but not restrictively) the data entered are
relative to an input of 1000 kg of crop product.
6.1.2
Overview of products and allocation principles
Each chapter in this document describes a different animal feed material production process. Unless
noted otherwise, the processes described in this document are treated as a single unit process with
multiple valuable output products, where allocation approach 1 is applied (see §5.3, Vellinga et al, 2012) in
which all products are treated as valuable by-products to which upstream emissions will be allocated
according to economic, energy, or mass allocation.
6.1.3
Structure of data
This document contains tables that reflect those data applied in the FeedPrint program. Additionally,
tables with background data are supplied, which are often inventories of encountered literature. Only the
tables that are used as data for the FeedPrint database and calculations are given a table number (see for
an example Table 6.1.1). Other tables that are not used in the FeedPrint database are not numbered and
have a simpler layout, see the example below.
Table 6.1.1 Example default inputs table for FeedPrint database.
Output
Values
Best estimate
Unit
Electricity
88
Error
1.4
Natural gas
245
1.4
(g2)
MJ/ton
MJ/ton
Example of background data not directly used in FeedPrint database
Source
Reference 1
Reference 2
Data found
80 MJ/ton
90 MJ/ton
Remarks
Older data from 1 processing facility.
Newer data from multiple facilities.
There are a number of recurring types of tables, usually in the following order:
1) Definition of feed materials related to the process;
2) Estimation of countries of origin of the crop and countries of processing;
3) Mass balances for the process;
4) Energy or material inputs needed for the process;
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
5) Allocation factors for the outputs from the process.
Unless explained otherwise in a specific chapter, these five tables are present for each process. Additional
sections or figures can give information on, for example, the definition of the process represented with a
flowchart. Each section also contains the references for cited sources. The usual structure of a section is
that first the default inputs for the FeedPrint database are presented, with the rest of the section
explaining in detail which data sources were used and why.
There are a number of different types of error ranges that can be given for each data point, and these are
applied for the energy and auxiliary inputs. More background information can be found in the overall
methodology document (Vellinga et al. 2012), which also explains the decision process followed to arrive
at the error ranges.
6.1.4
Glossary of terms
Below is a list of terms with definitions as applied in this document.
DMC
GE
6.1.5
Dry matter content in g/kg.
Gross Energy content in MJ/kg.
References
CVB-table: see appendix 1 in Vellinga et al. (2012)
European Commission. (2011). COMMISSION REGULATION ( EU ) No 575 / 2011 of 16 June 2011
on the Catalogue of feed materials. Official Journal of the European Union, (L 159), 25–65.
Marinussen et al (2012) Background data documents on cultivation. Blonk Consultants. Gouda, the
Netherlands
Vellinga, T.V., Blonk, H., Marinussen, M., van Zeist, W.J., de Boer, I.J.M. (2012) Methodology used in
feedprint: a tool quantifying greenhouse gas emissions of feed production and utilization
Wageningen UR Livestock Research and Blonk Consultants. Lelystad/Gouda, the Netherlands.
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
6.2
6.2.1
Fish meal and oil
By-products from fish meal and fish oil production
Feed materials derived from primary products are:
name CVB
DM
Protein
Fish meal Crude Protein<580
927
567
Fish meal Crude Protein 580-630
913
629
Fish meal Crude Protein 630-680
913
657
Fish meal Crude Protein>680
922
711
Within the scope of this research, no distinction will be made between the fish meal containing different
amounts of crude protein.
6.2.2
Note on allocation
The by-products of oil and meal are both valuable, and allocation approach 1 is applied for the fish meal
and oil industry (see §5.3, Vellinga et al, 2012). However, for the input of fish offals, the offals are
considered a residue and no upstream allocation from the fisheries is attributed to fish offals as input into
the factory (this is approach 3 as described in §5.3, Vellinga et al, 2012).
6.2.3
Sourcing
The Dutch feed industry sources the feed materials in the countries that are listed in Table 6.2.1,
according to industry information in the Netherlands. All fish is obtained from fishery and is presumed to
be landed in the country of processing. Since there is little information beyond total imports into the
Netherlands for generic fish oil and meal (according to FishStat), this document (as well as the
accompanying subreport on fishery industry), will focus products from Peruvian anchovetta and products
averaged from north-western European industry..
Table 6.2.1 Estimated countries of origin of the feed materials
Country
Fish species
Germany (fish meal)
Denmark (fish meal)
Norway (fish oil)
Herring (oily), Sandeel (oily), Blue Whiting
(lean), Norway pout (lean), European sprat
(oily) and Capelin (oily)
Peru (fish meal)
Anchovetta
Background data on fish species utilized for fish meal production in various countries.
Country
Fish
meal
production, Major species
2002/06 average (tonnes)
Peru
1714000
Anchovy
Chile
798000
Jac Mackerel, Acnhovy, Sardine
Thailand
402000
Various
China
348000
Various
USA
300000
Menhaden, Alaska Pollock
Denmark
246000
Sand eel, sprat, blue whiting, others
Japan
238000
Sardine, Japanese pilchard, Antarctic krill
Iceland
224000
Capelin, blue whiting, herring, trimmings
Norway
198000
Blue whiting, capelin, sand eel, trimmings
Other EU
210000
Trmmings, sand eel, sptrat, blue whiting, herring, a.o.
South Africa
103000
Anchovy, pilchard
Others
1176000
Mainly Anchovy
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
From ‘Fish processing: Sustainability and new opportunities’, George Hall, 2010.
6.2.4
Mass balance
Fish meal and oil production is a relatively simple step in which raw fish material goes through a number
of rendering steps (cooking, pressing, drying and milling) to produce crude protein meal and the extracted
fish oil. For gathering LCI data, we assume that this can be seen as a simple unit process.
In this document, also energy inputs required for fishery are described, and this will be seen as a single
unit process with energy requirements needed for the ‘production’ of fish. In simple terms, the flowchart
is shown in Figure 6.2.1.
Fish meal
Fisheries
Fish/fish waste
Processing
Fish oil
Figure 6.2.1 Simple flowchart of process included in the current document.
Default mass balance fish processing, applicable to all raw material inputs (both fish and fish offals can be
used). As explained in the remainder of the section, it is assumed that for the default data for North-West
Europe 25% of the meal is produced using fish offals, and in Peruvian industry no offals are used.
Table 6.2.2 Default mass balance for fish processing.
Input (Peruvian):
DMC (g/kg)
Mass (kg)
Fish
254
1000
Input (North-West Europe):
DMC (g/kg)
Mass (kg)
Fish
254
750
Fish offals
254
250
Output (both cases):
DMC (g/kg)
Mass (kg)
Fish meal
920
225
Fish oil
1000
47
There can be differences between the mass balances for different fish species, and even per season, as the
fat content can differ. We will first focus on a generic balance that, when possible, can be refined for a
specific type of fish (such as anchovies, which is the main fish stock used for fish meal and oil production
in Peru). Also of note is the fact that white fish is generally lower in fat content and the meal produced
reflects this in a lower oil content (Windsor, 2001). For raw material input with a fat content of lower than
3%, oil extraction does not take place (Hall, 2010), (FAO, 1986). Although this document mainly focuses
on processing where also oil is produced, it is worth noting that the processing of non-oily (lean) fish, is
less complicated and requires less energy, since some of the oil extraction steps are skipped. According to
a supplier of machinery, this can amount to 20 – 35% less electricity use of lean fish processing compared
to oily fish processing (http://www.fishmealmachine.com/).
Mass balances inventory on raw fish input (water loss, evaporation, is usually not reported directly).
Product
LCAFood
(b)
IFFO
(c)
W&L (d)
Jespersen
(e)
Ben Cat factory
(f)
Tacon
(g)
(S&T)2
(h)
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
Fish oil
4.5%
4.7%
4.6%
3.4%
4.0%
5%
7.6%
Fish meal
21.5%
22.5%
24.6%
21.6%
21.9%
22.5%
23.1%
Type of
Sand eel
Generic
Anchovis Europe
Sardine comp.
Pelagic
Peru
fish
generic
A (FAO, 1986); b (Olesen & Nielsen, 2000); c (Chamberlain, 2011); d (W&L, 2007) e (Jespersen et al., 2000) (f) (Ben
Cat Factory, 2011). g: (Tacon & Metian, 2008) h: ((S&T)2, 2005)
The multiple mass balances as above do not differ significantly amongst each other, and especially since it
will be always difficult to trace the exact origins of any specific batch of fishmeal, it is considered best to
use the industry averages provided by IFFO, resulting in a production of 4.7% fish oil and 22.5 % fish
meal per tonne of raw fish material. This actually includes fish waste as the raw material, and can thus be
used for all raw material inputs.
Differentiation according to fish species.
FAO (1986) mentions the following compositions of various fish species involved in fish meal processing.
The same source also notes the following on prospective fish meal and oil yields: Using the dry mater
content of around 91% for typical fishmeal (see also the CVB list) and assuming that 2 – 3% of oil is left
in the fish meal (FAO, 1986), (W&L, 2007), (Ben Cat Factory, 2011), the table below also incorporates
estimated oil and meal yields for the various fish species. Although these numbers are simply estimations,
they do give a nice insight in the variability across fish species. These figures have also been used to give
the range of dry matter contents in the default mass balance, with 25.4% as an approximate average that
also coincides with the dry matter contents in the mass balance outputs..
Composition of whole fish; average values over a number of years (in percent). Values taken from (FAO, 1986).
Fish species
Protein
Fat
Ash Water Estimated meal yield Estimated oil yield
per kg fis
per fish
Blue whiting. North Sea
17.0
5.0
4.0
75.0
24 – 25%
2 - 3%
Sprat. Atlantic
16.0
11.0
2.0
71.0
22 - 23%
8 - 9%
Norway pout
16.0
5.5
3.0
73.0
23 - 24%
2.5 – 3.5%
Anchoveta
18.0
6 - 7* 2.5
78.0
23 - 25%
3 - 5%
Herring. spring
18.0
8.0
2.0
72.0
24 - 25%
5 - 6%
Herring. winter
18.2
11.0
2.0
70.0
23 - 24%
8 - 9%
Capelin, Norway
14.0
10.0
2.0
75.0
20 -21%
7 - 8%
* Included data from (W&L, 2007): “The typical composition of industrial fish species like anchovy, being
7% oil, 20% solids and 73% water.”
Composition and meal and oil yield according to (Parker & P. H. Tyedmers, 2011).
Fish species
Meal yield Oil yield
Atlantic herring
20.0%
11.1%
Peruvian anchovy
20.5%
4.6%
Gulf menhaden
22.0%
16.1%
Blue whiting
19.5%
1.8%
This article also mentions standard deviations for these values, as well as protein and energy contents of
the fish meals. As is clear from the table above, the meal production is relatively constant, while the oil
yield can vary quite substantially across different fish species. The average values from IFFO for the final
mass balance (4.7% oil and 22.5% meal) are quite representative in all cases. The minimum and maximum
amounts of meals produced are taken from the tables above.
On the use of offals
The amount of offals (or fish trimmings) included is especially important because of the lower
environmental impact that is attributed to fish waste after allocation. The fish waste is considered to be a
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
residue product within our allocation methodology, and thus no upstream emissions are allocated to this
input. According to IFFO (Chamberlain, 2011), 25% of input is offals, and 75% is fresh fish for
worldwide production, but this can vary per region. It is assumed that it will be too difficult to trace
whether the fishmeal is produced with offals or fresh fish. However, fishmeal industry in Peru is mainly
from industrial fishing of Anchovy and no use of offals is assumed here. Forth North-Western Europe
the world average of 25% will be assumed.
6.2.4.1
Processing general fish to offals
The input for fish processing can be whole (often industrial or pelagic) raw fish, or offals from fish
processed into fillets for human consumption. For the energy required, no distinction is made in relation
to the percentage of offals in the raw materials (no information has been encountered to conclude
otherwise).
6.2.5
Fisheries: Inputs
The main energy and input requirements in fishery are diesel use of the fishing vessels, which is used for
combustion in the diesel engine and generating power to run cooling and other on-board facilities.
Common in LCA studies on fisheries is to express an amount of diesel used per tonne of fish landed. The
type of fishing vessel has a large influence on its efficiency (see for example (P. H. Tyedmers, 2004)). The
purse seine vessels, which are routinely used in pelagic (industrial) fisheries, are quite energy efficient
compared to, for example, trawler vessels. Interestingly, this efficiency is especially high for industrial
fishing and less so for human consumption, see for example (P. Tyedmers, 2001). As the purse seines are
very prevalent in pelagic fisheries for fish oil production, all values listed here concern this type of fishing
boats.
Inventory table of energy inputs for purse seine fisheries. Values energy used for amount of fish landed, so by-catch is included.
Source
(P. H. Tyedmers, 2004)
((S&T)2, 2005)
(P. Tyedmers, 2001)
(Driscoll & P. Tyedmers,
2010)
(Schau, Ellingsen, Endal,
& Aanondsen, 2009)
(Thrane, 2008)
Description
Capelin/Herring, NE atlantic
Menhaden, NW atlantic
Blue Whting, NE atlantic
Herring, Sand eels, NE atlantic
Peruvian fishery
Norway, 1998
Iceland, 1998
US, atlantic herring
Energy use*
20 liters/tonne
32 liters/tonne
85 liters/tonne
100 liters/tonne
43 liters diesel/tonne
85, 96, 126
19, 32
21
Norway
Iceland (capelin)
Danish industrial
Danish Mackerel
Danish Herring
90
20
65
59
119
* According to (Schau, Ellingsen, Endal, & Aanondsen, 2009), the typical fuel used for fishing vessels is marine gas oil (diesel)
which has an energy content (lower heating value) of 42.8 MJ/kg and a density of 0.86 kg/L.
6.2.5.1
Fisheries: Peruvian Anchovetta
By far the largest exporter of fishmeal and oil is Peru, which has a large industry based on the industrial
fishing of Peruvian Anchoveta. There is only one source that cites data directly based on the Peruvian
anchovetta industry. It lies quite comfortably within the range of the other purse seine fisheries. The
direct value by ((S&T)2, 2005) will be used as the mean value, while the upper and lower bounds are set
by the other sources available. The upper bound is taken from the value specific to industrial fishing from
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
Thrane, 2008. Some higher values than the chosen exist but, as noted before, industrial fisheries for fish
meal and offals are usually efficient.
Table 6.2.3 Energy use in Peruvian Anchovetta fishery, per tonne landed fish.
parameter
Value
Best estimate
Min
Max
1580
735
2390
Diesel
6.2.5.2
Unit
MJ/tonne landed fish
Fisheries: North-Western Europe generic industrial fish
Data indicates that most fish meal and oil is imported from Denmark, Norway, or Germany. However,
since a lot of re-export is takes place, it is more reasonable to focus on a generic North-Western
European industry (main contributors are Norway, Iceland, Denmark and Germany). The beginning of
this section already listed a range of sources for different countries. Since there is no easy way to distill a
best estimate average from these sources which list different types of fish species as well, it was decided to
assume a uniform distribution between the minimum and maximum values.
Table 6.2.4: Energy use in North-Western European fishery, per tonne landed fish.
parameter
Diesel
6.2.5.3
Value
Min
Max
735
2390
Unit
MJ/tonne landed fish
Fisheries: Fish offals
As noted earlier, according to our allocation method, no upstream emissions are attributed towards the
residue streams of fish offals. Thus, no data on fisheries is required.
6.2.6
Processing fish: Inputs
Below are the default energy and material inputs for processing fish and fish offal into fish meal and oil.
The data will be underpinned in the remainder of this section. The data are valid for both Peruvian and
North-Western European industries.
Table 6.2.5 Default energy inputs per tonne of fish or fish offal as input:
Parameter
Value
Unit
Mean
Min
Max
Fuel oil
1500
1300
2100
MJ/tonne
Electricity from the grid
144
90
180
MJ/tonne
Table 6.2.6 Default material inputs per tonne of fish or fish offal as input:
Parameter
Value
Unit
Best estimate
Error
Formaline (37 % formaldehyde)
2.3
1.2
g/kg
Antioxidants
0.1
1.2
g/kg
Sulphuric acid. H2SO4
0.5
1.2
g/kg
Sodiumhydroxide, NaOH
1.0
1.2
g/kg
Nitric acid, HNO3
0.1
1.2
g/kg
Hydrochloric acid. HCl
0.1
1.2
g/kg
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
Water for steam, cleaning
0.5
1.2
L/kg
Seawater
20
1.2
L/kg
Inventory of fuel and energy consumption for fish meal and oil production.
Reference
(W&L, 2007)
(Olesen & Nielsen, 2000)
(Hall, 2010)
(Blonk, Kool, Luske, de
Waart, & ten Pierick,
2008)
(Ben Cat Factory, 2011)
((S&T)2, 2005)
Location
Peru
Denmark
-
Fish type
Anchovis
Sand eel
-
Fuel oil
1440 MJ/ton
1330 MJ/ton
35–49 Liters/ton (1500 – 2100 MJ)
41.2 m3 gas/ton (1300 MJ)
Electricity
25-50 kWh/ton
41 kWh/ton
32 kWh/ton
40.4 kWh/ton
Vietnam
Peru
35 L / ton (1500 MJ)
53 Liters/ton
30-40 kWh/ton
30 kWh/ton
(Jespersen et al., 2000)
-
Sardine
Mainly
anchoveta
-
49 Liters/ton (2100 MJ)
32 kWh/ton
There is a quite extensive study by Pelletier et al., 2009, where energy and GHG emissions from various
different types of fish but only specify total energy use, and not composition and type. Although the
source can be considered reliable, it is unclear how the figures for different species of fish are obtained
and can unfortunately not be used. Also in (Pelletier, 2006) differentiation is made between three different
types of fish and location, obtained from a variety of older sources, but all lie within the range of values as
shown in the above table. Until more direct valid country-specific sources (especially industry averages)
are encountered, averages of 35 L of fuel oil and 40 kWh per tonne of incoming raw material is assumed
in all cases as a best estimate.
There is a source (Olesen & Nielsen, 2000) which also gives chemical inputs. Although these values might
not be absolutely representative for all fish meal production processes, some chemicals are likely to be
used in most locations and they are included in the LCI default data (see Table 6.2.6). The data on water
use in Table 6.2.6 are from (Jespersen et al., 2000).
6.2.7
Allocation
Table 6.2.7 Allocation data
Product
CVB Name
Mass
DMC (g/kg)
Fish meal
Fish meal Crude Protein<580 (46110)
Fish meal Crude Protein 580-630 (46120)
Fish meal Crude Protein 630-680 (46130)
Fish meal Crude Protein>680 (46140)
Fish oil (not on CVB list)
225
920
Price
(euro/kg)
0.76
47
1000
0.52
Fish oil
6.2.8
GE
16.3
14.9
15.0
15.4
37
References
CVB-table (2012): see appendix 1 in Vellinga et al. (2012)
Vellinga, T.V., Blonk, H., Marinussen, M., van Zeist, W.J., de Boer, I.J.M. (2012) Methodology used in
feedprint: a tool quantifying greenhouse gas emissions of feed production and utilization
Wageningen UR Livestock Research and Blonk Consultants. Lelystad/Gouda, the Netherlands.
(S&T)2. (2005). BIODIESEL GHG EMISSIONS USING GHGENIUS AN UPDATE. Energy. (S&T)2
Consultants Inc. Delta, BC Canada.
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
Aubin, J., Boissy, J., & Drissi, A. (2010). Life cycle assessment of fish feed ingredients. Aquamax project
REPORT WP1.1 Deliverable: D1.1.4 & D1.1.5. INRA UMR SAS report No.1. Version of March
2010. Recherche. INRA.
Ben
Cat Factory. (2011). Basic Fish Meal and Oil
http://bencatfishmeal.com.vn/index.php?vs=aboutus/process
Process.
Retrieved
from
Blonk, H., Kool, A., Luske, B., de Waart, S., & ten Pierick, E. (2008). Milieueffecten van Nederlandse
consumptie van eiwitrijke producten Gevolgen van vervanging van dierlijke eiwitten anno 2008.
Blonk Milieu Adives, Gouda.
Chamberlain, A. (2011). Fishmeal and Fish Oil – The Facts , Figures , Trends , and IFFO ’ s Responsible
Supply Standard. Production. International Fishmeal & Fish Oil Organisation.
Driscoll, J., & Tyedmers, P. (2010). Fuel use and greenhouse gas emission implications of fisheries
management: the case of the new england atlantic herring fishery. Marine Policy, 34(3), 353-359.
Elsevier. doi:10.1016/j.marpol.2009.08.005
Durand, N. S., & Seminario, M. G. (2009). Status of and trends in the use of small pelagic fish species for
reduction fisheries and for human consumption in Peru. Fish as feed inputs for aquaculture:
practices, sustainability and implications. FAO Fisheries and Aquaculture Technical Paper. No.
518. (pp. 325-369). FAO, Rome.
FAO. (1986). The production of fish meal and oil, FAO Fisheries Technical Paper - 142. FAO, Fishery
Industries
Division.
Retrieved
from
http://www.fao.org/DOCREP/003/X6899E/X6899E04.htm
Guillen, J., & Cheilari, A. (2010). Energy efficiency analysis and profitability of the european union fishing
fleets. Fuel. Energy Use in Fisheries: Improving Efficiency and Technological Innovations from a
Global Perspective, November 14-17, 2010.
Hall, G. (2010). Fish processing: Sustainability and new opportunities. Wiley-Blackwell.
Jespersen, C., Christiansen, K., & Hummelmose, B. (2000). Cleaner Production Assessment in Fish
Processing. Production. COWI Consulting Engineers and Planners AS, Denmark.
Marlen, B. V. (2009). Energy Saving in Fisheries (ESIF) Fish/2006/17 LOT3 - Final Report. Institute for
Marine Resources and Ecosystem Studies.
Olesen,
E., & Nielsen, P. H. (2000). Fishmeal and Oil Production.
http://www.lcafood.dk/processes/industry/fishmealproduction.htm
Retrieved
from
Parker, R. W. R., & Tyedmers, P. H. (2011). Uncertainty and natural variability in the ecological footprint
of fisheries: A case study of reduction fisheries for meal and oil. Ecological Indicators, In press,
6-13. Elsevier Ltd. doi:10.1016/j.ecolind.2011.06.015
Pelletier, N. L. (2006). Life cycle measures of biophysical sustainability in feed production for
conventional and organic salmon aquaculture in the northeast pacific, Master Thesis. Cycle.
Dalhousie University, Halifax, Nova Scotia.
Pelletier, N. L., Tyedmers, P., Sonesson, U., Scholz, A., Ziegler, F., Flysjo, A., Kruse, S., et al. (2009). Not
all salmon are created equal: life cycle assessment (LCA) of global salmon farming systems. Supporting Information. Environmental science & technology, 43(23), 8730-6.
doi:10.1021/es9010114
Schau, E., Ellingsen, H., Endal, a, & Aanondsen, S. (2009). Energy consumption in the Norwegian
fisheries.
Journal
of
Cleaner
Production,
17(3),
325-334.
Elsevier
Ltd.
doi:10.1016/j.jclepro.2008.08.015
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Tacon, A., & Metian, M. (2008). Global overview on the use of fish meal and fish oil in industrially
compounded aquafeeds: Trends and future prospects. Aquaculture, 285(1-4), 146-158. Elsevier
B.V. doi:10.1016/j.aquaculture.2008.08.015
Thrane, M. (2004). Environmental Impacts from Danish Fish Product. Development. Aalborg university,
Denmark.
Thrane, M. (2008). Energy Consumption in the Danish Fishery: Identification of Key Factors. Journal of
Industrial Ecology, 8(1-2), 223-239. doi:10.1162/1088198041269427
Tietze, U., & Thiele, W. (2005). Economic performance and fishing efficiency of marine capture fisheries,
FAO Fishereries Technical Paper 482. Fisheries (Bethesda). FAO, Rome.
Tyedmers, P. (2001). Energy Consumed by North Atlantic Fisheries. Environmental Studies. Fisheries
Centre Research Reports.
Tyedmers, P. H. (2004). Fisheries and Energy Use. Encyclopedia of Energy, Volume 2 (Vol. 2, pp. 683694). Elsevier Inc.
U.Tietze, Prado, J., Le Ry, J.-M., & Lasch, R. (2001). Techno-economic performance of marine capture
fisheries.
FAO
Fisheries
Technical
Paper
421.
Retrieved
from
http://www.fao.org/DOCREP/004/Y2786E/Y2786E00.HTM
W&L. (2007). Manufacturing technology for fish meal and fish oil, description “Planta Harina.”
Technology. Comercializadora Industrial W&L Ltda.
Windsor, L. M. (2001). Fish Meal, Torry Avisory Note No. 49. FAO. Retrieved from
http://www.fao.org/wairdocs/tan/x5926e/x5926e00.htm
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
6.3
6.3.1
By-products from dairy products
By-products
Main industrial processors of primary products producing for the Dutch feed industry are:
- Cheese/whey powder producers
- Milk powder producers
- Casein producers
The feed materials from dairy industry that are included in the CVB list are listed Table 7.1. Whey can
originate from both casein production and cheese production. These are considered to be sufficiently
similar to be treated as one product.
From the CVB list, the following product definitions and, partially, composition data is retrieved if
available in the CVB list.
name
Industry
DMC
(g/kg)
912
Casein
Caseine factory
Milk powder skimmed
Milk factory
Milk powder whole
Milk factory
Lactose
Whey processing
Permeate
Whey processing
Whey powder
Whey processing
Whey powder delactosed
Whey processing
Whey powder low in sugar Crude Ash<210
ASH
CP
CFAT
NFE
34
868
11
-1
945
79
350
16
500
949
62.6
279
224.9
382.4
980
80
130
9
761
Whey processing
956
177
254
53
472
Whey powder low in sugar Crude Ash>210
Whey processing
959
225
213
41
480
Whey protein concentrate (30%)
Whey processing
Whey protein concentrate (60%)
Whey processing
Whey protein concentrate (80%)
Whey processing
The specific production route of ‘kaas/kwark’ production is currently not taken into account, as no
specific information for this is known. However, especially for the dried whey products, the drying step is
important and this will be very similar in all whey production routes.
A number of whey related products of 6%, 30% and 60% dry matter content were added to the feed
ingredients list. These were treated using allocation approach 2 (see §5.3, Vellinga et al, 2012), and thus
only the drying step was included (with 6% dry matter whey products only having emissions from
transportation).
6.3.2
Note on allocation
Liquid whey is assumed to be a residue product and is treated according to allocation approach 2 (see
§5.3, Vellinga et al, 2012). The drying and concentration of whey into powder is directly attributed to the
products.
6.3.3
Sourcing
The Dutch feed industry source the feed materials in the countries that are listed in Table 6.3.1.
Table 6.3.1 Estimated countries of origin of the feed materials
Phase
Processing
Belgium
France
Germany
other EU
the Netherlands
10%
15%
30%
20%
25%
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
Animal husbandry in same country
6.3.4
100%
100%
100%
100%
100%
Flowcharts
In the figures below, the different production routes of dairy products considered here are shown. There
are basically three types of dairy industries considered here: 1) milk powder production, 2) casein and
casein whey production, and 3) cheese and cheese whey production. For milk powder, two different
drying techniques are shown. For casein whey and cheese whey, three types of drying techniques are
shown. Only for cheese whey, possible techniques are considered for extracting different fraction from
the liquid whey before drying. It can be assumed that these techniques can also be applied to liquid casein
whey.
Raw milk
Thermal
treatment, skim,
cool (A1)
Energy-input (natural
gas, electricity)
Cream
Energy-input (natural
gas, electricity)
Standardized milk
MVR evaporator
TVR evaporator
Dry tower (B)
TVR evaporator
Dry tower (C)
Milk powder, skimmed (1) or whole (2)
Figure 6.3.1 Flowchart of whole milk and skimmed milk powder production chain.
Raw milk
Thermal
treatment, skim,
cool (A1)
Energy-input (natural
gas, electricity)
In each process
Cream
Standardized milk
Casein/casein salts
production (D)
Liquid casein whey
Casein/casein salts
(3)
Figure 6.3.2 Flowchart of casein production chain
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
Raw milk
Thermal
treatment, skim,
cool (A2)
Cream
Standardized milk
Cheese production
(H)
Energy-input (natural
gas, electricity)
In each process
Liquid cheese whey
Cheese
Figure 6.3.3 Flowchart of cheese production chain
Liquid whey
Ultrafiltration (J)
Whey protein
concentrate (wet)
Energy-input (natural
gas, electricity)
In each process
Reversed osmosis
MVR evaporator
TVR evaporator
Dry tower (E4)
Reversed osmosis
TVR evaporator
Dry tower (E3)
Whey
permeate
(wet)
Crystallisation (I)
Lactose
MVR evaporator
TVR evaporator
Dry tower (E2)
Whey,
delactosed
TVR evaporator
Dry tower (E1)
Whey powder (5), whey powder, delactosed (6), whey
protein concentrate (7, 8 and 9), whey permeate (10)
Figure 6.3.4 Flowchart of liquid whey processing chain (liquid whey input from both casein and cheese production).
6.3.5
Input and output
Because there is no complete dataset available in literature and combining different datasets leads to large
errors, KWA Adviseurs was approached to supply a complete dataset from Dutch dairy industry with
mass balances and energy use. The data are presented here. It is assumed that these data are representative
for similar processing industries across Europe (in the final section of this chapter, a literature review is
given).
All electricity and natural gas data are from KWA Adviseurs. Mass output is based on dry matter balance
from KWA in case of a single product process or calculated based on dry matter from KWA and assumed
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
compositions. Composition data in the table is combined data from Ramirez (2009), KWA Adviseurs, the
CVB product list and was supplemented using knowledge on the mass balances in these sources.
Assumed compositions of the products
Product
Raw milk
Cream
Standardized milk (whole)
Milk powder (whole)
Cream
Standardized milk (skimmed)
Milk powder (skimmed)
Cream
Cheese
Whey (wet)
Whey powder
Lactose
Whey powder, delactosed (wet)
Whey powder, delactosed (dry)
Permeate (wet) (WPC30)
WPC30 (wet)
Permeate (wet) (WPC60)
WPC60 (wet)
Permeate (wet) (WPC80)
WPC80 (wet)
Permeate (dry) (WPC30)
WPC30 (dry)
Permeate (dry) (WPC60)
WPC60 (dry)
Permeate (dry) (WPC80)
WPC80 (dry)
Water
0.87
0.5
0.88
0.04
0.5
0.90
0.04
0.5
0.44
0.95
0.04
0.04
0.73
0.04
0.956
0.92
0.956
0.872
0.956
0.796
0.04
0.04
0.04
0.04
0.04
0.04
Fat
0.041
0.5
0.035
0.23
0.5
0.005
0.02
0.5
0.295
0.000
0.01
0.000
0.011
0.04
Protein
0.033
0
0.032
0.28
0
0.035
0.36
0
0.255
0.007
0.13
0.000
0.160
0.57
Carbohydrates
0.046
0
0.044
0.39
0
0.049
0.51
0
0
0.039
0.75
1.000
0.000
0.000
Ash
0.01
0
0.010
0.06
0
0.011
0.08
0
0.01
0.004
0.08
0.000
0.099
0.35
GE (MJ/kg)*
2.9
18.5
2.6
19.8
18.5
1.6
15.5
18.5
15.3
0.8
15.3
17.0
3.1
11.2
0.288
0.096
14.7
0.576
0.096
14.7
0.768
0.096
14.7
* Gross energy contents calculated based on composition data.
6.3.6
Production system: Milk powder.
Milk powder originates either from skimmed or full fat (whole) standardized milk. These options result in
two slightly different mass balances for the thermal treatment step as indicated in the table below. There is
no range of energy use given, but the contribution to the total energy use of end products is negligible.
Table 6.3.2 Unit process I: (A1) Thermal treatment, skim, cool for milk powder and casein
Energy inputs
natural gas
electricity from the grid
Outputs
Output (whole):
Cream (whole)
Standardized milk (whole)
Output (skimmed):
Cream (skimmed)
Standardized milk (skimmed)
Value
Mean
0
3.2
Unit
Error
10%
10%
MJ/ton raw milk
MJ/ton raw milk
DMC
(g/kg)
Mass
Unit
500
120
0.025
0.979
kg/kg raw milk
kg/kg raw milk
500
100
0.074
0.926
kg/kg raw milk
kg/kg raw milk
There are two ways in which the standardized milk can be dried, resulting in the milk powder. The three
step dryer option is more energy efficient (especially for natural gas usage) compared to the two step
dryer. The default is chosen to be the best case for the minimum and the worst case for the maximum,
because it is not known how much product is produced using the two step and how much using the three
step technology in the supplying countries.
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
Table 6.3.3 Unit process I: Drying to milk powder (B, C)
Parameter
Default values
natural gas
electricity from the grid
Mean
Min
Max
Unit
613
85
1041
98
MJ/ton std. milk
MJ/ton std. milk
Three step dryer (MVR):
natural gas
electricity from the grid
684
104
613
98
823
114
MJ/ton std. milk
MJ/ton std. milk
Two step dryer:
natural gas
electricity from the grid
922
72
859
66
1041
85
MJ/ton std. milk
MJ/ton std. milk
Outputs
For both drying options:
Milk powder (whole)
Milk powder (skimmed)
DMC (g/kg)
Mass
960
960
0.122
0.965
Unit
kg/kg st. milk
kg/kg st. milk
Data for allocation purposes in milk powder production. Allocation only takes place at the first step (A1
in Figure 6.3.1).
Table 6.3.4. Allocation data milk powder production
Process I:
Co-product
Name CVB
Mass
Cream, whole
Cream, skimmed
Standardized milk, whole
Standardized milk, skimmed
NA
NA
NA
NA
0.025
0.074
0.979
0.926
Mass
(dm)
0.0125
0.037
0.117
0.093
DMC
(g/kg)
500
500
120
100
Price*
1.31
1.31
0.48
0.39
GE
*
37
37
21.5
16.2
* GE (MJ/kg) are on dry matter basis. Prices on products as is, based on Dutch export data from Faostat for 20052009.
The standardized milk is then dried, with milk powder either resulting from a two-step dryer or a three
step dryer. These will be treated as two different products.
6.3.7
Production system: Casein/Cheese production factory (up until liquid whey)
Liquid whey can be produced from both casein or cheese production. Data is known for both process but no
distinction is made between these products as the processing is roughly similar. The most detailed data is available
for cheese production, while casein production is not completely covered in the data available. Thus, all whey
products will be based on data for this process.
Table 6.3.5. Unit process I: (A2) Thermal treatment, skim, cool for cheese or casein
Parameter
natural gas
electricity from the grid
Mean
7
6
Output:
Cream
Standardized milk
DMC (g/kg)
Error
10%
10%
Mass
0.025
0.979
Unit
MJ/ton raw milk
MJ/ton raw milk
Unit
kg/kg raw milk
kg/kg raw milk
* Values are based on cheese production
Table 6.3.6. Unit process II: (D) Casein/casein salts or cheese production*
Parameter
natural gas
electricity from the grid
Mean
109
56
Error
10%
10%
Unit
MJ/ ton st. milk
MJ/ ton st. milk
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
Output:
Cheese
Liquid whey
DMC (g/kg)
Mass
0.128
0.868
Unit
kg/kg st. milk
kg/kg st. milk
* Values are based on cheese production
Data for allocation purposes, based on cheese for all whey products. According to the allocation
methodology, liquid whey is considered a residue product and no upstream emissions are allocated
towards liquid whey.
Table 6.3.7. Allocation data for cheese production
Process I:
Process II
Co-product
Cream
Standardized milk
Mass
0.025
0.977
Mass (dm)
0.0125
0.117
Cheese
Liquid whey
0.128
0.868
0.072
0.043
DMC (g/kg)
Price
1.31
0.48
GE*
37.0
21.5
NA
0
NA
0
* GE (MJ/kg) are on dry matter basis. Prices on products as is.
The resulting liquid whey is further processed, see next sections.
6.3.8
Production system: Liquid whey processing:
Starting here with liquid whey from either cheese or casein factory. Three routes will be described. The
first concerns the direct drying of whey, where whey powder is produced and four types of drying are
discerned. The second option is crystallisation for lactose production, resulting in delactosed whey. The
third is the ultrafiltration of whey for the production of whey protein concentrates (WPCs) which also
results in whey permeate. These are both dried in a manner similar to whey powder. The default is chosen
to be the best case for the minimum and the worst case for the maximum, because it is not known how
much product is produced using the two step and how much using the three step technology in the
supplying countries.
6.3.8.1
Direct drying of liquid whey.
Processing of a kg of wet whey starting from a 95% moisture content.
Table 6.3.8. Unit process I: (E1, E2, E3, E4) Four drying options for whey products
Parameter
Default
natural gas
electricity from the grid
Mean
Min
Max
Unit
214
35
594
39
MJ/ ton liq. whey
MJ/ ton liq. whey
Four step drying:
natural gas
electricity from the grid
227
41
214
39
275
51
MJ/ ton liq. whey
MJ/ ton liq. whey
Three step drying (MVR):
natural gas
electricity from the grid
239
74
227
74
285
77
MJ/ ton liq. whey
MJ/ ton liq. whey
Three step drying (RO):
natural gas
electricity from the grid
278
34
270
31
370
38
MJ/ ton liq. whey
MJ/ ton liq. whey
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
Two step drying:
natural gas
electricity from the grid
Output:
Whey powder
556
31
548
30
DMC (g/kg)
960
594
35
Mass
0.053
MJ/ ton liq. whey
MJ/ ton liq. whey
Unit
kg/kg liq. whey
No allocation is necessary for whey powder drying, as it is a single input output process. However, the
four types of drying will be listed as seperate products.
Table 6.3.9. Allocation data for drying whey.
Process II (2-step):
Process II (3-step, RO):
Process II (3-step, MVr):
Process II (4-step):
6.3.8.2
Name CVB
Mass
Whey powder
Whey powder
Whey powder
Whey powder
NA
NA
NA
NA
Mass
(dm)
NA
NA
NA
NA
DMC
(g/kg)
960
960
960
960
Price
GE
NA
NA
NA
NA
NA
NA
NA
NA
Production of whey, delactosed:
Processing of a kg of wet whey starting from a 95% moisture content.
Table 6.3.10. Unit process I: (I) Crystallization
Parameter
natural gas
electricity from the grid
Mean
266
65
Output:
DMC
(g/kg)
Lactose
Whey powder, delactosed (wet, 27% dm)
270
Error
10%
10%
Mass
0.040
0.041
Unit
MJ/ton liq. whey
MJ/ton liq. whey
Unit
kg/kg liq. whey
kg/kg liq. whey
Allocation between lactose and whey occurs at this point. As wet whey is considered a residue stream, all
upstream emissions are allocated towards lactorse
Table 6.3.11. Allocation data for lactose production
Process I:
Name CVB
Mass
Lactose
Whey powder, delactosed (wet, 27% dm)
0.040
0.041
Mass
(dm)
0.039
0.011
DMC
(g/kg)
Price
NA
0
GE
(MJ/kg)
NA
0
The three types of low-sugar whey powder in the CVB list have not been distinguished and are listed as a
single group. The wet delactosed whey enters a single drying step, resulting in the whey powders.
Table 6.3.12. Unit process I: (G2) TVR evaporator Dry tower for whey powder, delactosed
Parameter
natural gas
electricity from the grid
Best estimate
2917
391.5
Error
10%
10%
Unit
MJ/ ton liq. whey
MJ/ ton liq. whey
Output (CVB listed):
Whey powder, delactosed
Whey powder low in sugar Crude Ash<210
Whey powder low in sugar Crude Ash>210
DMC (g/kg)
Mass
Unit
960
0.280
kg/kg liq. whey
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
6.3.8.3
Whey protein concentrate production
Processing of a kg of wet whey starting from a 95% moisture content. No specific energy inputs for the
first ultrafiltration step were encountered. The same drying steps are applied after this separation as for the
direct drying of liquid whey (for both WPC and permeate), see Table 6.3.8. Again, no allocation takes
places there, but the four drying options are listed below for each of the whey protein concentrates. Whey
permeate is considered to be a residual product and no upstream allocation takes place
Table 6.3.13. Unit process I: (J) Ultrafiltration
Parameter
natural gas
electricity from the grid
Best estimate
0
0
Error
0
0
Unit
MJ/ton liq. whey
MJ/ton liq. whey
Output options:
Whey protein concentrate 30
Whey permeate
DMC (g/kg)
Mass
0.167
0.833
Unit
kg/kg liq. whey
kg/kg liq. whey
Whey protein concentrate 60
Whey permeate
0.028
0.972
kg/kg liq. whey
kg/kg liq. whey
Whey protein concentrate 80
Whey permeate
0.005
0.995
kg/kg liq. whey
kg/kg liq. whey
Table 6.3.14. Allocation data for whey protein concentrate production
Process I (30)
Process I (60)
Process I (80)
Process II (2-step):
Process II (3-step,
RO):
Process II (3-step,
MVr):
Process II (4-step):
Name CVB
Mass
Whey protein concentrate (30%)
Whey permeate
Whey protein concentrate (60%)
Whey permeate
Whey protein concentrate (80%)
Whey permeate
Whey protein concentrate (30%)
Whey protein concentrate (60%)
Whey protein concentrate (80%)
Whey permeate
Whey protein concentrate (30%)
Whey protein concentrate (60%)
Whey protein concentrate (80%)
Whey permeate
Whey protein concentrate (30%)
Whey protein concentrate (60%)
Whey protein concentrate (80%)
Whey permeate
Whey protein concentrate (30%)
Whey protein concentrate (60%)
Whey protein concentrate (80%)
Whey permeate
DMC
(g/kg)
0.167
0.833
0.028
0.972
0.005
0.995
Mass
(dm)
0.0134
0.0367
0.0036
0.0428
0.0010
0.0438
Price
GE
NA
0
NA
0
NA
0
NA
0
NA
0
NA
0
NA
NA
960
NA
NA
NA
NA
960
NA
NA
NA
NA
960
NA
NA
NA
NA
960
NA
NA
From the background data in the literature review, also energy use for drying up to 30% and 60% dry
matter can be deduced, based on a 2 or 4-step system. The energy necessary then ranges from 38 to 275
MJ/ton wet whey (at 5% dry matter) input for drying to 60% dry matter. For drying to 30% this ranges
from 21 to 83 MJ ton wet whey. These values are based on the following assumptions (see also the
literature survey section for the background of these values.
 Membrane filtration requires 0.014 – 0.036 MJ per kg water removed (up to 18% dry matter).
 MVR requires about 0.1 MJ per kg water removed (up to 30% dry matter).
 TVR requires 0.2 – 0.3 MJ per kg water removed (up to 60% dry matter).
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
6.3.9
Production system: Casein production
This section deals exclusively with casein production (the liquid whey produced is aggregated together
with cheese production in the earlier sections). The energy inputs described below are taken from cheese
production (no direct data on casein production was encountered), and thus somewhat less reliable. The
difference with cheese production lies in a different mass balance in the second unit process.
Table 6.3.15. Unit process I: (A1) Thermal treatment, skim, cool for milk powder and casein
Parameter
natural gas
electricity from the grid
Output:
Cream
Standardized milk
Best estimate
7
6
Error
20%
20%
DMC (g/kg)
500
120
Unit
MJ/ton raw milk
MJ/ton raw milk
Mass (kg)
0.019
0.979
Unit
kg/kg raw milk
kg/kg raw milk
Table 6.3.16. Unit process II: (D) Casein/casein salts production
Parameter
natural gas
electricity from the grid
Output:
Casein
Liquid whey
Best estimate
109
56
Error
20%
20%
DMC (g/kg)
912
70
Unit
MJ/ton st. milk
MJ/ton st. milk
Mass
0.057
0.923
Unit
kg/kg st. milk
kg/kg st. milk
According to the allocation methodology, liquid whey is considered a residue product and no upstream
emissions are allocated towards liquid whey.
Table 6.3.17. Allocation data for cheese production
Co-product
Mass
Price
GE*
0.019
0.979
DMC
(g/kg)
500
120
Process I:
Cream
Standardized milk
1.31
0.48
20.7
16.0
Process II
Casein
Liquid whey
0.057
0.923
912
70
NA
0
NA
0
* Protein contents and GE (MJ/kg) are on dry matter basis. Prices on products as is.
6.3.10 Drying to 30/60% dry matter content of whey
A number of products in the FeedPrint relate to whey products dried to 30% and 60% dry matter,
respectively. Also for these products, the starting point is wet whey, and only a specified amount of drying
energy is attributed to the dried derived products. The energy values are based on the best and worst case
values and the mass balances as well as the inputs are listed in the tables below.
Table 6.3.18. Drying whey to 30% dry matter starting from 1 tonne of whey with a 95% moisture content
Parameter
natural gas
Output:
Whey at 30% DM
Min
21
Max
83
DMC (g/kg)
300
Unit
MJ/ ton liq. whey
Mass (kg)
167
Table 6.3.19. Drying whey to 60% dry matter starting from 1 tonne of whey with a 95% moisture content
Parameter
natural gas
Min
38
Max
275
Unit
MJ/ ton liq. whey
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
Output:
Whey at 30% DM
DMC (g/kg)
600
Mass (kg)
0.083
6.3.11 Literature review
The most important energy requirement for dairy processing is water extraction. Before drying, preconcentration takes place by membrane concentration (e.g. ultrafiltration, reverse osmosis) and/or by
evaporation using mechanical vapour recompression (MVR) and/or thermal vapour recompression
(TVR). According to Ramírez et al (2006):
 Membrane filtration requires 0.014 – 0.036 MJ per kg water removed
 MVR requires about 0.1 MJ per kg water removed
 TVR requires 0.2 – 0.3 MJ per kg water removed (some TVR techniques require 0.5 – 0.9 MJ per
kg water)
 Spray drying requires 3.4 (3-stage technique), 4.3 (2-stage) or 4.9 (1-stage) MJ per kg water
removed
So, the specific energy requirements not only depends on the applied combination of techniques, but also
on the dry matter contents of the starting material (standardized milk, liquid whey), and the dry matter
content at the end of each water extraction technique. The table below shows the dry matter percentages
for three products and the calculated energy requirements at a scenario of 0.2 MJ per kg water for predrying and 4.3 MJ per kg removed water for drying. This is very much in line with the default data
presented above.
Table 6.3.20 Dry matter contents of three products at different stages in the production process from Ramírez et al (2008)
and calculated energy requirements at an assumed scenario
Product
Units
Skimmed milk
Whole milk
Whey
powder
powder
Dry matter at start
%
10% (skimmed
12% (whole
6.5% (liquid
milk)
milk)
whey)
DM after pre-drying
%
48%
45%
40%
Water removed
kg/kg
8.6
7.1
13.6
product
DM after drying
%
96%
97%
95%
Water removed
kg/kg
1
1.2
1.4
product
Energy pre-drying (0.2
MJ/kg
1.7
1.4
2.7
MJ/kg)
product
Energy drying (4.3 MJ/kg
MJ/kg
4.3
5.0
5.9
water)
product
The BREF report gives an German example of a two-step drying process for milk powder production.
The initial dry matter is 11%, the pre-drying dry matter content is 50-60% and the final dry matter content
is 95-97%. The energy requirement is 1.1 MJ electricity per kg product and 7.4 MJ fuel per kg product.
This is an example of a relatively high energy requirement, but within the range of expectation.
IDF/FIL (2005) report 4.1 MJ fuel and 0.106 kWh electricity requirement per kg water evaporated for
drying milk powder (which is about the same as the 2-stage technique reported by Ramírez et al. (2006)).
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
For the pre-drying, they reported an energy requirement of 0.12 to 0.37 MJ and about 0.01 kWh per kg
water evaporated. Which is about the range at different combinations of pre-drying techniques reported
by Ramírez et al. (2006).
The conclusion is that the presented default data are in line with three important publications on energy
requirements for dairy processing. Moreover, the default data are representative for the (Northwest)
European dairy industry.
6.3.12 References
CVB-table (2012): see appendix 1 in Vellinga et al. (2012)
EC (2006) Integrated Pollution Prevention and Control Reference Document on Best Available
Techniques in the Food, Drink and Milk Industries. August 2006.
IDF/FIL (2005) Energy use in dairy processing. Bulletin of the International Dairy Federation 401/2005
Ramírez, C., Patel, M, Blok, K. (2006) From fluid milk to milk powder: Energy use and energy efficiency
in the European dairy industry. Energy 31 (2006) 1984–2004
Vellinga, T.V., Blonk, H., Marinussen, M., van Zeist, W.J., de Boer, I.J.M. (2012) Methodology used in
feedprint: a tool quantifying greenhouse gas emissions of feed production and utilization
Wageningen UR Livestock Research and Blonk Consultants. Lelystad/Gouda, the Netherlands.
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
6.4
6.4.1
By-products from slaughtering animals
Slaughter by-products
After animals are slaughtered, the carcass is cut into fresh meat fractions, offal and slaughter by-products.
Slaughter by-products can be used for processing into food ingredients (e.g. gelatine), feed materials (e.g.
plasma powder), materials (e.g. leather) and fuels (e.g. biodiesel). In this paragraph, the industrial
processing of slaughter by-products into feed materials (Table below) are described in schematic flow
charts and LCI data of the by-products are reported.
The following are the specific by-products and their related industries.
name
Feather meal hydrolysed
Cat 3 pig fat and meal
Cat 3 bovine fat and meal
Cat 3 chicken fat and meal
Food grade pig fat and greaves
Food grade bovine fat and greaves
Food grade chicken fat and greaves
Haemoglobin powder from porcine blood
Haemoglobin powder from bovine blood
Plasma powder from porcine blood
Plasma powder from bovine blood
Industry/bewerking
Cat 3 rendering
Cat 3 rendering
Cat 3 rendering
Cat 3 rendering
Food grade fat melting
Food grade fat melting
Food grade fat melting
blood processing industry
blood processing industry
blood processing industry
blood processing industry
At a later stage, the products in the CVB list are as follows, and defined for porcine and bovine origin:
Code
Name
44400
Blood meal spray dr
44500
Feather meal hydrolised
44610
Meat bone meal CFAT<100
44620
Meat bone meal CFAT>100
44700
Bone meal
44800
Fat from Animals
44920
Greaves meal
70700
Meat meal Dutch
93710
Meat meal CFAT<100
93720
Meat meal CFAT>100
All bone and meat meals are considered as a single animal meal product from cat 3 rendering at the
current stage. Blood powders are considered to be represented in general for blood meal (spray dried),
and no differentiation takes place for haemoglobin or plasma powder in the current database.
[Editor’s note: At a later stage in the project, some of the product definitions were changed. This section
should still be updated and cleaned up to clarify the connection with the new products listed.]
There is a detailed report of the EC (2005), which contains detailed data on mass balances and energy use
for processing animal by-products. For fat melting (production of food grade fat and greaves), the report
does not contain complete data. This data gap was filled in with information from a presentation (Ten
Kate, 2005). No other sources are included here as they are incomplete, out-dated and geographically not
relevant.
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
6.4.2
Note on allocation principles
As described in the allocation methodology section of the background methodology document (Vellinga
et al, 2012), the upstream emissions of animal husbandry and energy inputs at the slaughterhouse are not
allocated to cat 3 slaughter by-products1.
6.4.3
Sourcing
According to our assumptions, the Dutch feed industry sources the feed materials in the countries that are
listed in Table 6.4.1 and Table 6.4.2.
Table 6.4.1 Estimated countries of origin of the feed materials except feather meal.
Phase
Processing in:
Animal husbandry in same
country
the
Netherlands
35%
Germany
France
Belgium
35%
15%
15%
100%
100%
100%
100%
Table 6.4.2 Estimated countries of origin of the feed materials from hydrolysed feather meal production
Phase
Processing in:
Animal husbandry in same
country
6.4.4
the
Netherlands
30%
United
Kingdom
40%
France
Italy
20%
10%
100%
100%
100%
100%
Flowcharts
There are four different processes resulting in feed ingredients from animal by-products (Figure 1):
1. Fat rendering
2. Fat melting
3. Feather rendering
4. Blood rendering
Cat 3 slaughter by-products
Food grade slaughter by-products
Fuel/heat
Electricity
Fat rendering
Animal fat (cat 3)
Animal meal
Fuel/hea
t
Electricity
Fat melting
Animal fat
(food grade)
Greaves
1 It should be noted that this is not the case for the food grade fat melting process. However, for
practical reasons no upstream emissions are allocated to food grade slaughter by-products either.
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
Feathers
Blood
Fuel/heat
Fuel/heat
gas
Electricity
Feather rendering
Electricity
Blood rendering
Feather meal
Plasma powder
Hemoglobin powder
Figure 6.4.1 Flowcharts of the various process resulting in feed ingredients.
6.4.5
Mass balance
All data are from EC (2005). Blood products output are estimates and food grade materials output are
based on EPA (1995).
Table 6.4.3 Mass balances after rendering 1000 kg of various slaughterhouse by-products (VDI, 1996).
Raw material/ Finished products
Quantity
(kg)
1000
150
120
Proteins
(%)
9
60
0
Mineral
(%)
2
13
0
Fat
(%)
14
12
99
Water
(%)
74
5
1
Blood
Haemoglobin powder
Plasma powder
1000
140
40
12
88
88
1
5
5
0
2
2
87
5
5
Feathers
Feather meal
1000
330
28
85
1
2
2
7
69
6
Food grade slaughter by-products
Greaves
Food grade fat
1000
230
150
60
0
13
0
12
99
5
1
Cat 3 slaughter by-products
Animal meal
Animal fat
The sum of protein, mineral matter, fat and water portions need not be 100%, as there are other ingredients in the
substances mentioned, e.g. starch, nucleic acid and raw fibres. The figures serve only as a guide, as they depend on the actual
composition of the raw material.
6.4.6
Inputs
Energy input is based on EC (2005) and Ten Kate (2005), and presented in the Table below.
Table 6.4.4 Default energy inputs per 1000 kg slaughterhouse by-products (all countries)
Process
Dry rendering
Parameter
Min
Max
Unit
Heat
electricity
1400
126
1600
180
MJ/tonne
MJ/tonne
Heat
electricity
1433
241
2000
371
MJ/tonne
MJ/tonne
heat
800
1000
MJ/tonne
fuel
electricity
1600
360
1750
432
MJ/tonne
MJ/tonne
Fat melting
Feather meal
Blood products
In literature, a large range of energy use efficiencies was found. However, we expect that the higher range
of values is based on data from more than five to ten years ago and may include data from factories in
Eastern and Southern Europe. Our recommendation is to use a uniform distribution between the
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
minimum and average values. This most likely approximates the current situation in Northwest Europe
(Netherlands, U.K., France, Germany, Belgium). The choices for default values are shown in the table
below, and these values are further underpinned in the remainder of this section.
The numbers for the default inputs are based on the more detailed table below (see also the
accompanying text).
Inventory data for energy inputs for slaughterhouse by-products processing
Process
Dry rendering Europe
Parameter
Best estimate
Min
Max
Unit
Ref
Fuel
electricity
Fuel
electricity
1584
45.7
1400
35
3263
84.7
1650
50
MJ/tonne
kWh/tonne
MJ/tonne
kWh/tonne
1
1
3
3
Fuel
electricity
Fuel
electricity
1300
60
1433
67
1500
70
2935
103
MJ/tonne
kWh/tonne
MJ/tonne
kWh/tonne
3
3
2
2
800a
1000 a
MJ/tonne
kWh/tonne
1
1
1600
100
60
120
1750
120
80 a
140 a
MJ/tonne
kWh/tonne
kg/tonne
kWh/tonne
3
3
1
1
Fat melting
Feather meal
Fuel
electricity
814
Blood products
Fuel
electricity
Fuel
electricity
1 EC 2005
2 Ten Kate 2005
3 R. van Lijssel, VION Ingredients, pers. comm. November 2011
a Estimate
In case of dry rendering, there is a large range of values with a mean value. However, in the Dutch
situation it is more likely that the actual situation is near to the lowest value rather than the mean. The
energy use for fat melting exemplifies this: in the old situation (before 2002), the energy use was about
double compared to the energy use from 2002 onwards (according to Ten Kate, 2005). We have also
receiving input from an industry expert (van Lijssel, 2011) whose data should be very representative for
Dutch industry.
6.4.7
Allocation
Table 6.4.5 Data for allocation purposes.
By-products
Mass
(kg)
DMC
(g/kg)
Price
(euro/kg)
GE
(MJ/kg)
Fat rendering
Animal meal
Animal fat
150
120
950
990
0.21a
0.54a
14.6
36.6
Blood rendering
Haemoglobin powder
Plasma powder
140
40
950
950
1b
4b
15.7
15.7
Feather rendering
Feather meal
330
940
-
17.0
Fat melting
Greaves
Food grade fat
230
150
950
990
0.21a
0.87a
14.6
36.6
a: Based on pig fat and meal (FAO, average 2005-2009). b: Rough estimates.
Gross energies were calculated from the protein and fat contents at 17 and 37 MJ/kg respectively.
FeedPrint background data report on processing, version 2012, part 6/7: Animal products
6.4.8
References
CVB-table (2012): see appendix 1 in Vellinga et al. (2012)
EC. 2005. Integrated Pollution Prevention and Control Reference Document on Best Available
Techniques in the Slaughterhouses and Animal By-products Industries May 2005.
EPA 1995. Emission Factor Documentation for AP-42 Section 9.5.3 Meat Rendering Plants Final Report
For U. S. Environmental Protection Agency Office of Air Quality Planning and Standards
Emission Factor and Inventory Group EPA Contract No. 68-D2-0159 Work Assignment No. II03 MRI Project No. 4602-03 September 1995
R. van Lijssel, VION Ingredients, pers. comm. November 2011
Ten Kate. 2005. Duurzaamheid door samenwerken 12 oktober 2005. Platform hernieuwbare
grondstoffen. [Presentatie]
VDI (1996). "Emission Control Plants for the Utilization and Disposal of Animal Carcases, either Wholly
or Partially, and for the Processing of Animal Products (Rendering Plants)", VDI 2590.
Vellinga, T.V., Blonk, H., Marinussen, M., van Zeist, W.J., de Boer, I.J.M. (2012) Methodology used in
feedprint: a tool quantifying greenhouse gas emissions of feed production and utilization
Wageningen UR Livestock Research and Blonk Consultants. Lelystad/Gouda, the Netherlands.