Resources, Conservation and Recycling 56 (2011) 92–104
Contents lists available at SciVerse ScienceDirect
Resources, Conservation and Recycling
journal homepage: www.elsevier.com/locate/resconrec
Life cycle assessment of biogas digestate processing technologies
T. Rehl ∗ , J. Müller 1
Universität Hohenheim (440e), Institute of Agricultural Engineering, Garbenstraße 9, 70599 Stuttgart, Germany
a r t i c l e
i n f o
Article history:
Received 3 April 2011
Received in revised form 26 July 2011
Accepted 18 August 2011
Keywords:
Biogas plant
Digestate
Effluent
Greenhouse gas emissions
LCA
Primary energy demand
Acidification
a b s t r a c t
Driven by a high increase of large scale biogas plants based on bio waste, agricultural by-products and
waste from food industry, there is a rapid structural development of the agricultural holdings in Germany.
Particularly in regions with intensive livestock husbandry, this leads to an overprovision of nutrients. New
technologies have been introduced during the last years to treat biogas digestate for optimal transport and
application conditions. An environmental Life Cycle Assessment (LCA) was carried out in order to compare
the environmental impacts and the energy efficiency of seven treatment options of biogas digestate. The
treatment options include one conventional digestate management option (storage and application of
untreated manure on agricultural land), one stabilization process (composting), three mechanical drying
options (belt dryer, drum dryer and solar dryer), one option using thermal vaporization (concentration)
and finally one physical–chemical treatment (combination of separation, ultra-filtration, reverse osmosis
and ionic exchanger). Primary energy demand (PED), global warming potential (GWP) and acidification
potential (AP) were analysed and presented per kg of digestate on the input side of the system as functional
unit (fu). Based on the default parameter setting, four scenarios have been defined to analyse the influence
of different feedstock, different kinds of energy supply, different emission reductions techniques and
different logistic chains on the LCA results. In the overall comparison, solar drying, composting and
physical–chemical treatment were identified to be the most suitable options to reduce the use of resources
and environmental impacts compared to the conventional digestate management. Belt drying turned out
to be the handling process with the highest PED demand, GWP and AP among the compared options. Total
PED varies from −0.09 MJ/fu (i.e. savings) in the composting option up to 1.3 MJ/fu in the belt drying
option. The GWP was in a range between 0.06 CO2 eq./fu for solar drying to 0.1 kg CO2 eq./fu for belt
drying. The amount of AP ranged from 2.7 kg SO2 g eq./fu in composting to 7.1 g SO2 eq./fu in belt drying.
The results indicate that the environmental impact depends largely on nitrogen related emissions from
digestate treatment, storage and field application. Another important aspect is the amount and kind of
fuel used for heat supply (biogas, natural gas) and the procedure chosen for the allocation among heat
and power.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
Biogas production by anaerobic fermentation is a promising
method of producing an energy carrier from renewable resources
while achieving multiple environmental benefits. The economical
promotion of renewable energy sources by the Federal Government
of Germany has led to favourable economic conditions in Germany
for the installation of biogas plants. Nevertheless there is still large
potential for the use of biogas technology. The current technical
∗ Corresponding author. Present address: PE – INTERNATIONAL, Hauptstraße 113115, 70771 Leinfelden-Echterdingen, Germany. Tel.: +49 0711 45923112/34181772;
fax: +49 0711 45923298/34181724.
E-mail addresses: torsten.rehl@uni-hohenheim.de, t.rehl@pe-international.com
(T. Rehl), joachim.mueller@uni-hohenheim.de (J. Müller).
1
Tel.: +49 0 711 45922490; fax: +49 0 711 45923298.
0921-3449/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.resconrec.2011.08.007
available potential of energy generation by biogas for Germany was
estimated by Ramesohl to be 72.2 TWh per year (Ramesohl, 2006).
The estimation was made under consideration of the area demand
for other energy (e.g. bio-ethanol, bio-diesel), food or chemical
applications. It was projected that the energy potential from biogas
could be increased under an optimal yield, cost and area development to 105 TWh per year. At this level the potential of electricity
generation from biogas would be predominantly to more than 50%
provided by energy crops (Ramesohl, 2006). Taking the gross electricity consumption in 2007 into consideration (617.9 TWh) (BMU,
2009) the current contribution of electricity generation from biogas
could be increased from 1% (BMU, 2009) in 2007 to 6.5% in 2030.
However, the increasing number of biogas plants especially of
those larger than 500 kW electrical power results in larger transportation distances both on the input side (biomass feedstock) and
on the output side (digestate). Furthermore, an accumulation of
biogas plants in certain regions might lead to an oversupply of
T. Rehl, J. Müller / Resources, Conservation and Recycling 56 (2011) 92–104
digestate especially in regions with intensive livestock farming or
fermentation of organic residues and bio waste. If the agricultural
area is too small for adequate use of the digestate, surplus material
has to be transported to regions with nutrient deficits. The transportation of digestate, however, causes logistical problems since
the transported material consists of 95% water on average (KTBL,
2005). Various treatment options for reducing the amount of water
or separating the nutrients are discussed in literature and some are
already in use (Bressler, 1994; Forbes et al., 2005; Masse et al., 2007;
Mihoubi, 2004; Rehl et al., 2007; Thörneby et al., 1999) but environmental impacts are still unknown. Often, a new technology can
have a positive influence in one aspect but a negative influence in
another aspect and it may be difficult to find optimal compromises.
The primary objective of this study was to support policy-making
in the agricultural sector through a comparison of the environmental consequences of digestate processing from biogas plants.
Therefore, primary energy demand, material in- and output as well
as global warming potential of seven digestate treatment options
were evaluated.
2. Methodology
2.1. Scope
The purpose of the technologies compared in this study is to
dispose or to make use of the digestate from biogas plants. To adequately compare the seven technologies, a functional equivalent
has to be found. Therefore, impacts were scaled on the functional
unit (FU), which is defined as 1 kg of digestate from biogas plants.
The digestate treatment options were tracked starting at the digestate origin at the biogas plant via the treatment process to the
application as fertilizer in the field. The system boundaries were
chosen in a way to include all processes necessary for the operation
of the system. The following life-cycle stages are taken into account:
extraction and processing of raw materials, digestate production
and use of the product.
The life cycle assessment (LCA) of the options for digestate treatment comprises the life path sectors from production of raw and
auxiliary materials, the transport between the treatment steps as
well as transportation and application to the field. With the discharging of the storage tank of the biogas plant, the digestate is
entering the life cycle system at the input side. The application of
the fertilizer on the field constitutes the system limit on the output side. Biological degeneration and soil activation by digestate
application is not taken into consideration. Fuel and energy input
for the operation of farm machinery and the production of fertilizers is included in the system. The production of machinery and
buildings lies outside the system boundaries as they have only little influence on the biogas system (Borjesson and Berglund, 2007;
Börjesson and Berglund, 2006b; Edelmann et al., 2001; Hartmann,
2006; Scholwin et al., 2006; Wilfert et al., 2004). Tractors, trucks,
infrastructure (e.g. streets, electricity grid) and human labour were
excluded as they are beyond the scope of this study.
2.2. Data recovery, classification and assessment
Data for performing the LCA were collected from three biogas
plants at private farms in Baden-Württemberg (Germany) based
on data of existing technologies and bottom-up simulations of
new technologies. On-site data was complemented by literature
values and by personal information obtained from manufacturers of treatment technologies. Quantities of raw material, auxiliary
and operating material and energy used in the biogas production
process were assessed as annual mean values over the last three
years. Average values for crop cultivation and biogas combustion
93
in a cogeneration plant were determined with GEMIS (GesamtEmissions-Modell-Integrierter-Systeme), a program for modelling
energy production systems (Fritsche and Rausch, 2008). Extensive, non-linear, biogas and digestate models have been developed
using the GaBi 4 software tool (Eyerer, 2006) and the integrated
database for electricity generation, transportation, fuel production
and transport emissions.
Collected data was classified and characterised in the impact
assessment using of the CML method (Centrum voor Milieukunde
Leiden) (Dreyer et al., 2003; Goedkoop and Spriensma, 2001;
Guinée et al., 2002; Heijungs et al., 1992). Energy use, which represents the consumption of non-renewable energy according to
Frischknecht et al. (1996) is expressed as primary energy demand
(PED). This includes direct forms of energy such as diesel and
electricity as well as indirect forms such as energy credits from
substituted fertilizer production. Renewable energy stored in the
digestate itself is excluded from the analysis. Beside energy use the
global warming potential (GWP) for 100 years (Houghton et al.,
2001) measured in CO2 equivalents and the acidification potential
(Hauschild and Wenzel, 1998) measured in SO2 were investigated.
In a first step, the contribution of the most relevant resources and
emission is presented. In a second step, several unit processes are
added up to form six life cycle stages. In a first step, transportation effort is differentiated into transport done on the farm itself
(pumps, conveyor belts, wheel loader and tractor) and regional
transportation, which includes transport from regions with nutrient surplus to regions with nutrient deficits. “Treatment” refers to
the option of specific digestate handling to produce different outputs. The life cycle stage “storage” implies the duration of storage
and the engines to mix liquids or move final products. The application stage considers the tractor operations with the specific field
application technology. Both “storage” and “application” involve
the emission profile of specific technologies; this implies the chemical decomposition of nitrogen compounds into NH3 , N2 O, NO–,
NO3 − and NH4 + and organic material into CH4 . Finally, the life
cycle stage “credit” was created representing the leftover value of
the digestate. A credit for substituted mineral fertilizer production
(ammonia nitrate, triple super phosphate and potassium chloride)
was given for the amount of nutrients N, P2 O5 and K2 O of the
different digestate based fertilizers applied to field. The life cycle
inventory data for the substituted mineral N, P and K fertilizers as
well as diesel, electricity and thermal energy have been taken from
the database of the GaBi 4 software.
2.3. Inventory definition
2.3.1. Treatment options
For this study, seven digestate treatment options were analysed.
An overview of the technical system boundary, the process and
supply chain from the storage of biogas digestate to the application
of fertilizer on the field is presented in Table 1 and in Fig. 1.
In conventional digestate management (CM) the digestate is
stored for several months in a digestate storage tank and subsequently transported out of the region with a pump tank wagon and
applied on the field.
Composting (CO) can be divided into three stages, mechanical
pre-treatment, composting and manufacturing of the soil product. The mechanical pre-treatment comprises flocculation and a
separation of the digestate by use of centrifugal force into a solid
and a liquid phase. Solid–liquid separation is commonly applied
as a physical treatment process for animal waste, mainly for the
improvement of manure handling properties by removing coarse
solids and fibre from the slurry (Jacobson et al., 1999). To enhance
separation rates flocculation substrates are added to the digestate
(Luckert, 2004; Witte and Keding, 1992). The capacity range of
the decanter was 4.5 m3 /h with an electrical power of 11 kW. The
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T. Rehl, J. Müller / Resources, Conservation and Recycling 56 (2011) 92–104
Table 1
Set up and system boundary for the seven options of biogas digestate treatment and supply.
Abbreviation
Option name
Characteristics
Data source
CM
CO
Conventional digestate
management
Composting
(Clemens et al., 2001; KTBL, 2005; Sandars
et al., 2003; Voća et al., 2005)
(Tiquia et al., 2002; Amlinger et al., 2008;
Edelmann et al., 2001; Finnveden et al., 2000;
Fricke et al., 2005; IPCC, 2003; Käck, 1996;
Wallmann et al., 2001; Wilfert et al., 2004)
BD
Belt dryer
DD
Drum dryer
SD
Solar drying
TC
Thermal concentration
PT
Physical chemical
treatment
Storage in open storage and field application
with splash plate.
Composting of separated solid phase in
windrows – open storage and field application
with compost spreader. Untreated storage and
field application of liquid phase with splash
plate.
Drying by mixing of dry and fresh digestate up
to a water content of 20% – afterwards final
drying, pelletizing and packaging in plastic
bags.
Drying of separated solid phase – pelletizing,
packaging in plastic bags – field application
with adopted lime spreader. Liquid phase
stored in open tanks and field application with
splash plate.
Drying of separated digestate up to a water
content of 65%. Untreated storage in open
tanks and field application of liquid phase with
splash plate.
Solid phase open storage and application with
compost spreader. Evaporation of separated
liquid phase to separate nutrients. Storage in
tanks, application with mineral fertilizer
spreader. Condensate dumped into a recipient.
Treatment of liquid phase using
micro-filtration, reverse osmosis and ion
exchanger. Solid phase untreated – open
storage and field application with compost
spreader.
dry matter content of the solid phase varied between 20 and 30%,
depending on the dry matter content of the input substrate. The
liquid phase can be used for different applications; in the default
parameter setting the digestate is used for fertilizing purposes
outside the region. The digestate is therefore transported over a
distance of 50 km. The digestate is composted onsite at the biogas farm. In the first stage the solid phase of digestate separation
is dumped in heaps with dry matter content of 25% on average to
undergo composting for 10 weeks. Composting is done in closed
windrows with occasional mixing for aeration. A diesel operated
machine with a capacity of 600 m3 /h and a diesel consumption of
10 l/h is used for mixing. Drainage water is collected by a drainage
system and recycled to moisten the compost matter. After composting the heaps are moved with a wheel loader and transported
to open storage boxes. It was assumed that digestate is losing 50% of
carbon and 80% of initial water content during the composting process. In the final stage, compost leaves the heaps with a dry matter
of 57% on average. Emission calculations for NH3 have been done
based on information from Clemens et al. (Clemens and Wulf, 2005;
Hersener et al., 2002; Käck, 1996), N2 O and CH4 emissions base on
information from Amlinger et al. (Amlinger et al., 2008; Edelmann
and Schleiss, 2001; Hao et al., 2004). It was assumed that on average
20% of the NH4 + is emitted to air as NH3 , 1.4% of the total nitrogen
as N2 O and 8.12% of total carbon will be released to air as CH4 .
In the belt dryer (BD) option, fresh digestate is mixed with
already dried material to achieve a water content of around 20%.
This process, also called homogenization, is needed to charge the
belts of the dryer. In the dryer, air with a maximum temperature of 85 ◦ C is blown over the substrate. The digestate leaves the
drying processes with an average dry matter content of 85%. Subsequently it is stored in intermediate storage, before it is conveyed
to the pelletizing plant. Starch and lime flour are added to achieve
optimal pellet consistency. Finally the pellets are packed in 50 kg
polyethylene bags and are used for fertilizing purposes in agriculture, landscaping and horticulture.
(Chuvaree et al., 2006; Guinée et al., 2001;
Haaring, 2009; Mihoubi, 2004; Vetter and
Burger, 2006)
(Bongiovanni et al., 2000; Haaring, 2009;
Mihoubi, 2004; Vetter and Burger, 2006)
(Bongiovanni et al., 2000; Bux et al., 2002; Bux,
2007; Ekechukwu and Norton, 1999; Salihoglu
et al., 2007; Vetter and Burger, 2006)
(Clemens and Wulf, 2005; Hersener et al.,
2002; KTBL, 2008; Schulz et al., 2007; Vetter
and Burger, 2006)
(Anon., 2010; Masse et al., 2007; Schulze and
Block, 2005; Thörneby et al., 1999)
In the mechanical drum dryer (DD) option the digestate is
mechanically pre-treated like in the CO option before it is continuously applied as a thin film on the rotating drum. As the drum
turns and is heated from inside, the product dries on the surface of
the drum. The drum dryer requires 3.00 MJ of heat and 0.31 MJ of
electricity per kg of removed water (Vetter and Burger, 2006). In
the default parameter setting the heat is provided by natural gas
combustion in a thermal power station. After drying the substrate
is handled like in option BD.
The solar dryer (SD) option also makes use of the mechanical
pre-treatment before the water is evaporated in a solar-powered
greenhouse-dryer that is covered with transparent polycarbonate
sheets. This greenhouse-dryer is loaded by a pump and discharged
by a wheel loader. During drying the digestate is mixed by an automatic mixing tool and aerated by fans. The solar drying hall reaches
an average evaporation rate of between 0.6 and 3.5 t water per m2
drying area and year depending on waste heat use from the biogas
plant and mechanical dewatering before drying (Bux et al., 2002;
Bux and Starcevic, 2005). According to Vetter and Burger (2006)
200 kWh of electricity were needed per ton of removed water to
mix and aerate the digestate down to a water content of 35%. The
drying process was performed by using solar energy only.
Before vaporization starts in the TC option, a decanter is used to
separate solid and liquid fractions of the digestate. The solid phase
is stored onsite at the farm and applied to the field without additional treatment, while the liquid phase is vaporized to separate
substantial ingredients and water. Liquid hot steam with a pressure of 10 bar is used to heat up rotary discs and to vaporize the
water of the liquid phase. While the liquid phase starts, boiling sulphuric acid is added to expulse CO2 , to increase the pH value to
4.4–4.8 and to convert ammonia into ammonium. As a result of the
pH value rise, gaseous losses of nitrogen are completely avoided
(Clemens and Wulf, 2005; Hersener et al., 2002). However, as an
effect of the CO2 emissions during the concentration process foam
was created. This was reduced by the use of 0.083 l of antifoaming
T. Rehl, J. Müller / Resources, Conservation and Recycling 56 (2011) 92–104
95
Fig. 1. System boundary, mass flow and final products for the treatment options conventional digestate management (CM), composting (CO), belt drying (BD), drum drying
(DD), solar drying (SD), thermal concentration (TC) and physical–chemical treatment (PT).
agent per cubic meter of digestate (Schulz et al., 2007). After the
concentration process two substrates are available: a concentrate
with high nutrient content and condensed steam. Depending on
the nutrient content the condensate can be used for diluting of fermentation substrates, can be further treated in a waste water plant
or must be transported to a region with nutrient deficit (assumed
in the default parameter setting).
In the physical–chemical treatment option (PT) the solid part
of the digestate is separated and transported out of the region.
The liquid phase is treated subsequently by ultra-filtration by use
of membranes and pressure between 2 and 10 bar (Masse et al.,
2007; Schulze and Block, 2005; Thörneby et al., 1999), separating
the substrate into a permeate, which is water with low-molecular
substances and a retentate with the remaining concentrated highmolecular substances. The permeate is further treated by reverse
osmosis. A semi-permeable membrane retains the dissolved substances in the solvent by using static pressure of 40–100 bar
(Schulze and Block, 2005; Thörneby et al., 1999). The ion exchanger
allows displacing ions by using resins as ionic exchanger. Retentates from ultra-filtration and reverse osmosis are mixed and stored
together before transportation to the application area. The permeate can be discharged to a recipient (assumed in this study), used
as irrigation water in agricultural applications or as cleaning water
without any post-treatment (Thörneby et al., 1999).
The biogas model was set up in the Life Cycle software GaBi as a
generic model. The model is able to reproduce material and energy
flows for the different supply chains (e.g. manure, energy crops)
but also for the fermentation system separately. The fermentation
process was computed based on fugitive emission factors developed by Baserga (1998) and biogas yield experiments performed
by Amon et al. (2007). Self-energy demand of single processes,
e.g. pumps, stirring devices, conveyor belts etc. was calculated
based on the electrical power, throughput and operation time of
each device. Self-energy demand in terms of heat was calculated
based on the amount of energy necessary for heating the feedstock to the fermentation temperature of 38 ◦ C and the heat loss
of the fermentation tank using the specific heat transition coefficient of the materials. The emissions and energy input were
determined for the operation of a 186 kWel biogas plant using
two-stage digestion technology at mesophilic temperatures. Furthermore, 18% of the heat is used for heating the digestate in the
fermenter and during the year in average 10% surplus heat, occurring especially during summer period, is dissipated to air as waste
heat. The emissions originating from the exploration, refining and
combustion of fuels for the reference system were estimated using
factors used in the GaBi software (Eyerer, 2006). Methane emissions from the CHP, the fermenter as well as from leakages were
estimated based on several studies (Börjesson and Berglund, 2006a;
Dalemo, 1998; Nilsson and Dahl, 2001), to be 2% of total methane
production.
All transportations in the entire life cycle of the products, from
on-site small scale transports of the digestate at the treatment plant
and transports of ingredients to the treatment stations as well as
transport of the final product to the application area were included
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T. Rehl, J. Müller / Resources, Conservation and Recycling 56 (2011) 92–104
in the analysis. The initial parameter variation of all options is carried out with a truck of 38 t total capacity at a workload of 74%
and a distance of 50 km for a one-way route. The average field size
was set to 10 ha and a defined amount of 60 kg of nitrogen is to be
provided by each treatment option per hectare. Liquid substrates
with a water content below 15% are applied on the field by a tractor
(102 kW) equipped with a liquid manure trailer and an electric submersible pump (25 kW). Substrates with water content between
20% and 69% are spread with a compost spreader (67 kW), which
is filled by a wheel loader (68 kW). For solid substrates with water
content higher than 70%, an adopted lime trailer spreading machine
is used (83 kW) which is filled by a wheel loader (68 kW) equipped
with a mineral fertilizer scoop. The diesel consumption of application technologies is strongly dependent on the amount of product
applied to the field. Hence linear regression functions were computed based on the assumptions described above and data provided
by the KTBL (KTBL, 2008).
2.3.2. Emissions
The global warming potential of agricultural processes is mainly
affected by nitrous oxide and methane emissions from degradation of nitrogen and carbon. Beside those, emissions of nitrous
oxide (NO–), ammonia (NH3 ) and nitrate (NO3 − ) contribute to
acidification and global warming potential by influencing the
amount of available nitrogen for degradation processes in subsequent life cycle steps and furthermore by affecting the amount of
crop available nitrogen. Subsequently, assumptions made regarding emissions from treatment, storage and field application of
digestates are described.
2.3.2.1. Treatment. No emission factors were available for N2 O
emissions from digestate drying or evaporation. Therefore factors
were estimated by extrapolating values for digestate storage for
treatment conditions in terms of heat and air circulation. During
storage, N2 O emissions originate mostly from the surface of the
solid particles of liquid manure by nitrification of ammonia and
denitrification of nitrate (Amon et al., 2006). For NH3 , emission
factors have been determined by expert interviews (Bux, 2007;
Haaring, 2009) unpublished measurements and calculations. Since
NH3 emissions to air are correlated to the amount of NH4 + in liquid
phase, emissions are low in digestates with high dry matter content.
In options with high operation temperature or extensive digestate
surfaces a high amount of NH4 + is emitted as NH3 . It was assumed
that 85% of the available NH4 + of the treated substrate was emitted
to air as NH3 in all drying options.
2.3.2.2. Storage. Storage tank cover, dry matter content, surface
area, existence and thickness of crust cover and nitrogen content
of the substrate are major factors of NH3 and N2 O creation during storage of farm fertilizer (Amon et al., 2002, 2006). To make
the options comparable, simplifications were made to calculate the
emission factors applied to NH4 + content for NH3 emission and total
N content for N2 O emissions:
1. Neither storage tank cover nor floating crust cover is used in the
default parameter setting.
2. The dry matter content is the dominating factor influencing the
emission factors of NH3 and N2 O.
This approach is in line with Sommer et al. who investigated the
influence of manure separation on the amount of ammonia losses.
The authors found that NH3 emissions from the separated liquid
phase are significantly lower (Amon et al., 2006; Döhler, 1990;
Sommer and Olesen, 1991) while emissions from the solid phase
arise. N2 O emissions on the other hand are lower with increasing
dry matter content. Clemens found that no N2 O emissions arise
from farm fertilizer with a dry matter content of more than 20%
(Clemens et al., 2001). Based on results of various research groups
(Amon et al., 2002, 2006; Clemens et al., 2001, 2006; Clemens and
Wulf, 2005; Hüther et al., 1997; Hüther and Schuchardt, 1998;
Hüther, 1999; IPCC, 2006; Kryvoruchko, 2004; Külling et al., 2001;
Sommer, 1997; Sommer et al., 2000; Sommer and Hutchings, 2001)
regression functions were developed to calculate the NH3 and N2 O
emission factors. For calculation of NO– and N2 emissions a constant ratio was developed by Jarvis and Pain (1994), stating NO–
as one tenth and that of N2 emissions as a threefold of that of N2 O
emissions.
Methane emissions during storage were calculated using an
equation of IPCC (2000) for solid and liquid dung with the following
equation:
ECH4 = kMCF VS B0 Dm
(1)
where ECH4 is the methane emissions (kgCH4 ); kMCF is the methane
conversion factor (kgCH4 kgODM −1 ); VS is the amount of organic
material kgODM (organic dry matter); B0 is the maximum methane
producing capacity (mCH4 3 kgODM −1 ); Dm is the conversion factor
of m3 to kg CH4 (0.67) (kg m−3 ).
The mean methane conversion factor kMCF is a manure management system specific characterisation factor depending on dry
matter content of the substrate to be stored, the storage type and
the temperature. It reflects the portion of Bo that is finally achieved.
For fermented liquid dung it has been assumed to be 0.17 for
temperate zones without natural crust cover and 0.02 for solid
material with a dry matter content of higher than 20%. The methane
creation potential was assumed to be 0.2 mCH4 3 kgODMdm −1 as recommended by IPCC (2000).
2.3.2.3. Field application. Emission factors for NH3 , CH4 and N2 O for
field application of digestates have been calculated with a methodology provided by Wulf et al. (2002), who developed regression
functions based on measured data and field experiments as well as
literature data for manure based fertilizer. Emission factors were
related to the total amount of organic dry matter (for CH4 emission) and organic dry matter and NH4 + (for NH3 and N2 O emission)
applied per hectare. In the present study the default parameter
setting for field application of liquid fractions was assumed to be
performed by splash plate, as it is the most common technology in
Germany. Solid phases were applied by compost spreader, special
manure spreader or pellet spreading machines.
Wulf et al. (2006) considered only direct N2 O emissions and
no indirect emissions due to volatilisation, leaching and run-off
of NH3 , N2 , NO– and NO3 − . The amount of N2 O emission due to
volatilisation and leaching was calculated based on a default factor of 0.0075 kg N2 O per kg N, as recommended by IPCC (2006).
Finally the NO– emission factor has been taken from Stehfest and
Bouwman (2006) (1.2% of total nitrogen) and the N2 emission factor from a literature summary made by Dämmgen (2010) (8% of
total nitrogen). Beside nitrogen emissions to air, nitrogen is lost by
percolating water below the root zone (leaching). Leaching occurs
whenever the amount of precipitation or snow melt exceeds evapotranspiration and the soil is near saturation capacity. Soil water
moving downwards recharges groundwater or contributes to tile
drain flow carrying nitrate along. The amount of replaced soil water
in the effective root zone by infiltration causes leaching of NO3 −
(Brentrup et al., 2000). The leaching rate was calculated based on
a formula developed by Renger (Renger, 1992) for an average location in Baden-Württemberg. The crop usable water capacity was
assumed to be 260 mm for medium loamy sand. The total amount of
nitrate, which was leached to groundwater was calculated by subtracting N losses of NH3 –, N2 O, N2 , NO– and the nitrogen fixation
according to Büchter and Baichinger (Baichinger and Zander, 2004;
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97
Table 2
Structure of scenario analysis, performed on seven options for four sensitivity scenarios (S1–S4) with variation of feedstock F, energy supply E, emission reduction R and
logistic L.
Scenario 1 (S1): parameter variations feedstock (F)
F1: 50% maize
F2: 70% maize
F3: 30% maize
Scenario 2 (S2):Parameter variations energy supply (E)
E1: heat (h) from biogas CHP (allocation power motivated), power (p)
from German grid
E2: heat (h) + power (p) from biogas CHP (allocation by energy volume)
E3: heat (h) + power (p) from biogas CHP (allocation by market price)
E4: heat (h) from natural gas combustion, power (p) from German grid
Scenario 3 (S3): parameter variations emission reduction (R)
R1: no emission reduction
R2: treatment with filter use (bf = bio-filter/cf = chemical filter)
R3: storage cover of liquid (l)
R4: injection of liquid (l)
R5: incorporation of liquid (l), solid (s) substrates
Scenario 4 (S4): parameter variations logistic (L)
L1: truck 50 km (all substrates)
L2: truck 100 km (all substrates)
L3: tractor 25 km (all substrates)
L4: truck 50 km (only solid phase – liquid phase on farm use)
L5: increase of loading efficiency of trucks from 74% to 96%
Büchter et al., 2001) for maize, from the total N applied to field.
The amount of nutrients from digestate based organic fertilizer
available for maize crop replaces equivalent amounts of mineral
fertilizer and thereby environmental impacts. To show the value of
the digestates in comparison to the effort for treatment and storage,
energy demand and emissions of chemical fertilizer production (N2
– ammonia nitrate, P2 O5 – triple super phosphate, K2 O – potassium
chloride) were considered as credits and subtracted from the total
amount of primary energy demand and emissions.
2.4. Scenario definition
The model allows sensitivity analyses by parameter variations
to examine the effect of changes in system parameters on output results. Four scenarios were defined for the sensitivity analysis
with variation of the characteristic parameters of each. The following aspects have been chosen for sensitivity testing: (scenario 1)
influence of different feedstock F for biogas production, (scenario
2) influence of different energy supply E for digestate processing,
(scenario 3) influence of different technologies for emission reduction R and (scenario 4) influence of different logistic variables L. The
structure of the analysis is shown in Table 2.
2.4.1. Scenario 1: influence of feedstock
As biogas digestate is an extremely inhomogeneous substrate;
digestates from three biogas plants in Baden-Württemberg were
investigated for this study: default parameter F1 from a cofermentation plant with a mixture of 35% liquid manure, 50% maize
silage and 15% residues from food industry; F2 from a ‘NawaRo’
biogas plant (using bio-based feedstock beside manure) with a mixture of 30% liquid manure and 70% maize silage and F3 from a
co-fermentation plant with a mixture of 30% liquid manure, 20%
maize silage and 50% residues from food industry. The composition
of the three types of digestate is listed in Table 3.
2.4.2. Scenario 2: influence of energy supply
The default parameter setting E1 represents the status quo of
energy supply. The electricity mix is provided by the German electricity grid and heat is provided by the combined heat and power
plant (CHP) of the biogas plant. However, as listed in Table 4,
heat is provided for free to the digestate treatment technology. In
CM
CO
BD
DD
SD
TC
PT
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
p
p
p/h
p/h
p/h
p/h
p
p
p
p
p
p
p
p/h
p/h
p/h
p/h
p/h
p/h
p/h
p/h
p/h
p/h
p/h
p/h
p
p
p
X
X
bf
l
l
l/s
X
cf
X
cf
l
l
l/s
X
cf
l
l
l/s
X
cf
l
l
l/s
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
l
l
l
X
X
X
X
s
X
l
l
l/s
parameter variations E2 and E3, electricity and heat were assumed
to be produced by a biogas plant but environmental burdens of
energy production from biogas technology were shared according to the amount of electricity and heat in kWh produced by the
CHP (E2) and according to the economic value of both products
(E3). Data for heat were calculated based on prices for natural gas
(8.27 D cent kWh−1 ) and data for electricity were calculated based
on the average domestic consumer price (24.98 D cent kWh−1 ). The
allocation factors used are shown in Table 4. The biogas system was
modelled based on on-site data measurements from three different biogas plants in Baden-Württemberg with a capacity of 170,
350 and 500 kW using different biogas substrates as indicated in
Table 3. The methane losses were estimated based on literature
(Börjesson and Berglund, 2006b; Dalemo, 1999; Nilsson et al., 2001)
as 2% of total methane production. In E4 the electricity is taken from
the German grid while the heat is produced by a natural gas based
thermal power station.
2.4.3. Scenario 3: influence of emission reduction technology
For investigating the effect of emission reduction technologies, in parameter variation R2, the influence of a stricter control
of emissions during the treatment phase was analysed in contrast to default setting R1. In options BD, DD and SD a chemical
air waste cleaner and in CO a biological air cleaner were used
to reduce NH3 emissions. Based on data from Clemens (Clemens
and Wulf, 2005; Trimborn, 2006) it was assumed that 83% of NH3
emissions were removed by a chemical air cleaner in options BD,
DD and SD and 60% by the biological air cleaner by converting
into 16% N2 O, 11.5% NO–, and the remainder into N2 (Trimborn,
2006) in the composting option (CO). For parameter variation R3,
the digestate storage reservoir has been assumed to be covered.
According to Amon et al. (Amon et al., 2002, 2006; Dämmgen, 2010;
Horlacher and Marschner, 1990; Wulf et al., 2006) between 55% and
100% of the NH4 + in the digestate can be retained by the digestate storage cover during storage. Thus a reduction potential of
90% for an airtight cover was assumed for emissions of CH4 , N2 O
and NH3 .
In parameter variations R4 and R5 the use of different application technologies have been tested based on data from Wulf et al.
(2002). The application methods were applied for substrates with
a dry matter content of less than 12%, Table 5.
98
T. Rehl, J. Müller / Resources, Conservation and Recycling 56 (2011) 92–104
Table 3
Dry matter, macro-nutrient content of feedstock F1 (default), F2, and F3 in % and g/kg wet basis of the digestate, respectively.
Input substrates of
fermentation process
Dry matter, %
Organic dry matter, %
Mineral dry matter
C, g/kg
N2 , g/kg
NH4 + , g/kg
P2 O5 , g/kg
K2 O, g/kg
F1
F2
F3
35% liquid manure 50%
maize silage 15% crop
residues
64.00
54.40
9.60
21.76
5.05
3.31
1.12
3.60
30% liquid manure 70%
maize silage
30% liquid manure 20%
maize silage 50% bio waste
41.80
35.53
6.27
12.54
2.63
1.27
0.43
2.66
89.00
75.65
13.35
33.82
7.57
4.43
2.82
4.71
Table 4
Energy source and distribution (%) of primary energy demand and environmental burdens between heat and power with different ways of product allocation.
E1
E2
E3
E4
Allocation procedure
Heat
Power
Economic
Energy
Economic
Avoided by external energy provision
Biogas CHP – 0% burden
Biogas CHP – 51% burden
Biogas CHP – 31% burden
Natural gas based boiler
German grid mix
Biogas CHP – 49% burden
Biogas CHP – 69% burden
German grid mix
2.4.4. Scenario 4: influence of logistics
In scenario 4, transport distances and logistic chains were varied. In parameter variation L2 the distance was extended from a
default of 50 km to 100 km with an optimized truck logistic chain
at 74% payload. In L3 the distance was decreased to 25 km, assuming nutrient application within the region of biogas production. The
transport was carried out with a tractor-based logistic chain with
8 t transport capacity. In L4, the separated liquid phase in DD, SD,
TC and PT was assumed to be used on-farm at the biogas production site. In L5 loading efficiencies of transport trucks have been
increased from 74% to 96%.
3. Results and discussion
The overall energy and environmental performances of the processes are assessed for the default parameter setting F1, E1, R1 and
L1. The results of the analysis per functional unit (1 kg of digestate
managed) for PED, GWP and AP are depicted in Figs. 2–4.
3.1. Primary energy demand
In Fig. 2 it can be seen that the primary energy demand is by far
the largest for the option with belt drying (BD), also higher than for
conventional management (CM). With the exception of belt drying (BD), most of the energy consumed is from fuel oil used in the
production and supply of electricity for treatment processes and
diesel for regional and local transportation purposes. In belt drying (BD) and thermal concentration (TC), uranium, hard coal and
lignite are additional sources of primary energy, deriving from the
use of uranium in nuclear power stations and coal in power plants
to produce electricity as part of the German electricity mix. Credit
for primary energy demand was generated mainly from natural gas
which is used for heat provision in mineral fertilizer production. In
solar drying (SD) most of the energy used for drying is renewable
Table 5
Emission factors used in application parameter settings R4 and R5 based on default
parameter setting R1.
R1
R4
R5
Technology
NH3
N2 O
CH4
Splash plate
Incorporation
Injection
1.00
0.52
0.22
1.00
1.35
1.42
1.00
1.49
2.70
and thereby not presented in the graphs. Lowest primary energy
demand was found in the CM, CO and SD options. Without considering the positive effects of mineral fertilizer substitution the
primary energy demand is about 0.23 MJ per FU for those options.
A net saving may be generated if credits were taken into consideration. The height of credits is mainly affected by nutrient losses
during treatment, storage and application and is lowest in belt drying (BD) due to high ammonia losses during digestate drying. The
life cycle step “treatment” played a predominant role in the drying options BD and DD (99% and 50%), the thermal concentration
option TC (80%) and the physical–chemical treatment option PT
(60%). The reason for the noticeable difference between the drying options DD, SD and BD was found to be the separation process
used in drum drying (DD) and solar drying (SD). The separation
process reduces the amount of digestate substrate which has to be
treated within the drying process by about 84%. However, the mass
reduction of drum drying (DD) and solar drying (SD) options with
10% compared to BD with 95% is much lower and resulting in a
higher PED for transportation. Nevertheless, the positive effects of
mass reduction in belt drying (BD) do not compensate the high PED
within digestate treatment. In general, transportation was identified to be a dominant contributor to PED responsible for 85% of total
energy demand in CM, 80% in CO, 7% in BD, 40% in DD, 75% in SD,
8% in TC and 20% in PT. These results show firstly that the impact of
transportation on conventional digestate management is predominant and secondly that treatment options are able to reduce this
effort significantly.
3.2. Global warming potential
Global warming potential (GWP) shows a strong similarity to
primary energy demand. Belt drying (BD) has the highest GWP
with 0.1 kg CO2 eq./fu, while physical–chemical treatment (PT),
composting (CO) and solar drying (SD) showed best environmental
performance in terms of GWP, emitting only 50% of the CO2 eq./fu
of the belt drying option (BD). With the exception of BD and TC all
options show a better environmental performance compared to
the reference option (CM) with a reduction potential between 47%
(DD) and 84% (PT). The environmental profiles in treatment options
BD, DD and TC are predominantly affected by CO2 emissions from
the combustion of lignite and black coal in power plants to produce
electricity. Beside CO2 emissions, CH4 emissions are the second
most relevant greenhouse gas, arising mainly from digestate
T. Rehl, J. Müller / Resources, Conservation and Recycling 56 (2011) 92–104
99
Fig. 2. Primary energy demand (PED) in MJ per functional unit (fu) as covered by different energy sources (a) and as resulting from different life cycle stages (b) for default
parameter setting in different treatment options (see Table 1).
Fig. 3. Global warming potential (GWP) in CO2 equivalents per functional unit (fu) as resulting from different main emissions (a) and from different life cycle stages (b) for
default parameter setting in different treatment options (see Table 1).
storage and field application. Impact of storage and application is
highest in the GWP profiles of the CO, CM and SD options and is
lower in the mechanical drying and evaporation treatment options.
The predominant influence in CO is due to the high amount of CH4
emission from anaerobe degradation during the composting process. In spite of this, composting is one of the best options in terms
of GWP. Compared to conventional digestate management (CM)
all treatment options have a lower impact of CH4 emission because
of the avoidance of anaerobe conditions during storage and after
field application in the soil due to the separation of solid and liquid
fractions. N2 O emissions from storage and application have a predominant impact of 30% in conventional digestate management,
while there are credits or much lower impacts in the other options.
In those options the emissions of N2 O from storage and field application were overcompensated by the positive effects of mineral
fertilizer substitution. Some reasons were identified to cause this
effect: (i) digestate treatment (due to ammonia emissions) reduces
the availability of nitrogen for N2 O creation processes during storage and field application steps significantly; (ii) there is a smaller
C/N-ratio and a higher NH4 + content in the untreated digestate of
Fig. 4. Acidification potential (AP) in SO2 equivalents per functional unit (fu) as resulting from different main emissions (a) and from different life cycle stages (b) for default
parameter setting in different treatment options (see Table 1).
100
T. Rehl, J. Müller / Resources, Conservation and Recycling 56 (2011) 92–104
the CM option compared to dried or separated digestates in the
other options and (iii) the separation process reduces the biological
activity during storage and thereby the oxidation of nitrogen.
3.3. Acidification potential
Acidification is dominated to more than 98% by ammonia emissions in all options. It shows important contributions from digestate
handling (field application, storage and treatment), while emissions from fossil fuel combustion (NOx and SOx) are negligible in
terms of acidification. Fig. 4 shows that acidification varies highly
but shows best results for options CO and PT and worst results for
BD option. With the exception of the BD option, field application
is the life cycle step with highest contribution to the acidification
potential. This is again due to lack of separation. The separation
into solid and liquid phase before drying reduces the amount of
ammonium available for volatilisation in the form of ammonia significantly. Nevertheless, a treatment with hot air during drying is an
optimal condition for the creation of ammonia and has a stronger
influence on the total results than the storage process in the drying
options. If results of all options are compared with the conventional digestate management it can be seen that losses of nitrogen
during treatment have led to a reduction of the amount of available nitrogen for nitrification and de-nitrification processes during
storage and field application of digestate based fertilizer. Due to
the use of sulphuric acid in thermal concentration (TC) and due to
exclusion of the digestate from the air in the physical treatment
(PT) option, these options do not have any acidification potential
during treatment.
3.4. Scenario analysis
The scenario results are presented by indicating the results of
parameter variations (F2 + F3, E2 − E4, R2 − R5, L2 − L5) in relation
to the default setting (F1, E1, R1, L1) for each impact category.
Relative change was calculated as a factor of change (FC):
FCparameter =
SV − DV
|DV|
∀ DV =
/ 0
(2)
DV, default value [kg eq./fu]; [MJ/fu]; SV, scenario value [kg eq./fu];
[MJ/fu]
3.4.1. Scenario 1: influence of feedstock
Fig. 5 shows the influence of different feedstock on the primary
energy demand, the global warming potential as well as the acidification potential for each option. The use of different feedstock
has highest influence on the PED, followed by impacts on the AP
and GWP. The highest effects on the PED were found in SD, PT,
CM and CO with increase factors between 1.5 and 2.4 for digestate F2 and decrease factors between −1.8 and −2.8 for digestate
F3. The tendency is opposite for the AP and GWP. The main influencing factor on the PED was the fertilizer credit for the amount
of nutrients applied to the field. A high nutrient content as given
with feedstock F3 resulted in higher credits and thereby in avoided
PED. The effect is overlapped in the GWP and the AP by three main
aspects: (i) digestate based emissions of N2 O, NH3 and CH4 during
storage, treatment and field application are higher/lower per kg
of higher/lower concentrated digestate, (ii) the dry matter content
affects the energy demand, which is needed to get a defined dry
matter concentration in separation, drying and filtering processes
and (iii) the nitrogen content affects the PED that is needed for
transportation and field application to reach the nitrogen demand
of the maize crop. In summary it can be stated that the feedstock
affects not only the amount of direct emissions due to digestate
handling but also the efficiency of the treatment and application
technologies.
3.4.2. Scenario 2: influence of energy supply
The influence of different methods of allocating the environmental burdens between electricity and heat provision within the
biogas system (energy based allocation in E2 and economic allocation in E3) and the impact of fossil fuel combustion for heat
production (natural gas in E4) is presented in Fig. 6. It can be seen
that the E4 parameter setting is by far the most primary energy
consuming and greenhouse gas emitting parameter variation. For
instance a maximum of 3.5 times more primary energy is consumed
in drum drying (DD) compared to the reference parameter setting
E1 resulting in an increase of greenhouse gas emissions by a factor
of 1.4. Acidification potential on the other hand is not influenced
significantly by the use of thermal energy from natural gas combustion but by the application of the biogas allocation parameter
settings E2 and E3. Mainly NH3 emissions from energy crop production and nitrous oxide emissions from the biogas CHP affect the AP
of energy from biogas production. In E2 and E3 both, electricity and
heat are produced by the combustion of biogas. Depending on the
allocation parameter setting, environmental impacts and resource
use are distributed differently between heat and electricity. As presented in Table 4, allocation according to energy content allocates
larger amounts of environmental burdens to the output product
“heat” than in the market price based approach. Compared to the
burden free use of heat in the default parameter setting (E1) allocation procedure E2 has led to highest increase of AP – with strong
influence in the drying options DD and BD as well as in the TC
option. Thermal energy use in drying and thermal concentration is
the dominant energy demanding process and is essentially affected
by the energy supply parameter settings. Due to this dominance,
effects correlated to the variation of biogas electricity provision
were only found in terms of PED in the E2 and E3 parameter settings
of each option. The effect of electricity use from biogas production
in the parameter settings E2 and E3 is positive up to a factor of 1.3
in drum drying (DD) on account of the significantly higher fossil
PED of the German electricity mix.
3.4.3. Scenario 3: influence of emission reduction technology
Although emission reduction technologies are propagated to
reduce ammonia emissions, highest reduction potentials were
found for the PED (Fig. 7). Two reasons are responsible for this effect.
As with the feedstock parameter variation the nitrogen concentration plays a dominant role. Emission reduction technologies reduce
the nitrogen losses and thereby increase the nitrogen content of the
digestate. The higher nitrogen content fulfils the nitrogen demand
with a lower transport effort to and on the field as less substrate has
to be transported. Secondly, positive savings were generated by a
higher amount of leftover ammonia available for mineral fertilizer
substitution. The avoided production of high energy demanding
mineral fertilizer production has a significant effect on the PED of
each option. Those positive effects of mineral fertilizer saving were
partially overlapped in R4 and R5 parameter variations in the GWP
due to an increase in N2 O and CH4 emissions during field application of digestates. However, the emissions profile as presented
in Table 5 would suggest an increase of greenhouse gas emissions
compared to the reference parameter variation (R1). Hence an additional effect was found influencing the creation of N2 O emissions:
a reduction of NH3 emissions not only reduces total AP but also
reduces the amount of nitrogen available for N2 O emissions due to
volatilisation of NH3 (indirect N2 O emissions).
In all options beside conventional digestate management, injection has shown the highest potential for saving greenhouse gas
emissions, followed by storage cover, and incorporation into soil.
Although 90% of all NH3 , N2 O and CH4 emissions were assumed to
be avoided by the storage cover the effect is much lower compared
to emission effects on PED, GWP and AP from the field application parameter variations (R4, R5). In this case the nitrogen and
101
3
2
1
F1
F2
0
F3
-1
CM
CO
BD
DD
SD
TC
AP
GWP
AP
PED
GWP
AP
PED
GWP
AP
PED
GWP
AP
PED
GWP
AP
PED
AP
PED
PED
-3
GWP
-2
GWP
FCfeedstock indicated relative to F1 (F1=0)
T. Rehl, J. Müller / Resources, Conservation and Recycling 56 (2011) 92–104
PT
4
3
2
E1
E2
1
E3
E4
0
CM
CO
BD
DD
SD
TC
AP
GWP
PED
AP
GWP
PED
AP
GWP
PED
AP
GWP
PED
AP
GWP
PED
AP
PED
GWP
AP
-2
GWP
-1
PED
FCenergy indicated relative to E1 (E1=0)
Fig. 5. Relative primary energy demand (PED), global warming potential (GWP) and acidification potential (AP) of different feedstock (F2, F3) compared to default setting
(F1 = 0) (see Table 3) for different treatment options (see Table 1).
PT
Fig. 6. Relative primary energy demand (PED), global warming potential (GWP) and acidification potential (AP) of different energy supply parameter variations (E2, E3, E4)
compared to default setting (E1 = 0) (see Table 4) for different treatment options (see Table 1).
3.4.4. Scenario 4: influence of different logistic variations
Fig. 8 shows the change in PED, GWP and AP of selected logistic parameter variations. PED is influenced the most, while AP is
hardly affected by a variation of the logistic parameters. A doubling of the transport distance (L2) is the most energy demanding
parameter variation, which also contributes highest to GWP. However, the effect varies considerably from a factor of 0.01 in belt
drying (BD) to about 3 in solar drying (SD) for PED, and 0.01 (BD)
and 1.9 (SD) for GWP. A tractor based logistic chain was assumed for
a maximum transport distance of 25 km (L3) and has shown high
1,5
1,0
0,5
0,0
R1
R2
-0,5
R3
CM
CO
BD
DD
SD
TC
AP
GWP
PED
AP
GWP
PED
AP
GWP
PED
AP
PED
GWP
AP
PED
GWP
AP
-2,0
GWP
R5
PED
-1,5
AP
R4
GWP
-1,0
PED
FCreduction indicated relative to R1 (R1=0)
carbon pool of the digestate was saved and available for biological
decomposition processes in the field. The release of nitrogen based
emissions was mainly shifted to a high degree from storage to field
application.
The most negative effect on the environmental profile was found
in the GWP of composting (CO) due to N2 O emissions from NH3 bacterial decomposition into N2 O within the biological filter substrate.
Electricity demand for pumping and ventilation purposes within
the chemical filter technology (R2) in the drying options increased
the PED by a factor of 0.19 in drum drying and 0.35 in solar drying.
PT
Fig. 7. Relative primary energy demand (PED), global warming potential (GWP) and acidification potential (AP) of different emission reduction parameter variations (R2, R3,
R4, R5) compared to default setting (R1 = 0) (see Table 5) for different treatment options (see Table 1).
T. Rehl, J. Müller / Resources, Conservation and Recycling 56 (2011) 92–104
4
3
L1
2
L2
1
L3
0
L4
-1
L5
CM
CO
BD
DD
SD
TC
AP
GWP
AP
PED
GWP
AP
PED
GWP
AP
PED
GWP
AP
PED
GWP
AP
PED
GWP
AP
PED
-3
PED
-2
GWP
FClogistic indicated relative to L1 (L1=0)
102
PT
Fig. 8. Relative primary energy demand (PED), global warming potential (GWP) and acidification potential (AP) of different logistic parameter variations (L2, L3, L4, L5)
compared to default setting (L1 = 0) (see Table 2) for different treatment options (see Table 1).
potential for PED and GWP reduction compared to the reference
parameter variation (50 km by truck). Even higher reduction potentials can be generated if liquid phases of production processes can
be used on the farm (parameter variation L4). Liquid phases were
produced with similar dry matter and nutrient contents in options
CO, DD and SD by the separation process and in option PT by the
micro-filtration process. The on-farm use of those liquid phases, for
example, for dilution of fermentation substrates, irrigation or fertilizing purposes has shown the highest potential for reduction of PED
and GWP. Due to the high amount of liquid substrates produced by
the separation process (84% by mass in CO, DD and SD) a factor of
−3.0 of PED and −1.8 of GWP in solar drying (SD) can be achieved
by mass reduction of transported products. If there are no possibilities for on-farm applications for the liquid phases and transport
distance cannot be reduced, an increase of transport efficiency is
the cheapest method to reduce environmental burdens. The maximum reduction potential was found for solar drying (SD) with a
factor of −0.31 and for physical/chemical treatment (PT) with a
factor of −0.22 in terms of GWP. Only little impact was found for
the high energy demanding options drum drying (DD) and thermal
concentration (TC) in the logistic parameter variations.
4. Conclusions
The goal of the study was to quantify the life cycle environmental
profile of seven treatment options for biogas digestate from ‘cradle
to grave’.
Belt drying of non-separated digestate was the treatment
requiring the most primary energy and showed the highest global
warming potential and acidification potential, whereas solar drying of separated digestate was the best option. In general, solar
drying, composting and physical/chemical treatment were most
suitable to reduce resource use and environmental impacts. In a
sensitivity analysis, treatment, storage, regional transportation and
field application were identified to be life cycle stages with the
greatest potential for improvement. In the feedstock scenario, the
nutrient content affected the amount of mineral fertilizer credits
and nitrogen related emissions, whereas the dry matter content
affected the energy demand of the treatment options. An increase
in nutrient load increased greenhouse gas and acidifying emissions
but reduced primary energy demand due to savings of mineral
fertilizer.
Operation of the various digestate treatment facilities proved
to be one of the most energy-demanding components of digestate processing, consuming 20–98% of the total net energy demand.
Thus, assumptions made about how to allocate the energy output
of the CHP of the biogas plant among power and heat, i.e. either
based on the economic value or the amount of energy produced,
resulted in different environmental burdens. Compared to the reference parameter variation, with burden free heat supply for drying
and thermal concentration options, global warming potential and
acidification potential increased. Burdens of all treatment options
can be reduced if electricity from the biogas plant is used instead
of electricity from the German power grid.
Most of the emissions in the treatment options arise from
nitrogen decomposition into N2 O, NH3 and NO3 − and carbon compounds into CH4 . Those emissions can be reduced by filter, air
tight storage cover or special application technologies. The chemical air cleaner in the drying options showed the highest reduction
potential for acidification, while fertilizer credits due to higher
nitrogen efficiency makes the injection technology for field application of digestates to the optimum for reduction of primary energy
use and global warming potential. In every case, a holistic consideration of all impact categories is of eminent importance: the
reduction of one emission, e.g. reduction of NH3 emissions during
treatment, might lead to higher emissions during later life cycle
stages.
In this study, digestate treatment options were investigated
exclusively from an environmental point of view. However, not
only ecological aspects are relevant for decision making but also
socio-political aspects (promotion of small enterprises, remuneration), economic aspects, legal aspects, regional aspects (biogas
plant density, nutrient provision in the region) as well as technical aspects. The predominant criterion for choosing a treatment
option is the profitability. The profitability arise from a reduction
of the transportation costs, higher revenues due to an increase of
the product value and an extension of the market by novell fertilizer products (e.g. liquid fertilizer, pellets or compost, which can be
used in landscape and horticulture). However, at least for the heat
based concepts those revenues alone will not refinance investment
costs, which are about D 300.000 for a solar drying hall needed to
dry the digestate of a 500 kWel biogas plant (Bux and Baumann,
2003). In this case the major economic motivation to realize the
plant concept is given by the CHP bonus of two cents per kWh
of heat used, which is currently paid by the German Government
to increase the energy efficiency of biogas plants. Although economics are the major driving force further aspects like ground area
demand, energy availability, regional waste management structure, farm infrastructure, regional nutrient balanceand compliance
with legal regulations have to be taken into consideration for a
holistic assessment of the treatment options. In future research
recommendations for the selection of a specific digestate treatment option should be developed on a thorough analysis of those
issues.
T. Rehl, J. Müller / Resources, Conservation and Recycling 56 (2011) 92–104
References
Amlinger F, Peyr S, Cuhls C. Green house gas emissions from composting and
mechanical biological treatment. Waste Manage Res 2008;26:47.
Amon B, Kryvoruchko V, Amon T, Zechmeister-Boltenstern S. Methane, nitrous
oxide and ammonia emissions during storage and after application of
dairy cattle slurry and influence of slurry treatment. Agric Ecosyst Environ
2006;112:153–62.
Amon B, Moitzi G, Schimpl M, Kryvoruchko V, Wagner-Alt C. Methane, nitrous oxide
and ammonia emissions from management of liquid manures. Tech. Report
No. 1107. Austrian federal Ministry of Agriculture, Forestry, Environmental and
Water Management and Austrian Federal Ministry of Education, Science and
Culture, Vienna; 2002.
Amon T, Amon B, Kryvoruchko V, Zollitsch T, Mayer K, Gruber L. Biogas production
from maize and dairy cattle manure – influence of biomass composition on the
methane yield. Agric Ecosyst Environ 2007;118:173–82.
Anon. 30.10.2010. Wie aus Biogasgülle Wasser und Nährstoffe werden. Technischer Artikel; 2010, http://www.vp-hottinger.ch/technische/biogasguelle.php
[Accessed 11.01.2010].
Baichinger J, Zander P. Planungswerkzeuge zur Optimierung der Stickstoffversorgung in Anbausystemen des Ökologischen Landbaus – Standort- und
vorfruchtabhängige Kalkulation der N-Salden von Anbauverfahren. Ressortforschung für den ökologischen Landbau – Aktivitäten aus Bund und Ländern,
Landbauforschung Völkenrode, Braunschweig FAL Agricultural Research; 2004.
pp. 21–30.
Baserga U. Agricultural co-digestion biogas plants, biogas from organic residues and
energy grass. FAT-Berichte 1998:512.
BMU. Characteristica of renewable energies, numbers and values. Berlin, Germany;
2009,
http://www.umweltbundesamt-daten-zur-umwelt.de/umweltdaten/
public/theme.do?nodeIdent=3437.
Bongiovanni JM, Mousques P, Puiggali JR. Thermal drying of residual sludge. Water
Res 2000;34:4218–323.
Borjesson P, Berglund M. Environmental systems analysis of biogas systems – Part
II: The environmental impact of replacing various reference systems. Biomass
Bioenergy 2007;31:326–44.
Börjesson P, Berglund M. Environmental systems analysis of biogas systems – Part
I: Fuel-cycle emissions. Biomass Bioenergy 2006a.
Börjesson P, Berglund M. Environmental systems analysis of biogas systems – Part
I: Fuel-cycle emissions. Biomass Bioenergy 2006b;30:469–85.
Brentrup F, Küsters J, Lammel J, Kuhlmann H. Methods to estimate on-field nitrogen
emissions from crop production as an input to LCA studies in the Agricultural
Sector. Int J Life Cycle Assess 2000;5:349–57.
Bressler E. Physikalisch-chemische Methoden zur Gülleentsorgung. Doctoral thesis,
University of Regensburg, Regensburg, Germany; 1994.
Büchter M, Wachendorf M, Taube F. Nitratauswaschung unter Silomais in
Abhängigkeit von der Bewirtschaftungsform und der N-Düngungsintensität
– Ergebnisse aus dem N-Projekt Karkendamm, Mitteilungen der Arbeitsgemeinschaft für Grünland und Futterbau. Kiel: Institut für Pflanzenbau und
Pflanzenzüchtung – Grünland und Futterbau/Ökologischer Landbau, ChristianAlbrechts-Universität zu Kiel; 2001. p. 75–7.
Bux M. Solar drying of effluents and digestate. Emissions of ammonia and nitrous
oxide during drying. Stuttgart. Personal communication; 2007.
Bux M, Baumann R. Wirtschaftlichkeit und CO2 -Bilanz der solaren Trocknung von mechanisch entwässertem Klärschlamm. KA Abwasser. Abfall
2003;50:1169–77.
Bux M, Baumann R, Pinnelkamp J, et al. Volume reduction and biological stabilization of sludge in small sewage plants by solar drying. Drying Technol
2002;20:829–37.
Bux M, Starcevic N. Stand der Technik solarer und solarunterstützter Trocknungsverfahren für Klärschlamm. GWF Wasser Abwasser Jg 2005;146:504–9.
Chuvaree R, Nishida N, Otani Y, Tanaka T. Filter press dryer for filtration/squeezing
and drying of slurries. J Chem Eng Jpn 2006;39:298–304.
Clemens J, Trimborn M, Weiland P, Amon B. Mitigation of greenhouse gas emissions
by anaerobic digestion of cattle slurry. Agric Ecosyst Environ 2006;112:171–7.
Clemens J, Wolter M, Wulf S, Ahlgrimm H-J. Methan- und Lachgas-Emissionen bei
der Lagerung und Ausbringung von Wirtschaftsdüngern., Emissionen der Tierhaltung – Grundlagen, Wirkungen, Minderungsmaßnahmen. KTBL/UBA; 2001.
p. 331–2.
Clemens J, Wulf S. Reduktion der Ammoniakausgasung aus Kofermentationssubstraten und Gülle während der Lagerung und Ausbringung durch interne
Versauerung mit in NRW anfallenden organischen Kohlenstofffraktionen. Bonn,
Germany: Forschungsvorhaben im Auftrag des Ministeriums für Umwelt und
Naturschutz, Landwirtschaft und Verbraucherschutz des Landes NordrheinWestfalen; 2005. p. 41.
Dalemo M. Environmental systems analysis of organic waste management, the
ORWARE model and the sewage plant and aerobic digestion submodels. Doctoral
Thesis, Swedish University of Agricultural, Uppsala, Sweden; 1999.
Dalemo MS. LCA av biogas – miljö belastningsprofiler för produktion av biogas fö r
fordonsdrift [Life cycle inventory of biogas – the environmental impact of biogas
as vehicle fuel]. Uppsala, Sweden: Swedish Institute of Agricultural Engineering
(JTI); 1998.
Dämmgen U. Calculations of emission from German agriculture – National Emission Inventory Report (NIR) 2010 for 2008. Braunschweig: Johann Heinrich
von Thünen-Institut. Federal Research Institute for Rural Areas, Forestry and
Fisheries; 2010. p. 415. http://www.bfafh.de/bibl/lbf-pdf/landbauforschungsh/lbf sh324.pdf.
103
Döhler H. Laboratory and field experiments for estimating ammonia losses from
pig and cattle slurry following application. In: Seminar; Odour and ammonia
emissions from livestock farming. Elsevier Applied Science; 1990. p. 132–40.
Dreyer LD, Niemann AL, Hauschild MZ. Comparison of three different LCIA methods
EDIP97, CML2001 and Eco-indicator 99. Does it matter which one you choose?
Int J Life Cycle Assess 2003;8:191–200.
Edelmann W, Schleiss K. Ökologischer, energetischer und ökonomischer Vergleich
von Vergärung, Kompostierung und Verbrennung fester biogener Abfallstoffe.
Arbeitsgemeinschaft Bioenergie, arbi and Umwelt- und Kompostberatung
Schleiss-Baar; 2001. p. 122.
Edelmann W, Schleiss K, Engeli H, Baier U. Ökobilanz der Stromgewinnung aus
landwirtschaftlichem Biogas Bundesamt für Energie, Bern; 2001. p. 97.
Ekechukwu OV, Norton B. Review of solar-energy drying systems II: an overview of
solar drying technology. Energy Convers Manage 1999;40:615–55.
Eyerer P. Software and database for balancing of sustainability, GaBi. 4.4th ed.
Stuttgart, Germany: PE International GmbH and University of Stuttgart; 2006.
Finnveden G, Johansson J, Lind P, Moberg A. Life Cycle Assessments of Energy from
Solid Waste. Stockholm: Stockholm University; 2000. p. 214.
Forbes EGA, Easson DL, Woods VB, McKervey Z. An evaluation of manure treatment
systems designed to improve nutrient management. A report to the expert group
on alternative use of manures. Hillsborough: Agri-Food and Bioscience Institute;
2005. p. 114.
Fricke K, Santen H, Wallmann R. Comparison of selected aerobic and anaerobic
procedures for MSW treatment. Waste Management 2005;25:799–810.
Frischknecht R, Suter P, Bollens U, Bosshart S, Ciot M, Ciseri L, Doka G, Hischier R,
Martin A, Dones R, Gantner U. Ökoinventare von Energiesystemen, Grundlagen
für den ökologischen Vergleich von Energiesystemen und den Einbezug von
Energiesystemen in Ökobilanzen für die Schweiz. 3rd ed. Bern: Bundesamt für
Energiewirtschaft (BEW/PSEL); 1996.
Fritsche U, Rausch L. Globales Emissions-Modell Integrierter Systeme (GEMIS)
Version 4.5. Darmstadt: Hessisches Ministerium für Umwelt, Energie und Bundesangelegenheiten unter Mitarbeit des Öko-Institut; 2008.
Goedkoop M, Spriensma R. The Eco-indicator 99: a damage oriented method for
Life Cycle Impact Assessment. Tech. Report No. 3. Amersfort, Netherlands:
Netherlands Ministry of Housing, Spatial Planning and the Environment; 2001.
Guinée JB, Gorrée M, Heijungs R, Huppes G, Kleijn R, de Koning A, van Oers L,
Wegener Sleeswijk A, Suh S, Udo de Haes HA, de Bruijn H, van Duin R, Huijbregts MAJ, Lindeijer E, Roorda AAH, Weidema BP. Life cycle assessment:
an operational guide to the ISO standards. The Hague/Leiden, Netherlands:
Ministry of Housing, Spatial Planning and Environment (VROM) and Centre
of Environmental Science (CML); 2001. p. 101. http://cml.leiden.edu/research/
industrialecology/researchprojects/finished/new-dutch-lca-guide.html.
Guinée JBE, Gorrée M, Heijungs R, Huppes G, Kleijn R, de Koning A, van Oers L,
Wegener Sleeswijk A, Suh S, Udo de Haes HA, de Bruijn JA, van Duin R, Huijbregts
MAJ. Handbook on life cycle assessment: operational guide to the ISO standards.
Dordrecht; 2002.
Haaring H. Data collection and personal communication. Data sheet Dorset drying
technologies. Power, efficiency, energy demand and emissions of drying plants.
Varsseveld, Netherlands: Dorset Agrar- und Umweltechnik GmbH; 2009.
Hao X, Chang C, Larney FJ. Carbon, nitrogen balances and greenhouse gas emission during cattle feedlot manure composting. J Environ Qual 2004;33:
37–44.
Hartmann JK. Life-cycle-assessment of industrial scale biogas plants. Göttingen,
Germany: LCA, University of Göttingen; 2006.
Hauschild M, Wenzel H. Environmental assessment of products. United Kingdom/Hingham, MA, USA: Chapman & Hall/Kluwer Academic Publishers; 1998.
Heijungs R, Guinée J, Huppes G, Lankreijer RM, Udo de Haes HA, Wagener Sleeswijk
A, Ansems AMM, Eggels PG, van Duin R, de Goede HP. Environmental life cycle
assessment of products. Guide & backgrounds. Centrum foor Milieukunde (CML).
Leiden, The Netherlands: Centre of Environmental Science; 1992.
Hersener J-L, Meier U, Dinkel F. Ammoniakemissionen aus Gülle und deren Minderungsmassnahmen unter besonderer Berücksichtigung der Vergärung. Bern:
Amt für Umweltschutz Kanton Luzern und Forschungs- und P+D Programm
Biomasse des Bundesamtes für Energie; 2002. p. 97.
Horlacher D, Marschner H. Schätzrahmen zur Beurteilung von Ammoniakverlusten nach Ausbringung von Rinderflüssigmist. Zeitschrift Pflanzenernährung
Bodenkunde 1990;153:107–15.
Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Xiaosu D. Third
assessment report: climate change 2001. Cambridge, United Kingdom: Intergovernmental panel on climate change (IPCC); 2001.
Hüther L. Entwicklung analytischer Methoden und Untersuchung von Einflussfaktoren auf Ammoniak-, Methan- und Distickstoffmonoxidemissionen aus
Flüssig- und Festmist. Braunschweig: Landbauforschung Völkenrode; 1999. p.
200.
Hüther L, Schuchardt F. Einflußfaktoren auf die Schadgasfreisetzung bei der
Lagerung/Kompostierung tierischer Exkremente. Braunschweig-Völkenrode:
Bundesforschungsanstalt für Landwirtschaft (FAL); 1998, 104.
Hüther L, Schuchardt F, Willke T. Emissions of ammonia and greenhouse gases
during storage and composting of animal manures; 1997. p. 327–34.
IPCC. Good practice guidance and uncertainty management in national greenhouse
gas inventories. Intergovermental Panel on Climate Change (IPCC); 2000. p.
4.1–.83. http://www.ipcc.ch/pub/guide.htm.
IPCC. Good practice guidance for land use, land-use change and forestry. In: Change
IPoC, editor. National Greenhouse Gas Inventories Program; 2003.
IPCC. IPCC guidelines for national greenhouse gas inventories. The Intergovernmental Panel on Climate Change. Japan: Institute for Global Environmental
104
T. Rehl, J. Müller / Resources, Conservation and Recycling 56 (2011) 92–104
Strategies (IGES); 2006, http://www.ipcc-nggip.iges.or.jp/public/2006gl/
index.html.
Jacobson LD, Moon R, Bicudo J, Janni K, Zhu J, Schmidt D, McGinley C, Engineer MG, Associates PG, Nicolai R. Generic environmental impact statement
on animal agriculture. Minnesota, MN, USA: University of Minnesota; 1999.
p. 177.
Jarvis SC, Pain BF. Greenhouse gas emissions from intensive livestock systems: their
estimation and technologies for reduction. Clim Change 1994;27:27–38.
Käck M. Ammoniakemissionen bei der Kompostierung separierter Feststoffe aus
Flüssigmist in belüfteten Rottereaktoren. Doctoral thesis, University of Hohenheim, Stuttgart, Germany; 1996.
Kryvoruchko V. Methanbildungspotential von Wirtschaftsdüngern aus der Rinderhaltung und der Wirkung der Abdeckung und anaeroben Behandlung auf
klimarelevante Emissionen bei der Lagerung von Milchviehflüssigmist. Doctoral
thesis, Universität für Bodenkultur, Wien; 2004.
KTBL. Faustzahlen für die Landwirtschaft. 13th ed. Darmstadt: YARA GmbH & Co.
KG, KTBL e.V.; 2005.
KTBL. KTBL-Datensammlung Betriebsplanung Landwirtschaft. 21st ed. Darmstadt:
KTBL-Schriftenvertrieb im Landwirtschaftsverlag Münster-Hiltrup GmbH;
2008.
Külling D, Menzi H, Neftel K, Sutter P, Lischer P, Kreuzer M. Emission of ammonia,
nitrous oxide and methane from different types of dairy manure during storage
as affected by dietary protein content. J Agric Sci 2001:235–50.
Luckert K. Handbuch der mechanischen Fest-Flüssig-Trennung. Essen: Vulkan Verlag GmbH; 2004.
Masse L, Massé DI, Pellerin Y. The use of membranes for the treatment of manure: a
critical literature review. Biosyst Eng 2007;98:371–80.
Mihoubi D. Mechanical and thermal dewatering of residual sludge. Desalination
2004;167:135–9.
Nilsson M, Linné M, Dahl A. Life cycle inventory of biogas as vehicle fuel. Malmö,
Sweden: Swedish Gas Centre; 2001.
Nilsson M, Dahl LMA. Livscykelinventering för biogas som fordonsbränsle [Life cycle
inventory of biogas as vehicle fuel]. Malmö, Sweden: Swedish Gas Centre; 2001.
Ramesohl D-IS. Analyse und Bewertung der Nutzungsmöglichkeiten von Biomasse;
2006, Endbericht, Wuppertal, Leipzig, Oberhausen, Essen.
Rehl T, Doluschitz R, Jungbluth T, Müller J. Environmental impact of different types
of biogas effluent processing. VDI Berichte 2007:323–33.
Renger M. Bestimmung der Bodenwasserhaushaltskomponenten. DVGW Schriftenreihe Wasser 1992;72:283–98.
Salihoglu N-K, Pinarli V, Salihoglu G. Solar drying in sludge management in Turkey.
Renew Energy 2007;32:1661–75.
Sandars DL, Audsley E, Canete C, Cumby TR, Scotford IM, Williams AG. Environmental
benefits of livestock manure management practices and technology by Life Cycle
Assessment. Biosyst Eng 2003;84:267–81.
Scholwin F, Michel J, Schröder G, Kalies M. Ökologische Analyse einer Biogasnutzung
aus nachwachsenden Rohstoffen. Leipzig, Germany: Institut für Energetik und
Umwelt gemeinnützige GmbH; 2006. p. 88.
Schulz W, Heitmann S, Hartmann D, Jahn K, Manske S, Ehlers B, Peters Erjawetz
S, Havran T, Risse S, Schnober M, Räbiger M, Schlüter M. Leitfaden. Verwertung
von Wärmeüberschüssen bei landwirtschaftlichen Biogasanlagen. Bremen: Bremer Energie Institut. Bundesministerium für Ernährung, Landwirtschaft und
Verbraucherschutz; 2007. p. 355.
Schulze D, Block R. Ökologische und ökonomische Bewertung von Fermenterabwasseraufbereitungssystemen auf der Basis von Praxisversuchen und
Modellkalkulationen für das Betreiben von Biogasanlagen. Rheinberg: Ministerium für Umwelt und Naturschutz, Landwirtschaft und Verbraucherschutz
des Landes Nordrhein Westfalen; 2005. p. 49.
Sommer SG. Ammonia volatilization from farm tanks containing anaerobically
digested animal slurry. Atmos Environ 1997;31:863–8.
Sommer SG, Hutchings NJ. Ammonia emission from field applied manure and its
reduction – invited paper. Eur J Agron 2001;15:1–15.
Sommer SG, Olesen JE. Effects of dry matter content and temperature on ammonia
loss from surface-applied cattle slurry. J Environ Qual 1991;20:679–83.
Sommer SG, Petersen SO, Sogaard HT. Greenhouse gas emission from stored livestock slurry. J Environ Qual 2000;29:744–51.
Stehfest E, Bouwman L. N2 O and NO emission from agricultural fields and soils under
natural vegetation: summarizing available measurement data and modelling of
global emissions. Nutrient Cycling Agroecosyst 2006;74:207–28.
Thörneby L, Persson K, Trägårdh G. Treatment of liquid effluents from dairy cattle
and pigs using reverse osmosis. J Agric Eng Res 1999;73:159–70.
Tiquia S, Richard T, Honeyman M. Carbon, nutrient, and mass loss during composting.
Nutrient Cycling Agroecosyst 2002;62:15–24.
Trimborn M. Biofilter/Biowäscher an Tierhaltungsanlagen als relevante Quelle
von Lachgas durch Ammoniakabscheidung? Bonn: Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES) Lehr- und Forschungsbereich
Pflanzenernährung; 2006. p. 59.
Vetter H, Burger S. Energetische Optimierung der Klärschlammaufbereitung. Bern,
Switzerland: Bundesamt für Energie, Forschungsprogramm Elektrizität; 2006.
p. 71. http://www.energieforschung.ch/.
Voća N, Krička T, Ćosić V, Rupić, Jukić Ž, Kalambura S. Digested residue as a fertilizer after the mesophilic process of anaerobic digestion. Plant Soil Environ
2005;51:262–6.
Wallmann R, Cuhls C, Frenzel J, Hake J, Fricke K. Nachrotte von Vergärungsrückständen aus dem Valorga-Verfahren. Müll und Abfall 2001;11:624–8.
Wilfert R, Nill M, Schattauer A. Biogasgewinnung aus Gülle, organischen Abfällen
und aus angebauter Biomasse – Eine technische, ökologische und ökonomische
Analyse. Tech. Report No. DBU Projekt 15071. Leipzig: Institut für Energetik und Umwelt; 2004, http://www.dbu.de/phpTemplates/publikationen/
pdf/101106090257217.pdf.
Witte H, Keding M. Produktgruppe ,,Zeolithe”. Anforderung, Angebot, Auswahl und
Qualität. 6. Karlsruher Flockungstage. Universität Karlsruhe, Tagungsband der
6. Karlsruher Flockungstage; 1992. p. 137–54.
Wulf S, Jäger P, Döhler H. Balancing of greenhouse gas emissions and economic
efficiency for biogas-production through anaerobic co-fermentation of slurry
with organic waste. Agric Ecosyst Environ 2006;112:178–85.
Wulf S, Maeting M, Clemens J. Application technique and slurry co-fermentation
effects on ammonia, nitrous oxide, and methane emissions after spreading: I.
Ammonia volatilization. J Environ Qual 2002;31:1789–94.