Optimization of the green gas supply chain. Identification of critical ...

PREFACE
This master thesis report has been prepared in the final stage of the master program Energy & Envi-
ronmental Sciences (MSc.). This program is offered by the Centre for Energy and Environmental
Studies (IVEM) at the Faculty of Mathematics and Natural Sciences of the University of Groningen
(NL). This research has been performed at KEMA Gas Consulting & Services in Groningen.
To start with, I would like to express my gratitude to senior consultant Harm Vlap, who provided this
research opportunity and who did perfect supervision at KEMA. Additionally I would like to thank
J. Holstein, R. van Ommen, O. Florisson and K. Schlebusch at KEMA for their valuable input.
Secondly, I would like to acknowledge prof. dr. H.C. Moll and dr. ir. A.A. Bellekom at the University
of Groningen for their supervision. They provided my work and process with sharp and useful re-
marks. In addition I would like to thank prof. dr. R.H. Teunter at the department for Operations Re-
search (OR) of the University of Groningen for his expertise and assistance in the OR related methods
conducted in this thesis.
During the period at KEMA I have been accompanied by fellow student Anne S. Braaksma. Part of
the literature and data used in this research was analyzed and modeled in cooperation with
A. Braaksma. His sharp view on energy analysis and the inspiring discussions we had were very help-
ful and motivating for me. Consequently, I would like to express my gratitude to him. Additionally I
would like to recommend his master thesis report, which focuses on the overall sustainability of green
gas. In that research, he focused on the environmental (emissions), economic and social (employment
and competition with food) aspects of green gas.

Summary
Global climate change and the need for security of energy supply drive the development of bio-energy
production and utilization. Green gas is defined as bio-based gas that has been upgraded to natural gas
quality. Amongst other energy carriers, green gas can be applied in an existing infrastructure, offering
the possibility for a gradual transition to a more renewable energy system. Within the transition from
the current energy system to a new, more sustainable one, an increasing demand emerges to dispose of
a generic model to determine the optimal pathway from biogas production to green gas injection.
Gaining knowledge about system integration is indispensable for further developments of green gas
initiatives and is of great importance regarding the potential of green gas to contribute to the sustain-
ability goals. Therefore this research has the following problem statement: Which system design of a
green gas supply chain is most efficient from an energetic and an economic perspective, based on a
generic model approach?
This research focuses on the identification of critical choices, e.g. capacity scale or choice for tech-
nologies or infrastructure, in designing an efficient (regarding energy requirements and total costs)
green gas supply chain (GGSC) and their consequences. A generic model has been developed to
model the possible pathways from biomass to green gas injection, including data to assess the energy
requirements and economics of the total GGSC. The model focuses on the Dutch situation for green
gas from anaerobic digestion of biomass.
The first important finding is that scale effects were mainly found in the economics of the GGSC,
which is the result of depreciation in capital costs. With regard to the energy requirements, pipeline
construction shows significant scale effects. Additionally, marginal scale effects were found for diges-
tion. In the other process steps the energy requirements are linearly related to the output. Secondly, the
capacity scale determines the grid in which green gas will be injected, since the Distribution Grid
(DG) is only limited susceptible for injection of green gas (max 150 Nm 3 /h green gas, depending on
the exact location). Hence, the Regional Grid (RG) is the only option for injection of capacities ex-
ceeding 150 Nm 3 /h. The grid choice also determines whether or not transportation (either biomass or
biogas) is required. The most efficient transportation manner from both the energetic and economic
perspectives depends on total distance and capacity scale.
Furthermore, the choice for a certain upgrading technique is critical since it influences the energy de-
mand for compression to grid pressure. Pressurized water scrubbing and chemical scrubbing were
found to be most economical and efficient respectively.
Concluding, a configuration of centralized digestion, upgrading and injection is found to be optimal,
based on the developed model. The configuration in which decentralized digestion, upgrading and in-
jection into the DG is performed was found to be more beneficial than the configuration that includes
biomass (manure and maize silage) transport over more than 60 km. However, since injection into the
DG is limited to 150 Nm 3 /h green gas, the development of additional green gas initiatives in the same

DG area is hampered when applying this system configuration. In contrast, a configuration of decen-
tralized digestion, centralized upgrading and injection in the RG or the configuration in which also the
digester is located centrally offers the opportunity for other (maybe small) green gas initiatives to join.
These conclusions were based on certain distances between the farms and/or to the specific gas grids.
Changing these distances will influence the absolute results. However, the overall conclusion remains
the same, since the system configuration in which all process steps are performed decentralized is lim-
ited to the injection capacity of the DG. Furthermore, it should be noted though that cryogenic separa-
tion seems promising since the gas output pressure could potentially minimize the overall chain energy
requirements and costs.
Further research is recommended on the possibilities to enable the DG for injection of capacities >150
Nm 3 /h green gas. Additionally it should be noted that promising techniques for in-situ methane en-
richment might result in substantial efficiency enhancement and are thereby potentially very important
in further developments with regard to optimization of the GGSC.

Samenvatting [NL]
Wereldwijde klimaatverandering en de noodzaak om energievoorziening in de toekomst zeker te stel-
len scheppen een voedingsbodem voor de ontwikkeling van bio-energieproductie en -toepassing.
Groengas is gedefinieerd als gas dat afkomstig is van biomassa, wat is opgewaardeerd naar gas van
aardgaskwaliteit. Als een van de hernieuwbare-energiedragers kan groengas toegepast worden in een
huidige infrastructuur. Dit geeft mogelijkheden voor een geleidelijke transitie naar een duurzamere
energievoorziening. In de transitie van de huidige energievoorziening naar een meer duurzame, zijn er
in toenemende mate belangen bij een generiek model voor de bepaling van de optimale 'route' van bi-
omassa naar groengas. Systeem analyse zal onvermijdelijk zijn in verdere ontwikkelingen van groen-
gas en is van groot belang voor het behalen van duurzaamheiddoelstellingen. In dit onderzoek zal de
volgende onderzoeksvraag beantwoord worden: Bij welke groengas keteninrichting zijn de totale kos-
ten geminimaliseerd en is de ketenefficiëntie het hoogst, gebaseerd op een generiek gasketen model?
Dit onderzoek heeft zich gericht op het identificeren van bepalende keuzes bij het ontwerpen van een
optimale groengas keten. Deze keuzes kunnen betrekking hebben op bijvoorbeeld de schaalgrootte,
technologiekeuze of keuze van infrastructuur. Een generiek model is ontwikkeld voor de modellering
van de mogelijke groengas routes. Hiermee zijn de totale energiebehoeften en kosten in de keten be-
paald. Het model is gericht op de Nederlandse situatie voor groengas productie uit biomassa vergis-
ting.
Een van de meest belangrijke bevindingen van het onderzoek (i) is dat schaaleffecten vooral aanwezig
zijn voor de economische kant van de groengasproductie en invoeding. Gekeken naar de energiebe-
hoeften voor de productie van groengas bleek dat vooral pijpleiding constructie schaaleffecten liet
zien. Bovendien zijn er marginale schaaleffecten gevonden voor vergisting. In de andere processtap-
pen bleek een lineair verband te liggen met betrekking tot schaalgrootte, hier werden dus geen schaal-
voordelen voor de energiebehoefte waargenomen.
Daarnaast (ii) is de keuze voor het gasnet waarin gas ingevoed zal worden van belang omdat het dis-
tributienet, afhankelijk van de exacte locatie, een maximale invoedingscapaciteit van 150 Nm 3 /uur
groengas heeft. Hierdoor is invoeding van grotere capaciteiten gebonden aan het regionale gasnet. Het
beschikbare type gasnet bepaalt over het algemeen ook of er transport van biomassa of biogas nodig
is. Indien transport is vereist, is de keuze tussen biomassa transport (vrachtwagen) of biogas transport
(pijpleiding) sterk afhankelijk van de transport afstand en biogas capaciteit.
De keuze met betrekking tot de biogas opwaardeertechniek is tevens van belang (iii). Pressurized wa-
ter scrubbing (PWS) en 'chemical scrubbing' blijken respectievelijk het meest economisch en efficiënt.
Concluderend, de optimale systeemconfiguratie voor groen gas is degene waarin biomassa van meer-
dere boerderijen centraal wordt vergist en centraal wordt opgewaardeerd waarna het groengas in het
regionale gasnet (4.0 MPa) ingevoed wordt. De configuratie waarin alles decentraal gebeurt en waarbij
het gas op het distributienet wordt ingevoed, scoort beter dan de centrale verwerkingsoptie waarbij de

transportafstanden van mest en maïs silage groter zijn dan 60 km. Echter, vanwege de beperkte invoe-
dingscapaciteit in het distributienet zal verdere ontwikkeling van groengasprojecten gehinderd worden
bij deze configuratie. Invoeding in het regionale gasnet heeft geen capaciteitsbeperkingen en zal juist
meer mogelijkheden bieden voor groengas ontwikkeling omdat andere (ook kleine) boerderijen zich
zouden kunnen aansluiten bij de configuratie waarin alles centraal verwerkt wordt of waarin opwaar-
dering en invoeding centraal gedaan wordt.
Deze conclusies zijn gebaseerd op systeemconfiguraties bij bepaalde afstanden. Veranderingen in de
onderlinge afstanden zal de (absolute) resultaten doen veranderen. Echter, omdat het distributienet (al-
leen relevant voor de configuratie waar geen transport aan te pas komt) maar beperkte mogelijkheden
biedt voor invoeding zal de uiteindelijke conclusie niet veranderen als functie van de bepaalde afstan-
den.
De mogelijkheden om het distributienet toegankelijk te maken voor invoeding van groengas capacitei-
ten >150 Nm 3 /uur zullen moeten worden onderzocht in vervolg studies. Daarnaast zal er meer onder-
zoek (zowel fundamenteel als praktisch) gedaan moeten worden naar innovaties die zich richten op 'in-
situ methaanverrijking'. Deze technieken zouden voor substantiële systeemoptimalisatie kunnen zor-
gen.

Glossary
Biogas Biologically produced gas from anaerobic digestion, consisting of mainly CH4
(50-75 vol%) and CO2 (25-45 vol%), also traces of a.o. H2O, H2S, NH3, O2 and H2
can be found.
CapEx Capital expenditures
CH4
Methane (main component of natural gas)
CHP Combined Heat and Power unit; production of electricity (38%) and heat (50%)
from biogas.
CO2
Carbon dioxide
CRF Capital Recovery Factor
DG Distribution Grid
GGSC Green Gas Supply Chain
Green gas Biogas that has been upgraded to natural gas quality.
H2S Hydrogen sulfide
HG High pressure Grid
HDPE High-Density Polyethylene
kWh KiloWattHours (1 kWh = 3.6 MJ)
Manure-to-maize Refers to the substrate ratio (manure & maize silage) that is fed to the digester,
based on weight percentages
MJ Mega Joule (10 6 Joule); in this report MJ refers to 'thermal MJ' (in contrast to elec-
trical MJ)
Nm 3 A m 3 of gas at 'normal' conditions, being at a pressure of 0.1013 MPa and a tem-
perature of 273.15 K.
MPa Mega Pascal (10 6 Pascal) = 10 bar
OpEx Operational expenditures
PSA Pressure Swing Adsorption
PWS Pressurized Water Scrubber
RG Regional Grid
TotEx Total expenditures
Vol% Percentage of volume
WACC Weighted Average Cost of Capital: the minimum return that should be earned to
satisfy providers of the capital, or that might be earned by an investment else-
where. In the Dutch situation it is assumed to consist of 80% liability at 6% inter-
est and 20% equity at 15% interest.

Table of contents
1 Introduction ................................................................................................................................. 11
1.1 Relevance of this Research.................................................................................................................. 11
1.2 Research Outline.................................................................................................................................. 12
1.2.1 Research Aim .................................................................................................................................. 12
1.2.2 Research Question........................................................................................................................... 12
1.2.3 Sub Questions.................................................................................................................................. 12
1.3 Structure of this Report........................................................................................................................ 13
2 Green Gas Supply Chain ............................................................................................................ 15
2.1 Basic Steps........................................................................................................................................... 15
2.1.1 Anaerobic Digestion........................................................................................................................ 15
2.1.2 Gas Upgrading................................................................................................................................ 16
2.1.3 Compression & Injection ................................................................................................................ 18
2.2 Relations within the GGSC ................................................................................................................. 20
3 Methodology................................................................................................................................. 23
3.1 System Design of this Research .......................................................................................................... 23
3.2 A Generic Model Approach................................................................................................................. 24
3.2.1 Pathways ......................................................................................................................................... 25
3.2.2 Biomass and Biogas Transport Distances....................................................................................... 27
3.2.3 Gas Compression ............................................................................................................................ 27
3.3 Economics of the GGSC...................................................................................................................... 28
3.4 Energy Requirements of the GGSC..................................................................................................... 32
3.5 Susceptibility for Optimization............................................................................................................ 36
4 Results........................................................................................................................................... 39
4.1 Scale effects......................................................................................................................................... 39
4.1.1 Scale effects on the Economics in the GGSC .................................................................................. 39
4.1.2 Scale effects on the Energy Requirements of the GGSC.................................................................. 44
4.2 Transition Points of Transport Means ................................................................................................. 44
4.3 Upgrading Techniques......................................................................................................................... 45
4.4 Pathways.............................................................................................................................................. 47
4.5 Sensitivity Analysis Transport............................................................................................................. 49
5 Conclusions .................................................................................................................................. 53
6 Discussion..................................................................................................................................... 55
7 Recommendations for further research .................................................................................... 57
References ............................................................................................................................................ 61

List of figures
Figure 2.1: Basic steps in a green gas supply chain. ............................................................................. 15
Figure 2.2: Annual consumption pattern of natural gas from the Distribution Grid. ............................ 19
Figure 3.1: System design of the generic GGSC model developed. ..................................................... 24
Figure 3.2: Generic overview of possible pathways in green gas supply chain.................................... 25
Figure 3.3: Generic model approach of the GGSC. .............................................................................. 26
Figure 3.4: Overview of literature about the energy requirements in the green gas supply chain. ....... 33
Figure 4.1: Scale effect on the total costs for digestion......................................................................... 40
Figure 4.2: Scale effect on pipeline construction costs. ........................................................................ 40
Figure 4.3: Scale effect on the total costs for upgrading techniques..................................................... 41
Figure 4.4: Scale effect on the economics of compression. .................................................................. 42
Figure 4.5: Total overview of scale effects on the economics of the GGSC......................................... 43
Figure 4.6: Total overview of scale effect on the energy requirements in the GGSC........................... 44
Figure 4.7: Transition lines of most efficient and economic transport means....................................... 45
Figure 4.8: Energy requirements of system configurations with different upgrading techniques......... 46
Figure 4.9: Total costs of system configurations with different upgrading techniques......................... 46
Figure 4.10: Normalized results of upgrading techniques..................................................................... 47
Figure 4.11: Total energy requirements of different pathways ............................................................. 48
Figure 4.12: Total costs of different pathways...................................................................................... 48
Figure 4.13: Normalized results of pathways........................................................................................ 49
Figure 4.14: Sensitivity of transition points. ......................................................................................... 50
Figure 4.15: Sensitivity of pathways to changes in total distance......................................................... 51
Figure 4.16: Sensitivity of pathways to biogas yield of maize silage. .................................................. 52
List of tables
Table 2.1: Biogas production and methane content per substrate ......................................................... 15
Table 2.2: Overview of Dutch gas grid taxonomy ................................................................................ 19
Table 3.1: Main parameters used in the economic analysis of the generic model. ............................... 29
Table 3.2: Specific economics of the green gas supply chain............................................................... 30
Table 3.3: Energy requirements of the green gas supply chain............................................................. 33
Table 3.4: System designs to identify the most efficient pathways....................................................... 37
Table 4.1: Trendlines functions in economics of process steps in GGSC............................................. 43

1 INTRODUCTION
Production, distribution and application of (natural) gas has been a big driver in the development of
the Dutch economy and resulted in an extended and sophisticated gas grid. This fact is now seen as a
major opportunity for developments in renewable gas utilization in the Netherlands. Renewable gasses
can be applied in the existing gas grid, offering the possibility for a gradual transition to a more sus-
tainable energy system (Hagen et al., 2001). Within the transition from the current energy system to a
new, more sustainable one, an increasing need emerges to develop and use a generic model to deter-
mine the optimal pathway from biogas production to green gas injection (Bekkering et al., 2010).
Green gas is defined as bio-based gas that has been upgraded to natural gas quality. Several system
configurations, including an extensive set of technologies, can be considered. The choice for a system
design depends on a wide range of parameters that are characteristic for each specific case, lacking a
generic framework (Carlos & Khang, 2009). Buchholz et al. (2009) address the need for tools that ap-
ply system thinking in bio-energy systems and Pöschl et al. (2010) add the need for insight in the po-
tential for integrated efficiency enhancement and minimization of costs.
This research focuses on the identification of critical choices, e.g. capacity scale or choice for technol-
ogy, in designing an efficient (focused on the energy requirements and total costs) green gas supply
chain and what their consequences could be. In order to determine the critical aspects and their conse-
quences and to draw conclusions on the most efficient system design a generic model will be devel-
oped, which includes data on energy requirements and costs of the processes in the green gas supply
chain. The model is considered to be 'generic' because the used methods are not fixed for a specific
geographical area and can be applied to various green gas supply chain configurations. The generic
model focuses on the Dutch situation for green gas supply.
1.1 Relevance of this Research
The Dutch government set the goal to realize a 20% sustainable energy share by 2020, relative to the
1990 level (VROM, 2007). According to Welink et al. (2007) about 10% of the natural gas in the
Netherlands can be replaced by green gas in the near future (2030). Bio-based gas (biogas) is being
produced mainly from the treatment of wet organic waste streams in the absence of oxygen, referred to
as anaerobic digestion. Currently, practically all biogas produced is utilized in combined heat and
power (CHP) units to produce electricity and heat (Mann et al., 2009). Since this heat can barely be
fully used (Gebrezgabher et al., 2010) the efficiency of biogas when applied in a CHP is likely to be
lower than biogas that has been upgraded to green gas and injected into the gas grid (Gebrezgabher et
al., 2010; Welink et al., 2007 & Bekkering et al., 2010).
Bekkering et al. (2010) reviewed the possibilities for optimization in the green gas supply chain. They
state that knowledge about this supply chain is available on three levels, being macro, meso and micro.
Macro-level knowledge is e.g. about the biomass potential of a country or how this is related to gov-
ernmental sustainability goals. On the opposite is the micro-level knowledge which focuses on e.g.
technical and economical aspects of specific technologies. The level in between, the meso-level, in-
volves system integration (Bekkering et al., 2010). This meso-level is relevant for the understanding of
11

energy systems and changes within energy systems (Schenk et al., 2007). According to Bekkering et
al. (2010) the meso-level is important when designing or engineering systems. They found that there is
insufficient knowledge about this meso-level at this moment. Political dissonance in energy transition
strategies partly results from a knowledge gap in energy systems' meso-level dynamics (Schenk et al.,
2007).
Bekkering et al. (2010) address the need for research on how different components of the green gas
supply chain can be combined in order to optimize the supply chain of green gas. Besides that they
address the need to gain insight in optimization measures related to capacity benefits or application
properties. Optimization of the overall chain is essential for a feasible expansion of green gas produc-
tion (Mann et al., 2009). Thus, gaining knowledge about system integration is indispensable for further
developments of green gas initiatives and is of great importance regarding the potential of green gas to
contribute to the sustainability goals.
1.2 Research Outline
Within the transition from the current energy system to a new, more sustainable one, a lack emerges
on the meso-level knowledge of the green gas supply chain. To fill this gap, relations between the
steps of the green gas supply chain (from an energetic and economic perspective) and to what extent
these relations could lead to chain optimization are identified.
1.2.1 Research Aim
The aim of this research is to identify which steps in the green gas supply chain are susceptible for
optimization (being minimization of the energy requirements and costs) and which measures could
result in optimization throughout the chain. A generic model will be developed to determine the criti-
cal aspects in the supply chain and their consequences and to draw conclusions on the most efficient
system configuration. The model will include data on energy requirements and costs of all processes
(incl. different capacity ranges) in the green gas supply chain and focuses on the Dutch situation.
1.2.2 Research Question
Which system design of a green gas supply chain is most efficient from the energetic and economic
perspective, based on a generic model approach?
1.2.3 Sub Questions
1 Which routes can be considered in the green gas chain, and how are the steps interrelated?
2 How can a green gas supply chain be generically modeled?
3 What are critical choices when designing a green gas supply chain and what are their conse-
12
quences?
4 What is the effect of increasing scale to the costs and energy requirements in each process step?
5 What are the effects of different system configurations on the total energy requirements and costs
of the green gas supply chain, according to model calculations?
6 Can transition points in the system configuration be identified with regard to energy requirements
and costs?

1.3 Structure of this Report
In chapter 2 the process steps of the green gas supply chain and their reciprocal relations are dis-
cussed. In chapter 3 the methodology used in this research is explained. Section 3.1 describes the sys-
tem design and research boundaries of this research, section 3.2 focuses on the generic model ap-
proach. In section 3.3. the approach to the economics in the green gas supply chain (GGSC) are dis-
cussed, followed by the approach to the energy requirements (in Section 3.4). In section 3.5 methods
are discussed to find the susceptibility within the GGSC for optimization. In chapter 4 the results are
presented, followed by the conclusion in chapter 5. At last, the results and conclusions of this re-
search are discussed (chapter 6), where after some recommendations for further research are given
(chapter 7).
13

2 GREEN GAS SUPPLY CHAIN
The aim of this chapter is to give an overview of the basic steps in a green gas supply chain. In general
a complete bio-energy system includes feedstock production, conversion and conditioning technolo-
gies and energy allocation (Buchholz et al., 2009). Considering the green gas supply chain (GGSC)
four basic steps can be identified, being (i) feedstock supply, (ii) anaerobic digestion, (iii) upgrading of
biogas and (iv) compression and injection of upgraded biogas (green gas) into the natural gas grid,
presented in Figure 2.1. These process steps are discussed in this chapter. Feedstock supply (cultiva-
tion and harvesting) has not been included in this research.
Feedstock
supply
Transport
of Biomass
Figure 2.1: Basic steps in a green gas supply chain.
2.1 Basic Steps
2.1.1 Anaerobic Digestion
The process of biologically decomposing of organic material, in the absence of oxygen, is called an-
aerobic digestion and produces biogas and digestate (digested residue). Biogas is a mixture consisting
of mainly methane (50 – 75 vol% A ) and carbon dioxide (25 – 45 vol%), furthermore traces of a.o. wa-
ter, hydrogen sulfide, ammonia, oxygen and hydrogen can be found (Poeschl et al., 2010; FNR, 2009).
An overview of all components present in biogas is presented in Appendix I.
The amount of biogas produced and its quality is highly dependent on the feedstock digested. Since all
biomass is composed of carbohydrates, proteins and fats the biogas and methane production per sub-
strate can be expressed as a function of the composition in these compounds, see Table 2.1. Anaerobic
digestion is common practice at landfills, wastewater treatment plants, composting plants and food and
agricultural residue processing (Abatzoglou et al., 2009).
Table 2.1: Biogas production and methane content per substrate (FNR, 2009).
Biogas production (m 3 / tonne o.d.m. a ) Methane content (vol%) A
Carbohydrates 700 – 800 50 – 55
Proteins 600 – 700 70 – 75
Fats 1,000 – 1,250 68 – 73
a Organic dry matter (o.d.m.)
Anaerobic
Digestion
When processing agricultural products by anaerobic digestion, a distinction can be made between cen-
tralized and decentralized digestion. In the Netherlands 37% of all biogas was produced at farms (de-
A Percentage of total gas volume (vol%)
Biogas pipeline
& compression
Gas Upgrading
Green gas pipeline
& compression
Compression &
Injection
15

centralized) in 2008 (CBS, 2009). Small-scale digesters process typically 10,000 tonne per annum
(Pöschl et al., 2010; Gebrezgabher et al., 2010), which is 36,000 tonne per annum
(>500 Nm 3 /h biogas). In the Netherlands the average farm-scale biogas production is approximately
300 Nm 3 /h biogas (Bekkering et al., 2010). According to Jingura & Matengaifa (2009) centralized
(large scale) anaerobic digestion is standard technology in Europe.
The abovementioned 50:50 manure-to-maize ratio is considered to be common practice in Europe,
which is related to European regulations (EC 1774/2002 & EC 208/2006) prescribing that a maximum
of 50% (weight percentage) of co-products (products other than manure) is allowed to be digested
when the digestate will be used as fertilizer on agricultural land (Gebrezgabher et al., 2009). Pöschl et
al. (2010) report that 75% - 80% of the biogas plants operate with co-digestion of cattle manure and
maize silage.
2.1.2 Gas Upgrading
Raw (bio)gas derived from anaerobic digestion has to be upgraded in order to comply with natural gas
quality for transmission by the Dutch natural gas grid. In this research upgrading is defined as the re-
moval of CO2, H2S, H2O, NH3 (and some trace components) from biogas in order to realize a methane
content of >95% (Reppich et al., 2009; Electrigaz, 2008). The product gas is referred to as 'green gas'.
Upgrading techniques can basically be divided into three categories, being biological, physical and
chemical upgrading, or a combination of the latter two. Biological processes are currently applied
solely for the removal of H2S from raw biogas (Petersson & Wellinger, 2009; Lems, 2010), not yet for
complete biogas upgrading. Physical upgrading techniques use pressure and/or temperature effects for
methane purification, where chemical processes use chemicals to realize specific absorption properties
in order to dissolve the contaminants.
In this section the following upgrading techniques will be discussed: pressurized water scrubbing
(PWS), pressure swing adsorption (PSA), chemical scrubbing, membrane separation and cryogenic
separation.
Pressurized Water Scrubbing (PWS)
This technology is based on purely physical properties of gases. The difference in solubility character-
istics of carbon dioxide (CO2), hydrogen sulfide (H2S) and ammonia (NH3) compared to methane
(CH4) is used by this technology for gas separation. Using water as scrubbing agent, carbon dioxide,
hydrogen sulfide and ammonia are washed out counter-currently in a pressurized (

clean water or energy for regeneration (Wellinger & Lindberg, 1999; Reppich et al., 2009). In this case
regeneration is often realized by stripping the solvent with air or by slight heating of the solvent, for
the controlled release of CO2, H2S and NH3 (DENA, 2009). This energy or water requirement is the
main reason that this technology is often to be found at sewage water treatment plants, which are able
to supply water at low costs (Bruinsma, 2007). According to Petersson & Wellinger (2009) PWS is the
most applied biogas upgrading technique.
Pressure Swing Adsorption (PSA)
With pressure swing adsorption carbon dioxide is separated from the (bio)gas stream by specific ad-
sorption characteristics on a certain adsorbing material, which generally is activated carbon or molecu-
lar sieves (zoelites) (Petersson & Wellinger, 2009). Compressed biogas (0.5 – 1.4 MPa) is cooled to <
40 °C and enters the PSA column where carbon dioxide will be adsorbed by the adsorbing material
(Reppich et al., 2009). After the methane rich gas has left the column, the pressure inside is decreased
to atmospheric pressure in order to regenerate the absorbing material (CO2 is being released again)
(Klinski, 2006). In order to realize high regeneration of the adsorbent, the last step generally involves
vacuum pressure. Therefore, this technology is also referred to as Vacuum Pressure Swing Adsorption,
VPSA (DENA, 2009; Reppich et al., 2009). In practice four, six or nine vessels are being used to op-
erate these processes of adsorption and desorption of CO2. Since H2S is adsorbed irreversibly, the ad-
sorbing material will get heavily damaged when adsorbed H2S is brought into contact with water or
moisture (Persson, 2009). Therefore hydrogen sulfide removal and gas conditioning are required be-
fore PSA is applied. However, these steps are generally included in full scale PSA units.
Chemical scrubbing
Just as water scrubbing, chemical scrubbing uses solubility characteristics of gases in some agent.
However, with chemical scrubbing the solubility characteristics are enforced by addition of specific
chemicals to the scrubbing agent in order to realize high dissolving rates, at atmospheric pressure
(DENA, 2009; Persson, 2003). The chemical binding forces are stronger than the purely physical
forces, meaning that a much larger loading of the scrubbing agent can be achieved (Urban et al.,
2009). The chemicals that are often used are mainly Monoethanolamine (MEA) or Diethanolamine
(DEA). Therefore this technique is also known as Amine wash (Reppich et al., 2009). A drawback of
these technologies is that regeneration of the scrubbing agent has high energy requirements. Generally
the chemical sorbents are regenerated by a high thermal energy input, e.g. by boiling of the scrubbing
agent or by addition of steam (DENA, 2009; Persson, 2003).
Membrane separation
By using a membrane gas separation takes place through a mutual difference in transport rates of
components through a membrane. Whether or not gas is transported through the membrane depends
on particle size or the affinity (de Hullu et al., 2008). The rate of permeation is determined by the dif-
fusion characteristics (difference in partial pressure) of the components (Kapdi et al., 2005). Accord-
ing to Reppich et al. (2009) and Kapdi et al. (2005) the permeability of CO2 and H2S are 20 and 60
17

times, respectively, higher than the permeability of methane. Generally, 2.5 – 4.0 MPa is needed to
operate the process (Hagen et al., 2001). Depending on the exact membrane used and the exact biogas
composition, pre treatment of biogas might be required in order to avoid damage or fouling of the
membrane (Wellinger & Lindberg, 1999; de Hullu, 2008). Usually biogas passes through a filter in
order to capture aerosols, water or oil droplets before entering the membrane (Petersson & Wellinger,
2009).
Cryogenic separation
Cryogenic separation (or low temperature condensation and distillation) involves separation based on
physical gas properties (Kapdi et al., 2005). By a combination of compression and cooling, gases are
liquefied at their specific dew point (Reppich et al., 2009). Biogas is compressed to approximately 8.0
MPa and cooled to – 45 °C in order to produce liquefied CO2 (Kapdi et al., 2005). However, these op-
erating conditions vary per supplier. De Hullu et al. (2008) report information of the cryogenic tech-
nology of DMT (a Dutch supplier of biogas upgrading facilities) that operates at –90 °C and 4.0 MPa.
Low temperature distillation is a very effective technology to separate different compounds from bio-
gas. Although the energy requirements of the cryogenic technology are substantial (Zwart, 2009), it
has two major advantages. The product gas (98% methane) becomes available at operating pressure.
Besides, the waste stream from cryogenic upgrading, being liquid CO2, has a significant market value
(Janssen et al., 2010).
2.1.3 Compression & Injection
Utilizing biogas in the national gas grid requires gas conditioning and upgrading (see section 2.2) to
specific grid specifications, addition of THT (Tetrahydrothiofeen) for odorization, compression to grid
pressure and continuous measurements of gas quality and specifications. Since the Netherlands con-
sists of different grid types, gas specifications can vary. In this section the different Dutch gas grids
are discussed. This part is based on previous IVEM research done by Braaksma (2010) on the techni-
cal potential for green gas utilization, documentation from Gas Transport Services (GTS, 2009) and
interviews with experts from KEMA (Vlap, 2010; Holstein, 2010)
Grid Taxonomy
Green gas is currently only injected in the Groningen quality natural gas (G-gas) network. G-gas
transport in the Netherlands is done on three levels. Upstream the High Pressure Grid (HG) operates at
a pressure of 6.7 MPa (ranging from 6.0 – 8.0 MPa). The HG is connected to large storage facilities
resulting in a great ability to handle seasonal fluctuations caused by varying demand. Furthermore the
HG is connected to the downstream Regional Grid (RG), large industry and border connections with
neighboring countries. Downstream, gas is transmitted by the RG, operating at a pressure of 4.0 MPa.
This RG is connected to industry and to gas reduction stations for pressure reduction to the distribution
network. The distribution network operates at pressures

Netherlands (Tilburg et al., 2008a; Vlap, 2010). In Table 2.2 the main characteristics of the different
grid types are presented.
Table 2.2: Overview of Dutch gas grid taxonomy (Braaksma, 2010; GTS, 2009; Vlap, 2010).
Characteristics High Pressure Grid
(HG)
Regional Grid (RG) Distribution Grid
(DG)
Pressure 6.7 MPa 4.0 MPa

Gas specifications and interchangeability
Raw bio-based gases cannot be directly injected into the natural gas infrastructure, gas upgrading is
needed to comply with the specifications of the specific infrastructure. The exact specifications to
which green gas should comply in order to be injected in the natural gas grid in the Netherlands are
presented in Appendix I. Changes in the gas composition might have negative downstream effects on
the gas infrastructure and/or end use appliances. Significant concentrations of CO2 in the high pressure
natural gas infrastructure might result in pipeline corrosion, particularly when coming in contact with
water or water vapor (Levinsky, 2009). Similar effects on pipeline integrity occur when H2S is present.
Ammonia corrodes copper and some rubbers (present in domestic equipment) and siloxanes present in
gas might deposit as sand in equipment when combusted (Levinsky, 2009).
Furthermore, hydrogen, carbon monoxide and carbon dioxide radically change combustion behavior
(Levinsky, 2009). E.g. the concentration in which hydrogen is present in gas determines the burning
velocity, resulting in higher burning velocity when higher concentrations of hydrogen are present and
vice versa (Levinsky, 2009; Gersen et al., 2008). Since the gas infrastructure and downstream appli-
ances are designed for a very specific gas composition, small changes might have major safety conse-
quences. Hence, biogas upgrading is required for injection in the natural gas network.
2.2 Relations within the GGSC
A supply chain in which biomass is converted and finally utilized as an energy source is a complex
system in which numerous logistic options are available (Diekema et al., 2005). Locating a facility is
seen as a critical aspect of strategic system design in a wide range of sectors (Rentizelas & Tatsiopou-
los, 2010). The decision on a certain location might have great effects on the system design and will
also affect investment and operational costs since both upstream and downstream consequences have
to be taken into account (Rentizelas & Tatsiopoulos, 2010). Besides, the location of a bio-energy facil-
ity affects the optimal scale of the plant (Polman et al., 2007). According to Amigun & Blottnitz
(2010) the consideration whether to build small/medium scale decentralized biogas plants or large
scale centralized biogas plants is of great importance.
Scale effects vs. transport
When considering the economic relations within the green gas supply chain, economies of scale might
have influence on the total costs for green gas. For both digestion and upgrading the capacity scale
influences the specific costs (Welink et al., 2007). Taking advantage of economies of scale for diges-
tion would often require centralized anaerobic digestion (cD), meaning that transport of biomass from
numerous sources to the cD plant is needed (Ghafoori & Flynn, 2007). In this case the upgrading tech-
nology would also benefit from economies of scale since large amounts of biogas are produced. An-
other option to benefit from economies of scale in the upgrading step is to operate decentralized diges-
tion (dD) at various locations and transport of raw biogas to a centralized upgrading (cU) plant, in this
way biomass transport is avoided. Such a system design is referred to as a "Biogas Hub" or "Green
Gas Hub" and is gaining popularity in The Netherlands. The first project to realize such a hub in the
Netherlands has started since February 2010 and more projects are planned (PNG, 2010).
20

Combining technologies
According to Ooka & Komamura (2009) the optimal design of an energy system is, amongst others,
dependent on the choice for certain technologies within the system. Application of biogas in the natu-
ral gas grid requires upgrading and gas conditioning, after which it must be compressed to grid pres-
sure to be injected. The choice for a certain upgrading technique can influence downstream costs (en-
ergetic and economic) for compression (van Tilburg et al., 2008b). This might have major conse-
quences for both the energetic and economic efficiency of the green gas supply chain.
21

3 METHODOLOGY
In this research a generic model approach to the green gas supply chain (GGSC) is presented. Section
3.1 deals with the system design of this research and describes the system boundaries. In section 3.2
the generic model approach is discussed. In section 3.3. the approach to the economics in the GGSC is
discussed, followed by the approach to the energy requirements (in Section 3.4). Section 3.5 deals
with the methods used to find the susceptibility for optimization within the GGSC.
3.1 System Design of this Research
The focus of this research is on the energy requirements and costs of green gas production. Revenues
of green gas are excluded since these are highly dependent on subsidy regulations, which may vary
over time. The system boundaries of this research are presented in Figure 3.1. This figure is specified
for the system design of the assessment of the energy requirements considered in this research. Con-
cerning the economics of the GGSC the figure is similar, except for the fact that 'Indirect' effects (such
as costs embedded in the capital needed to produce the digestion or upgrading facility) are excluded.
Furthermore, the focus is on the Dutch situation for green gas.
Feedstock supply has not been included, since feedstock supply is needed for all supply chain configu-
rations. Therefore, this will not be a critical factor in the identification of the susceptibility to optimi-
zation of the supply chain.
Transport of substrates (manure and maize silage) and digestate is included since that is assumed to be
inevitable when performing centralized anaerobic digestion, besides, strong indications exist that
transport is a critical factor in the GGSC (Ghafoori et al., 2007 and Caputo et al., 2005). Transporta-
tion of gas is assumed to be done only by HDPE pipelines. These pipelines are assumed to transport
either biogas or green gas (dependent on the system configuration) at a pressure of 0.5 MPa. Digestion
of substrates is in this research restricted to a manure-to-maize ratio of 50:50 (as in Pöschl et al.,
2010), with a capacity range of 100 – 2000 Nm 3 /h biogas. Injection into the HG (6.7 MPa) is excluded
from this research since the grid operator (Gas Transport Services) does not allow for injection into
this grid (Vlap, 2010).
23

Figure 3.1: System design of the generic GGSC model developed.
Direct energy requirements considered in this research are translated through the direct fossil fuel use,
electricity use and heat consumption. The indirect energy requirements considered are the energy re-
quirements embodied in the vehicle, maintenance of the road, production of the materials for pipelines
and the conversion of electricity to primary energy at centralized power plants. Primary energy em-
bodied in the vehicles represents the energy needed to produce the vehicle materials and for the pro-
duction of the spare parts that will be applied over its useful life.
The energy requirements for injection are reflected through compression to grid pressure. In the eco-
nomic assessment costs for injection are composed of both compression costs and costs for the physi-
cal connection to the grid (including measurement and control equipment). Energy requirements and
costs for odorization of green gas are not included in this research since this is still in an experimental
phase for the relatively low flow rates of green gas (Vlap, 2010). Also digestate processing is not in-
cluded, however it is assumed that manure transport to e.g. a centralized digester inevitably results in
digestate transport back to the farms. It is assumed that 90% of the total substrate input will become
digestate (Bermejo & Ellmer, 2010).
3.2 A Generic Model Approach
A generic GGSC model is in this report defined as a model that can be applied to various GGSC con-
figurations and is not fixed to a specific geographical area within the Netherlands.
24
Manure
Maize
silage
Refinery
Indirect Energy Requirements
Transport of
Biomass
Embodied
in vehicle
Materials road
Anaerobic
Digestion
- Manure
- Maize silage
Direct Energy Requirements
Maintenance
road
Disposal
Biogas pipeline
& compression
Digestate
processing
Materials
pipeline
Capital goods
Gas Upgrading
- PWS
- Membrane
- PSA
- Cryogenic
- Chemical
scrubbing
Electricity production
(Efficiency: 40%)
Green gas pipeline
& compression
System Boundary
Compression &
Injection
- DG (

3.2.1 Pathways
Numerous pathways from biomass to green gas injection can be considered. An overview of the possi-
ble pathways is given in Figure 3.2.
Feedstock
Transportation of Biomass
Anaerobic Digestion
Biogas pipeline & compression
Gas Upgrading
Green gas pipeline & compression
Gas Compression & Injection
National gas grid
D = Digestion
U = Upgrading
I = Injection
d = decentralized
c = centralized
b = biogas transport
g = green gas transport
t = truck transport Pathway: 1 2 3 4 5 6 7 8
dD-dU-dI
Figure 3.2: Generic overview of possible pathways in green gas supply chain.
dD-dU-g-cI
The figure shows an overview of the possible pathways from a starting point to a destination. When
starting, biomass can either be digested on the spot (decentralized) or transported by truck to a central-
ized digestion plant, where after the biogas is upgraded. When upgraded, green gas can be injected in
the DG or in the RG. Approaching this figure from a system-analysis point of view it can be stated that
transportation is a critical choice in designing a green gas supply chain, since digestion, upgrading and
injection are inevitable (also reported by Ghafoori et al., 2007). In practice transportation of biomass
or biogas means that larger capacities are being digested or upgraded, respectively. The size of the
dots in Figure 3.2 represent the capacity processed, indicated as 'large' or 'small'. It is likely that the
large dots can benefit from economies of scale.
dD-b-cU-cI
The methodology to model these pathways generically is derived from a method used in the field of
Operations Research to find the shortest path in transportation networks. This method is known as the
Shortest Path method, aiming at finding the shortest path P from a source-node s to a destination-node
d in a graph G, where the accumulated costs of the edges (flows) and nodes have to be minimized
(Post, 2004). The first and most classical Shortest Path procedure was described by Edsger Dijkstra in
dD-b-cU-g-cI
t-cD-b-cU-g-cI
t-cD-cU-g-cI
t-cD-b-cU-cI
t-cD-cU-cI
25

1959 and is widely used in, amongst others, network routing protocols (Campos & Ricardo, 2008;
Dreyfus, 1968).
Figure 3.3 presents a methodological approach to the possible pathways from biomass to biogas utili-
zation in an existing infrastructure. In Figure 3.2 all possible pathways are presented. However, not all
of those pathways will be assessed in this research since some are only realistic for very specific geo-
graphical areas. Figure 3.3 is derived from Figure 3.2 and represents an overview of the pathways that
can be modeled generically.
Figure 3.3: Generic model approach of the GGSC.
The upper part of the graph represents decentralized treatment (gas production, upgrading and injec-
tion), where the bottom represents centralized treatment. Node 'dI' and 'cI' represent injection into the
distribution network and in the RG respectively. The characters t, b and g on the edges in the graph
represent transport (t = truck, b = biogas, g = green gas). In order to use this generic model for model
calculations, the edges and nodes will be assigned values representing the associated energy require-
ments or costs. The generic model developed in this research is based on the following mathematic
formulation, derived (where after slightly adapted to fit the GGSC) from the generic equation for
shortest path methods (after Festa, 2006):
26
∑
( e − + N ) + N
[1]
Minimize: ( j 1,
j)
( j)
0
j∈[
1,...
n]
Where: j = node; e = edge flow costs; N = node costs; N0 = starting point; n = number of nodes in the pathway.
However, in this research it is assumed that feedstock supply (node N0 in the equation) is excluded,
meaning that N0 can be set to 0. This results in following equation:
∑
Anaerobic Digestion Gas Upgrading Compression & Injection
dD dU dI
Decentralized Decentralized
s d
b g g
Centralized t Centralized
cD cU cI
Anaerobic Digestion Gas Upgrading Compression & Injection
Minimize: e + N )
[2]
( ( j−
1,
j)
j∈[
1,...
n]
( j)

3.2.2 Biomass and Biogas Transport Distances
The net costs for transportation of biomass to the digester are reported as a critical factor when design-
ing a biogas system (Ghafoori et al., 2007, Caputo et al., 2005; Hiremath et al., 2007). Changes in the
costs for transportation influence the optimal system design (Ghafoori et al., 2007). In order to deter-
mine the energy requirements and costs of transportation of biomass or biogas from farms to gas grids,
information about the distances is in this model determined by a simple heuristic method, being the
"Centre of Gravity" method D . Based on Euclidean distances the method is used to determine the best
location for a facility from an infinite number of potential locations with a fixed amount of demanders
(or farms to be suppliers in case of this research). The method requires the input of coordinates (x, y)
and flows. Within the GGSC the facility to be located is referred to as 'centre', representing a location
where all flows are bundled. Following equations determine the x and y-coordinates of the 'centre' (af-
ter Teunter, 2010):
( ∑ f j x j ) /( ∑ f j
c ∑ f j y j ) /( ∑
x )
= c
j j
y ( f )
[3]
= j j
Where: xc = x-coordinate of centre; yc = y-coordinate of centre; j = farm; fj = flow of farm j; xj = x-coordinate of
farm j; yj = y-coordinate of farm j.
Subsequently the associated Euclidean distances can be derived as following (after Rentizelas &
Tatsiopoulos, 2010; Valezquez-Marti & Fernandez-Gonzalez, 2010):
Distance to centre (m):
Distance from centre to grid (m):
d ∆
j
2
2
j,
c = ( ∆x
j,
c ) + ( y j,
c )
[4]
d ∆
2
2
c,
g = ( ∆xc,
g ) + ( yc,
g )
[5]
Total distance from farms to grid: ( ∑ d + d
[6]
= d t
j,
c c,
g
j
)
Where: d = distance (m); c = centre; j = farm; g = gas grid; t = total.
The coordinates used in this research originate from the RD-coordinates (Rijksdriehoeks-coordinaten)
system, which is a system that is extensively used in the Netherlands.
In this research eight farm locations and one gas grid location were assumed, in an area nearby Gron-
ingen (NL). Appendix V includes the x- and y-coordinates of that specific area. The distances found
with the Centre of Gravity method are in this research used for determination of energy requirements
and costs of transportation. It is assumed that the transport distances are equal for both pipeline trans-
port of gas and truck transport of biomass.
3.2.3 Gas Compression
Compression of gas is required for transport of gas and for injection into the gas grid. The type of gas
to be compressed determines, amongst others, the electricity demand of the compressor. This is caused
by the difference in characteristics between gases. Biogas consist of about 40% CO2, resulting in a
D The Centre of Gravity method is used in the field of Operations Research for the warehouse location problem.
27

higher specific electricity demand for compression compared to compression of green gas (>95 CH4).
Therefore, the distinction between compression of green gas and biogas is important. In this research
gas injection is done by compression of green gas and transportation is done by compression of both
biogas and green gas (depending on the system configuration). So, the exact energy requirements and
costs for compression are dependent on the type of gas compressed, the gas compression capacity and
the pressure lift required. Compression for transportation purposes was assumed to be done to 0.5
MPa, compression for injection in DG and RG is done to 0.8 MPa and 4.0 MPa respectively.
A mathematical approach is used to determine the electricity demand, and hence the costs and primary
energy requirements, of compression. This methodology was presented in Damen (2007) and cited in
Koornneef et al. (2008). Following equation shows the generic method to determine the specific work
for compression of gas to a certain pressure:
28
C2
⎡⎛
p ⎞ ⎤
2 = C ⎢
⎜
⎟
1 * −1⎥
⎢⎣
⎝ p1
⎠ ⎥⎦
W [7]
Where: W = specific work (kJ/kg gas); C1,2 = constants; p1 = suction pressure; p2 = final pressure.
In this report p1 and p2 can be considered as variables, since p1 depends on the technology used for
upgrading biogas to green gas (see section 2.2) and p2 depends on the purpose (transport or injection)
for compression. After determining the specific work needed for compression, the value is converted
to primary energy requirements. In Appendix II the methodology is specified, also the constants and
variable used for the determination of the energy demand for compression of both biogas and green
gas (methane) are given in this appendix.
3.3 Economics of the GGSC
Calculations on the costs of digestion are based on information given mainly by Fraunhofer (Urban et
al., 2009), which is a German scientific research institute on technical innovation. Additional to the
Fraunhofer report (Urban et al., 2009), data from DENA (2009) was used, which is an extension of
Urban et al. (2009). These reports were also used to gain part of the information about costs for biogas
upgrading. According to Poeschl (2010) Germany is at this moment leading in biogas technology and
therefore Urban et al. (2009) is in this research seen as valuable literature. Little information is avail-
able on the total costs for biogas upgrading for different gas capacities, numerous studies (a.o. Tilburg
et al., 2008a & 2008b; Petersson & Wellinger, 2009; Reppich et al., 2009;) performed on the econom-
ics of upgrading based their conclusions on Urban et al. (2009).
In the generic model all costs are calculated to ct€/MJ produced. Basically the total costs are composed
of investment costs and variable costs. A capital recovery factor (CRF) is used to determine the annual

capital costs based on an annuity, resulting from the investment, by including the depreciation period
and interest rates (or WACC E ).
CRF
Where: CRF = Capital Recovery Factor (-), i = Interest rate (%), n = depreciation period (yrs)
The depreciation period (n) was set to 12 years since Dutch subsidy regulations are fixed for this pe-
riod of time (Lensink et al., 2010). In practice the period for which an initiative is granted subsidy de-
termines the acceptable depreciation period (Vlap, 2010). Similar effects of legislation on the effective
lifetime of a biogas installation were reported by Walla & Schneeberger (2008). Solely pipeline con-
struction costs are based on a depreciation period of 30 years (Koornneef et al., 2008). The interest
rate is included in the WACC and is set to 7.8% which was reported by Lensink et al. (2010).
Table 3.1 gives an overview of the main parameters concerning the economic analysis.
Table 3.1: Main parameters used in the economic analysis of the generic model.
Parameter Value Unit Reference
Depreciation period 12 Years Lensink et al., 2010
WACC a 7.8 % Lensink et al., 2010
Operational hours 8000 Hours / year Vlap, 2010
Electricity price 0.10 € / kWh Janssen & Bogaard, 2009; de Hullu, 2008
Caloric value Biogas 21.5 MJ / Nm 3
Biogas yield:
- Manure
- Maize
n
i(
1+
i)
( 1+
i)
−1
= n
23
200
m 3 / tonne
m 3 / tonne
[8]
Noyola et al., 2006; Wellinger & Lindberg, 1999;
Murphy et al., 2004
Bruinsma, 2007; Pöschl et al., 2010; Walla & Sch-
neeberger, 2008
a WACC (Weighted Average Cost of Capital) is the minimum return that should be earned to satisfy providers of the capital,
or that might be earned by an investment elsewhere. In the Dutch situation it is assumed to consist of 80% liability at 6%
interest and 20% equity at 15% interest.
More specific information was found about every step in the green gas supply chain. This data is pre-
sented in Table 3.2.
E Weighted Average Cost of Capital (WACC)
29

Table 3.2: Specific economics of the green gas supply chain.
Process Value Unit Reference
Transportation of Biomass
(manure ; maize)
- Fixed
- Variable
(1.5 a ; 0.35)
(0.04 ; 0.05)
€ / tonne
€ / tonne.km
Luesink, 2010; Walla & Schnee-
berger, 2008
Walla & Schneeberger, 2008
Anaerobic Digestion (avg.) b 0.65 ct€ / MJ Urban et al., 2009
Biogas Pipeline (construction) c
- 110 mm (avg.)
- 310 mm
90
190
silage. The specific costs for transportation are composed of fixed costs and variable costs (derived
30
€ / m
€ / m
KEMA; Enexis, 2009 d
KEMA
Gas Upgrading (avg.) e 0.35 ct€ / MJ Urban et al., 2009
Gas Compression
0.8 MPa
0.8 – 4.0 MPa
Green gas injection facility
(

from Walla & Schneeberger, 2008). Manure is assumed to be transported at 0.04 €/tonne.km and
loaded and unloaded (fixed costs) at 1.5 €/tonne. For maize these values are 0.05 €/tonne.km and 0.35
€/tonne respectively. The large difference between loading/unloading costs of the substrates can be
explained by the legal obligation, according to Dutch law, to sample manure before transportation,
accounting for approximately 1.0 €/tonne (Luesink, 2010). The same costs for loading/unloading hold
for transportation of digestate. These values and their reference can be found in Table 3.2. The specific
costs are subsequently multiplied by the distance and converted to ct€/MJ. In this calculation the costs
for return trips with digestate (being 90% of the total substrate input, after Bermejo & Ellmer, 2010)
are included.
Anaerobic Digestion
As mentioned in section 3.3 the economics of digestion were derived from Urban et al. (2009). In that
report all specific costs that are involved in the construction and operation of a digester were specified
in detail. The authors discuss two types of digesters: a vessel for digestion of a biomass input with a
manure-to-maize ratio of 90:10 and one with a manure-to-maize ratio of 10:90. Since the largest share
of digesters process biomass with a manure-to-maize ratio of approximately 50:50 (Pöschl et al., 2010)
all costs accompanying digestion of 50:50 (manure-to-maize) were calculated based on the costs for
the plants given by Urban et al. (2009). The costs of a 50:50 plant were assumed to be directly propor-
tional to the average costs of the 90:10 and 10:90 plants. The investment costs were multiplied by the
CRF to gain annual capital costs. Both the annual capital and operational costs were subsequently cal-
culated to specific costs, expressed in ct€/MJ, based on the parameters given in Table 3.1.
Biogas Pipeline Construction
The 'Centre of Gravity' method (explained in section 3.2.1) was used to calculate transport distances.
The material of the biogas pipeline is assumed to be high-density polyethylene (HDPE). Two diame-
ters were used: 110mm for transportation of a single biogas flow from a digester to a 'centre' (see
3.2.1) and 310mm for a bundled flow of biogas from the 'centre' to the gas grid. These pipelines were
assumed to have specific costs of 90 €/m and 190 €/m respectively for the total construction (see Table
3.2). These costs are indicative and derived as rounded values. Total construction costs are composed
of costs for materials, construction, right of way, labor and miscellaneous and are assumed to be 40%
'difficult' and 60% 'easy'. The difficulty is determined by the obstacles that might hinder the construc-
tion (e.g. if the pipeline will cross road-, rail- or water ways). The investment costs are calculated to
annual capital costs with the CRF method. For pipeline construction a depreciation period of 30 years
is assumed (after Koornneef et al., 2008).
Gas Upgrading
Information about the costs for upgrading were found in Urban et al., 2009 & DENA, 2009 (PWS,
PSA, chemical scrubbing); de Hullu et al., 2008 (membrane, cryogenic); Dirkse, 2007 (PWS); Jensen
& Jensen, 2000 (membrane); Janssen & Bogaard, 2009 (cryogenic); Colsen, 2009 (cryogenic) and
KEMA (cryogenic). A comparison of specific costs of the upgrading techniques is presented in Ap-
31

pendix III. However, it should be noted that information about the economics of cryogenic and mem-
brane separation was directly or indirectly derived from quotations from suppliers of upgrading tech-
niques. Since biogas upgrading has not been done very extensively yet it is likely to assume that com-
mercial interests are translated through these quotations, meaning that given costs cannot be inter-
preted as reliable. From literature study it can be stated that reliable information on the economics of
upgrading techniques, especially about cryogenic separation and membrane separation, is difficult to
find.
The data given by Urban et al. (2009), DENA (2009) and Dirkse (2007) was specified in detail, giving
a clear overview of how total costs are composed. The other references mostly specified investment
costs and energy costs. The investment costs were multiplied by the CRF (equation [8]) to gain annual
capital costs. Both the annual capital and operational costs were calculated to specific costs, expressed
in ct€/MJ, based on the parameters given in Table 3.1.In the generic model the average of each tech-
niques per capacity was taken to generate data in which commercial interest is reflected least possible.
Gas Compression
The methodology used to determine the electricity demand for compression of biogas or green gas is
explained section 3.2.2. An electricity price of 0.10 €/kWh (see Table 3.1) is assumed in this research
to determine the operational costs. The capital costs for compression (investment and maintenance of
compressor) were derived from information from KEMA. A (confidential) data analysis, combined
with the CRF methodology (equation [8]) is used to gain data on the specific costs, which is confirmed
by KEMA employees.
Green Gas Injection Facility
Injection of gas is done in an injection facility and requires compression. However, this section only
describes the costs for the injection facility. Compression of gas is discussed in section 3.2.2 and in
Appendix II.
Costs for an injection facility are solely composed of capital costs for the physical connection to a gas
grid and the costs for measurements and control equipment. The costs for the physical grid connection
are highly depending on the gas grid of choice. A connection to a DG requires an investment of ap-
proximately 25*10 3 € (KEMA; Vlap, 2010). Connection to a RG involves more sophisticated labor
(welding of a pipeline at high pressure) and materials. Consequently, a connection to a RG requires an
investment of approximately 250*10 3 € (KEMA; Vlap, 2010). Measurement and control equipment
accounts for approximately 100*10 3 €. These figures are also presented in Table 3.2. A CRF (equation
[8]) was used to calculate the investment to annual and subsequently specific costs (ct€/MJ).
3.4 Energy Requirements of the GGSC
In this section the energy requirements for the different process steps in the GGSC are discussed. The
system boundaries are presented in Figure 3.1. The energy requirements are expressed in this report as
primary energy requirements (MJpe/MJ).
32

The energy requirements of the different processes within the green gas supply chain have been re-
ported in literature, see Figure 3.4. Pöschl et al. (2010) address the need for an overall study on the
energy requirements of the total biogas production and utilization chain.
Bekkering et al. (2010)
Berglund & Borjesson (2006)
Borjesson (1996, 2008)
Braaksma (2010)
Damen (2007)
Gerin et al. (2008)
Koornneef et al. (2008)
Polsch et al. (2010)
Welink et al. (2007)
Zwart (2006)
Figure 3.4: Overview of literature about the energy requirements in the green gas supply chain.
The information presented by Braaksma (2010) was largely based on Bekkering et al. (2010), Damen
(2007), Welink et al. (2007) and Zwart (2006). Pöschl et al. (2010) used Berglund & Borjesson
(2006), Borjesson (2008), Gerin et al. (2008) and other specific references.
The data found in literature, with regard to the energy requirements of the process steps in the green
gas supply chain, are given in Table 3.3. The green gas injection facility is not included in this table, it
is assumed in this research that no energy requirements are involved.
Table 3.3: Energy requirements of the green gas supply chain (given in MJ primary energy per MJ gas).
Process Value Unit Reference
Transportation of Biomass a
-Manure
-Maize silage
1.73
4.55
MJpe / tonne.km
MJpe / tonne.km
Anaerobic Digestion (avg.) b 0.17 MJpe / MJ
Biogas Pipeline c
(grid construction)
3922 GJpe / km
Gas Upgrading (avg.) 0.09 MJpe / MJ
Gas Compression
- 0.8 MPa
- 4.0 MPa
Feedstock
supply
0.024
0.057
Transport
of Biomass
MJpe / MJ
Anaerobic
Digestion
Pöschl et al., 2010; Berglund & Borjesson, 2006; Bor-
jesson, 1996
Urban et al., 2009; Pöschl et al., 2010; Braaksma, 2010;
Berglund & Borjesson, 2006
Koornneef et al., 2008; Sakai et al., 2004; Worrell et al.,
1993
Bekkering et al., 2010; Zwart, 2009; Urban et al.,
2009 d ; Braaksma, 2010
Koornneef et al., 2008; Damen, 2007; Pöschl et al.,
2010 e ; Braaksma, 2010
a Based on 2.8 MJpe / tonne.km for manure and 7.5 MJ pe / tonne.km (incl. empty returns) for maize silage, transported over
mid-range distance (2 < 20 km). Energy requirements for fossil fuel use and deterioration of infrastructure are included. Also
primary energy embodied in the vehicle (materials, maintenance) is included, accounting for 0.04 MJ/tonne.km (Borjesson,
1996). A factor of 0.6 is used to correct for the empty returns.
b Berglund & Borjesson (2006) give 24 MJpe/MJ, Pöschl et al. (2010) give avg. 13 MJ pe/MJ, Braaksma (2010) reports 17.5
MJ pe/MJ and the values from Urban et al. (2008) are calculated from energy use (heat & electricity), where the direct electric-
ity use is multiplied by 2.5 to determine the primary energy use.
Biogas pipeline
& compression
Gas Upgrading
Green gas pipeline
& compression
Compression &
Injection
33

c Based on a pipeline of HDPE
d Calculated from energy use (heat & electricity), where the direct electricity use is multiplied by 2.5 to determine the pri-
mary energy use.
e Pöschl et al. (2010) reports 0.18 MJ/Nm 3 biogas for compression to 1.6 MPa (note: authors did not encounter conversion to
primary energy)
In a study of Koornneef et al. (2008) life cycle inventory data was given for an onshore CO2 pipeline
infrastructure. However, since that study was based on data about natural gas pipelines it was consid-
ered to be useful data in the case of green gas. This data informs about the materials and quantities
needed for such an infrastructure. Subsequently the energy requirements were found (figures on en-
ergy requirements for the materials were found in Sakai et al., 2004; Worrell et al., 1993 and Bu-
chanan & Honey, 1994). In the same study of Koornneef et al. (2008) the authors present an equation
for the calculation of energy requirements for compression, which they derived from Damen (2007),
see Appendix II. Calculating with these equations the results correspond to those given by Pöschl et al.
(2010).
Transport of biomass
The primary energy requirements for road transport of biomass are derived from Pöschl et al. (2010).
Energy requirements embodied in vehicle and infrastructure are given by Borjesson (1996). The article
by Berglund & Borjesson (2006) was used to determine a correction factor for the difference between
the energy requirements for empty or fully loaded return trips. In the article by Pöschl et al. (2010)
truck transport of different substrates (including cattle manure and maize silage specifically) was re-
ported in three distance ranges and the authors assumed empty returns. Since in this research it is as-
sumed that 90% (after Bermejo & Ellmer, 2010) of the biomass will become digestate and hence has
to be returned to the farms, it was not possible to use the values given by Pöschl et al. (2010) directly.
To correct for digestate returns a factor was used that was found by simple calculations on the energy
requirements for transportation of biomass given by Berglund & Borjesson (2006). The authors re-
ported different values for transportation including and excluding empty returns. It was found that the
difference was a factor 0.6 (MJ/tonne.km excl. empty returns divided by MJ/tonne.km incl. empty re-
turns). Consequently, the values used to express the energy requirements for transportation of biomass
in this research are 1.73 MJpe/tonne.km and 4.55 MJpe/tonne.km for transport of cattle ma-
nure/digestate and maize silage respectively. Determination of transport distances is done by the 'Cen-
tre of Gravity' method, described in section 3.2.1.
34

Anaerobic Digestion
Urban et al. (2009) reported in detail the costs for digestion of different substrate ratios, composed of,
amongst others, the costs for thermal and electrical energy supply to the digester. The authors supple-
mented detailed information on the price for heat and electricity they assumed. With that information
the costs were calculated to amounts of heat (kWth) and electricity (kWe) that is needed to operate the
specific digester (depending on capacity and substrate ratio of input). A conversion factor of 40% (ηe)
for the calculation of the primary energy input for the electricity production in a power plant is in-
cluded (see Figure 3.1). The average of the resulting energy requirements is in line with the general
values given in Berglund & Borjesson (2006), Braaksma (2010) and Pöschl et al. (2010). Appendix IV
shows the effect of substrate input ratio on the energy requirements of the digester heat demand.
Gas Pipeline Construction
The materials needed for a natural gas pipeline were reported in detail by Koornneef et al. (2008).
However, the authors supplied information about steel pipelines only. To add HDPE to the generic
model it is assumed that the same volume of material (steel or HDPE) is needed for the construction of
the particular pipeline. Therefore the values of the amount of steel needed (given by Koornneef et al.,
2008) were calculated to HDPE on basis of the specific material densities, which are 7750 kg/m 3 and
941 kg/m 3 respectively. All the materials and diesel needed for the construction were based on a 50
km pipeline in Koornneef et al. (2008), which is corrected in this research to values per meter. The
energy requirements for the construction of the pipeline materials were derived from Worrell et al.
(1993), Sakai et al. (2004) and Buchanan & Honey (1994). The distance was determined by the ‘Cen-
tre of Gravity' method, explained in section 3.2.1. It is assumed that the energy requirements for pipe-
line construction are written off in 30 years.
Gas Upgrading
In the report by Urban et al. (2009) and Dirkse (2007) the costs (€) for different upgrading techniques
(PWS, PSA, chemical scrubber), except for membrane and cryogenic separation, were specified in
detail. The costs for electricity and/or thermal energy use of the different upgrading techniques and
their capacities were used in this research to find the electricity (kWe) and heat (kWth) demands since
both references supplemented information about their assumptions on the electricity and heat price
(€/kWh). A conversion factor of 40% (ηe) for the calculation of the primary energy input for the elec-
tricity production in a power plant is included (see Figure 3.1). An average was calculated based on
the values resulting from the abovementioned methodology and the energy requirements given by
Bekkering et al. (2010) and Zwart (2009). Information about the primary energy requirements of
membrane and cryogenic separation were derived from Bekkering et al. (2010) & Zwart (2009).
Gas Compression
The method to determine the energy requirements for compression of gas for both transportation and
injection purposes is stated in section 3.2.2.
35

Green Gas Injection Facility
It is assumed that no energy requirements are involved in the green gas injection facility. The energy
requirements for injection of green gas into the gas grid consider solely electricity demand for com-
pression. The method to determine the energy requirements for compression for injection purposes is
discussed in section 3.2.2.
3.5 Susceptibility for Optimization
As stated in section 2.2 system integration might result in scale benefits, transportation of biogas or
biomass and efficiency enhancement as a result of the choice for certain technologies. These aspects
determine the total chain energy requirements and costs of a certain green gas production pathway.
The generic model developed in this research is used to identify the effects of these aspects on the total
energy requirements and costs of the process steps in the GGSC. This section describes the methods
used for identification of possibilities for optimization. First, the method to identify scale effects is
described. Since scale effects are related to transportation in the GGSC, this aspect follows after de-
scription of the methods for scale effects identification. Then, the method to assess the effects of ap-
plication of a certain upgrading techniques is explained. Following, process for the assessment of the
effect of certain transport means and the optimal pathway is described.
Scale Effects
Sensitivity of the energy requirements and costs as a function of the capacity will be identified for
every process step in the GGSC. The data will be plotted to identify what kind of relation exists be-
tween capacity scale and the total energy requirements and costs of each step in the GGSC. A trend
line will be added to quantify the effects. The trend line with the highest correlation to the data (indi-
cated by the R-squared) will be plotted. Comparing the accompanying trend line function gives infor-
mation about which process step is most sensitive to scale effects.
Transition Points of Transport Means
Transportation is reported as a critical factor in a bio-energy chain. Within the GGSC truck transport
of biomass and pipeline transport of biogas can be considered. When biomass is transported by trucks
in order to be digested centralized, it is likely that scale effects will occur for digestion. This is in con-
trast with transportation of biogas by pipelines from decentralized digesters to a centralized upgrading
and injection plant. Consequently, transportation of either biomass or biogas cannot be considered
solely, digestion has to be included.
In this research a transition point, indicating the effect of capacity and distance on the energy require-
ments and costs, is identified. This is done by modeling the two means of transport for several dis-
tances for both the energy requirements and costs as a function of capacity, and subsequently obtain-
ing the break-even points of the lines. Plotting the break-even points in one graph gives an overview of
the optimal means transport at a certain distance and capacity.
For biomass truck transport it was assumed that digestate is transported back over the same distance as
substrates were supplied. Pipeline transport includes pipeline construction and compression of biogas
to 0.5 MPa.
36

Upgrading Techniques
Within the different pathways (section 3.2) there are five upgrading technique. One system configura-
tion is used to assess the different upgrading techniques. Pathway 3 (hub configuration) is chosen to
assess the effect of application of PWS, PSA, chemical scrubber, membrane separation and cryogenic
separation. The effect of different upgrading techniques on the total costs and energy requirements in
the GGSC is identified. Take into consideration that the data used to assess the economics of mem-
brane and cryogenic separation is uncertain. Data about these costs were reported very modest and
considered to be highly influenced by commercial interests.
Optimal Pathway
By modeling different system configurations with the generic model, possible effects of the choice for
certain pathways on the economics and energy requirements can be identified. The system configura-
tions that are chosen are presented in Table 3.4. According to section 3.2.1 five pathways can be con-
sidered. However, since the DG is limited to a green gas injection of max 150 Nm 3 /h, only four path-
ways are assessed. The pathways are expressed according to Figure 3.3. In all the pathways PWS is
used for biogas upgrading since this is the most applied upgrading technique.
Table 3.4: System designs to identify the most efficient pathways.
Fig 3.2
Pathway
Fig. 3.2 & 3.3
1 dD-dU-dI
2 dD-dU-g-cI
3 dD-b-cU-cI
8 t-cD-cU-cI
Description
Decentralized digestion of 50:50 (manure-to-maize) at 250 Nm 3 /h biogas; upgrading
(PWS) at the farm and injection in DG at farm. Pipeline of 200 m to DG assumed.
Decentralized digestion of 50:50 (manure-to-maize) at 250 Nm 3 /h biogas; upgrading
(PWS) at the farm; transport of green gas by pipelines (HDPE) over a distance of 12
km and injection in the RG (4.0 MPa).
Decentralized digestion of 50:50 (manure-to-maize) at totally 2000 Nm 3 /h biogas (=
8 farms of 250 m 3 /h); centralized (bundled) upgrading (PWS); biogas transport by
pipelines (HDPE) over a total distance of 50 km and injection in the RG (4.0 MPa).
Transportation of biomass (50 km totally); centralized digestion of 50:50 (manure-to-
maize) at 2000 Nm 3 /h biogas; centralized upgrading (PWS) and injection in the RG
(4.0 MPa). Digestate is transported back to the farms.
With regard to pathways 3 and 8 the Centre of Gravity method was used to determine the transporta-
tion distances (explained in section 3.2.1). For the locations assumed in this research a total distance of
50 km was found for transportation of both biomass and biogas. In pathway 2 it is assumed that diges-
tion and upgrading is done decentralized and that green gas is transported to the RG. The distance of
12 km is the average distance from all farms to the centre (bundle point) plus the distance from centre
to the gas grid. For decentralized digestion, upgrading and injection (pathway 1) it is assumed that a
pipeline is constructed over a distance of 200 m for injection in the Distribution Grid.
37

Interpretation of results
In order to draw conclusions on which system configuration is the optimal one, normalization is used
to combine the results of the energetic and economic assessment of the pathways. The results with re-
gard to the energy requirements and costs are considered equally important. Normalization is done by
dividing the total expenditure (energy requirements or economics) per pathway by the maximum of all
considered pathways. If this is done for all pathways in both the energetic and economic assessment,
the normalized values can be summed up and plotted in one single graph, where the pathway with the
lowest total expenditures is the optimal pathway.
38

4 RESULTS
In literature the existence of scale effects on the total chain expenditures was indicated. It was also
indicated that transportation is a critical factor. Transport of biomass or biogas is directly related to the
location (centralized or decentralized) and capacity of digestion and upgrading. Therefore, these as-
pects are discussed in each other’s follow-up. Furthermore, it was indicated that the choice for a cer-
tain upgrading technique might have influence on the total chain expenditures.
In this chapter the results are presented. Section 4.1 describes the effects of capacity scale on the eco-
nomics of every step in the GGSC. Section 4.2 focuses on scale effects regarding the energy require-
ments. Where after section 4.3 presents transition points between the biogas capacity and the most
efficient means of transportation. In section 4.4 the effect of the choice for a certain upgrading tech-
nique is shown. In section 4.5 an overview of the total costs and energy requirements of different sys-
tem configurations is given.
4.1 Scale effects
This section handles with the effects of scale on the economics and energy requirements in the green
gas supply chain.
4.1.1 Scale effects on the Economics in the GGSC
The total specific costs for green gas consist of capital costs and operational costs. This distinction is
of significance when identifying scale effects. Economies of scale are mainly the result of scale effects
in the capital costs (investment) of the installations. The operational costs are mainly determined by
the energy requirements, which are directly proportional to the output capacity of the installation.
Transport of Biomass
Truck transportation of biomass was found to be linear in relation to increasing capacity. Namely, the
biogas production capacity requires delivery of a substrate load that is directly related to that capacity.
Anaerobic Digestion
For digestion an exponentially decaying curve is found, which shows the existence of economies of
scale (Figure 4.1). Such scale effects on the costs for digestion were also reported by Amigun & Blott-
nitz (2010), Hornbachner et al. (2005), Ghafoori & Flynn (2007) and Welink et al. (2007). The graph
presents the total expenditure (TotEx) of anaerobic digestion, where the different digester types are
specified for the specific manure-to-maize ratios (costs for the substrates are excluded). The trend in
the cost function clearly shows the benefit of large scale operation. The exponent in the equation of the
line in the graph is -0.18.
39

ct€ / MJ
40
0.90
0.80
0.70
0.60
0.50
0.40
Scale effect on economics of digestion
Total expenditure (ct€/MJ)
y = 1.98x -0.18
R 2 = 0.99
0 500 1000 1500 2000
Nm 3 /h biogas
Figure 4.1: Scale effect on the total costs for digestion.
Gas Pipeline Construction
A strong capacity scale effect on the specific costs for pipeline construction was found, see Figure 4.2.
The exponent in the trend line equation is found to be -1, which indicates very strong scale effects.
ct€ / MJ
4
3.5
3
2.5
2
1.5
1
0.5
0
Scale effect on economics of pipeline construction
Capital Expenditure (ct€ / MJ)
y = 342.94x -1.00
R 2 = 1.00
0 500 1000 1500 2000
Nm 3 /h biogas
Figure 4.2: Scale effect on pipeline construction costs.
HDPE pipeline
(50 km)
This strong scale effect is the result of the fact that in pipeline construction costs only capital costs ex-
ist. When using a pipeline for higher capacities of gas, the investment can be written off much faster
compared to low capacities since more MJ’s are transported over its lifetime.
Gas Upgrading
When considering different upgrading techniques, it appears that in general the upgrading techniques
show a specific cost (TotEx) function that is exponentially decaying. Similar scale effects were also

eported by Welink et al. (2007), Hornbachner et al. (2005) and Persson (2003), of which the latter
two, however, showed a stronger exponential function. The results by Hornbachner et al. (2005) and
Persson (2003) can be explained by the fact that only investment costs were included in these studies.
Since a large part of the TotEx consists of operational costs, the scale effect showed in Figure 4.3 was
weaker than reported by Hornbachner et al. (2005) and Persson (2003).
ct€ / MJ
0.60
0.55
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
Scele effect on economics of upgrading techniques
Total expenditure (ct€/MJ)
y = 2.92x -0.31
R 2 = 0.98
y = 2.41x -0.28
R 2 = 0.91
y = 6.78x -0.46
R 2 = 0.99
0 500 1000 1500 2000 2500
Nm 3 /h biogas
Figure 4.3: Scale effect on the total costs for upgrading techniques.
PWS
PSA
Chemical
scrubber
The exponents in the equations of the lines of the upgrading techniques are -0.46 for PWS, -0.28 for
PSA and -0.31 for the chemical scrubber, meaning that PWS shows the strongest reciprocal scale ef-
fect on the total costs. A graph in which all the upgrading techniques are presented can be found in
Appendix III.
Gas Compression
Compression is applied for both transportation of gas in pipelines as for injection of gas into the gas
grid. Some scale effects can be found in the costs for compression, see Figure 4.4. This is related to
the investment costs of the compressor. The operational expenditures are found to be linear with re-
gard to the capacity of the installation since the energy requirements of compression are directly pro-
portional to the output.
41

ct€ / MJ
42
0.140
0.130
0.120
0.110
0.100
0.090
0.080
0.070
Scale effect on economics of compression
Total expenditure (ct€/MJ)
y = 0.25x -0.12
R 2 = 0.93
y = 0.25x -0.12
R 2 = 0.93
y = 0.18x -0.10
R 2 = 0.93
0 500 1000 1500 2000 2500
Nm 3 /h gas*
Figure 4.4: Scale effect on the economics of compression.
0.1 - 0.5 MPa (biogas*)
0.1 - 0.8 MPa (green gas*)
0.8 - 4.0 MPa (green gas*)
The share of compression in the total costs of the GGSC strongly depends on the pressure lift and on
the type of gas compressed. As the graph shows, compression of biogas to 0.5 MPa (for gas transport
purposes) has the highest costs. This is the result of the biogas characteristics (density and molar
mass), which are specified in Appendix II. Furthermore, the graph shows higher costs for compression
from 0.1 MPa to 0.8 MPa than for compression from 0.8 to 4.0 MPa. This can be explained by the fact
that compression from 0.1 MPa to 0.8 MPa requires compression by a factor 8, where compression
from 0.8 to 4.0 MPa requires compression by only a factor 5.
Green Gas Injection Facility
The injection facility for green gas consists solely of capital (investment) costs. The total investment
strongly depends on the gas grid, see section 3.4. A very strong scale effect was found for the costs of
an injection facility. The exponent in the trend line function is -1, meaning that this step is extremely
sensitive to scale effects. However, the share of the injection facility in the total costs for a GGSC is
marginal.
Summary of scale effect on Economics
This section summarizes the results with regard to the scale effects on the economics of the GGSC.
Figure 4.5 shows the results regarding the economics of the steps in a GGSC. This figure does not rep-
resent a specific system configuration but summarizes all possible process steps in one graph. When a
specific system configuration is considered, the total sum of that chain will therefore be lower than the
total sum of the steps in Figure 4.5. Table 4.1 gives an overview of the scale effects of all steps.

ct€ / MJ
5.00
4.50
4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
Scale effect on economics of steps in the GGSC
100 250 500 1000 1500 2000
Nm 3 /h biogas
Figure 4.5: Total overview of scale effects on the economics of the GGSC F .
Injection facility
Gas compression (0.8-4.0 MPa)
Green gas transport
Gas Upgrading (PWS)
Biogas transport
Pipeline construction (50 km)
Anaerobic Digestion (50:50)
Biomass transport (50 km)
Remarkable is the share of pipeline construction as a function of capacity scale. At low capacities
pipeline construction determine a large proportion of the total chain expenditures, however, this pro-
portion strongly decreases as capacity increases. At high capacities, digestion and upgrading contrib-
ute the larger proportion.
Table 4.1: Trend lines functions in economics of process steps in GGSC
Process Trend line function Exponent Remark
Transportation of Biomass (50 km) y = 0.28 x
Anaerobic Digestion y = 1.98x -0.18 -0.18
Biogas Transport See 'Compression' - -
Pipeline Construction y = 342.94x -1.00 -1
Gas Upgrading:
PWS
PSA
Chemical scrubbing
Membrane
Cryogenic
y = 6.78x -0.46
y = 2.41x -0.30
y = 2.92x -0.31
y = 18.58x -0.65
y = 31.58x -0.62
-0.46
-0.28
-0.31
-0.65
-0.65
Green gas Transport See 'Compression' - -
Gas Compression (MPa):
0.1-0.5 (Biogas)
0.1-0.8 (Green gas)
0.8-4.0 (Green gas)
y = 0.25x -0.12
y = 0.25x -0.12
y = 0.18x -0.10
Green gas injection facility y = 0.16x -1 -1
-0.12
-0.12
-0.10
No exponential
function
Unreliable
Unreliable
F the value with regard to biogas upgrading with PWS at 100 Nm 3 /h was extrapolated based on the trend line
function given in Table 4.1.
43

The strongest scale effect is found for pipeline construction and the green gas injection facility. Up-
grading and digestion show weaker scale effects but contribute a larger proportion at higher capacities.
Since data about membrane and cryogenic separation was very modestly found in literature and con-
sidered to be uncertain, the results with regard to these upgrading techniques were found to be unreli-
able.
4.1.2 Scale effects on the Energy Requirements of the GGSC
In general solely the energy requirements for pipeline construction and digestion show scale effects.
The strong scale effects (exponent = -1) found for pipeline construction are the result of the ‘capital’
energy requirements. A limited scale effect is found for the energy requirements of digestion (expo-
nent = -0.12). This is mainly the result of the relative decrease in surface area (heat losses) per unit of
water (containing heat energy) in the digester as total volume increases.
The other process steps in the GGSC show linearity in relation to increasing capacity scale. This is the
result of the fact that the energy requirements in the supply chain of green gas are mainly determined
by the operational (direct) energy requirements. This finding was also reported in a more general con-
text by Moll (1993), who studied the direct and indirect contribution of energy and materials to the
lifecycle effects of products. Figure 4.6 summarizes the energy requirements and their susceptibility
for scale effects, for the process steps in the GGSC, not specified for a certain system configuration.
Since this figure includes all possible steps in a supply chain, the total energy requirements in a spe-
cific system configuration are lower than the sum of the steps presented here.
MJ pe / MJ
44
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Energy requirements of steps in the GGSC
100 250 500 1000 1500 2000
Nm 3 /h biogas
Gas Compression (4.0 MPa)
Green gas transport
Gas Upgrading (avg.)
Biogas transport
Figure 4.6: Total overview of scale effect on the energy requirements in the GGSC.
Pipeline construction (50 km)
Anaerobic Digestion (50:50)
Biomass transport (50 km)
4.2 Transition Points of Transport Means
Transportation of biomass to a centralized digestion plant results in scale effects, which is in contrast
with biogas transportation from decentralized digesters to a centralized upgrading and injection plant.
This is of great importance when assessing the total chain effects of the transport means. Intersections

etween biomass truck transport and biogas pipeline transport, as a function of distance and capacity
scale, are identified. The results are presented in Figure 4.7. The left side of each line represents the
area in which truck transport of biomass (1) is optimal and the area to the right of each line represents
that pipeline transport of biogas (2) is optimal. In the area between the lines (1 or 2) it depends on ones
preferences which means of transport is considered to be optimal.
Biomass truck transport was found to be more economical than biogas transport for distances up to
200 km at 1750 Nm 3 /h biogas or 150 km at 2000 Nm 3 /h biogas. With regard to the energy require-
ments transport is more efficiently done by biomass trucks than biogas pipelines at distances up to 200
km at 480 Nm 3 /h biogas or 40 km at 2000 Nm 3 /h biogas.
Exceeding these break-even points means that pipeline transportation of biogas is more economical or
efficient than transportation of biomass to a centralized digester.
km
200
150
100
50
0
Transition of transport means
0 500 1000 1500 2000
Nm 3 /h Biogas
Figure 4.7: Transition lines of most efficient and economic transport means.
Capacities exceeding 2000 Nm 3 /h were not assessed since data about digestion was not considered for
larger scales. With regard to the distance evaluated, it was assumed that distances up to 200 km are
relevant for the Dutch situation since system configurations in which a coalition of farms or digesters
is used, might cover 200 km. Longer distances were considered to be unrealistic for green gas system
configurations.
1
1 or 2
4.3 Upgrading Techniques
In order to assess the performances of different upgrading techniques, the results of five scenarios as
described in section 3.3 are presented here (Figure 4.8 and Figure 4.9). It should be noted though that
the results of the economic assessment with regard to membrane and cryogenic separation are consid-
ered to be unreliable. Data about the economics of these two upgrading techniques was reported very
modest and with high uncertainty. Therefore, these upgrading techniques are indicated with '*'.
2
MJ
€
45

MJpe / MJ
46
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
Scenarios upgrading techniques
Energy requirements
PWS PSA Chemical
srubbing
Membrane* Cryogenic*
Anaerobic Digestion Pipeline construction Biogas transport
Gas Upgrading Gas compression
Figure 4.8: Energy requirements of system configurations with different upgrading techniques.
ct€ / MJ
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
Scenarios upgrading techniques
Economics
PWS PSA Chemical srubbing Membrane* Cryogenic*
Anaerobic Digestion Pipeline construction Biogas transport
Gas Upgrading Gas compression Injection facility
Figure 4.9: Total costs of system configurations with different upgrading techniques.

Index
2.00
1.80
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
Total normalized score upgrading
1.00 0.93 0.88
0.87
0.84
0.88 0.94 1.00 0.90 0.86
PWS PSA Chemical
srubbing
Membrane* Cryogenic*
Normalized economics Normalized energy requirements
Figure 4.10: Normalized results of upgrading techniques.
PWS, PSA and chemical scrubbing were found to the similar overall index numbers, meaning that
there is no 'optimal' upgrading technique when considering the energy requirements and costs equally
important. PWS is the most economical upgrading technique and chemical scrubbing the most effi-
cient one. Including cryogenic and membrane separation, the cryogenic technology is the optimal one.
This is mainly the result of the integrated compression to 4.0 MPa which makes separate compression
to RG pressure redundant. This effect of the choice for a specific upgrading technique was also indi-
cated by Tilburg et al. (2008b) and Vlap (2010).
4.4 Pathways
The pathways assessed in this research (defined in section 3.5.1) show significant differences in both
the energy requirements and the total costs. The results for the assessment of both energy requirements
and costs are shown in Figure 4.11 and Figure 4.12.
47

48
MJ pe / MJ
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Energy requirements of pathways
1 2 3 8
Biomass transport Anaerobic Digestion Pipeline construction
Biogas/Green gas transport Gas Upgrading Gas compression
Figure 4.11: Total energy requirements of different pathways (defined in section 3.3).
ct€ / MJ
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Economics of pathways
1 2 3 8
Biomass transport Anaerobic Digestion Pipeline construction
Biogas/Green gas transport Gas Upgrading Gas compression
Injection facility
Figure 4.12: Total costs of different pathways (defined in section 3.3).
In the graphs it is clearly visible that digestion has, on average, the largest share in the total energy
requirements and costs of the pathways in which biogas is upgraded to green gas. The share of upgrad-
ing in the energy requirements (Figure 4.11) is large, but in the total costs (Figure 4.12) the proportion
is highly dependant on the capacity scale. Biogas transport, compression and injection contribute to a
smaller fraction of total. The scenario in which biomass is transported by truck to a centralized diges-
tion plant is found to be the most economic and efficient scenario. This is mainly the result of the large
impact of the scale effects in digestion.
The net best pathway for both the energy requirements and costs is found by plotting the sum of the
normalized values of the pathways in one graph, presented in Figure 4.13.

Index
2.50
2.00
1.50
1.00
0.50
0.00
0.82
0.73
Total normalized score pathways
1.00
1.00
0.89
0.71
1 2 3 8
Figure 4.13: Normalized results of pathways.
Normalized economics Normalized energy requirements
The main result from this figure is that pathway 8 (t-cD-cU-cI) is the net best pathway, based on the
system configurations described in Table 3.4. Pathway 8 represents the system configuration in which
biomass is transported by truck from farms to a centralized digester where after biogas is upgraded
centralized and green gas injected in the RG.
4.5 Sensitivity Analysis Transport
Transportation is found to be a critical aspect within the green gas supply chain. A sensitivity analysis
is performed to gain insight in the effects of changes in certain parameters on the results with regard to
transportation.
First the sensitivity of the transition points in section 4.3 is identified, followed by the sensitivity with
regard to the results of the optimal pathway (section 4.5).
Transition Points of Transport Means
Following graph (Figure 4.14) shows the sensitivity of the transition points at 150 km in Figure 4.7 to
the change of some relevant parameters. The transition points at this distance is chosen for the
assessment of the sensitivity because no transition point regarding the economics was found for
distances shorter than 150 km.
The y-axis represents the change (%) of the parameter and the x-axis the effect of that change on the
transition point.
0.92
0.59
49

50
Change to parameter (%)
60%
40%
20%
0%
-20%
-40%
-60%
Sensitivity of transition point 150 km
0 500 1000 1500 2000
Figure 4.14: Sensitivity of transition points.
Nm 3 /h Biogas
MJ Biogas yield Manure
MJ Biogas yield Maize silage
MJ Transport Manure
MJ Transport Maize silage
MJ Pipeline + Compression
€ Biogas yield Manure
€ Biogas yield Maize silage
€ Transport Manure
€ Transport Maize silage
€ Pipeline + Compression
Changes in the biogas yield from maize silage show significant effects. This means that the actual
realistic biogas production from maize silage (dependant on different process parameters in digestion)
or the assumption on the biogas yield in calculations should carefully be considered. Also the changes
in the expenditures (energy requirements and costs) for biogas transport (pipeline + compression) have
significant effects on the transition points. Further more, the changes in the expenditures for biomass
truck transport show significant effects. This can be explained by the fact that more transport is
required when the biogas yields decrease. This results in increasing expenditures for truck transport,
meaning that the transition point at which pipeline transport becomes more interesting shifts to the left
(at lower capacities).
The reason that no shifts occur to higher capacities (exceeding 2000 Nm 3 /h biogas) is that no data was
considered for digestion of larger capacities.
Optimal Pathway
The results in Figure 4.13 show that pathway 8 is the optimal pathway within the system configura-
tions as defined in Table 3.4. Before concluding on this result it is relevant to identify the effect of the
total distance on the results, since transportation is found to be a critical factor. Furthermore, the bio-
gas yield from maize silage was found to have great influence on the expenditures for transportation
(see Figure 4.14).
Figure 4.15 and Figure 4.16 show the effects of changes in distance and biogas yield from maize si-
lage, respectively, to the energy requirements, economics and normalized values of the pathways. On
the x-axis the pathways are presented. The y-axis presents the values corresponding to either the en-
ergy requirements, economics or normalized values. In the legend the effects on the normalized values
are indicated with an 'N', the effects on the economics with a '€'-sign and the energy requirements with

'MJ'. The percentages in Figure 4.15 are the percentages change to the distance of 50 km (reference
situation) and in Figure 4.16 to the biogas yield from maize silage (200 Nm 3 /tonne).
(MJpe/MJ) - (ct€/MJ) - (Normalized)
2
1.5
1
0.5
0
Sensitivity to Distance
1 2 3 8
Pathways
Figure 4.15: Sensitivity of pathways to changes in total distance.
Normalized (-)
Economics (ct€/MJ)
Energy Requirements (MJpe/MJ)
Figure 4.15 shows that at a total distance exceeding 60 km (is +25%) pathway 3 and 8 have the same
normalized value. It also shows that from that distance pathway 1 (dD-dU-dI) becomes the optimal
pathway, since it is not dependant on distance. The increase in the economics of pathway 2 as a func-
tion of distance can be explained by the fact that the costs for a single pipeline to the RG has to be
written off over only 250 Nm 3 /h, which is in contrast with pathway 3 where it can be written off over
2000 Nm 3 /h.
The effect of biogas yield from maize silage is also important to consider, see Figure 4.16. If maize
silage has a lower biogas yield, more truck transport is needed to maintain the production capacity.
This is the reason that the effect of biogas yield from maize silage is only visible through pathway 8.
In the other pathways it is assumed that the substrates are available at the farm location. Thus, changes
to the biogas yield from maize silage have significant effects on the conclusion about the optimal
pathway.
N -50%
N -25%
N +25%
N +50%
€ -50%
€ -25%
€ +25%
€ +50%
MJ -50%
MJ -25%
MJ +25%
MJ +50%
51

52
(MJpe/MJ) - (ct€/MJ) - (Normalized)
2
1.5
1
0.5
0
Sensitivity to Biogas yield Maize silage
Normalized (-)
Economics (ct€/MJ)
1 2 3 8
Pathways
Figure 4.16: Sensitivity of pathways to biogas yield of maize silage.
Energy Requirements (MJpe/MJ)
N -50%
N -25%
N +25%
N +50%
€ -50%
€ -25€
€ +25%
€ +50%
MJ -50%
MJ -25%
MJ +25%
MJ +50%

5 CONCLUSIONS
In this chapter the final conclusions about optimization of the GGSC are presented. Firstly the critical
choices are described, followed by the effect of capacity scale or the choice for a certain upgrading
technique on the different process steps. Finally the most optimal system configuration is presented.
Critical Choices
Firstly, determination of the potential biogas production in an area is of importance since it determines
the grid in which injection is possible. Injection in the Distribution Grid has the lowest energy re-
quirements and costs because it operates at low pressures and is widely available. However, the Dis-
tribution Grid is only limited susceptible for injection of green gas (max 150 Nm 3 /h green gas, depend-
ing on the exact location), hampering large scale green gas injection and thereby causing meso-level
problems. System configurations of centralized, upgrading and injection in Regional Grid offer oppor-
tunities for more green gas initiatives, since nearby farms can decide to 'join' the infrastructure. There
exists no maximum injection capacity for the Regional Grid.
The choices with regard to the gas grid subsequently determine whether or not transportation (either
biomass or biogas) is required. If transportation is required it depends on the total distance and produc-
tion capacity which means of transport is preferred. Furthermore, the choice for a certain upgrading
technique is critical since it influences the energy demand for compression to grid pressure.
Consequences of Optimization
It was found that optimization by increasing the capacity scale mainly occurs in the economics of a
green gas supply chain. Optimization in the economics of a total green gas supply chain can be real-
ized by the installation of a large scale (great capacity) upgrading facility. Additionally, the effect of
scale on the costs for digestion is significant. It can be concluded that large scale upgrading and diges-
tion (within boundaries) is beneficial. With regard to the choice for an upgrading technique it can be
concluded that integration of the Pressurized Water Scrubbing technology results in minimization of
the total chain costs. However, integration of Chemical Scrubbing for biogas upgrading results in
minimization of the total chain energy requirements. The Regional Grid should be used for injection of
green gas since it is not limited by a maximum injection capacity and will, consequently, not hamper
the development of more green gas initiatives in a certain area.
Optimal System Configuration
The optimal system configuration, based on the generic model, is a configuration in which biogas up-
grading is done centralized where after green gas is injected in the Regional Grid. Whether biomass
has to be digested centralized or decentralized depends on the total distances and gas capacities con-
sidered. Centralized digestion, upgrading and injection into the Regional Grid is beneficial from both
the energetic and economic perspective within the condition that a total biogas capacity up to 2000
Nm 3 /h at a maximum total distance of 150 km is available. Exceeding these conditions would favor
the system configuration in which decentralized digestion, upgrading and injection into the Regional
Grid is done. However, based on the normalized values this transition point exists at 65 km’s.
53

With regard to large scale upgrading installations Pressurized Water Scrubbing, Pressure Swing Ad-
sorption and Chemical Scrubbing scored similar on the normalized scale. When the choice is driven by
economics, Pressurized Water Scrubbing is better and when driven by energy requirements, Chemical
Scrubbing is better.
Sensitivity analyses showed that especially the biogas yield from maize silage, transportation distances
and transportation costs/energy requirements have great influence on the overall results and conclu-
sions. The results and conclusions reported here are based on a generic model approach, in specific
situations results and conclusions can be different.
54

6 DISCUSSION
In this chapter some results and conclusions are discussed. Additionally, some aspects that need some
extra attention or aspects that were excluded from this study are discussed in this chapter.
Centre of Gravity method
The methodology used in this research for the determination of distances in the generic model, is
based on Euclidian distances. It is likely to assume that in reality no point-to-point connections will be
constructed, meaning that the distances for road transport will probably be larger in reality than those
calculated with this method. Additionally, the Centre of Gravity method is not able to design grid
structures. The biogas pipeline structure applied in this research is likely to be suboptimal. Both these
aspects will have effects on the results and thus on the final conclusions.
Availability of data
A large part of the data in the generic model was based on data from Fraunhofer (Urban et al., 2009;
DENA, 2009). This data was based on the German situation. According to Vlap (2010) and Ommen
(2010) German data on the economics of digestion and upgrading might be relatively high compared
to the Dutch situation.
Data about cryogenic and membrane separation was very modestly found in literature. The data that
was obtained was derived directly or indirectly from quotations. This means that it is likely to assume
that commercial interests are translated through these data. Results were therefore considered unreli-
able, and consequently, no conclusions were drawn with regard to these techniques. However, espe-
cially cryogenic separation seems promising (also reported by Olajire, 2010). Since the output gas
pressure becomes available at about 4.0 MPa, it is likely that application of cryogenic upgrading
avoids further compression (with accompanying costs and energy requirements).
Methane slip
In this research the loss of methane (methane-slip) in all steps of the green gas chain has not been con-
sidered. These losses are on average in the range of a few percentages in the overall chain, where the
upgrading step contributes the largest share. Especially membrane separation has high methane losses,
typically >20%. However, there are indications that membrane’s off gas has sufficient energetic value
to fulfill digesters heat demand. This would mean that biogas can be utilized for 100% when mem-
brane separation is applied near the digester. Since this research did not include this aspect, conclu-
sions on the optimal system configuration might change when taking this aspect into consideration.
Pre-desulphurization
Application of membrane separation for biogas upgrading does require pre-desulphurization in prac-
tice. This has not been included in this study. This means that the total costs and energy requirements
in a system configuration in which membrane separation is applied will be higher than reported in this
study.
55

Gas-overflow
In the developments of the green gas market and injection of green gas to the natural gas system, some
gas grid operators are studying the possibilities to enable gas transmission in two directions (up-
stream-downstream and the other way around). This would mean that it might become possible to in-
ject large capacities of gas into the low-pressure grids without over pressurizing the network. Gas is
than transmitted from low pressure grids to grids with higher operating pressures (e.g. from Distribu-
tion Grid to RG). This principle is called 'gas overflow' and is in practice not yet applicable. Enabling
this possibility would offer full scale decentralized injection of gas and might consequently lower
overall supply chain costs and energy requirements. The 'gas overflow' technology is a promising
meso-level solution for green gas supply chain optimization. However, since the developments in this
issue are just commencing, this option was excluded in this study.
Compressor waste heat
With regard to decentralized compression for transportation and injection purposes, the possibility to
utilize waste heat from compression has been left out of scope. Thermal energy generated in the com-
pressor might be applied for heating the digester, which would lower the demand for external energy.
However, since the specific heat capacity of water is larger than that of gasses, expectations are that
the generated heat from biogas compression could only supply a fraction of the total heat demand of
the digester.
Combined Heat and Power (CHP)
In practice a critical choice for initiators of a biogas project is to apply either a green gas chain or a
biogas CHP unit. In the generic model the CHP option was included in all assessments since KEMA
was highly interested in such a comparison. The assessment showed that a total biogas utilization sys-
tem in which a CHP unit is applied, is the cheapest option, however, since a large part of the waste
heat is emitted to the atmosphere the CHP has very high 'energy requirements'. The overall normalized
results showed that application of a CHP unit for biogas utilization has the highest overall score. This
means that the production of green gas from biogas is a better option than utilization in a CHP when
the energy requirements and economics are considered to be equally important.
56

7 RECOMMENDATIONS FOR FURTHER RESEARCH
This chapter discusses the subjects that need more research in the future. Firstly some novel technolo-
gies are mentioned that might lower overall chain energy requirements and costs. Thereafter, briefly
the availability of information, sustainability of green gas and the need for operations research tech-
niques are discussed.
Innovative upgrading techniques
In the GGSC the largest share of the total costs and energy requirements is composed of digestion and
upgrading. In literature some interesting innovative technologies can be found that could drastically
increase the chain efficiency and lower the total costs. Biological biogas upgrading and/or in-situ
methane enrichment are such technologies. According to Bekkering et al. (2010) the total costs for in-
situ methane enrichment are estimated to be significantly lower than the costs for conventional post-
upgrading of biogas. Since these techniques are still in an experimental phase, no accurate data is
available on the energy requirements and costs. Nevertheless, these promising technologies might
have great positive influence on the total chain efficiency of green gas.
Biological biogas upgrading
Mann et al. (2009) presented results from laboratory experiments of biogas upgrading with micro al-
gae (Chlorella vulgaris.), on a scale of 0.45 liter of culture volume. The authors reported removal effi-
ciencies of 97% for CO2 and 100% for H2S. Parallel to gas upgrading, micro algae allow for the pro-
duction of marketable products like proteins, feedstock, and pharmaceutical valuable substances. A
drawback of this method for biogas upgrading is that the algae produce significant amounts of oxygen
(>23 vol%). Besides the effect of higher concentrations of oxygen on the flammability of the methane
gas mixture, gas grid specifications do not allow for such concentrations of oxygen. Removal of oxy-
gen is not possible with current biogas upgrading techniques, so realization of high methane contents
will probably be even more difficult when algae are applied than when conventional biogas upgrading
is applied (Vlap, 2010).
In-situ methane enrichment
Concepts for in-situ methane enrichment were proposed by Hayes et al. (1990), Jewell et al. (1993),
Richards et al. (1994), Lindberg & Rasmuson (2006a, 2006b) and Lindeboom et al. (2009). Lindberg
& Rasmuson (2006a, 2006b) proposed selective desorption of CO2 from a circulating liquid stream
from the digester. A bubble column was installed to treat the liquid stream with air for CO2 desorption.
The lowest methane loss reported was 2%. With regard to H2S the authors reported successful desorp-
tion but mentioned uncertainties. Similar to this technology are those of Hayes et al. (1990), Jewell et
al. (1993) and Richards et al. (1994), where leachate recirculation enabled gas stripping outside the
digester. All authors reported methane concentrations of >90% in the gaseous phase of the digester.
When in-situ methane enrichment is performed, biogas transportation (compression) costs and energy
requirements will decrease significantly as a result of lower CO2 concentrations in biogas (decreasing
total gas volume and changing biogas characteristics).
57

Lindeboom et al. (2009) proposed a new process for anaerobic digestion (‘Autogenerative High Pres-
sure Digestion’; AHPD) in which biologically produced pressure resulted in increasing methane con-
tents (>90%). Since the solubility of gasses is directly proportional to the prevailing pressure, CO2 and
H2S dissolve at increasing pressure. Additionally, the authors mentioned the value of bio-pressure to
drive pumps or to force media through membranes. Pressures of 9.0 MPa were reported without de-
creasing biological activity. This bio-pressure might be valuable when green gas injection into the gas
grid is part of the supply chain. It is expected that the costs for digestion will increase since stronger
materials are required to sustain the high pressures. However, this technology might replace conven-
tional post-upgrading and compression, resulting in substantially improved chain efficiency.
Gas overflow
As explained in the discussion, distribution grids may be enabled in the future for injection of capaci-
ties exceeding 150 Nm 3 /h green gas. This should be achieved by enabling the grid for two directions
of gas transmission (upstream-downstream and the other way around). More research should be done
to the possibilities of such a system for further GGSC optimization. A variant to the gas overflow
could be an overflow in which a CHP unit is applied. This would mean that excess green gas would be
utilized in a CHP for the period of time that the gas supply to the DG exceeds the demand. In this case
a large proportion of the energy will be lost by thermal emissions to the atmosphere, however, it
would save on compression from DG pressure (

Operations Research
The generic model developed in this research is based on two methodologies originating from the field
of operations research (OR). In bio-energy system designing, optimization, planning and logistics, op-
erations research techniques combined with system analysis on meso-level could significantly
strengthen the developments of green gas production and utilization. OR expertise could contribute to
improvement in GGSC system designing and logistics planning.
59

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Appendix I – Gas composition
This appendix presents the typical gas quality of raw biogas and the quality to which upgraded biogas
should comply in order to be injected in the Dutch natural gas grid. The gas grids considered here are
RG and Distribution Grid, which distribute Groningen-gas (G-gas) quality. The composition of Gron-
ingen-gas is given NMa (2006). Since biogas has varying compositions, three sources of biogas are
considered here; anaerobic digestion (AD), landfill gas and biogas from sewage water treatment plants
(SWTP). The composition of biogas produced by anaerobic digestion is derived from Rasi (2009). The
composition for landfill gas is derived from Rasi et al. (2007). The list is completed with values given
in the article of Appels et al. (2008).
Component Unit
I
BIOGAS NATURAL GAS
AD Landfill SWTP G-gas
Caloric value MJ/Nm 3 21.5 16 20 31.67
Wobbe index MJ/Nm 3 20 20 20 43.46-44.41
Methane nr. >130 >135 >135 (>80)
Methane % 60 (50-75) 54 55
Carbon dioxide % 40
(25-50)
33
(15-40)
37
(34-38)
Sulphur (total) mg/Nm 3 0.9
H2S mg/Nm 3

Appendix II – Gas compression
The equation given in section 3.2.3 to calculate the energy demand for compression (for either trans-
portation of gas or injection of gas into a grid), is based on following mathematical formulations, de-
veloped by Damen (2007) and cited in Koornneef et al. (2008). In the second equation the calculation
from electricity requirement to primary energy requirement is integrated.
ZRT ⎡⎛
⎞
1 Nγ
p2
W = * * ⎢
⎜
⎟
M γ −1
⎢
⎣⎝
p1
⎠
E
primary
γ −1/
Nγ
⎛ W ⎞ ⎛ 1 ⎞
⎜
⎟ * ρ *
⎜
⎟
⎝η
isη
m ⎠ ⎝η
el
=
⎠
LHV
⎤
− 1⎥
⎥
⎦
Where: W = specific work (kJ/kg gas); Z = compressibility factor (-); R = gas constant (J/mole.K); T1 = suction
temperature (K); M = molar mass (g/mole); N = number of compressor stages; γ = specific heat ratio (-); p =
pressure (MPa) E = specific electricity requirement (kWh); ρ = density (kg/Nm 3 ); η = efficiency (-); LHV =
lower heating value (MJ/Nm 3 ).
Following tables presents the constants and variables used in the calculations for the energy require-
ments for compression of green gas and biogas. In the calculation with regard to green gas, the gas
characteristics of methane are considered. The variables are depending on the system design that is
chosen in the green gas supply chain.
Constants used in calculations for energy requirements of compression.
Constants Value Unit
Z 0.9942 -
R 8.3145 J/mole.K
T1 288.15 K
M (methane) 16.043 g/mole
M (biogas) 32.9 g/mole
N 2 Stages
γ 1.293759 -
η (isentropic ; mechanical ; electrical) (80 ; 99 ; 40) %
ρ (methane) 0.717 kg/Nm 3
ρ (biogas) 1.222 kg/Nm 3
LHV (methane) 35.9 MJ/Nm 3
LHV (biogas) 21.5 MJ/Nm 3
II

Variables used in calculations for energy requirements of compression.
Variable Value Unit
P1 (PWS) 1.0 MPa
P1 (PSA) 1.0 MPa
P1 (Chemical scrubbing) 0.101325 MPa
P1 (Membrane separation) 0.101325 MPa
P1 (Cryogenic separation) 4.0 MPa
P2 (pipeline transport biogas) 0.5 MPa
P2 (Distribution Grid) 0.8 MPa
P2 (Regional Grid) 4.0 MPa
III

Appendix III – Upgrading techniques
This appendix presents a graph in which the upgrading techniques are included. Note that the informa-
tion about the economics was derived, directly or indirectly, from quotations from suppliers of upgrad-
ing techniques. Since biogas upgrading has not been done very extensively yet it is likely to assume
that commercial interests are translated through these quotations, meaning that given costs cannot be
interpreted as reliable data. At this moment however, given information is the best that is available.
During extensive literature study it can be stated that reliable information on the economics of upgrad-
ing techniques, especially about cryogenic separation and membrane separation, is difficult to find.
ct€ / MJ
1.20
1.00
0.80
0.60
0.40
0.20
0.00
y = 2.92x -0.31
y = 31.58x -0.62
Economics of Upgrading techniques
Total expenditure (ct€/MJ)
y = 18.58x -0.65
y = 2.41x -0.28
y = 6.78x -0.46
0 500 1000 1500 2000 2500
Nm 3 /h biogas
P WS
P SA
Chemical scrubber
Membrane
Cryogenic
IV

Appendix IV – Energy requirements digestion
The energy requirements for anaerobic digestion are presented in this graph. The energy requirements
were calculated from the costs for heating and electricity given by Urban et al. (2009) and DENA
(2009). The difference can be explained by the difference in heating demand for digestion of varying
substrate ratios (manure-to-maize), which is directly related to substrate’s water content. Manure has a
water content of >90% where maize silage consists for approximately 65% of water.
V
MJpe / MJ
0.30
0.25
0.20
0.15
0.10
0.05
-
Energy Requirements Digestion
MJ pe / MJ biogas
y = 0.30x -0.12
0 500 1000 1500 2000
Nm 3 /h biogas
Manure-to-Maize (90:10)
Manure-to-Maize (50:50)
Manure-to-Maize (10:90)

Appendix V – Centre of Gravity method
This appendix presents the specific area that is used to determine transport distances in this research.
The table with the coordinates shows the specific villages and fictitious locations of farms/digesters.
Using the Centre of Gravity method the distances as presented in the figure are determined.
Input RD-system
Anaerobic Digestion RD Coordinates Distance to centre
Village nr X Y km
Oldehove 1 223547 590866 6.056
Feerwerd 2 225879 594095 7.654
Sauwerd 3 230783 590655 4.825
Den Horn 4 225653 583012 4.385
Garnwerd 5 229440 591639 5.121
Niehoeve 6 220866 589412 7.596
Noordhorn 7 222890 587110 5.097
Adorp 8 231494 587696 3.650
Gas Grid Gas Grid (Regional Grid) 231282 581577 6.126
X Y ∑ meters
Centre point Centre Location 227973 586733 50.510
596000
594000
592000
590000
588000
586000
584000
582000
580000
6
7
1
4
2
220000 222000 224000 226000 228000 230000 232000 234000
5
3
8
Anaerobic Digestion
Centre Location
Gas Grid
VI