Faculteit BedrijfskundeRijks

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Sustainable bio-ethanol production in the Energy Valley region

Master Thesis

Faculteit

Bedrijfskunde

Rijksuniversiteit Groningen

Date: August 2007

Specialization: Technology Management Course: Master Thesis

Supervisors RuG: drs. ing. G.J. Nanninga dr. ir. W. Klingenberg

Supervisors Energy Valley: dr. ir. J. Gigler drs. ing. P. Cnubben

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Preface

This master thesis is not just a report about the research performed, it is more than that. This report represents also the end of great time as a student and the beginning of the often so called “real” life, hence this report is a milestone in my life.

If someone had told me ten years ago that I would graduate with a master degree I would classify him as somebody with a great sense of humor. Something many of my secondary school teachers would agree with.

After secondary school I started studying mechanical engineering at the MTS in Zwolle. During these four years I found out while I was doing my internships what the value of a diploma can be. It made me decide to study mechanical engineering at the Hanzehogeschool in Groningen. By the time I graduated I was at a point were I knew something about how to make a product (MTS), what to make (HTS) but not yet how to make it a success. This made me decide to study the master Technology Management at the Rijksuniversiteit Groningen. Now ten years after leaving secondary school I am again at the point where I have to make a decision on what to do next. I am going to start working… and I am looking forward to it!

In these last four months I got the opportunity to perform research in a very dynamical and interesting field, to know bio-fuels. For that I would like to thank all colleagues at Energy Valley, in particular Jorg Gigler and Patrick Cnubben.

As supervisors from the RuG I would like to thank Warse Klingenberg for willing to function as the second reader. And last but certainly not least I would like to thank Gejo Nanninga for his support, especially in the field of writing a proper research report.

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Summary

The Dutch government signed the Kyoto protocol in 1997 in which they commit themselves to reduce the greenhouse gas (GHG) emissions with 5%,. Part of the package to reduce the greenhouse gas emissions is the bio-fuel directive EC/2003/30. In this directive it is decided to blend 5,75% bio-ethanol with petrol by 2010, and there are intentions to blend 10% bio-ethanol by 2020. It is because of this directive that bio-ethanol has become a commercial viable transportation fuel since it can not economically compete with petrol. Therefore the bio-ethanol market is qualified as an by government regulated market.

The Dutch government developed criteria for sustainable biomass production. These criteria are an answer to the concerns which originated from increasing biomass production for among others bio-ethanol. Goal of these criteria is to actively protect nature & environment and the social and economical situation. These biomass criteria are going to be applicable in the Netherlands in such a way that bio-ethanol producers who can not satisfy the criteria are not allowed to count the produced bio-ethanol to the bio-fuel directive.

In the above outlined context the objective of this report is to investigate how (planned) first generation bio-ethanol production chains in the Energy Valley region can or should be organised to meet the future sustainability criteria for biomass.

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-50,00% -40,00% -30,00% -20,00% -10,00% 0,00% 10,00% 20,00% Synthetic fertilizer -25,90% -14,89% -44,08% -33,10% Organic fertilizer -18,85% -11,93% -19,80% -2,11% Diesel tillage activities -10,24% -10,22% -12,57% -12,00% Diesel transportation -1,20% -5,02% -1,20% -5,15% Naturals gas -32,47% -41,94% -32,47% -41,94% Electricity -4,63% -10,06% -4,63% -10,06% DDGS 5,81% 5,81% straw 3,65% 3,65% Vinasse 4,34% 4,34% Pulp 3,18% 3,18% Total GHG balance 16,18% 13,45% -5,29% 3,14% sand wheat sand sugar beet clay wheat clay sugar beet

Considering the research objective it turned out that the greenhouse gas balance is a potential barrier for the future. This is underpinned by the results presented in this summary. To improve the greenhouse gas performance it is recommended to bio-ethanol producers in the Energy Valley region to take the following improvement measures into account when setting up bio-ethanol production chains.

Cultivating energy crops on sandy soils improves the greenhouse gas balance with approximately 5% and 11% for sugar beet and wheat respectively;

Importing biomass by sea vessel can improve the GHG balance for sugar beet bio-ethanol with 10-15% and 25-36% for wheat bio-ethanol, depending on the soil type;

Producing electricity in-house at the side by means of an internal combustion engine with heat recovery reduces the emissions associated with electricity consumption with

approximately half;

Applying waste heat from other industries can improve GHG efficiency with approximately 31% and 40% respectively wheat and sugar beet ethanol;

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Glossary

Bio-ethanol ethanol derived from biomass which can serve as a replacement for petrol.

Bio-fuels transportation fuels derived from biomass, such as bio-ethanol and bio-diesel.

Biomass material based on organic matter that can be used as a feedstock for different purposes, in this report as feedstock for the production of bio-ethanol.

CO2eq. standard towards different kind of greenhouse gasses are

evaluated.

First generation bio-fuels bio-fuels derived from edible parts of plants e.g. sugar-beets and wheat (food purposes).

GHG Greenhouse Gas.

LCA Life Cycle Assessment.

N-fertilizer Nitrogen containing fertilizer.

Second generation bio-fuels bio-fuels derived from the non-edible part of plants, e.g. straw and wood (non-food purposes).

Sustainable energy energy which is replenishable within a human lifetime and causes no long-term damage to the environment.

tbe ton bio-ethanol

WTW Well to Wheel (in this report, starting with the production of biomass until the bio-ethanol usage).

VEM Energy content per unit of measurement (e.g. /ha /kg) also used

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1. Context from where the research is initiated

The drivers behind bio-ethanol production are among others the increasing dependence on oil and carbon dioxide emissions (Annevelink et al., 2006, Ryan et al., 2006). In the period between 1970 and 2004 carbon dioxide emissions grew with 80% of which the lion’s share comes from the transportation sector. One of the measures to put a hold on the increasing growth of carbon dioxide emissions is the Kyoto protocol. Countries who signed this treaty committed themselves to reduce greenhouse gas emissions with 5% based on 1990 emissions over the period 2008 till 2012. Part of the package to reduce the greenhouse gas emissions is the bio-fuel directive EC/2003/30. In this directive it is decided to blend 5,75% bio-ethanol with petrol by 2010 and there are intentions to blend 10% fuels by 2020 based on energetic value. To this extent bio-ethanol has the potential to become a sustainable transportation fuel for the future. Sustainable because it reduces greenhouse gas emissions (and thus decreases the effects of global warming) and fossil fuel depletion. However currently produced bio-ethanol in Europe, the so called first generation, can not economically compete with fossil fuels (Ryan et al., 2006). This means for now that the bio-ethanol market is classified as a by government regulated market.

On 14th of July 2006 a commission lead by the current minister Cramer of Housing, Spatial Planning and the Environment (VROM) presented the report “Criteria for sustainable biomass production1”. These criteria cope with the themes greenhouse gas balance, competition with food and feedstock, bio-diversity, welfare, wellbeing and environment (appendix A) and are developed to actively protect nature & environment and social and economical situation (Criteria for sustainable biomass production 2006). With these sustainability criteria for biomass the Netherlands is taking a leading role in Europe. The commission Cramer intends only to count bio-ethanol to the fuel directive which suffices the biomass criteria (Cramer 2007; Criteria for sustainable biomass production 2006). Bio-ethanol produced in the Netherlands must therefore fulfil the sustainable biomass criteria because it can not compete with fossil fuels on price. Until now there were no sustainability regulations or criteria applicable to bio-ethanol production. Current commercial produced bio-ethanol in the Netherlands is referred to as the first generation bio-ethanol (Annevelink et al., 2006). This means that the biomass is either starch or sugar based (Kampman et al., 2005, Deurwaarder et al., 2006). From an economical point of view the two most promising common agriculture crops in the Netherlands are sugar beet and wheat (van der Voort 2007).

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On March the 28th 2007 during the 2nd expert meeting “National biomass action plans”2 concerns were expressed of competition for land use and prices for feed and food. Growing sugar beets or wheat on large scale in the Netherlands for bio-ethanol production will likely have a negative effect on theme 2 (appendix A) of the sustainable biomass criteria. To this extent current studies on the net greenhouse gas reductions of first generation bio-ethanol results in reductions of 40-60% carbon dioxide, however these studies are not peer reviewed. Peer reviewed studies show more pessimistic figures (Reijnders et al., 2006). If this is the case theme 1 (appendix A) is also under pressure. Meaning that in the foreseeable future it is very likely that sustainable biomass criteria will cause barriers for bio-ethanol production in the Energy Valley region.

1.1 Research objective formulated by Energy Valley

The initial research objective formulated by Energy Valley, in light of the research context outlined in the previous section, is:

How (planned) first generation bio-ethanol production chains in the Energy Valley region can or should be organised to meet future sustainability criteria for biomass.

Chapter 2 continuous with this research objective by formulating more specified research questions and setting the research boundaries.

1.2 Energy Valley’s research motivation

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1.3 Reading guide

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2. Translation of the research objective to a practical

research approach

Based on the research context and the management question from Energy Valley (chapter 1) a research approach is determined. This chapter elaborates on how to approach this research, which steps to make and how these steps are going to be executed.

2.1 Applied theoretical framework

Based on the research objective, this research aims at contributing to the decision how to intervene in the bio-ethanol production chain in order to meet the future sustainability criteria. To this extent this research is qualified as a practical focused research (Verschuren et al., 2005). This kind of research often recognized as a research in which the problem owner can be overconfident on what is causing the problem (Verschuren et al., 2005).

The research objective is an initial high aggregated problem statement formulated by Energy Valley. Before continuing it is important to perform research in order to identify the problem situation since initial problem statements perceived by the problem owner can be symptoms of underlying problems (de Leeuw 2000).

This research will therefore use the first two steps of the intervention cycle according to Verschuren et al. (2005). These steps consist of first analysing the problem and determining what the problem situation is. Based on this problem situation research questions are defined. The second step is to study the background to gain insight in the problem situation recognized in the first step. The research questions from step 1 will be leading during this study. The results of the second step are insights which point towards the solutions to overcome the problem. Hence an answer to the management question.

2.2 Translation theoretical framework to practical approach

Before continuing with the first step a decision is made from which perspective the problem is analyzed. Based on this perspective a problem analysis is performed by means of an external analysis according to the model of Wissema (1999). The reason to choose this method is to assure an inventory of facts without assuming that there is a problem. Based on the results a comparison is made between the actual situation and the desired situation (complying with the sustainable biomass criteria). The final result will be the problem situation.

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The diagnosis of the problem situation is lead by the research question derived from the problem situation in step 1. Before continuing a theoretical review is performed on the relevant issues from the research questions. This should guarantee viable and scientific justifiable results.

2.3 Applied information sources during research

Information with a qualitative nature is abundantly available especially for the first step. The developments in the field are mainly directed by government decision (as will become clear in chapter 3). These decisions either on national or European level are available for everybody. Together with information gathered during expert meetings this will be the basis to perform the first step from the Verschuren et al. (2005) model.

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3. Analysis of the research objective

This chapter explores the problem situation in order to qualify solid research questions. First a decision is made from which perspective the research is performed. Subsequently an environmental analysis is carried out from this perspective to gain insight into the problem situation. The chapter finishes with the research questions and demarcation of the research.

3.1 Research perspective

The current first generation bio-ethanol supply chain comprises of the following links: biomass supply, bio-ethanol production, bio-ethanol distribution and bio-ethanol consumption (Kondili et al., 2007). Bio-ethanol production requires multiple parties to cooperate. Based on the supply chain the following (direct) stakeholders can be identified:

Biomass suppliers (farmers, industries)

Bio-ethanol producers converting biomass to bio-ethanol Oil refineries who are obliged to blend bio-ethanol with petrol Distributors

Petrol station owners Consumers

Considering the mission statement of Energy Valley which is “… strengthen the economy and employability in the Energy Valley region…” the perspective should be from a stakeholder within the production chain who is permanently situated in the Energy Valley region. For this reason it is chosen to approach the research from the perspective of a bio-ethanol producer in the Energy Valley region.

3.2 Environmental analysis from the research perspective

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Figure 1 External analysis model Wissema et al. 1999

3.2.1 Developments of the directly related forces on bio-ethanol

production in the Energy Valley region

The direct circle represents developments of the forces which directly influence the business in the middle. The forces according to the model in Figure 1 are assessed in this section.

As mentioned in the introduction bio-ethanol can not compete with fossil fuels but it is commercial a viable transportation fuel because the bio-fuels directive imposes binding targets to fossil fuel producers. At the moment refineries buy the bio-ethanol, turn it into bio-ETBE and blend it with petrol. The petrol is than distributed to petrol stations and finally sold to consumers. To this extent refineries are the ones who need to comply with the bio-fuel directive in order to continue their petrol sales. The total bio-ethanol demand in the Netherlands can be derived from the total petrol sales. Table 1 presents the bio-ethanol demand assuming a linear growth in petrol consumption of 1.8%. Note that the blend percentage is based on the energetic value of petrol and bio-ethanol.

Table 1 forecast domestic bio-ethanol demand (million litres)

Year 2006 2010 2020 Blend percentage 2,00% 5,75% 10,00% Petrol * 5563 5974 7141 Bio-ethanol 161 502 1043

* source www.vnpi.nl linear extrapolation over growth 2005-2006 (1.8%)

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becomes a scarce resource for refineries. This increases the bargaining power of bio-ethanol producers and enables them to charge premium prices (McGee et al., 2005). It is however likely that these extra costs are charged to the 2nd and 3rd buyer in the chain. The point is that ethanol production is profitable as long as the fuel directive obliges refineries to blend bio-ethanol and the demand supply ratio is greater than 1. By the time the ratio drops beneath 1 the refineries will gain bargaining power and premium prices can not be charged anymore. From this moment ethanol face competition with each other based on price since the quality of bio-ethanol is the same, referring to the definition of customer value: quality minus the cost (Rajendran et al., 1996). Considering the European demand and installed capacity in 2005 this is not the case yet.

The two most promising energy crops from an economical point of view, which can be cultivated in the Netherlands, are sugar beets and wheat (van der Voort 2007). In the Netherlands there is a total of 820.944 ha agricultural land, 221.323 ha of temporarily grass land and 23.854 ha of set aside land available for energy crop cultivation (Land- en tuinbouwcijfers 2005). Table 2 presents the percentage of the total agricultural land in the Netherlands which is needed to fulfil the domestic bio-ethanol demand derived from (Table 1). Calculations are based on bio-ethanol yields from sugar beet and wheat on clay calculated in chapter 6.

Table 2 percentage required total available agricultural land in the Netherlands

2006 2010 2020

Sugar beets 2,9% 9,0% 18,7% Wheat 5,1% 15,8% 32,8%

Based on the results it seems inevitable that the energy crop cultivated in the Netherlands will suppress current crop cultivation because there is not enough set aside land. It is therefore very likely that biomass for bio-ethanol production will compete with food and feedstock, hence theme 2 of the Cramer sustainability criteria (appendix A).

3.2.2 Developments of the indirectly related forces on bio-ethanol

production in the Energy Valley region

The indirect circle represents the developments which have indirect influence on bio-ethanol production in the Energy Valley region. These are developments which become part of the direct circle in the future or which influence the direct forces.

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that carbon dioxide is the greenhouse gas which is responsible for the lion’s share of global warming. This is remarkable because until then scientist did not make statements of this kind because there were too many uncertainties on what was truly causing global warming. This is just a selection of events which are currently creating awareness among the public that our climate is changing due to global warming and that something needs to be done to put a hold on it. Reducing carbon dioxide emissions is seen as an effective measure by many. To this extent governments, which are usually chosen by the public, are taking measures. Examples are the Kyoto protocol, bio fuel directive and, specifically in the Netherlands the biomass sustainability criteria.

In May 2007 current Minister Cramer wrote a letter to the Dutch parliament in which she clarifies the government policy on sustainable production of biomass for energy purposes. The implementation of the sustainable biomass criteria is one of priorities of Minister Cramer (Cramer 2007). For bio-ethanol producers in the Energy Valley region it is therefore certain that they have to deal with these criteria. The direct consequences for bio-ethanol producer will be that they have to report on the sustainability of the produced bio-ethanol of which the greenhouse gas reduction is an essential part (Cramer 2007) The calculation methodology to assess the greenhouse gas reduction is already developed (Bergsma et al., 2007) and currently the methodology is tested and default values are being set. In the same letter a short note is spent on what the policy will be on the expected increasing competition with food and feedstock (theme 2). The government will take the consequences for food- and feedstock for granted until 2010 (Cramer 2007). This is decided because the Netherlands has committed themselves to the bio-fuel directive until 2010. After 2010 the government is able to adapt the blend percentage according to biomass availability and this will take into account the competition with food- and feedstock. By that time a tool will be developed which assesses the competition with food- and feedstock on macro levelsince this is not possible on individual company level due to land shifts (Cramer 2007).

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second generation (Seminar “the future of bio-fuels” 2007). As a result between now and at least 2012 bio-ethanol will be produced according the first generation technology.

3.3 Research question

The external analysis revealed that greenhouse gas reduction is currently the only biomass criteria which can be quantified by means of the greenhouse gas calculation methodology of Bergsma et al. (2007). At the same time it is made clear that it is expected from bio-ethanol producer that they have to report on the greenhouse gas reduction of their bio-ethanol. However it is not yet clear what the limit of the greenhouse gas reduction will be in order to comply with the sustainable biomass criteria. The research results will therefore not focus on a certain limit. In light of the research objective the greenhouse gas performance, according to the calculation methodology of Bergsma et al. (2007) will be determined for bio-ethanol producers in the Energy Valley region and successive supply chain improvements are investigated and proposed.

The following research question is formulated:

What is the greenhouse gas balance performance according to the calculation methodology of Bergsma et al. (2007) for ethanol produced in the Energy Valley region and how should bio-ethanol production chains be set up to achieve an increased performance which can better meet the sustainability criteria?

The research question is divided in the following sub-questions.

Sub question 1: What is the greenhouse gas balance performance of bio-ethanol according to the calculation methodology of Bergsma et al. 2007?

Sub question 2: How to set up bio-ethanol production chains to improve the green house gas balance performance?

3.4 Research demarcation

The report is written for Energy Valley. This means the results are specified for bio-ethanol production in the Energy Valley region. The assessment will focus on the technical aspects of the bio-ethanol production chain. Carbon dioxide after bio-ethanol production are either neglect able4 or not assigned to bio-ethanol5 (blending at the refinery). This means that the greenhouse gas

4

distribution 37,8 grCO2/tbe.km 27 ton truck Essent et al., 2003 and refineries are in Bottleck or Ruhr area

(Germany)

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reduction is assessed until bio-ethanol production (cradle to the gate principal Figure 2). Finally the level of aggregation is restricted to the primary in- and outputs of each part in the chain.

Figure 2 Bio-ethanol process steps

Pre- treatment Fermentation Distillation Dehydration

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4. Theory of greenhouse gas calculation methodology

The chapter starts with the theory of the greenhouse gas calculation methodology of Bergsma et al. (2007). This theory is used in order to perform a proper life cycle assessment.

The calculation methodology of Bergsma et al. (2007) is based on the Life Cycle Assessment (LCA) according to ISO 14040. This technique is specifically developed to assess the environmental aspects and potential impacts associated with a product, in this report bio-ethanol by:

Compiling an inventory of relevant inputs and outputs of a product system

Evaluating the potential environmental impacts associated with those inputs and outputs Interpreting the results of the inventory analysis and impact assessment phases in relation

to the objectives of the study.

A proper LCA study includes definition of goal and scope, inventory analysis, impact assessment and interpretation of results (Figure 3).

Figure 3 Methodological framework LCA (ISO 14040)

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evaluation of in- and outputs (Reijnders et al., 2006; Blottnitz et al., 2007; Malça et al., 2006; Björkland 2002). To this extent the ISO 14040 has the following limitations.

The nature of choice and assumptions made in LCA may be subjective.

Models used for inventory analysis or to assess environmental impacts are limited by their assumptions, and may not be available for all potential impacts or applications.

Results of LCA studies focused on global and regional issues may not be appropriate for local applications, i.e. local conditions might not be adequately represented by regional or global conditions.

The accuracy of LCA studies may be limited by accessibility or availability of relevant data, or by data quality, e.g. gaps, types of data, aggregation, average and site specifications.

The lack of spatial and temporal dimensions in the inventory data used for impact assessment introduces uncertainty in impact results. This uncertainty varies with the spatial and temporal characteristics of each impact category.

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5. Greenhouse gas balance of bio-ethanol produced in

the Energy Valley region

The greenhouse gas balance is assessed for first generation bio-ethanol. This is the only commercial available bio-ethanol and likewise currently the only relevant conversion technology in the Energy Valley region. The life cycle assessment (LCA) is performed according the methodological framework depicted in Figure 3 (chapter 4). Each phase of the methodological framework is presented in successive order. The final result will be the greenhouse balance performance of current bio-ethanol production units in the Energy Valley region according to the calculation methodology of Bergsma et al. (2007). It provides an answer for sub question 1.

5.1 Definition of life cycle assessments goal and scope

Function life cycle assessment and functional unit of comparison

This function of the LCA study is to compare the total carbon dioxide emissions of petrol as a transportation fuel with the total emissions needed to produce bio-ethanol in the Energy valley region, including biomass production. If bio-ethanol production requires more carbon dioxide emissions compared to the total carbon dioxide emissions from burning petrol it is a logic conclusions that the use of bio-ethanol as a transportation is a more polluting alternative. Hence it does not contribute to the Kyoto protocol.

The unit of measurement for comparison with petrol should be, according to the calculation model, driving 1 km with a standard car. This is a relative intangible unit of measurement. Therefore it is based on the net energy content of bio-ethanol and petrol assuming that the efficiency of a standard car remains the same using either bio-ethanol or petrol. For convenience purposes the final unit of measurement will be 1 ton of bio-ethanol (tbe). The CO2 emissions are

recalculated to this unit of measurement according to the flow sheet parameters in Figure 5 and Figure 6. Based on Table 3, the final comparison will be the carbon dioxide emissions from 1 ton bio-ethanol with those of 636 kg petrol.

Table 3 Energy content and density (source: Kavalov 2003) fuel Net calorific value

MJ/kg1

Density1_(kg/l)

Petrol 41.8 0,745

Bio-ethanol 26.6 0,798

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Life cycle assessments system boundaries

The system boundaries are included in the calculation methodology of Bergsma et al. (2007), Figure 4. All relevant in- and outputs are described in the calculation methodology manual (Bergsma et al., 2007).

Figure 4 System boundary (Bergsma et al. 2007)

Quality requirement for life cycle assessment data

The calculation is specified for the energy valley region. All data used are specified for this region and time frame when possible. If data are used that are doubtful concerning its applicability to the Energy Valley region it will be mentioned explicitly. Two soil types and two crop types are assessed which are relevant for the Energy Valley region. To know;

“Clay Northern Netherlands “ and “Northern sand”, (KWIN 2006) from now on referred to with the terms clay and soil.

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The LCA study results are based on first generation conversion technology. Data are collected from literature, representative reports of well-established institutions, previous LCA studies, data from specified data collections and experts.

The next section continues with the data collection and quantifies each element within the system boundary. These values are recalculated to the unit of comparison, 1 ton bio-ethanol (tbe).

5.2 Inventory analysis of associated carbon dioxide emissions

This section describes relevant data and calculation procedures to quantify the in- and outputs of the system. The first part will address all general data like product yields and mass/energy flow charts. Subsequently all relevant elements within the system boundary (Figure 4) are assessed and inputs/outputs are qualified and quantified. Table 4 presents the carbon dioxide emissions associated with the fossil fuels used as either comparison (petrol) or primary system input (diesel, natural gas and electricity).

Table 4 Emissions primary energy sources source Primary energy factor

GJp/GJ2 Direct CO2 emission CO2gr./MJ1 Emission factor CO2gr/MJ Petrol 1,14 73 83,2 Diesel 1,16 74.8 86,8 Natural gas 1,06 58.3 61,8 Electricity Netherlands 1613 ₁₆₁ 1

Upper limit IPCC 2006 report vol.2 ch.1 pg.1.24

2_{Motimer et al. 2004, LowCvp 2004} 3_{2005 value 580 CO}

2 gr/kWh, Energy database www.enegiened.nl

Due to the transition to commercially available fuel e.g. mining and transportation, the total energy needed to produce and distribute 1GJ of the fossil fuels requires an energy factor. To this extent the carbon dioxide emissions are multiplied by the same factor. The carbon dioxide associated with electricity consumption depends on the Dutch energy mix. The energy mix represents the average emissions from all energy production units that supply the Dutch market (e.g. coal, gas, nuclear, hydro-power, …).

General description of the life cycle inventory

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Table 5 Energy crop yield per soil type (source: KWIN 2006)

Clay Sand

Wheat 15% moisture (ton/ha) 8,4 7,8

Wheat straw (ton/ha) 4,4 4

Sugar beets clean product (ton/ha) 53,3 53,5

The inventory data are based on the flow charts depicted in Figure 5 and Figure 6. The flow chart is based on the production of 1 ton bio-ethanol (tbe) in a first generation bio-ethanol installation. For both flow charts it is explained were the figures are retrieved. The first flow chart represents bio-ethanol from wheat and the second from sugar beets.

Figure 5 Mass/energy balance of bio-ethanol from sugar beets

One ton of bio-ethanol requires 3499 kg wheat with 16% moisture content (Mortimer et al., 2004, LowCVP 2004). Cultivation of biomass yields on sandy soil 7800 kg wheat (15% moisture) and 4000 kg straw and on clayey soils 8400 kg wheat (15% moisture) and 4400 kg straw (KWIN 2006). This results in a land usage of respectively 0,45 and 0,42 ha. The by-product is 1,85 ton

Biomass cultivation Transportation Conversion Sowing seed 1 ton bio-ethanol (99.5%) Straw 1,80 ton 48,3 litre diesel DDGS 1,14 ton 3.,49 ton wheat 0,45 ha 0,42 ha 92,4 kg N 39,2 liter diesel 72,0 kg N Straw 1,85 ton Clay Sand

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straw on clay and 1,8 ton straw on sand per ton ethanol. The nitrogen input per ton bio-ethanol is 92,4 kg for clay and 72 kg for sand (PPO 2005). Co-product after distillation is 1.14 ton/tbe of DDGS (Dark Dry Grain Solubles) (Mortimer et al., 2004 and LowCvp 2004, Perry et al., 2006).

Figure 6 Mass/energy balance of bio-ethanol from sugar beets

The molecular weight of ethanol is 92 and 180 mol for glucose/fructose. This means that 1 kg glucose yields 0,51 kg ethanol and 0,49 kg CO2 (Murphy et al., 2004). 1 ton of pure bio-ethanol

(100% purity) requires 1005 kg ethanol 99,5%. The conversion efficiencies of glucose to bio-ethanol and sucrose to glucose are respectively 95% and 99% (Murphy et al., 2004). Hence, 1 ton bio-ethanol requires 2095 kg sucrose. The sugar content (sucrose) of clay and sand sugar beets are respectively 16,4 and 16,3% (KWIN 2006) requiring 12,774 and 12,852 ton sugar beets at the input. With tara percentages of 18% for clay and 15% for sand (KWIN 2006) 15,6 ton and 15,1

Biomass cultivation Transportation Conversion Sowing seed 1 ton bio-ethanol (99.5%) 43,1 litre diesel 15,6 - 15,1 ton 0,24 ha 0,24 ha 37,0 kg N 36,8 liter diesel 40,6 kg N Clay Sand Pulp 0,78 ton Vinasse 0.72 ton

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ton off land product is required. The product yield is 65 ton on clay and 63 ton on sand (KWIN 2006) requiring 0.24 ha on both types of soil.

The nitrogen input per ton bio-ethanol is 37 kg for clay and 40,6 kg for sand (Swaaij 2005). The co-products of 1 ton bio-ethanol are 0,78 ton pulp with a moisture content of 9% and 0,72 ton vinasse (Smeets et al., 2005 and Mortimer et al., 2004).

Based on these flow charts the carbon dioxide emissions of the relevant elements within the system boundary (Figure 4) are qualified and quantified.

Carbon dioxide emissions from farm machinery

Emissions are caused by diesel consumption. Emissions released during the production of auxiliary equipment are neglectable over their life time (Bergsma et al., 2007). Included are all tillage activities according to KWIN. For sugar beets on top of the KWIN figures additional fuel for harvesting and sowing is required and for both crops additional spreading of animal manure is necessary. Table 6 summarizes the total CO2 emission per ton bio-ethanol according to equation 1

and based on the data admitted in appendix B.

tbe diesel diesel fm tot ha ef fm F CO2 _ =

∑

_ × Equation 1

CO2tot_fm Total carbon dioxide per ton bio-ethanol farm machinery (kg/tbe)

Fdiesel_fm fuel consumption diesel farm machinery (liters)

efdiesel emission factor diesel (kg/l)

hatbe hectare per ton bio-ethanol (ha)

Table 6 total CO2 emissions from farm machinery

Total carbon dioxide emission kgCO2/tbe

wheat clay 156,93

wheat sand 124,85

sugar beet clay 134,92

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Carbon dioxide emissions associated with fertilizer production

The emission factor for N-fertilizer production is 6,69 kg CO2-eq./kg N, according to LowCvp

(2004) and 6,85 kg CO2-eq./kg according to Kramer et al. (1997). Bergsma et al. (2007) used a

guide value similar to the one of LowCvp (2004) the final value is being discussed with experts . The emissions for P- and K- fertilizer production are neglected in the calculation model. Their impact is neglectable relative to N-fertilizer emissions (Bergsma et al., 2007). The total emissions depend on the total amount of synthetic N-fertilizer used. It is common practice to apply organic fertilizer: 100 kg/ha and 110kg/ha, respectively on sand and clay, before sowing the wheat (Darwinkel 1997). Before sowing sugar beets 108 kg/ha and 19kg/ha organic fertilizer is applied, respectively on sand and clay (Swaaij 2005).

Table 7 summarizes the total CO2 emission per ton bio-ethanol according to equation 2 and based on the data admitted in appendix B.

tbe fp manure tot fp tot ha ef N N CO2 _ =

∑

( − )× Equation 2

CO2tot_fp total carbon dioxide per ton bio-ethanol fertilizer production (kg/tbe)

Ntot Total N-fertilizer (kg)

Nmanure N-fertilizer from animal manure (kg)

effp emission factor fertilizer production (kg CO2eq./kgN)

Table 7 total CO2 emission associated with fertilizer production

sugar beet sand 97,05

N2O soil emissions from fertilizer application

Soil emissions depend on type soil, climate and quantity of fertilization rate (IPCC 2006). Total emissions constitute direct and indirect emissions in the form of N2O. This greenhouse gas is 296

times worse than CO2. The total emissions are calculated according to protocol 5434 “direct

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×

+

×

=

− Equation 3

CO2tot_N20-N total carbon dioxide per ton bio-ethanol from fertilizer (kg/tbe)

ef1 direct soil emissions (N2O-N/ha)

ef2 indirect soil emissions (N2O-N/ha)

The direct and indirect emissions are calculated separately and results are admitted in appendix C Further readings on the used formulas in IPCC 2006 vol. 4.

Table 8 Total CO2eq. soil emissions from fertilizer application

Carbon dioxide emissions associated with transporting

Transportation of biomass to a bio-ethanol installation is assumed by diesel truck. Considering the Energy Valley region it is assumed that the average distance, one way, is 100 km from the farmers to the bio-ethanol installation. The emissions are calculated according to the methodology of Essen et al. (2003). Emissions are based on driving 10% through urban areas 30% through rural areas and 60% on highways by truck with a load capacity of 27 ton. The truck is assumed to be dedicated to biomass transport meaning half of the distance is travelled empty (full load to the bio-ethanol installation and empty back to the biomass supplier).

Table 9 summarizes the total CO2 emission per ton bio-ethanol according to equation 4 and based on the data admitted in appendix B.

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CO2tot_N20-N total carbon dioxide per ton bio-ethanol from transportation (kg/tbe)

Efl energy consumption full load (MJ/km)

Eemp energy consumption empty (MJ/km)

efdiesel emission factor diesel (kg/MJ)

dist distance one way (km)

BMinput biomass input per ton bio-ethanol (kg)

There is small difference between clay sugar beet and sand sugar beet due to the tarra percentage.

Table 9 Total CO2 emissions associated with transporting

Carbon dioxide emissions associated with conversion

The energy input for wheat ethanol conversion is divided over milling, hydrolysis, fermenting, distillation, dehydration and drying of DDGS. The energy input for sugar beet ethanol conversion is divided over shredding, diffusion, fermentation, distillation and dehydration.

The conversion inputs for both energy crops are electricity and steam. Steam can be produced by means of an industrial gas fired steam boiler with a thermal efficiency of 80% (Ozalp et al., 2006). Table 10 summarizes the total CO2 emission per ton bio-ethanol according to equation 5 and based on the data admitted in appendix B.

elek elek gas boiler steam conver tot E ef E ef CO × + ×      = . _ 2

η

Equation 5

CO2tot_N20-N total carbon dioxide per ton bio-ethanol from transportation (kg/tbe)

Esteam energy consumption steam (MJ/tbe)

ηboiler boiler efficiency (%)

efgas emission factor gas (kg/MJ)

Eelek. energy consumption electricity (MJ/tbe)

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There is a small difference between clay sugar beet and sand sugar beet due to the tarra percentage.

Table 10 Total CO2 emissions associated with conversion

wheat 821

Sugar beets 1151

Emissions associated with co- and by-product allocation

Allocation will be based on function and physical relationship with a reference product. Subdivision of the system is not possible and system extension leads to complex calculation especially when the replaced product is part of another system and not produced independently which is often the case for bio-fuels (Malça et al., 2006). The allocation proposed in this section should not be considered as the best. There are many other potential allocation functions and bases. Table 11 summarizes the amount of CO2 emissions per ton bi-ethanol allocated to the by

products, which product it substitutes and the physical relation on which the allocation is based. The results are based on appendix B.

Table 11 emissions associated with co- and by-product allocation

Co-product Substitutes Allocation base CO2 emission

allocated kg

Straw N-fertilizer Nitrogen content 80,7

DDGS Soya schoot Protein content 128,7

Pulp Corn VEM 70,3

Vinasse N-fertilizer Nitrogen content 96

Emissions associated with land use change

IPCC 2006 distinguishes crop land, forest land, grass land, wet land, settlements and other land. The current annual crops cultivated in the Netherlands are grown on so called crop land. De Groot et al. (2005) states that the change in carbon content of the soil does not change when cultivating different types of annual crops. In the same article it is stated that emissions due to land use changes from temporarily grassland to cropland are marginal. Therefore these emissions are neglected since we assumed that energy crops are cultivated in the Energy Valley region.

Carbon dioxide emissions associated with displacement of prior crop production

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wheat for bio-ethanol production. Even though these emission are not incorporated at least until 2011 (Bergsma et al., 2007; Cramer 2007), this report will take this into account.

Emissions associated with energy crops production will be the emissions for cultivating the same crops. This means that the emissions calculated for emissions of farm machinery, fertilizer production and N2O emissions from fertilizers are calculated twice. Table 12 summarizes the

additional carbon dioxide emissions.

Table 12 Emission associated with displacement of prior crop production

CO2 emission land use change kgCO2/tbe

Carbon dioxide emissions associated with displacement of prior use of residues

The residue of wheat is straw and remains available. Residues of sugar beet production, organic matter, remain in the field. Therefore the emissions associated with the replacement of residues are assumed to be zero.

Carbon dioxide emissions from fossil energy reference

As already mentioned in the beginning of the chapter petrol is compared with bio-ethanol. There are no residue or by products of petrol production which have to be taken into account. The reference value will be the CO2 emissions from 636 kg petrol. The total carbon dioxide emissions

associated with the combustion of 636 kg petrol is 2211,9 kg (Table 3 and Table 4).

The next chapter will continue on the results from the inventory analysis. It summarizes the inventory analysis by quantifying for each scenario the greenhouse has balance.

5.3 Impact assessment of the life cycle inventory analysis

This section contains the results, in the form of the greenhouse gas balance for each scenario, of the LCA according to the methodology of Bergsma et al. (2007). First a recap of the assumptions made will be given:

Biomass is cultivated according to common agricultural practices in the Netherlands; Fertilization consists of partly animal manure and partly synthetic nitrogen;

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Conversion of sugar beet ethanol includes shredding, diffusion, fermenting, distillation, dehydration;

Fossil energy inputs characteristics are according Table 4;

DDGS is a substitute for soya schoot and allocated based the protein content, straw is a substitute for synthetic fertilizer and allocated based on nitrogen content;

Vinasse is a substitute for synthetic fertilizer and allocated based on the nitrogen content, pulp is a substitute for corn and allocated based on the VEM content;

Emission due to land use change are included; No replacement of co- by products are necessary;

Comparisons are based on energy content and related weight of petrol;

The assessment is performed starting with the biomass supply until bio-ethanol production;

Only the primary system in- and outputs are considered.

Table 13 presents the results of the Green house gas balance, according to equation 6.

petrol emission l bioethano emission petrol emission balance CO CO CO GHG _ _ 2 _ _ 2 _ _ 2 ∑ − = Equation 6

GHGbalance Green House Gas balance

CO2_emission_bioethanol Total Carbon dioxide emission bio-ethanol production

CO2_emission_petrol Carbon dioxide emission petrol

Table 13 Greenhouse gas balance

Green house gas balance %

wheat clay -5,29

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The next section will interpret the results from table Table 13 by investigating the contributions of each element according to the calculation methodology of Bergsma et al. (2007) from Figure 4

and each individual input.

5.4 Interpretation of the greenhouse gas balance results

For both types of crops and soils pie charts are made to assess the impact of each relevant system element (Figure 4) on the calculated greenhouse gas balances. The marginal effects of each in- and output is assessed by aggregating the carbon dioxide emissions to those in- and outputs.

Figure 7 and Figure 8 represent the absolute contribution of each element on the total greenhouse gas balance of wheat bio-ethanol based on the greenhouse gas balances from Table 13 (note that the total does not cumulate till 100%). E.g. avoiding displacement of prior crop production increases the greenhouse gas balance with 27,5 and 38,2% respectively wheat bio-ethanol on sandy and clayey soils.

wheat bioethanol on sand

5,64% 7,63% 14,23% 1,20% 4,63% 32,47% 27,49% -3,65% -5,81%

Energy inputs, farm machinery Emission from fertilizer production Emission due to soil fertilization N2O Energy inputs machinery and transports Energy inputs machinery and conversion electricity Energy inputs machinery and conversion heat Displacement of prior crop production Allocation straw

Allocation DDGS

Wheat bioethanol on clay

7,1% 13,0% 18,2% 1,2% 4,6% 32,5% 38,2% -4% -6%

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Sugar beet bio-ethanol on sand 5,6% _4,4% 8,5% 5,0% 10,1% 41,9% 18,5% -4,3% -3,2%

Energy inputs, farm machinery Emission from fertilizer production Emission due to soil fertilization N2O Energy inputs machinery and transports Energy inputs machinery and conversion electricity Energy inputs machinery and conversion heat Displacement of prior crop production Allocation vinasse

Allocation pulp Sugar beet bio-ethanol clay

6,1% 9,7% 7,8% 5,1% 10,1% 41,9% 23,6% -4,3% -3,2%

Figure 8 Impact of system elements on total greenhouse gas balance of sugar beet ethanol

The system in- and outputs and their contribution to the total greenhouse gas balance are depicted in Figure 9. The replacement of prior crop is incorporated by doubling the inputs for biomass supply. -50,00% -40,00% -30,00% -20,00% -10,00% 0,00% 10,00% 20,00% Synthetic fertilizer -25,90% -14,89% -44,08% -33,10% Organic fertilizer -18,85% -11,93% -19,80% -2,11% Diesel tillage activities -10,24% -10,22% -12,57% -12,00% Diesel transportation -1,20% -5,02% -1,20% -5,15% Naturals gas -32,47% -41,94% -32,47% -41,94% Electricity -4,63% -10,06% -4,63% -10,06% DDGS 5,81% 5,81% straw 3,65% 3,65% Vinasse 4,34% 4,34% Pulp 3,18% 3,18% Total GHG balance 16,18% 13,45% -5,29% 3,14% sand wheat sand sugar beet clay wheat clay sugar beet

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Table 14 presents the marginal effects on the greenhouse gas balance of the primary inputs which means the effect of increasing/decreasing 1 unit of primary in- or output on the total greenhouse gas balance per ton bio-ethanol. Based on these figures together with Figure 9 it is able to calculate the system in- and outputs. Note that these figures are rounded numbers.

Table 14 Marginal effect inputs on GHG balance

Marginal effects in- and outputs /tbe unit

Synthetic fertilizer 1 kg N -0,51%

Organic fertilizer 1 kg N -0,23%

Diesel tillage activities 1 liter -0,14%

Diesel transportation 1 liter -0,14%

Naturals gas 100 MJ -0,28% Electricity 100 MJ -0,73% DDGS 100 kg 0,51% Straw 100 kg 0,20% Vinasse 100 kg 0,60% Pulp 100 kg 0,41%

Based on the presented figures the following conclusions are made.

cultivating energy crops on sandy soils improves the greenhouse gas balance with 5,09% and 10,73% for respectively sugar beet and wheat;

one ton bio-ethanol from sugar beets requires nearly half of the total land requirement when using wheat;

converting sugar beet to 1 ton bio-ethanol requires 1,4 times more carbon dioxide compared to wheat;

sugar beet conversion is responsible more than half of the total carbon dioxide emissions; wheat to bio-ethanol conversion is responsible for less than half of the total carbon

dioxide emissions;

emissions associated with the biomass supply for sugar beets are approximately one and a half time lower compared to wheat;

to this extent is the burden from replacing prior crop production 1,5 times higher for wheat compared to sugar beets;

emissions associated with the input from electricity are 2,6 times higher compared to gas; replacing synthetic fertilizer with organic fertilizer can reduce emissions per kg N with

more than half;

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6. Production

chain

improvement

measures

to

increase the greenhouse gas performance

This chapter answers the second research question being “Which improvement measures can be made in the set up of the bio-ethanol production chain to improve its greenhouse gas balance. Numbers and figures used in this chapter are derived from appendix C unless mentioned otherwise. Figure 10 presents the emissions which influence greenhouse gas emissions. Accordingly improvements are investigated. Whenever reliable data are available greenhouse gas reductions were calculated, if not then an estimate of the potential reduction is made.

Figure 10 Sources of GHG emissions

6.1 Improvements within the biomass supply chain

To reduce GHG chain emissions the best option is to use waste products because it does not add any additional carbon dioxide emissions. These emissions are than assigned to the product life cycles were it comes from. However there are two exceptions;

if the waste product is used in an other industry the burden for replacing the waste by an other product has to assigned to the LCA of bio-ethanol (e.g. molasse which is also used as a animal feedstock);

as soon as emissions are allocated to the waste, by the LCA from where the waste is originated, than these have to be taken over by the LCA of bio-ethanol (waste heat input from e.g. biogas fired Combined Heat and Power (CHP) installation who need to comply to the sustainability criteria and therefore allocate when possible emissions to the heat (by-product of biogas)).

Biomass supply Conversion By- and co-products Cultivation Crop/land management Transport Land use change Displacement prior crops

Heat Electricity DDGS and

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The savings which can be achieved are similar to the total emissions from crop cultivation.

Table 15 savings on GHG balance when using waste products

CO2 emission land use change kgCO2/tbe

wheat clay 38,2%

wheat sand 27,4%

sugar beet clay 23,6%

sugar beet sand 18,5%

In case of growing energy crops the inputs depicted in Figure 10 matter. The remaining of this paragraph deals with improvement measures to reduce these emissions.

Emissions from farm machinery (cultivation)

The contribution of carbon dioxide emissions from farm machinery is relatively small. Equation 1 (chapter 5.2) shows that emissions are determined by the fuel consumption. This variable is directly related to machine capacity. Figures used in this report are based on common averaged agricultural practice. There are no accurate data available to calculate the effect of increasing machine capacity. However based on the KWIN (2006) figures an estimation will be made on what the impact is of increased machine capacity.

Table 16 estimates the savings on the greenhouse gas reduction per ton bio-ethanol. The calculations are based on data admitted in Appendix C.

Table 16 greenhouse gas savings due to increased farm machinery capacity

Estimation savings GHG balance increased capacity /tbe %

wheat sand 0.94%

wheat clay 0.88%

sugar beet sand 0.54%

sugar beet clay 0.54%

These estimations show marginal savings. Based on these results it does not seem worth to increase capacity in order to reduce the total greenhouse gas.

Emission associated with fertilizer production and soil emissions (Crop/land management)

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account because these emissions are approximately 0.12 kgCO2/km (0,0058% per km) in the worst case (wheat on clay) (Essen et al., 2003).

However it is not possible to increase the amount of animal fertilizer because fertilization is spread over multiple periods. As soon as the crop is sowed the option of animal manure turns obsolete because the manure has to cultivate in the ground. Avoiding synthetic fertilizer, not replacing, has a marginal effect on the greenhouse gas balance of 0,51% per kg N (0,3% per kg N due to production emissions and 0,21% per kg N due to reduced soil emissions).

Reducing the amount of nitrogen can yield significant reduction of the total GHG emissions. It is though a complex matter to determine the optimal fertilization rate suited best for the bio-ethanol production since it depends on the type soil, weather conditions, moment of spreading and the product yield (IPCC 2006). No estimations are proposed since there are too many contingencies involved enough to make worth it a research on its own.

For now it is assumed that the nitrogen management is optimized to gain maximum product yield with applying the maximum amount animal manure. This is likely since farmers receive equity for using animal manure and the maximum amount nitrogen per ha is restricted by regulations.

Emissions associated with transport

The effects of carbon dioxide emissions from transport are relative small, (a marginal effect on the greenhouse gas balance of 0,05% per km by truck). However the option to transport by inland vessel is examined. Main goal is to show what the relative difference is between the two transportation modes. Table 17 presents the results assuming a distance of 100 km and no extra transportation from and to the harbour. These results are based on Appendix C.

Table 17 Greenhouse gas balance improvements of transport set-up changes

Transport by inland vessel savings GHG balance /tbe Sugar beet 2,74%

Wheat _0,56%

The transport by inland vessel does not yield significant savings on the greenhouse gas balance, especially realizing that transportations to and from the harbor is not taken into account.

Emissions associated with displacement of prior crop production

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emissions for prior crop displacement were included in the LCA. For Eastern Europe these figures (replacement) vary between 0.99% till 3% (Kondili et al., 2007). It is assumed that importing biomass from these countries avoids displacement of prior crop production. This means for the GHG balance that emissions associated with prior crop production are replaced by emissions for importing biomass per sea vessel and transportation over land when the bio-ethanol installation is not at a harbour. Therefore the assumption is made that a sea vessel has to travel 1000 km, an additional 100 km is driven by truck and energy crops are cultivated on similar circumstances and with the same agricultural practices. Table 18 presents the GHG balance reduction. The results are based on appendix C.

Table 18 Greenhouse gas balance improvements due to biomass import

Emission associated with biomass import Savings GHG balance /tbe

wheat clay 36,25%

wheat sand 25,51%

sugar beet clay 15,72%

sugar beet sand 10,60%

Importing biomass by sea vessel only adds 0.01725 kg/km for wheat and 0.06275 kg/km for sugar beets. Therefore the savings on the total GHG balance are significant because it replaces the emission associated with prior crop production. This is the 3rd largest contributor to the total greenhouse gas balance for sugar beet bio-ethanol and 2nd largest for wheat bio-ethanol.

6.2 Improvements within the conversion chain

The carbon dioxide emission from electricity and heat are responsible for the lion’s share respectively sugar beet and wheat bio-ethanol, of the total greenhouse gas balance. There is a lot to gain by reducing these emissions. The options which are investigated are;

electricity production at site. applying waste heat.

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Ozalp et al. (2006) examined the conversion efficiencies of industrial steam and electricity production configurations. Electricity generation by means of an internal combustion engine are used for capacities between 100 kW and 5MW. Wheat ethanol conversion requires approximately 2.3MW and sugar beet ethanol 4,7MW (Appendix B). Therefore electricity is generated with an internal combustion engine.

There are also examples of ethanol production installations which use waste heat, e.g. bio-ethanol production in Wijster using waste heat from Essent. In these cases all emission associated with steam production can be subtracted. Table 19 summarizes the greenhouse gas balance improvement when generating electricity in-house with an internal combustion engine and using the waste heat for steam production. These results are based on appendix C.

Table 19 Greenhouse gas balance improvements due to conversion set-up changes

GHG balance improvements /tbe Wheat bio-ethanol Sugar beet ethanol

Internal combustion engine with heat recovery total -0,72% -1,57%

of which saving on electricity -1,74% -3,77% of which savings on steam production 1,01% 2,20%

Utilizing waste heat from other industries 32,47% 41,94%

Even though the emissions of natural gas is a factor 2,6 lower it can not reduce the carbon dioxide emissions compared to grid electricity. This is caused by the higher electrical efficiencies of large power plants approximately 38% compared to the 27,5% from the combined heat and power (CHP) unit investigated in this report. However the use of waste heat reduces the net loss.

6.3 Improvements by different allocation bases

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Table 20 Allocation beet pulp to electricity and heat

Allocation pulp to electricity and heat Savings on GHG balance /tbe Allocation excess electricity to grid 10,51%

Allocation process steam heat recovery 4,87%

Replacement process electricity 26,51%

Total savings by allocation pulp 41,88%

In case of wheat ethanol the DDGS can be burnt in a steam generator. It replaces the function of gas and the physical relation will be the energy content. When excess heat can not be utilized it is not allowed to allocate the remaining heat to natural gas. Therefore the excess DDGS is used as feedstock. Total savings are summarized in Table 21. Calculations are admitted in appendix C.

Table 21 Allocation DDGS to heat and feedstock

Allocation DDGS to heat and feedstock Savings on GHG balance /tbe Allocation excess DDGS to feedstock 1,81%

Replacement process heat 32,47%

Total savings by allocation DDGS 34,28%

Another by-product of bio-ethanol production which is not been assessed in any study is the low value waste heat and steam from the bio-ethanol conversion process. This low value waste heat and steam can, due to its low temperature, not be used in the process. However this kind of heat and steam can be used for e.g. city heating or factory heating. In these cases the emissions associated with the heating of these factories/cities can be allocated to the waste heat and steam from the bio-ethanol process.

These two examples of alternative allocations show what is possible with the allocation of by- and co-products. The main message is that allocation should be based on function and correlating physical relationship. Therefore it can be worth while to upgrade, separate or combine by- and co-products. E.g. for wheat ethanol there are also options to create more by-co-products. Currently they are all combined to DDGS.

An example is not to dry the co-product DDGS from wheat bio-ethanol. In the current calculation these emissions are incorporated. Not drying the DDGS would reduce the heat input with 4272 MJ (Mortimer et al., 2004) equivalent to 330 kg/tbe 6 and has an impact of 14,9% on the GHG balance.

6

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6.4 Interpretation improvement results

Based on the results in this chapter the following conclusions are made:

GHG performance improvements due to increasing farm machinery capacity is relatively small;

Applying animal manure is restricted to the first nitrogen gift, before sowing.

Reducing the synthetic nitrogen can lead to significant improvements of the GHG balance;

Reducing nitrogen reduces product yield, therefore creating a trade off between reducing CO2 emissions and increasing land usage per ton bio-ethanol.

Transporting biomass by inland vessel yields marginal improvements of the GHG balance;

Importing biomass by sea vessel can improve the GHG balance for sugar beet bio-ethanol with 10-15% and 25-36% for wheat bio-ethanol, depending on the soil type;

Producing electricity by means of an internal combustion engine with heat recovery at the side does not reduce the total carbon dioxide emissions due to the relative low electrical efficiency of the CHP unit.

Applying waste heat avoids the emissions due to steam production;

Allocating by- and co-products on different functions can lead to GHG improvements in the order 28% for DDGS and 42% for pulp.

Faculteit BedrijfskundeRijks

Sustainable bio-ethanol production in the Energy Valley region

Faculteit

Bedrijfskunde

Rijksuniversiteit Groningen

Preface

Summary

Table of contents

Glossary

1.

Context from where the research is initiated

1.1 Research objective formulated by Energy Valley

1.2 Energy Valley’s research motivation

1.3 Reading guide

2.

Translation of the research objective to a practical

research approach

2.1 Applied theoretical framework

2.2 Translation theoretical framework to practical approach

2.3 Applied information sources during research

3.

Analysis of the research objective

3.1 Research perspective

3.2 Environmental analysis from the research perspective

3.2.1 Developments of the directly related forces on bio-ethanol

production in the Energy Valley region

3.2.2 Developments of the indirectly related forces on bio-ethanol

production in the Energy Valley region

3.3 Research question

3.4 Research demarcation

4.

Theory of greenhouse gas calculation methodology

5.

Greenhouse gas balance of bio-ethanol produced in

the Energy Valley region

5.1 Definition of life cycle assessments goal and scope

5.2 Inventory analysis of associated carbon dioxide emissions

∑

∑

ha

ef

ef

CO

296

)

(

28

44

2

×

+

×

=

η

5.3 Impact assessment of the life cycle inventory analysis

5.4 Interpretation of the greenhouse gas balance results

6.

Production

chain

improvement

measures

to

increase the greenhouse gas performance

6.1 Improvements within the biomass supply chain

6.2 Improvements within the conversion chain

6.3 Improvements by different allocation bases

6.4 Interpretation improvement results