Temperature optimization of anaerobic digestion at the Käppala Waste Water

Treatment Plant Temperaturoptimering av Käppalas rötningsprocess

Sofia Bramstedt

Industrial and Environmental Biotechnology

Royal Institute of Technology

Master Thesis 2015

2

ABSTRACT

The Käppala Waste Water treatment plant treats water from 11 municipalities in Stockholm, Sweden. In

addition to treating wastewater, Käppala uses sludge to produce biogas. Biogas has a high economic value.

Käppala upgrades biogas from ca 65% methane to 97% methane before it is sold to Stockholms Länstrafik

(SL). The sale of methane gas generates an income of around 27 MSEK each year. Käppala wants to

investigate if the process could be optimized in order to increase the profit.

Today, the anaerobic digestion at Käppala is operated at 37 ⁰C in two digesters; R100 and R200. In general,

anaerobic digestion processes are often operated in either a mesophilic temperature interval (30-40 ⁰C)

or a thermophilic temperature interval (50-60 ⁰C). The literature regarding whether it is possible to

establish a stable digestion process in the temperature interval between mesophilic and thermophilic is

inconsistent. In this report, the optimal temperature for Käppala´s anaerobic digestion process is

investigated. Economic aspects, environmental effects, process stability and seasonal variations are

considered when determining the optimal temperature. It should also be determined if a stable process

can be obtained in the temperature interval between mesophilic and thermophilic.

The project is divided into two parts; a laboratory part and a modelling part. In the laboratory investigation,

the anaerobic digestion process in R100 is mimicked with respect to substrate. The process is evaluated

for different temperatures and organic loading rates. Two reactors were set to a temperature of 37 °C,

two were set to 45 °C and the remaining two were set to 55 °C. The organic loading rate is first set to 3

kgVS/(m3,day) in all reactors, then increased with 25%, VS stands for volatile solids. During a period of four

and a half months, the process stability is evaluated for the three different temperatures. The evaluation

is done by measuring the concentration of volatile fatty acids, pH and alkalinity in the digested sludge as

well as measuring the biogas production and the methane content of the produced gas. The results

indicate that the lab scale process in general was more instable than the large-scale process. However,

the differences in process stability between the different temperatures were small.

The data from the measurements are used in the modelling part as well as in the evaluation of the process

stability for the different temperatures. The most important analyses are the biogas production

measurements and methane content measurements. There is an obvious difference in methane

production between the different temperatures. The digestion run at 37 ⁰C produces the most methane

gas. In the modelling part, a mathematical model was created through literature search, laboratory data

and function determinations. The input variables in the mathematical model are digestion temperature,

organic loading rate, methane content after upgrading and the partitioning between the three current

applications for the produced gas. The outputs are the system’s monetary profit and carbon dioxide

footprint. The profit for the system at 37 ⁰C as digestion temperature is 10-20% larger than for the other

digestion temperatures. The total carbon dioxide footprint from the system at 37 ⁰C is 3-12% higher than

for the other temperatures. Despite the higher total carbon dioxide footprint, the environmental impact

from the system at 37 ⁰C is regarded as more positive than the environmental impact from the system at

45 ⁰C or 55 ⁰C. This conclusion is based on the fact that the system at 37 ⁰C lowers the carbon dioxide

footprint from fossil energy sources with 6-12% more than the system at the other temperatures. This

output result is independent on variation in organic loading rate and heating requirements.

Keywords: Biogas, Methane gas, Anaerobic digestion, Wastewater treatment

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SAMMANFATTNING

Käppala är ett avloppsvattenreningsverk som renar vatten från 11 kommuner i Stockholm. Förutom att

rena avloppsvatten använder Käppala slam för att producera biogas. Biogasen är en ekonomiskt värdefull

produkt. Käppala uppgraderar biogasen från ca 65% metan till 97% metan innan den säljs vidare till

Stockholms länstrafik (SL). Försäljningen av metangasen genererar en inkomst på runt 27 MSEK per år.

Käppala vill ta reda på om processen går att optimera för att öka vinsten.

Idag kör Käppala sin anaeroba rötningsprocess vid 37 ⁰C i två stycken rötkammare, R100 och R200.

Generellt körs anaeroba rötningsprocesser oftast i antingen mesofilt temperaturintervall (30-40 ⁰C) eller

termofilt temperaturintervall (50-60 ⁰C). Det finns motstridig litteratur på om det är möjligt att etablera

en stabil rötningsprocess i temperaturintervallet mellan mesofilt och termofilt, det är troligtvis beroende

på den totala processen. I denna rapport är den optimala temperaturen för Käppalas anaeroba

rötningsprocess undersökt. Temperaturen ska optimeras med avseende på ekonomi, miljöpåverkan,

processtabilitet och säsongsvariationer. Det ska även undersökas om det är möjligt att upprätta en stabil

process i temperaturintervallet mellan mesofilt och termofilt.

Projektet är uppdelat i två delar; en laborativdel och en modelleringsdel. I den laborativa undersökningen

är den anaeroba rötningsprocessen i R100 härmad i sex småskaliga reaktorer förutom temperaturen och

den organiska belastningen. Temperaturen i reaktorerna är satt till 37 ⁰C, 45 ⁰C respektive 55 ⁰C för två

reaktorer var. Den organiska belastningen är först satt till 3 kgVS/(m3,dag) i alla reaktorer för att sedan

ökas med 25%, VS står för det engelska uttrycket volatile solids som på svenska översätts till glödförlust.

Under en period på fyra och en halv månad är processtabiliteten utvärderad för de tre olika

temperaturerna. Utvärderingen är gjord genom att mäta koncentrationen av flyktiga syror, pH och

alkalinitet på det rötade slammet, samt genom att mäta biogasproduktionen och metanhalten i den

producerade gasen. Resultatet är att processen i labbskala generellt är mindre stabilt än processen i

fullskala. Dock är skillnaderna i processtabilitet mellan de olika temperaturerna små.

Förutom utvärdering av processtabiliteten av olika rötningstemperaturer används data från mätningarna

i modelleringsdelen. De viktigaste mätningarna är produktionen av biogas och metanhalt . Det är en tydlig

skillnad i metanproduktionen mellan de olika temperaturerna. Rötningsprocess som körs i 37 ⁰C

producerar mest metangas.

I modelleringsdelen är en matematisk modell konstruerad genom litteratursökning, data från den

laborativa delen och funktionsbestämningar. Inputvariablerna i den matematiska modellen är

rötningstemperatur, organisk belastning, metanhalt efter uppgradering och uppdelning av gasen på de

tre befintliga användningsområdena. Output från modellen är en ekonomisk balans över systemet och

systemets koldioxidavtryck. Vinsten från systemet vid rötningstemperatur 37 ⁰C är 10-20% högre än för

de andra temperaturerna. Det totala koldioxidavtrycket för systemet vid 37 ⁰C är 3-12% högre än för de

andra röttemperaturerna. Trots det högre totala koldioxidavtrycket anses miljöpåverkan från systemet

vid en röttemperatur på 37 ⁰C som mer positivt än miljöpåverkan från systemet vid 45 ⁰C eller 55 ⁰C.

Denna slutsats baseras på att systemet vid 37 ⁰C sänker koldioxidavtrycket från fossila energikällor med

6-12% mer än vad systemet gör vid de andra temperaturerna. Modelleringsresultaten för ekonomi och

miljö är oberoende av säsongsvariation i organisk belastning och uppvärmningsbehov.

Nyckelord: Biogas, Metangas, Anaerob rötning, Avloppsrening

4

CONTENT

Abstract ......................................................................................................................................................... 2

Sammanfattning ............................................................................................................................................ 3

Abbreviations ................................................................................................................................................ 6

1 Introduction .......................................................................................................................................... 7

1.1 Background ................................................................................................................................... 7

1.1.1 Water treatment plant .......................................................................................................... 7

1.1.2 Anaerobic Digestion .............................................................................................................. 7

1.1.3 The Käppala Waste Water Treatment Plant ....................................................................... 12

1.2 Aim and goal ............................................................................................................................... 16

2 Method ............................................................................................................................................... 17

2.1 System analysis ........................................................................................................................... 17

2.2 Laboratory part ........................................................................................................................... 18

2.2.1 Start up ................................................................................................................................ 19

2.2.2 Reactor maintenance .......................................................................................................... 19

2.3 Modelling part ............................................................................................................................. 21

2.3.1 Determination of total carbon footprint ............................................................................ 22

2.3.2 Energy survey ...................................................................................................................... 22

2.3.3 Survey over the disposal of digested sludge ....................................................................... 26

2.3.4 Biogas usage survey ............................................................................................................ 28

2.3.5 Evaluation of lab scale parameters ..................................................................................... 29

2.3.6 Concluding equations.......................................................................................................... 31

3 Results ................................................................................................................................................. 32

3.1 Laboratory part ........................................................................................................................... 32

3.1.1 Analysis results .................................................................................................................... 32

3.1.2 Stability ............................................................................................................................... 37

3.2 Modelling part ............................................................................................................................. 38

3.2.1 Input .................................................................................................................................... 38

3.2.2 Output ................................................................................................................................. 39

4 Discussion ............................................................................................................................................ 40

4.1 Comparison with full scale process ............................................................................................. 40

4.1.1 Foaming ............................................................................................................................... 41

5

4.1.2 Centrifuge - dewatering not effective ................................................................................. 41

4.1.3 Smell .................................................................................................................................... 42

4.2 Process stability .......................................................................................................................... 42

4.3 Seasonal variation ....................................................................................................................... 44

4.4 Profit ........................................................................................................................................... 45

4.5 Environmental aspects ................................................................................................................ 46

4.6 Reliability of the results .............................................................................................................. 47

5 Conclusion ........................................................................................................................................... 50

6 References .......................................................................................................................................... 51

Appendix 1 – Raw data ............................................................................................................................... 53

Appendix 2 – Equations to the mathematical model ................................................................................. 58

Appendix 3 – Constant to the mathematical model ................................................................................... 60

Appendix 4 – Output from mathematical model ........................................................................................ 63

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ABBREVIATIONS

SRT Solid retention time [day]

HRT Hydraulic retention time [day]

OLR Organic loading rate [kgVS/m3,day]

VS Volatile solids [%]

TS Total solids [%]

FS Fixed solids [%]

VFA Volatile fatty acid [mg/l]

TA Total alkalinity [mekv/l]

BA Bicarbonate alkalinity [mekv/l]

%CH4 Methane content [%]

m Mass [kg]

GWP Global warming potential

CDF Carbon dioxide footprint [kg]

Em Emission of greenhouse gases [kg]

CF Emission from consumption of a fuel [m3]

TV Thermal value [GJ/ m3]

EF Emission factor [kg/GJ]

E Energy [J]

Exp Expense [SEK]

Q Flow [m3/day]

ρ Density [kg/ m3]

cP Heating capacity [J/kg,K]

T Temperature [K]

P Power [W]

𝜂 Efficiency

R2 Coefficient of determination

V Volume [m3]

d Distance [km]

FC Mean consumption of fuel [m3/km]

7

1 INTRODUCTION

1.1 BACKGROUND

1.1.1 Water treatment plant

The main role of a wastewater treatment is to clean the wastewater from industries, communities etc.

The process has several environmental and financial challenges. Wastewater treatment has to be an

inexpensive service, while the quality of the water that is released into the environment has to remain

high. Also, environmental laws regarding pollution are becoming increasingly strict, and therefore it is a

challenge to operate a treatment processes in a sustainable and financially favourable way.

A water treatment plant can separate and utilize by-products of incoming waste water in order to produce

products with a monetary value a product that is both environmentally and financially sound is biogas.

Waste water treatment plants can produce biogas using sludge; small, solid particles that can be

separated from wastewater. The sludge is treated with microorganisms in an anaerobic environment that

degrades organic matter to biogas - this process is called anaerobic digestion. In Swedish wastewater

treatment plants, the cost of sludge treatment is as high as the cost of water treatment. The three largest

expenses related to sludge treatment are expenses for final disposal, personnel, heat, power, and water.

Anaerobic digesters are often ill optimized with respect to energy consumption due to the fact that the

processes where initially designed when the demand on biogas was low and biogas was less valuable

(Larsson et al., 2005).

1.1.2 Anaerobic Digestion

Anaerobic digestion is a microbiological process that occurs in the absence of free oxygen. The process

utilizes an anaerobic food chain that degrades organic compounds and produces biogas. In the

wastewater treatment industry, sludge is an organic by-product from the treatment process and its

nutrient content varies little. Therefore, the sludge is well suited as a substrate for stable anaerobic

digestion (Gerardi, 2003; Jarvis & Schnürer, 2009).

1.1.2.1 Microbiological activity

There is a large diversity of microorganisms that has the ability to use different sources of energy and

carbon. In anaerobic digestion, organotrophs are over-represented. Organotrophs are microorganisms

that use organic compounds as energy and carbon source. Anaerobic digestion also contains

microorganisms, known as chemoautotrophs, that can utilize inorganic compounds as substrate. In

anaerobic digestion process there are four different stages of degradation of organic compounds to

methane and to carbon dioxide. The stages are dependent on each other due to that products in one

stage is used as substrate in another stage. Some of the microorganisms in the process have a syntrophic

relationship, which means that the function of one type of microorganism is dependent of the function of

another (Gerardi, 2003; Jarvis & Schnürer, 2009).

Microorganisms are divided into five subgroups with respect to their response to free oxygen. The first

group includes strict aerobes, which require free molecular oxygen to preform respiration that leads to

growth. The second group are facultative anaerobes. This group uses respiration in the presence of oxygen.

However, under anaerobic conditions they are able to switch their metabolism to either fermentation or

anaerobic respiration. Aerotolerants are microorganisms that can grow in the presence of oxygen

8

although they preform fermentation. Microaerophiles perform respiration when the concentration of free

molecular oxygen is below 20% of the atmospheric pressure. If the pressure is higher than that or in

absence of oxygen the microorganisms are not able to grow. The last group of microorganisms is the strict

anaerobes, which perform either anaerobic respiration or fermentation. These microorganisms cannot

grow in the presence of free molecular oxygen. In anaerobic digestion, facultative aerobes, aerotolerants

and strict anaerobes dominate (Jarvis & Schnürer, 2009).

Microorganisms use different types of final electron acceptors in their metabolisms. If the final electron

acceptor is oxygen, the metabolism is called respiration. If the final electron acceptor is another molecule

than oxygen, the metabolism is called anaerobic respiration. Fermentation is a type of respiration where

the final electron acceptor is an organic compound. The amount of energy that microorganisms obtain

from a metabolic reaction depends on what type of electron acceptor the microorganisms use (Figure 1).

If the microorganism can utilize different electron acceptors, the electron acceptor with the highest

reduction potential is primarily used. In anaerobic digestion there is a competition for the hydrogen

between the methane producing microorganisms and the sulphate-reducing microorganisms. To favour

the methane producing microorganisms that produce valuable methane gas, the oxygen-reduction

potential (ORP) must be below -300 mV (Gerardi, 2003; Jarvis & Schnürer, 2009).

Figure 1 – The magnitude of the reduction potential for final electron acceptors.

There are four stages of anaerobic digestion of organic material to methane and carbon dioxide. Each

requires different types of microorganisms (Figure 2). Firstly, large organic compounds such as proteins,

fats and polysaccharides are hydrolysed to smaller organic compounds such as amino acids, fatty acids,

simple sugars and some alcohols. Most microorganisms that hydrolyse excrete enzymes and hydrolysis

occurs outside of the cells. The hydrolysis products can then be obtained by the microorganisms from the

medium. The degradation of proteins and small sugar chains to alcohols, fatty acids, ammoniac, carbon

dioxide and hydrogen gas is called fermentation. The types of products that are produced depend on both

the type of microorganisms that are present and the environment in the reactor, due to that some

microorganisms change their metabolism depending on the environment. The third stage of anaerobic

digestion is the anaerobic oxidation where fatty acids, alcohols, and some amino acids and aromatic

compounds are oxidized to mostly acetate and carbon dioxide. The third stage is strongly connected to

the last stage, which is the methane producing stage. In the third stage hydrogen gas is produced by

microorganisms during oxidation but the microorganisms in the fourth stage are only able to achieve

oxidation if the concentration of hydrogen gas is low. The concentration of oxygen is kept low by some of

the methane producing microorganisms that are using hydrogen as a substrate. This is an example of a

9

syntrophic relationship and this particular relationship is called Inter species Hydrogen Transfer (IHT)

(Gerardi, 2003; Jarvis & Schnürer, 2009; Witkiewicz, 2012).

Figure 2 – The order of the anaerobic digestion stages.

All types of methanogens that produces the methane belong to the domain archaea. Normally, the most

common methanogens in sludge digestion are acetotrophic methanogens. These microorganisms

produce methane and carbon dioxide through cleavage of acetate (RE 1). The second most common

methanogens, hydrogenotrophic methanogens, produce methane gas through utilization of hydrogen gas

and carbon dioxide (RE 2) (Gerardi, 2003; Jarvis & Schnürer, 2009).

𝐶𝐻3𝐶𝑂𝑂𝐻 → 𝐶𝐻4 + 𝐶𝑂2 RE 1

10

𝐶𝑂2 + 𝐻2 → 𝐶𝐻4 + 𝐻2𝑂

RE 2

1.1.2.2 Operational conditions

Methanogens are highly sensitive to changes in pH, alkalinity and temperature, often more so than the

rest of the microorganisms in the same digestion. More about the temperature effect on anaerobic

digestion can be found in section 1.1.2.3. Due to this sensitivity, it is important to have a stable process

with respect to operational conditions in order to obtain a high production of biogas with a high content

of methane. The optimal operational conditions are different for different strains of microorganisms and

the optimum of the total process can be thought of as a compromise between the optima for the different

microorganisms. Plants have different operational condition optima due to process differences. However,

besides from the above mentioned operational conditions there are other important factors to maintain

constant, such as gas composition , oxidation reduction potential (ORP), concentration of volatile acids,

retention time, organic loading rate, and stirring (Gerardi, 2003). The parameters that can be easily

controlled are temperature, substrate, stirring, organic loading rate and retention time. The value of the

other parameters is a consequence of the controlled ones.

The methane content of the gas is vital for Käppala since it is the gas that has a financial value. A low

methane concentration is also an indication that the methanogens are inhibited in some way. The ORP

value plays an important role in the microbiological relationship - different values on ORP favours different

types of microorganisms. Finally, the concentration of volatile acids is closely related to pH and alkalinity.

The operational conditions are related to each other either directly or indirectly and is it therefore

important to determine the cause of the change in the operational conditions (Gerardi, 2003).

Methanogens are slow growing microorganisms and in order to have a stable growing culture in the

reactor the retention time is an important parameter. There are two different retention times; solid

retention time (SRT), which is the time it takes to exchange all the solids/microorganisms in the reactor,

and hydraulic retention time (HRT), which is the time it takes to exchange the sludge/wastewater. SRT

and HRT are equal if the process does not recycle any digested and thickened sludge. To avoid wash-out

of microorganisms, the retention time needs to be longer than the generation time for the

microorganisms. Typically, the generation time for methane forming microorganism is in the range 1-12

days. Another aspect to take into consideration for determination of the retention time is the degree of

degradation; a longer degradation time means more degradation of the sludge and more production of

biogas. In batch processes, the sludge is added in portions, and the rate of degradation decreases with

time after a portion is added. Therefore, it is not favourable to drive the process to 100% degradation

before a new batch is added. Moreover, it is not possible to drive a continuous process to a complete

degradation. A combination of degree of digestion, total biogas production and microbiological

generation time determines the optimal retention time (Jarvis & Schnürer, 2009).

Organic loading rate (OLR) is the addition rate of organic material. If the organic loading rate is high and

the substrate has a high content of easily degraded molecules, volatile fatty acids are accumulated. This

is due to fermentation occurring at a higher rate than methane production. This can be avoided by

decreasing OLR, by diluting the sludge or by decreasing the retention time. Substitution of substrate with

a high content of simple organic molecules to a substrate with a high content of complex molecules, for

11

which hydrolysis and fermentation takes longer than methane production, is also a useful practice for

avoiding accumulation of volatile acids (Jarvis & Schnürer, 2009).

Stirring in the reactor prevents accumulation of solids at the bottom of the reactor as well as foaming.

Furthermore, stirring facilitates the contact between the microorganisms and the substrate (Jarvis &

Schnürer, 2009).

If changes in the operational conditions occur the process stability can be compromised. Different types

of indicators exist for evaluating the stability of anaerobic digestion processes. The indicators are pH,

alkalinity, volatile fatty acid concentration, ratio between volatile solids and total solids, biogas production

and methane production. However, to obtain a stable process there are several parameters that can be

controlled and maintained as constant. If a larger change in one of the process conditions is done, the

expected time for the process to reach stability again is approximately one month. A stable process has

low changes in the gas production of biogas and the ratio between acid and alkalinity (Gerardi, 2003).

1.1.2.3 Temperature effect

Microorganisms are active at different temperature ranges and have different temperature optima where

their cellular reactions are working optimally. Overall, methanogens are the most sensitive

microorganisms with respect to changes in temperature, as even a few degrees difference influence the

stability of the process. Methanogens are often classified according to four temperature intervals where

they can be active (Table 1) and some methanogens are active in more than one interval. Anaerobic

digestion is often operated at temperatures in either the mesophilic temperature range (30-40 ⁰C) or the

thermophilic temperature range (50-60 ⁰C) where most of the methanogens are active (Jarvis & Schnürer,

2009).

Table 1 – Temperature intervals for microorganisms and their names.

Temperature interval Temperature range

Psychrophilic 4-25 ⁰C

Mesophilic 30-40 ⁰C

Thermophilic 50-60 ⁰C

Extremophilic >65 ⁰C

The most favourable temperature interval is different for different waste water treatment plants. In the

mesophilic temperature interval different methanogens are active, the endogenous death rate is lower,

the volatile acid concentration is lower, the operational conditions are more stable and the

microorganisms are slightly more resistant to temperature changes. In the thermophilic temperature

interval the rate of methane production and digestion rate of organic compounds are 25-50% higher,

inactivation of pathogens is higher, microbial growth is faster and the equilibrium between ammoniac and

ammonium is driven to ammoniac gas. The temperature optimum is a trade-off between several different

parameters such as the cost of heating the system, the production of biogas, the disposal expenses, the

stability in the process and the environmental impact etc. (Gerardi, 2003; Jarvis & Schnürer, 2009; Larsson

et al., 2005).

There are different opinions regarding the possibility of having a stable process in the temperature

interval between mesophilic and thermophilic. According to New data on temperature optimum and

temperature changes in energy crop digesters (Lindorfer, et al., 2008) it is possible to establish a stable

12

process at the temperature interval in between mesophilic and thermophilic for digestion of energy crops.

However, according to (Gerardi, 2003; Larsson et al., 2005) it is not possible for the digestion of sludge.

Changes in the process temperature are possible. Considering the biogas production rate it is more

favourable to change from a mesophilic to a thermophilic process than the opposite. The reason for this

is that the change from a mesophilic temperature to a thermophilic will kill some of the mesophilic

methanogens. If the temperature is changed back, there are fewer microorganisms left that have a high

activity in mesophilic range. Thermophiles survive a temperature change to mesophilic temperatures even

though they lose activity. In this case, the mesophilic specialists are knocked out and the thermophilic

specialists become less effective. After a major temperature change it takes approximately one month to

obtain a stable process, if it even is possible. If the temperature is changed stepwise, it will take longer or

the process to stabilize (Boušková et al., 2005; Jarvis & Schnürer, 2009).

1.1.3 The Käppala Waste Water Treatment Plant

1.1.3.1 Water treatment plant

The Käppala Waste Water Treatment Plant has been in use since 1969 and treats water from 11

municipalities in Sweden. The plant is located on Lidingö, north of Stockholm. The plant has the capacity

to treat wastewater from 700 000 population equivalents (p.e.), and after an expansion in 2001 the

Käppala Waste Water Treatment Plant treats water from over 500 000 p.e. Käppala is the third largest

wastewater treatment plant in Sweden and has mechanical, chemical and biological cleaning (Witkiewicz,

2012).

A chart of Käppala’s wastewater treatment process is shown in Figure 3. The first step is a meva stepscreen,

which separates water from larger particles. In the following step the water is purified form sand in a grit

chamber. The third stage is presedimentation, where sedimentation of particles to the bottom occurs.

The particles, i.e. sludge, are collected with a sludge scrape and the water is led to a biological treatment.

In the biological treatment step, nitrogen concentration in the water is reduced by nitrification (RE 3) and

denitrification (RE 4). Anaerobic denitrification occurs in the first part of the basin and the nitrification

occurs later in an aerated zone, which requires recirculation of the nitrate rich water. The final settling

occurs after the biological step when the sludge is led to centrifugation as secondary sludge for digestion

preparation. Finally, before water is released into the sea, it is filtrated through a sand filter (Erikstam,

2013).

13

Figure 3 – Process chart over the Käppala Waste Water Treatment Plant.

𝑁𝐻4 → 𝑁𝑂3

RE 3

𝑁𝑂3 → 𝑁2

RE 4

1.1.3.2 The anaerobic digestion at the Käppala Waste Water Treatment Plant

The anaerobic digestion is run in a mesophilic environment, at 37 ⁰C in two reactors of 9000 m3 each. The

first reactor uses the sludge from the first sedimentation, i.e. primary sludge, as substrate and has a

retention time of circa 13 days. The second reactor digests a mix of digested sludge from reactor one and

secondary sludge from the final settling after the biological treatment, i.e. excess sludge. The retention

time in the second reactor is circa 10 days. The separation of the two digestion processes is done because

the sludge treated after the biological step contains large filaments of microorganisms. A high content of

filament in the sludge and a high biogas production cause a high probability of foaming. The gas

production is reduced in the second reactor by mixing the secondary sludge and digested sludge from the

first reactor, which contains lower concentrations of easily degradable organic material. Approximately

80% of the gas production comes from reactor one and 20% from reactor two (Biogasföreningen, 2005;

Witkiewicz, 2012) (Personal communication, C. Grundestam, 2015).

1.1.3.3 Production and usage of biogas

Besides the two reactors where biogas is produced, the biogas system consists of one gasometer, one

torch, one power generator and one gas treatment plant (Figure 4). The gasometer collects the biogas

coming from the anaerobic digesters and acts as a buffer to even out variations in the gas production.

Some of the biogas is then led to the gas treatment plant, where it is upgraded to 97% methane. Finally,

the upgraded gas is transported to Stockholms Länstrafik (SL) to be used as fuel for the busses in Lidingö.

Part of the produced gas is continuously led to the power generator. Lastly, the torch is used when the

upgrading system is down or when SL cannot receive the methane gas produced.

14

Figure 4 – Process chart over the biogas system at Käppala. Biogas is produced in the anaerobic digesters, R100 and R200. The gas is then transferred to the gasometer before the flow is divided between power generator, a torch and a gas upgrading system.

In 2013 the biogas production was 6.3 million Nm3. Of the produced gas 0.1 million Nm3 where used for

heat production and 0.4 million Nm3 was burned in the torch. The last 5.8 million Nm3 biogas was

upgraded to 3.9 million Nm3 97% methane (Witkiewicz, 2012). A volume of methane gas corresponding

to 38 500 MWh was delivered to SL in 2013 (Käppala, 2013; Witkiewicz, 2012).

1.1.3.4 Heating system

The sludge in the digesters is heated with a series of heat exchangers and a heat pump. Water and sludge

are used as heat carriers as they are pumped through this sludge heating system. Electricity is used for

the circulation pumps in addition to increasing water temperature in the heat pump (Figure 5). The sludge

heating system can be coupled to other connecting heating systems, generating an even more complex

system.

15

Figure 5 – Process chart over the sludge heating system. Blue boxes are heating exchanger, the pink box is the heat pump and the yellow circles are circulation pumps. R100 and R200 are digesters.

The heating system is regulated with respect to the temperature difference between incoming sludge and

digestion temperature through two parameters. The first parameter that is regulated at Käppala is the

temperature of the water out from the heating pump on the warm side, noted as THP,out in Figure 5. The

second regulated parameter is the water circulation flow on the warm side of the heat pump. The flow is

regulated with two circulation pumps, noted as SB00-P151 and SB00-P251 in Figure 5. The other pumps

are run at a constant rate.

16

1.1.3.5 Environmental policy

Käppala has six goals in its environmental policy. The first is that the emission requirements and laws

should be fulfilled with a decent margin, which Käppala managed to do during 2013. The second goal is

that the water treatment plant should produce sludge with a fertilizer grade quality that allows nutrients

to be recycled back to farmland. Moreover, Käppala should educate the personnel further and engage

them in the environmental work as well as educate and inform the public in order to minimize the amount

of non-treatable substances in incoming sewage. The final goal is to consider the environment when

procurement of goods and services occurs, to decrease the usage of energy and chemicals and to

successively improve the environmental work (Käppala, 2013, 2015).

1.2 AIM AND GOAL The anaerobic digestion at the Käppala Waste Water Treatment Plant is run at a mesophilic temperature

(37 ⁰C), but the process temperature has not been optimized for the plant. Käppala together with IVL

Swedish institute of environment and Syvab (Sydvästra Stockholm VA-verksaktiebolag) are interested in

the optimization of the anaerobic digestion process with respect to the heating requirement and a

seasonal variation in organic loading rate. The overall goal of the project can be divided into three;

Determine whether it is possible to establish a stable anaerobic digestion process in the

temperature interval between mesophilic and thermophilic (40-55 ⁰C)

Determine the optimum digestion temperature in the temperature interval 37-55 ⁰C with respect

to economics, environmental impact and process stability.

Evaluate if the seasonal variation in organic loading rate and heating requirement affects the

optimal digestion temperature.

The project is demarcated, the projects boundaries and simplifications are;

For the laboratory examinations three temperatures (37C, 45 ⁰C and 55 ⁰C) and two organic

loading rates (3 kgVS/(m3,day) and 3.75 kgVS/(m3,day)) are to be examined

The sludge heating system is seen as one unit and the connecting heating system that can be

coupled is neglected.

The areas of usage for the produced gas are those that Käppala already use.

The system boundaries are shown in section 2.1 (Figure 6).

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2 METHOD

2.1 SYSTEM ANALYSIS This article investigates a system of processes at Käppala that are the most affected by a change in

digestion temperature (Figure 6). The effect on other processes at Käppala is for all intents and purposes

regarded as neglectable and therefore disregarded. The investigation aimed at building a mathematical

model of the system that could determine its monetary profit and its total carbon footprint depending on

digestion temperature, organic loading rate, usage of produced gas and season. The article also

investigates the stability of the anaerobic digestion process at Käppala with respect to digestion

temperature.

Figure 6 – Overview of the system investigated in this article. The system consists of the processes at Käppala that are affected by changes in digestion temperature. Red arrows indicate whether the process generates a monetary income or contributes to the total expenses of the system. Red arrows also indicate what processes contribute to the system’s total greenhouse gas emission. Black arrows symbolise heat transfer, green arrows symbolise biogas transfer and yellow arrows symbolise sludge transfer.

In this report, the method section is divided into two parts, laboratory part (Section 2.2) and a modelling

part (Section 2.3). The laboratory method was a laboratory scale investigation. The large scale digestion

18

was mimicked using six lab scale reactors, where all process parameters were kept at the regular levels

that Käppala normally uses, except for the temperature and the organic loading rate, which were varied.

The resulting biogas production rate, methane content of the biogas, percentage of volatile solids (%VS)

and percentage of total solids (%TS) of the digested sludge as a function of temperature and organic

loading rate were investigated. These functions were later used in the modelling. The result also contained

an evaluation of the process stability depending on the digestion temperature. The modelling part of the

work consisted of a collection of data and relationships for building a mathematical model. The

mathematical model was used for the economic and environmental evaluation of the process as functions

of temperature and organic loading rate.

2.2 LABORATORY PART The purpose of the digestion in the lab scale reactors was to determine changes in gas production,

methane content of the gas, %TS and %VS of the digested sludge as a function of temperature. The process

stability of the digestion depending on the temperature was also investigated. The temperature interval

40-55 ⁰C was compared to the full scale process temperature, 37 ⁰C. In the lab scale reactors, two different

values for organic loading rate, 3 kgVS/(m3,day) to 3.75 kgVS/(m3,day), were evaluated. Anaerobic digestion

were operated in six small scale reactors under the same operational conditions as the full scale digestion

in reactor, R100, at Käppala except from the temperature, the size and the organic loading rate. The

reference temperature 37 ⁰C was used in two of the lab scale reactors. The following two reactors were

run at a temperature of 45 ⁰C, which is in the middle of the temperature interval between mesophilic and

thermophilic. The last two reactors were run at 55 ⁰C, which is in the middle of the thermophilic

temperature interval.

The set-up that was used for the small scale digestion was the Automatic Methane Potential Test System

(AMPTS II. Bioprocess Control, Lund, Sweden). The system consisted of six sets of glass reactors with one

stirring device, one outlet valve, one feeding tube, one carbon dioxide trap and one membrane each. The

carbon dioxide traps were connected to a gas analyser which logged the methane production continuously.

The solution in the carbon dioxide trap reacts with the carbon dioxide in the produced biogas. Hence, only

methane gas is led do the gas analyser. The gas analyser uses liquid displacement and buoyancy as

measuring principle. Moreover, the reactors were heated with water baths and the temperatures were

logged continuously with three external Pt-100 thermometers (Figure 7) (Bioprocess Control Sweden AB,

2013).

19

Figure 7 – Laboratory scale process set up.

2.2.1 Start up

Firstly, the gas analyser was tested by passing a known volume of gas into the six cells that were to be

used. In the software, each flow cell was adjusted until all the cells registers had the same value for the

same amount gas.

Thereafter, the six 2000 mL glass reactors were mounted with one stirring device, one feeding tube and

one outlet valve. On the stirring device, one membrane gas sampling port was connected with a plastic

tube. All six reactors were pressure tested before they were placed in the same water bath (37 ⁰C). Each

reactor was inoculated with 1000 mL primary sludge from one 10 L container and 800 mL from another

10 L container. One gas trap; a glass bottle containing 400 mL of 3 M Sodium hydroxide (NaOH) and 5

mL/L 4% Thymolphthaline, was connected to each reactor and to the gas analyser. The recording of the

gas started one hour after the inoculation.

2.2.2 Reactor maintenance

During the experiment, feeding (Section 2.2.2.1) and analyses (Section 2.2.2.2) were performed daily. The

first 17 days all the reactors were run at 37 ⁰C to ensure a stable process in each of them as well and to

control that the measured process parameter values for all reactors were similar. After 17 days, two of

the reactors were adjusted to 45 ⁰C and two reactors to 55 ⁰C. During the first two weeks, some

modifications from the normal feeding process were made. The reactors that showed a large instability in

the process were not fed or fed less. These changes were introduced in order to ensure that the reactors

would have enough time to adapt to the new temperature.

2.2.2.1 Feeding process

To be able to mimic the full scale process at Käppala the organic loading rate (OLR) was 3 kgVS/m3, day

which gives 5.4 gVS/day. To avoid feeding the reactor on weekends the reactors were fed twice as much

20

on Fridays and the last portion was evenly distributed between the other days. Hence, each reactor was

fed with 6.4 gVS/day of primary sludge once a day, Monday to Thursday and on Fridays with 10.8 gVS/day.

The OLR was increased with 25% the last month in order to investigate if the reactors produced less gas

than expected due to that the VS content had already been degraded. Primary sludge for approximately

two weeks of feeding was taken from the large scale waste water treatment process and frozen down in

portions. Before feeding each day the primary sludge was thawed in a water bath to room temperature.

During the feeding process, the gas flow was blocked. The primary sludge was placed in the feeding tube,

the outlet valve was opened and the sludge was pushed down using an air flow from a 50 mL syringe that

was coupled to the tube at the feeding funnel. The digested sludge could then be siphoned out from the

open outlet valve. The digested sludge was collected for analyses.

2.2.2.2 Analyses

The analyses performed on digested sludge were alkalinity, pH, volatile fatty acid (VFA) content, total

solids (TS) and volatile solids (VS). Biogas was analysed for methane content. All parameters are indicators

for process stability.

Methane content was analysed once a day Monday through Friday before the feeding procedure. The

methane content results and the methane production results were used in the mathematical model in

order to determine the system’s methane production as well as its total biogas production. Gas from the

reactors was sampled with a 10 mL syringe that was pressed through the membrane gas sampling port

placed on the stirring device. A more concentrated solution than in the gas-traps (7 M NaOH) (Section

2.2.1) was placed in an Einhorn pipe and 5 mL of the reactor gas was injected into the solution. The carbon

dioxide could then react with the solution while the methane stayed in gas phase. The volume gas left in

the pipe, which was assumed to be 100% methane gas, was noted. The methane content was calculated

as in equation 1.

%𝐶𝐻4 =𝑉𝐶𝐻4

[𝑚𝑙]

𝑉𝑇𝑜𝑡𝑎𝑙[𝑚𝑙]=

𝑉𝐶𝐻4[𝑚𝑙]

5 [𝑚𝑙] EQ 1

The pH of the digested sludge was measured with a pH-electrode directly after each feeding procedure.

Alkalinity and pH is related to each other as alkalinity is a measure of a solutions buffering capacity. Once

a week, the alkalinity was measured on the digested sludge using a titration robot (916 Ti-Touch, Metrohm,

USA). Both the total alkalinity (TA), a measurement on the total amount basic ions, and the bicarbonate

(HCO3-) alkalinity (BA), a measurement related to the buffering due to the amount bicarbonate ions, were

measured. The titration was performed with 0.05 M hydrochloric acid (HCl) until pH 5.75 to determine BA

and until pH 4.0 to determine TA (EQ 2 and 3)(Jarvis & Schnürer, 2009).

𝐵𝐴 = 380 ∗ 𝑉𝐻𝐶𝑙 [𝑚𝑔𝐻𝐶𝑂3−/𝐿]

EQ 2

𝑇𝐴 = 380 ∗ 𝑉𝐻𝐶𝑙 [𝑚𝑔𝐵𝑎𝑠𝑖𝑐 𝑖𝑜𝑛𝑠/𝐿]

EQ 3

Once a week, the volatile fatty acid (VFA) content was measured. The digested sludge was filtered with a

suction filter with a pore size of 0.45 μm (Whatman, GE Healthcare Life Sciences, Germany) directly after

the pH measurement. The filtrate was analysed with LCK 365, a cuvette test from HACH LANGE (Sweden).

The cuvette was analysed in a spectrophotometer and the result is given in gacetate/L. Acetate represents

21

most of the amount VFA, and this is the reason why the results are approximated to the total

concentration of VFA.

The percentage of total solids in the digested sludge and the percentage fixed solids of total solids (%FS)

were measured once a week. The %TS and percentage of total solids are shown in Figure 15 and Figure

16 respectively.

The last analyses were total solids (TS) and fixed solids (FS). The percentage volatile solids of total solids

(%VS) is the fraction that is not fixed solids. Volatile solid content of the sludge is used to determine the

degree of digestion in the digested sludge. Aluminium cups were burned in a furnace for two hours at 550

⁰C. The cups were weighted (mCup) before digested sludge from the reactors was put in them. The cups

with the sludge were weighted (mSludge) again and placed in an oven at 106 ⁰C for at least 20 hours. At last,

the cups were weighted (mTS), burned again at 550 ⁰C in the ignition residue oven for two hours and then

weighted (mFS) for the last time. The percentage of TS and FS were calculated as in equations 4 and 5.

%𝑇𝑆 =𝑚𝑇𝑆[𝑔] − 𝑚𝐶𝑢𝑝[𝑔]

𝑚𝑆𝑙𝑢𝑑𝑔𝑒[𝑔] − 𝑚𝐶𝑢𝑝[𝑔]

EQ 4

%𝐹𝑆 =𝑚𝐹𝑆[𝑔] − 𝑚𝐶𝑢𝑝[𝑔]

𝑚𝑇𝑆[𝑔] − 𝑚𝐶𝑢𝑝[𝑔]

EQ 5

2.3 MODELLING PART In this part, the procedure for determining the relationships and the constants that were needed for

building the mathematical model is described. The aim with the model was that it should be able to

provide an economic and environmental evaluation on the anaerobic digestion process at Käppala for

temperatures in the range between 37 ⁰C and 55 ⁰C. The model was also made to be able to take two

organic loading rates into account, 3 and 3.75 kgVS/(m3,day).

The modelling method section is divided into six parts. The first part is a literature search on how to

determine the impact on the environment with respect to the total carbon footprint of the system

(Section2.3.1). The second part is an investigation of the system’s energy consumption as well as the

running cost and carbon dioxide footprint associated with said consumption (Section 2.3.2). The system’s

running cost was defined as a function of digestion temperature and season. The system’s carbon dioxide

footprint connected to the electricity usage was defined as a function of digestion temperature and

season. The third part is an investigation of the carbon dioxide footprint and cost associated with the

disposal of digested sludge (Section 2.3.3). The carbon dioxide emission from the silos with respect to

digestion temperature is determined. Additionally, the changes in transport expenses and the CO2-

footprint as a result from changed digestion temperature and season is determined. The fourth part is an

investigation of the carbon dioxide footprint and financial income associated with biogas production and

usage as well as methane content (Section 2.3.4). At Käppala, the produced biogas is partitioned between

three usage applications: the torch, the upgrading system and the power generator. The fifth part is an

evaluation of the lab scale parameters which is used in the mathematical model (Section 2.3.5). The

concluding part is a compilation of the profit and the carbon dioxide footprint from the system (Section

2.3.6).

22

For all fitted functions, the significant figures are important, however in the text the numbers are rounded.

All significant figures that are used in the mathematical model can be found in Appendix 1 – Raw data.

2.3.1 Determination of total carbon footprint

By determining the total carbon footprint as a function of temperature and season, the environmental

effect was investigated. The total carbon footprint is the sum of greenhouse gases that are emitted. All

greenhouse gases have different global warming potentials (GWP) and carbon dioxide was used as a

reference gas for translation of the different gases to carbon dioxide equivalents (Table 12, Appendix 3 –

Constant to the mathematical model). In the total gas emission, the greenhouse gases are emitted during

production of energy, and chemicals used in the process added to the emission of gas in the process.

There were no chemicals used in the anaerobic digestion, the contribution to the total carbon footprint

arose from energy consumption, transports, emission and burning of produced gas. The part of the

produced biogas that replaces fossil fuel contributes to a negative carbon footprint. Any leakage from the

system was neglected (Erikstam, 2013).

The carbon dioxide footprint (CDF) was measured in kilogram emitted carbon dioxide. The emission of

other greenhouse gases (Em) than CO2 was measured in kilogram and then multiplied with the GWP value

of that gas (EQ 6). The emission from consumption of a fuel (CF) [m3] was calculated by using the thermal

value (TV) [GJ/m3] and the emission factor (EF) [kg/GJ] for the fuel (EQ 7). For combustion of fossil fuel,

only the emission of carbon dioxide was considered. For combustion of biofuels the emission of methane

and nitrous oxide was also considered. The diesel that was used for vehicle fuel was a mix of 11% FAME

and 89% diesel. The thermal values and emission factors are stated in Table 13 (Appendix 3 – Constant to

the mathematical model) and represents values for pure substances.

𝐶𝐷𝐹 [𝑘𝑔] = 𝐺𝑊𝑃 ∗ 𝐸𝑚 [𝑘𝑔]

EQ 6

𝐶𝐷𝐹 [𝑘𝑔] = 𝐺𝑊𝑃 ∗ 𝐶𝐹 [𝑚3] ∗ 𝑇𝑉[𝐺𝐽/𝑚3] ∗ 𝐸𝐹 [𝑘𝑔/𝐺𝐽]

EQ 7

The carbon dioxide footprint can be divided into emission from usage of fossil fuel and biofuel. Emission

of greenhouse gases from fossil fuels generates a larger environmental effect than emission of

greenhouse gases from biofuels. Both the contribution from fossil fuel and biofuel were determined in

each step, and the environmental effect was evaluated with the total carbon footprint and the carbon

footprint from the fossil fuels (Naturvårdsverket, 2014; Svenska Petroleum & Biodrivmedel Institutet,

2014) (Personal communication, Naturvårdsverket, 2015).

2.3.2 Energy survey

The running cost of the system was determined by the sum of expenses for the constant energy use and

the variable energy use. However, this report is interested in the change in cost with respect to

temperature. Therefore, only the expenses for variable energy consumption was taken into account in

the mathematical model, except from the energy cost from constant circulation pumps. For the anaerobic

digestion, there are three parts that consume electricity, they are either directly or indirectly dependent

on the digestion temperature. Firstly, this report described the heating system of the reactors (Section

2.3.2.1) and secondly the upgrading of the produced gas to 97% methane content (Section 2.3.2.2). Lastly,

the sludge dewatering after digestion for transport is outlined (Section 2.3.2.3).

The total energy consumption (ETotal) is the sum of the energy consumptions (EHeating, EUpgrading, EDewatering)

(EQ 8).

23

𝐸𝑇𝑜𝑡𝑎𝑙 [𝑘𝑊ℎ] = (𝐸𝐻𝑒𝑎𝑡𝑖𝑛𝑔 + 𝐸𝑈𝑝𝑔𝑟𝑎𝑑𝑖𝑛𝑔 + 𝐸𝐷𝑒𝑤𝑎𝑡𝑒𝑟𝑖𝑛𝑔) [𝑘𝑊ℎ]

EQ 8

Furthermore, in all three cases the same type of electricity was used. The total energy consumption was

multiplied with the price on electricity (PriceElectricity) for determining of the total running cost (ExpRunning)

(EQ 9). The price of energy for Käppala was approximated by dividing the expenses on electricity for 2013

with the consumed electricity for 2013, the average price was 0.76 SEK/kWh (Käppala, 2013;

Käppalaförbundet, 2013).

𝐸𝑥𝑝𝑅𝑢𝑛𝑛𝑖𝑛𝑔[𝑠𝑒𝑘] = 𝐸𝑇𝑜𝑡𝑎𝑙 [𝑘𝑊ℎ] ∗ 𝑃𝑟𝑖𝑐𝑒𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 [𝑆𝐸𝐾/𝑘𝑊ℎ]

EQ 9

Käppala is using Nordic mix electricity that has an average greenhouse gas emission at 80 g CO2

equivalents per kWh. 94.2% of the electricity is produced from renewable sources, and the rest of the

electricity is produced from fossil fuels. The emission caused by electricity consumption is determined

with equations 10 and 11 (Energi & Klimat rådgivning, n.d.; Vattenfall, 2014) (Personal communication, A.

Thunberg, 2015).

𝐶𝐷𝐹𝐸𝑙𝑒𝑐𝑡𝑟𝑐𝑖𝑡𝑦𝐵𝑖𝑜 [𝑘𝑔] = 0.942 ∗ 𝐸𝑇𝑜𝑡𝑎𝑙 [𝑘𝑊ℎ] ∗ 𝐸𝑚𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 [𝑘𝑔/𝑘𝑊ℎ]

EQ 10

𝐶𝐷𝐹𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦𝐹𝑜𝑠𝑠𝑖𝑙 [𝑘𝑔] = 0.058 ∗ 𝐸𝑇𝑜𝑡𝑎𝑙 [𝑘𝑊ℎ] ∗ 𝐸𝑚𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 [𝑘𝑔/𝑘𝑊ℎ]

EQ 11

The rest of the energy consuming activities were set to be constant even though the power used for

stirring of the two reactors in theory would slightly decrease with temperature. This phenomenon is due

to that the viscosity of the sludge decreases with increasing temperature. However, this was not taken

into account.

2.3.2.1 Heating system

The heating system requires energy for pumping water and sludge in the system. The energy requirement

related to pumping is changed by two factors, the first one due to changes in viscosity for the sludge or

water. The viscosity of water as a function of temperature is more constant than that of the sludge,

therefore, the pumping energy consumption as a function of viscosity is set to constant (Section 2.3.2).

The second factor that can change the energy requirement is the frequency of the circulation pumps. The

frequency controls the flows of sludge and water in the heating system. The main energy-consuming

source for the anaerobic digestion is the heating pump, and the energy is used to increase the heat on the

water out from the pump on the warm side.

In section 1.1.3.4 it is stated that the temperature in the digestion reactors is regulated in two ways. The

temperature out from the heating pump can be changed and water flow on the warm side of the heating

pump can be changed. The sum of this energy consumption represents the variable part of the energy

consumption of the heating system and was compared to the energy requirement for heating the

digesters.

Energy requirement for heating each reactor (EHeating) is a function of the flow of sludge (QSludge ), the

density of sludge (ρSludge), the specific heating capacity of the sludge (cP,Sludge), and the difference between

the incoming sludge and the digestion temperature (ΔT) (EQ 12). The density of the sludge was

approximated to the density of water (1 kg/m3) and the specific heating capacity was approximated to the

specific heating capacity of water. The energy requirement for heating both of the two reactors is the sum

of the energy required to heat the incoming sludge to each reactor. In this case, it was assumed that the

sludge transported from R100 to R200 did not lose any heat, and therefore the energy requirement was

24

calculated using the sum of heating the primary sludge that was introduced into R100, with the heating

the secondary sludge that was transported into R200, (EQ 13). QPS,R100 is the flow of primary sludge in to

R100, ΔTD-PS is the temperature difference between the digestion temperature and the primary sludge,

QES,R100 is the flow of secondary sludge in to R200 and ΔTD-ES is the temperature difference between the

digestion temperature and the secondary sludge (Erikstam, 2013).

𝐸𝐻𝑒𝑎𝑡𝑖𝑛𝑔[𝐽/𝑑𝑎𝑦] = 𝑄𝑆𝑙𝑢𝑑𝑔𝑒[𝑚3/𝑑𝑎𝑦] ∗ 𝜌𝑆𝑙𝑎𝑚[𝑘𝑔/𝑚3] ∗ 𝑐𝑃[𝐽/𝑘𝑔, 𝐾] ∗ ∆𝑇 [𝐾]

EQ 12

𝐸𝐻𝑒𝑎𝑡𝑖𝑛𝑔[𝐽/𝑑𝑎𝑦] = 𝜌𝑆𝑙𝑎𝑚[𝑘𝑔/𝑚3] ∗ 𝑐𝑃[𝐽/𝑘𝑔, 𝐾](𝑄𝑃𝑆,𝑅100 ∗ ∆𝑇𝐷−𝑃𝑆 + 𝑄𝐸𝑆,𝑅200 ∗ ∆𝑇𝐷−𝐸𝑆) [𝐾, 𝑚3/𝑑𝑎𝑦]

EQ 13

Due to the complexity in the heating system, all the circulation pumps, all the heating exchangers and the

heating pump are seen as one unit that delivers heat to the digesters and consumes electricity (Figure 5,

Section 1.1.3.4). The heating system’s energy consumption was divided into two parts. The first part

consists of the energy consumption from the circulation pumps that have a constant energy demand

(Section 2.3.2.1.1). The second part was made up by the energy consumption from the controlled

circulation pumps and the heating pump as a function of flow and temperature (Section 2.3.2.1.2). The

rated values that have been used for the energy consumption calculations are stated in (Table 7, Appendix

1 – Raw data).

2.3.2.1.1 The heating system’s constant energy consumption

The maximal energy consumption for each circulation pump in the heating system was determined by its

rated power (PRated). The efficiency (𝜂) of the variable-frequency drive is assumed to be 97%. For the

circulation of pumps that is assumed to be constant, the specific energy consumptions were determined

with a logged value on the percentage of the maximal effect (EQ 14) (Variable frequency drive, 2015-04-

21) (Personal communication, C. Mikkelsen, 2015).

𝐸 [𝑘𝑊ℎ/𝑚𝑜𝑛𝑡ℎ] =𝑃𝑅𝑎𝑡𝑒𝑑 [𝑘𝑊] ∗ 24 [ℎ] ∗ %𝑜𝑓 𝑚𝑎𝑥 ∗ 𝐷𝑎𝑦𝑠𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐 𝑚𝑜𝑛𝑡ℎ [𝑑𝑎𝑦𝑠/𝑚𝑜𝑛𝑡ℎ]

𝜂

EQ 14

The constant energy consumption for the circulation pumps is 88 000 kWh/month and this value was

added in the mathematical model as a constant.

2.3.2.1.2 The heating system’s variable energy consumption

For the variable energy consumptions in the heating system the power was determined as a function of

sludge flow into the reactors and the temperature differences (Section 2.3.2.1). For the heating pump,

the used current was logged, and a mean value of the current used for each week was determined (Acurve,

Period 2014-04-01 to 2015-03-30). The mean current divided with the rated current gives the percentage

of the maximal effect that the heating pump was operated at. Mean values on the percentage of the

maximal effect for the two regulated circulations pumps were determines by logged values (Acurve,

Period 2014-04-01 to 2015-03-30). The effect for both the heating pump and the two circulation pumps

were determined according to (EQ 15).

𝑃 [𝑘𝑊] = 𝑃𝑅𝑎𝑡𝑒𝑑 [𝑘𝑊] ∗ %𝑜𝑓 𝑚𝑎𝑥

EQ 15

A relationship between energy consumption and temperature difference was determined by plotting the

variable part of the power requirement against the sum of the product between flow and temperature

difference for each reactor (EQ 16).

𝑃𝐻𝑒𝑎𝑡𝑖𝑛𝑔 ∝ (𝑄𝑃𝑆,𝑅100 ∗ ∆𝑇𝐷−𝑃𝑆 + 𝑄𝐸𝑆,𝑅200 ∗ ∆𝑇𝐷−𝐸𝑆) EQ 16

25

The temperature difference was assumed to be the same between both the primary sludge and digestion

temperature and between the secondary sludge and the digestion temperature. The temperature of the

sludge that was transported from R100 to R200 was assumed to be constant. The sum of the variable

effects was first plotted against temperature differences for each week and then against the flows for

each week (Figure 25 resp. Figure 26, Appendix 1 – Raw data). From the first plot it is evident that the

effect is strongly dependent on the temperature difference. However, from the second plot there is no

obvious trend. To find a relationship between effect, temperature and flow, the effect was plotted against

the flow times the temperature difference to the power of an integer. The integer was chosen so that the

curve fit had the highest possible coefficient of determination (R2), both a linear and a logarithmic curve

fit were tested (Figure 28, Appendix 1 – Raw data) (EQ 17). In the same way, a function was fitted for the

effect on the heating pump connected to the flow and the temperature difference (Figure 27, Appendix 1

– Raw data) (EQ 18). The temperature difference was determined using equation 19.

𝑃 [𝑘𝑊] = 63.313 ∗ ln (∆𝑇5 [℃5

] ∗ 𝑄 [𝑙/𝑠]) − 936.78

EQ 17

𝑃 [𝑘𝑊] = 73.149 ∗ ln (∆𝑇4 [℃4

] ∗ 𝑄 [𝑙/𝑠]) − 900.04

EQ 18

∆𝑇 [℃] = 𝑇𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛[℃] − 𝑇𝐼𝑛𝑐𝑜𝑚𝑖𝑛𝑔 𝑆𝑙𝑢𝑑𝑔𝑒[℃]

EQ 19

These equations were added in the mathematical model for determining the effect for a specific flow and

temperature difference, according to season of the year. The mean flows and temperatures on incoming

sludge for each month is stated in Table 14 (Appendix 3 – Constant to the mathematical model) (Acurve,

Period 2014-04-01 to 2015-03-30). The variable energy consumption for the total heating system and the

heating pump was calculated for each month, the efficiency (𝜂) of the variable frequency drive was

included as in section 2.3.2.1.1 (EQ 20).

𝐸 [𝑘𝑊ℎ/𝑚𝑜𝑛𝑡ℎ] =𝑃 [𝑘𝑊] ∗ 24 [ℎ] ∗ 𝐷𝑎𝑦𝑠𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐 𝑚𝑜𝑛𝑡ℎ [𝑑𝑎𝑦𝑠/𝑚𝑜𝑛𝑡ℎ]

𝜂

EQ 20

2.3.2.2 Gas upgrading system

The gas upgrading system upgrades the produced biogas to 97% methane content. This system requires

energy and is indirectly dependent on the digestion temperature, as the gas production is dependent on

the digestion temperature. The energy consumption (EUpgrading) is in turn dependent on the volume

upgraded gas (VDelivered Gas) (EQ 21 and 22). The energy consumption depends on the month (Table 15,

Appendix 3 – Constant to the mathematical model) (Personal communication, M. Medoc, 2105).

𝑉𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝐺𝑎𝑠 [𝑚3/𝑦𝑒𝑎𝑟] = 𝑉𝐵𝑖𝑜𝑔𝑎𝑠 [𝑁𝑚3/𝑦𝑒𝑎𝑟] ∗%𝐶𝐻4,𝐵𝑖𝑜𝑔𝑎𝑠

%𝐶𝐻4,𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝐺𝑎𝑠

EQ 21

𝐸𝑈𝑝𝑔𝑟𝑎𝑑𝑖𝑛𝑔[𝑘𝑊ℎ/𝑦𝑒𝑎𝑟] = 𝐸𝑈𝑝𝑝𝑔𝑟𝑎𝑑𝑖𝑛𝑔 [𝑘𝑊ℎ/𝑚3] ∗ 𝑉𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝐺𝑎𝑠 [𝑚3/𝑦𝑒𝑎𝑟]

EQ 22

The values for energy consumption for each month (Table 15, Appendix 3 – Constant to the mathematical

model) were used in the mathematical model with equation 10. The production volume for each month

was determined with equation 23.

26

𝑉𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝐺𝑎𝑠 [𝑁𝑚3/𝑚𝑜𝑛𝑡ℎ] =𝑉𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝐺𝑎𝑠 [𝑁𝑚3/𝑦𝑒𝑎𝑟]

12

EQ 23

2.3.2.3 Sludge dewatering

To reduce the volume of the sludge after digestion, the sludge is dewatered. This is in order to decrease

the transport expenses even though it requires energy. At the time of purchase of the dewatering

centrifuges it was stated that the energy consumption is 28 kWh/tonneTS,in. The electricity consumption

for each month was determined with equation 24 (Personal communication, M. Medoc, 2015).

𝐸𝐷𝑒𝑤𝑎𝑡𝑒𝑟𝑖𝑛𝑔[𝑘𝑊ℎ/𝑚𝑜𝑛𝑡ℎ] = 𝐸𝐷𝑒𝑤𝑎𝑡𝑒𝑟𝑖𝑛𝑔[𝑘𝑊ℎ/𝑡𝑜𝑛𝑛𝑒] ∗ 𝑚𝑇𝑆,𝑖𝑛[𝑡𝑜𝑛𝑛𝑒/𝑚𝑜𝑛𝑡ℎ]

EQ 24

The mass of total solids for each month (mTS,in) are determined by using the %TS value for the sludge with

the sludge flow to the reactor.

To determine the energy requirement for dewatering, data on the average mass of total solids for each

month was needed. The flow into the reactors cannot be considered as the same as the flow out from the

reactors due to volume decrease depending on evaporation of water and organic material that are

converted into gas. The flow in to the dewatering is logged, a mean flow for each month into the

dewatering was determined (Table 16, Appendix 3 – Constant to the mathematical model) (Acurve, Period

2014-04-01 to 2015-03-31). The value of %TS is given in section 2.3.5.3 and the density in section 2.3.2.1.

2.3.3 Survey over the disposal of digested sludge

After the digestion follows sludge disposal. Before the sludge dewatering, the sludge is stored in silos and

after the dewatering the sludge is transported to sludge storages before it is transported to farms. As for

the dewatering step, covered in section 2.3.2.3, the storage of sludge in silos and transport of sludge

contributes to the emission of greenhouse gases. Also, the transport contributes to the system-related

expenses.

2.3.3.1 Silos

The methane emission from sludge silos that are open to the air was calculated with equation (EQ 25).

The emission rate of methane in the silos is noted as EmCH4 and TSilo is the mean temperature of the sludge

in silos (Erikstam, 2013).

𝐸𝑚𝐶𝐻4[𝑁𝑚3/𝑡𝑜𝑛𝑛𝑒, ℎ] = 0.0004𝑒0.159∗𝑇𝑆𝑖𝑙𝑜[°𝐶]

EQ 25

The emission rate was multiplied with the mass of sludge (mSludge,Silos) in the silos and the VS content in the

sludge (%VS) for determining the total emission rate. Additionally, the emission of methane was

calculated to CO2-equialents by using equation 26 (Erikstam, 2013).

𝐶𝐷𝐹𝑆𝑖𝑙𝑜𝑠𝐵𝑖𝑜 = 𝐶𝐷𝑊𝐶𝐻4

∗ 𝑄𝐶𝐻4[𝑁𝑚3/𝑡𝑜𝑛𝑛𝑒, ℎ] ∗ %𝑉𝑆 ∗ 𝑚𝑆𝑙𝑢𝑑𝑔𝑒,𝑆𝑖𝑙𝑜𝑠[𝑡𝑜𝑛𝑛𝑒]

EQ 26

VS content is not only affecting the methane production in the silos, but also the production of nitrous

oxide gas through the nitrification/denitrification. This emission is assumed to be 3.33 kgN2O/tonneTS,year,

the carbon dioxide footprint is determined with equation 27 (Erikstam, 2013).

𝐶𝐷𝐹𝑆𝑖𝑙𝑜𝑠𝐵𝑖𝑜 = 𝐶𝐷𝑊𝑁2𝑂 ∗ 𝐸𝑚𝑁2𝑂[𝑘𝑔/𝑡𝑜𝑛𝑛𝑒, 𝑦𝑒𝑎𝑟] ∗ %𝑇𝑆 ∗ 𝑚𝑆𝑙𝑢𝑑𝑔𝑒,𝑆𝑖𝑙𝑜𝑠[𝑡𝑜𝑛𝑛𝑒]

EQ 27

To determine the methane emission from the silos, the sludge temperature in the silos was needed. The

temperature will be different depending on the digestion temperature. The outdoor temperature was

27

assumed to not affect the temperature inside the silos due to the mass of sludge. Also, it was assumed

that independent of the digestion temperature, the temperature difference between the digestion

temperature and the sludge temperature in the silos would be the same. The mean value of the

temperature difference between the temperature of the sludge after the last heat exchanger and the

digestion temperature was determined to be 22.7 ⁰C. The mean value was calculated based on data from

the period 2014-11-01 to 2015-05-31 since the sludge was not transported though the last heat

exchangers the months prior to that period (Acurve, Period 2014-11-01 to 2015-05-31). The temperature

in the silo was determined by equation 28.

𝑇𝑆𝑖𝑙𝑜 = 𝑇𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛 − ∆𝑇𝐻𝑒𝑎𝑡𝑖𝑛𝑔 𝑠𝑦𝑠𝑡𝑒𝑚

EQ 28

The total mass of sludge in the four silos has been more or less constant form the day that they were put

into use. The mean value of the mass was 170 tonne (Acurve, Period 2014-11-01 to 2015-05-31).

2.3.3.2 Transport

The transport of digested sludge after dewatering is an important parameter that contributes to the

expenses and carbon dioxide footprint. This report will consider transportations to storages in the

Mälaren region in Sweden. The expenses for transport and the carbon dioxide emission are functions of

the amount of produced dewatered sludge noted as mDW Sludge (EQ 29-32). The price for transport

(PriceTransport) is 100 SEK/tonne, the average transport distance (dTransport) is 60 km, and each truck can

transport 36 tonnes at one time (mSludge/Truck). Diesel is used as the fuel and the mean consumption for a

truck ( FCDiesel,Truck) is 0.4*10-3 m3/km (Personal communication, C. Bertholds, 2015) (Svenska Elvägar AB,

2009).

𝐸𝑥𝑝𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡[𝑆𝐸𝐾] = 𝑚𝐷𝑊 𝑆𝑙𝑢𝑑𝑔𝑒[𝑡𝑜𝑛𝑛𝑒] ∗ 𝑃𝑟𝑖𝑐𝑒𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡[𝑆𝐸𝐾/𝑡𝑜𝑛𝑛𝑒]

EQ 29

𝑉𝐷𝑖𝑒𝑠𝑒𝑙 [𝑚3] = 𝑚𝐷𝑊 𝑆𝑙𝑢𝑑𝑔𝑒[𝑡𝑜𝑛𝑛𝑒]

𝑚𝑆𝑙𝑢𝑑𝑔𝑒𝑇𝑟𝑢𝑐𝑘

[𝑡𝑜𝑛𝑛𝑒]∗ 𝑑𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡[𝑘𝑚] ∗ 𝐹𝐶𝐷𝑖𝑒𝑠𝑒𝑙,𝑇𝑟𝑢𝑐𝑘[𝑚3 𝑘𝑚⁄ ]

EQ 30

𝐶𝐷𝐹𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡𝐹𝑜𝑠𝑠𝑖𝑙 [𝑘𝑔𝐶𝑂2

] = 𝑉𝐷𝑖𝑒𝑠𝑒𝑙 [𝑚3] ∗ 0,89 ∗ 𝐸𝐹𝐷𝑖𝑒𝑠𝑒𝑙[𝐺𝐽/𝑚3] ∗ 𝑇𝑉𝐷𝑖𝑒𝑠𝑒𝑙 [𝑘𝑔/𝐺𝐽]

EQ 31

𝐶𝐷𝐹𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡𝐵𝑖𝑜 [𝑘𝑔𝐶𝑂2

] = 𝑉𝐷𝑖𝑒𝑠𝑒𝑙 [𝑚3] ∗ 0,11 ∗ 𝑇𝑉𝐹𝐴𝑀𝐸 [𝐺𝐽

𝑚3] ∗

∗ (𝐸𝐹𝐹𝐴𝑀𝐸,𝐶𝑂2[𝑘𝑔/𝐺𝐽] + 𝐸𝐹𝐹𝐴𝑀𝐸,𝑁2𝑂[𝑘𝑔/𝐺𝐽] )

EQ 32

Today, the amount of produced dewatered sludge at Käppala equals 30 000 tonnes per year. According

to Novel anaerobic digestion process with sludge ozonation for economically feasible power production

from biogas (Komatsu et al., 2011), the amount of water in the dewatered sludge (%waterDW Sludge)

depends on %VS. (EQ 33) The ratio between VS and TS as a function of digestion temperature was

determined by the small scale experiments (2.2). The amount transported sludge per year was determined

using Equation 34 (Personal communication, C. Bertholds, 2015).

%𝑤𝑎𝑡𝑒𝑟𝐷𝑊 𝑆𝑙𝑢𝑑𝑔𝑒 = 87.5 ∗ %𝑉𝑆 + 22.4

EQ 33

𝑚𝐷𝑊 𝑆𝑙𝑢𝑑𝑔𝑒[𝑡𝑜𝑛𝑛𝑒] = 30000[𝑡𝑜𝑛𝑛𝑒] ∗%𝑤𝑎𝑡𝑒𝑟𝐷𝑊 𝑆𝑙𝑢𝑑𝑔𝑒

%𝑤𝑎𝑡𝑒𝑟𝐷𝑊 𝑆𝑙𝑢𝑑𝑔𝑒,𝑅𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒

EQ 34

28

The reference value on percentage water in the sludge after the dewatering was determined by

considering measurements on %TS for the five centrifuges (Table 8, Appendix 1 – Raw data) (Acurve,

Period 2014-11-01 to 2015-05-31). The mean value for the reference was 74.25% water after dewatering.

2.3.4 Biogas usage survey

The produced biogas that is a function of digestion temperature and organic loading rate is either

upgraded and delivered to SL as methane gas, used as fuel in the power generator for heat production, or

burned in the torch if Käppala cannot take care of the gas. The carbon dioxide footprint and the income

from each part are connected to the amount of gas that is used.

2.3.4.1 Gas deliver to SL

The upgraded gas (97% methane) is sold for 7 SEK/m3 to SL (EQ 22). The delivered gas is used as fuel for

buses and replaces diesel. This results in a positive CDF from the emission of greenhouse gases as a result

from the usage of the methane gas (EQ 23) and a negative CDF from the diesel that is replaced (EQ 35-

38). The amount of diesel that is replaced is determined by using the average fuel consumption per

kilometre for both diesel and methane gas and the delivered methane gas (EQ 24). The biogas

consumption for a bus is 0.5 m3/km and the diesel consumption for a bus is 0.45*10-3 m3/km (Norrman et

al., 2005; Stockholm läns landsting, 2014) (Personal communication, A. Thunberg, 2015).

𝐼𝑛𝑐𝑜𝑚𝑒𝑆𝐿[𝑆𝐸𝐾] = 𝑉97%𝑚𝑒𝑡ℎ𝑎𝑛𝑒 [𝑚3] ∗ 𝑃𝑟𝑖𝑐𝑒𝑆𝐿[𝑆𝐸𝐾/𝑚3]

EQ 35

𝐶𝐷𝐹+𝑆𝐿𝐵𝑖𝑜[𝑘𝑔] = 𝑉𝐵𝑖𝑜𝑔𝑎𝑠[𝑚3] ∗ 𝑇𝑉𝐵𝑖𝑜𝑔𝑎𝑠[𝐺𝐽/𝑚3] ∗ (𝐸𝐹𝐶𝑂2,𝐵𝑖𝑜𝑔𝑎𝑠 + 𝐶𝐷𝑊𝐶𝐻4

∗ 𝐸𝐹𝐶𝐻4,𝐵𝑖𝑜𝑔𝑎𝑠

+ 𝐶𝐷𝑊𝑁2𝑂 ∗ 𝐸𝐹𝑁2𝑂,𝐵𝑖𝑜𝑔𝑎𝑠 )[𝑘𝑔/𝐺𝐽]

EQ 36

𝑉𝐷𝑖𝑒𝑠𝑒𝑙[𝑚3] = 𝑉𝐵𝑖𝑜𝑔𝑎𝑠[𝑚3] ∗𝐹𝐶𝐷𝑖𝑒𝑠𝑒𝑙,𝐵𝑢𝑠[𝑚3/𝑘𝑚]

𝐹𝐶𝐵𝑖𝑜𝑔𝑎𝑠,𝐵𝑢𝑠[𝑚3/𝑘𝑚]

EQ 37

𝐶𝐷𝐹−𝑆𝐿𝐹𝑜𝑠𝑠𝑖𝑙[𝑘𝑔] = −0,89 ∗ 𝑉𝐷𝑖𝑒𝑠𝑒𝑙[𝑚3] ∗ 𝑇𝑉𝐷𝑖𝑒𝑠𝑒𝑙[𝐺𝐽/𝑚3] ∗ 𝐸𝐹𝐷𝑖𝑒𝑠𝑒𝑙 [𝑘𝑔/𝐺𝐽]

EQ 38

𝐶𝐷𝐹−𝑆𝐿𝐵𝑖𝑜[𝑘𝑔] = −0,11 ∗ 𝑉𝐷𝑖𝑒𝑠𝑒𝑙[𝑚3] ∗ 𝑇𝑉𝐹𝐴𝑀𝐸[𝐺𝐽/𝑚3] ∗ 𝐸𝐹𝐹𝐴𝑀𝐸 [𝑘𝑔/𝐺𝐽]

EQ 39

2.3.4.2 Gas used for heat production

The non-upgraded biogas can be used as fuel instead of oil in the power generator for production of heat.

The replacement of oil is seen as it contributes to an income to the system, the income is equal to the

cost of the replaced oil. In the same way, a negative contribution to CDF is added from the unused oil. At

last, the used biogas generates a positive contribution to CDF. The price of oil is 10 000 SEK/m3, the energy

content is 10 000 kWh/m3. The energy content of biogas is 6.5 kWh/Nm3. The replaced oil was determined

with Equation 40, the income with Equation 42 and the CDF contributions with Equations 42 and 43

(Energi & Klimat rådgivningen, n.d.; Svenskt Gasteknsikt Center, 2012).

𝑉𝑂𝑖𝑙[𝑚3] = 𝑉𝐵𝑖𝑜𝑔𝑎𝑠[𝑚3] ∗𝐸𝐵𝑖𝑜𝑔𝑎𝑠[𝑘𝑊ℎ/𝑚3]

𝐸𝑂𝑖𝑙[𝑘𝑊ℎ/𝑚3]

EQ 40

𝐼𝑛𝑐𝑜𝑚𝑒𝑆𝐿[𝑆𝐸𝐾] = 𝑉𝑂𝑖𝑙 [𝑚3] ∗ 𝑃𝑟𝑖𝑐𝑒𝑂𝑖𝑙[𝑆𝐸𝐾/𝑚3]

EQ 41

𝐶𝐷𝐹+𝑆𝐿𝐵𝑖𝑜[𝑘𝑔] = 𝑉𝐵𝑖𝑜𝑔𝑎𝑠[𝑚3] ∗ 𝑇𝑉𝐵𝑖𝑜𝑔𝑎𝑠[𝐺𝐽/𝑚3] ∗ 𝐸𝐹𝐵𝑖𝑜𝑔𝑎𝑠 [𝑘𝑔/𝐺𝐽]

EQ 42

𝐶𝐷𝐹−𝑆𝐿𝐹𝑜𝑠𝑠𝑖𝑙[𝑘𝑔] = −𝑉𝑂𝑖𝑙 [𝑚3] ∗ 𝑇𝑉𝑂𝑖𝑙 [𝐺𝐽/𝑚3] ∗ 𝐸𝐹𝑂𝑖𝑙 [𝑘𝑔/𝐺𝐽]

EQ 43

29

2.3.4.3 Torch

The methane emission from burning biogas in the torch was determined in the same way as if it had been

used for heat production (EQ 42).

2.3.5 Evaluation of lab scale parameters

The mathematical model needed results from the laboratory part to be able to derive functions on gas

production, gas content, total solids in the sludge and volatile solids in the sludge. The results that were

evaluated are stated in section 3.1.1. To be able to evaluate the results, a period where the process is

stable is desired. From the VFA measurements it was determined that the processes were stable between

2015-03-25, until the experiment finished 2015-06-16. 2015-04-24 the OLR was increased from 3

kgVS/(m3,day) to 3.75 kgVS/(m3,day) but the stability was unchanged (Section 3.1.2).

2.3.5.1 Methane production as function of temperature and OLR

From the results on methane production in section 3.1.1.1 the mean values for each reactor, OLR and

weekday were determined. Also, an overall daily mean production was calculated for each reactor and

OLR (Table 9 resp. Table 10, Appendix 1 – Raw data). Relationships between gas production and

temperature were derived using the daily mean methane production from the 37 ⁰C reactors as a

reference. The mean methane production in the 45 ⁰C and 55 ⁰C reactors was divided with the reference

production to determine the percentage change of methane production when the temperature is

changed (Table 2).

Table 2 – The mean values for the methane production measurements of the lab scale investigations.

OLR [kgVS/m3] Temperature [⁰C] Mean methane production [NmL/day]

%methane production

3

37 1776.21 100%

45 1670.71 94%

55 1635.84 92%

3.75

37 2173.52 100%

45 2000.89 92%

55 2129.56 98%

A function for each OLR was defined by fitting a quadratic polynomial curve to the percentage values in

Excel (Figure 31, Appendix 1 – Raw data). The functions are only valid in the temperature range 37-55 ⁰C.

Even though the functions were used to make a rough estimation, they were used in the mathematical

model to determine the methane volume at a given temperature and OLR. The percentage was multiplied

with a reference value on volume produced methane during a year. The reference value was determined

by the total gas production, 6 300 000 Nm3Biogas/year, and the percentage of methane in the gas, 65%,

gives 4 100 000 Nm3Methane/year. Equations 44 was used for OLR 3 kgVS/(m3,day) and 45 for OLR 3.75

kgVS/(m3,day).

𝑉𝑀𝑒𝑡ℎ𝑎𝑛𝑒 [𝑁𝑚3/𝑦𝑒𝑎𝑟] = 4100000(3.03 ∗ 10−4 ∗ 𝑇𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛2 [0𝐶2] – 3.23 ∗ 10−2 ∗ 𝑇𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛[0𝐶] + 1.78)

EQ 44

𝑉𝑀𝑒𝑡ℎ𝑎𝑛𝑒 [𝑁𝑚3/𝑦𝑒𝑎𝑟] = 4100000(8.80 ∗ 10−4 ∗ 𝑇𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛2 [0𝐶2] – 8.21 ∗ 10−2 ∗ 𝑇𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛[0𝐶] + 2.83)

EQ 45

30

2.3.5.2 Methane content as a function of temperature and OLR

As for the methane production above, two functions were derived from the results on methane content

measurements in section 3.1.1.2, one for each OLR period. Equations 46 is used for OLR 3 kgVS/(m3,day)

and 47 for OLR 3.75 kgVS/(m3,day). The functions were derived by plotting the mean methane content for

each OLR against the digestion temperature (Table 3) (Figure 29, Appendix 1 – Raw data).

Table 3 – The mean values for the methane content measurements of the lab scale investigations.

OLR [kgVS/m3] Temperature [⁰C] Methane content

3

37 65.0%

45 61.9%

55 62.2%

3.75

37 66.7%

45 62.6%

55 62.8%

%𝐶𝐻4 = 2.33 ∗ 10−4 ∗ 𝑇𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛2 [0𝐶2] – 2.30 ∗ 10−2 ∗ 𝑇𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛[0𝐶] + 1.18

EQ 46

%𝐶𝐻4 = 2.23 ∗ 10−4 ∗ 𝑇𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛2 [0𝐶2] – 2.21 ∗ 10−2 ∗ 𝑇𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛[0𝐶] + 1.17

EQ 47

2.3.5.3 TS and VS content as function of temperature and OLR

From the measured values on %TS and %VS on the sludge in the small scale reactors (Section 3.1.1.6), the

mean value for each OLR period and temperature was stated (Table 4). The mean values on %TS were all

rounded to 3% which is the value that is used in the mathematical model for %TS.

The %VS had a larger variation and therefore two functions were derived as for the methane content

(Section 2.3.5.2). The values were plotted against temperature for each OLR and a quadratic polynomial

was fitted for each period of OLR (Figure 30, Appendix 1 – Raw data). Equations 48 was used for OLR 3

kgVS/(m3,day) and 49 for OLR 3.75 kgVS/(m3,day).

Table 4 – The mean values for the %TS and %VS measurements of the lab scale investigations.

OLR [kgVS/m3] Temperature [⁰C] %TS %VS

3

37 2.5% 31%

45 2.5% 27%

55 2.6% 29%

3.75

37 2.6% 27%

45 2.8% 24%

55 2.6% 26%

%𝑉𝑆 = 4.20 ∗ 10−4 ∗ 𝑇𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛2 [0𝐶2] – 3.99 ∗ 10−2 ∗ 𝑇𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛[0𝐶] + 1.21)

EQ 48

%𝑉𝑆 = 2.91 ∗ 10−4 ∗ 𝑇𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛2 [0𝐶2] – 2.72 ∗ 10−2 ∗ 𝑇𝐷𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛[0𝐶] + 0.875)

EQ 49

31

2.3.6 Concluding equations

The last equations that were added to the mathematical model were the profit calculations (EQ 50), total

carbon dioxide footprint calculations (EQ 51) and total carbon dioxide footprint from fossil fuels

calculations (EQ 52).

𝑃𝑟𝑜𝑓𝑖𝑡 [𝑆𝐸𝐾] = ∑ 𝐼𝑛𝑐𝑜𝑚𝑒 [𝑆𝐸𝐾] − ∑ 𝐸𝑥𝑝 [𝑆𝐸𝐾]

EQ 50

𝐶𝐷𝐹𝑇𝑜𝑡𝑎𝑙 [𝑘𝑔] = ∑ 𝐶𝐷𝐹𝐹𝑜𝑠𝑠𝑖𝑙 [𝑘𝑔] + ∑ 𝐶𝐷𝐹𝐵𝑖𝑜 [𝑘𝑔]

EQ 51

𝐶𝐷𝐹𝑇𝑜𝑡𝑎𝑙𝐹𝑜𝑠𝑠𝑖𝑙[𝑘𝑔] = ∑ 𝐶𝐷𝐹𝐹𝑜𝑠𝑠𝑖𝑙 [𝑘𝑔]

EQ 52

32

3 RESULTS

The result section is divided into two parts, results from the laboratory part and the result from the

modelling part. In the results from the lab scale investigation the results from the analyses and the stability

are stated (Section 3.1). In the result for the modelling part, a summary of the mathematical model, input

and outcome from the model are stated (Section 3.2).

3.1 LABORATORY PART The reactors were started 2015-01-27. Cell 5 at the gas analyser seemed to consistently register 2% less

gas than the other cells when the cell was moved from one reactor to another. This was compensated for

in the system software. Before the reactors were set to different temperatures, reactor 37a produced less

than the other reactors while reactor 37b produced more than the other reactors, these reactors were

chosen as references. The temperature change of the other reactors was done 2015-02-13. Reactor 37a

and 37b were placed in 37 ⁰C, reactor 45a and 45b were placed in 45 ⁰C and reactor 55a and 55b were

placed in 55 ⁰C. None of the reactors were fed until 2015-02-17, thereafter reactor 45a, 45b, 55a and 55b

were fed sporadically until 2015-03-09 at what point the microorganism culture in the reactors had

adapted to the new environment.

At day 2015-03-23, the processes were considered to be stable (Section 3.1.2). During the first time the

organic loading rate was set to 3 kgVS/(m3,day) as in the large scale reactors. The dynamics of methane

production rate for each reactor indicated that all of the reactors had digested most of the readily

digestible compounds during the week as the biogas production rate was extremely low during the last

hours before the feeding procedure during Monday. The organic loading rate was increased with 25% to

3.75 kgVS/(m3,day) 2015-04-24 to evaluate if any reactors had additional digestion capacity. The stability

of the processes did not noticeably change with the change of OLR (Section 3.1.2).

3.1.1 Analysis results

During the lab scale evaluation eight parameters were measured both to evaluate the stability of the

processes and for additional information with respect to the modelling part. The measured parameters

included methane production (Section 3.1.1.1), methane content (Section 3.1.1.2), pH (Section 3.1.1.3),

alkalinity (Section 3.1.1.4), volatile organic acids concentration (Section 3.1.1.5), percentage of total solids

and percentage of volatile solids (Section 3.1.1.6).

3.1.1.1 Methane production

The rate of methane production was logged continuously during the small scale digestion. Figure 8 shows

the methane gas flow for a typical week when the processes were assumed to be stable. The pattern was,

with few exceptions, repeated weekly.

33

Figure 8 – The methane gas flow during a typical week when the lab scale processes where stable (37a and 37b – 37 ⁰C reactors, 45a and 45b – 45 ⁰C reactors, 55a and 55b – 55 ⁰C reactors).

The resolution of the gas data log was 15 minutes. During the feeding procedure, the gas flow tube to the

analyser was clamped to avoid registration of air instead of produced biogas when the reactor was opened.

In some cases during feeding, a larger portion of the gas in the reactor was replaced by air. When the gas

flow to the analyser was started, again, this air was registered as pure methane which gave rise to an

abnormal high peek in the production rate. The high peak signals were corrected in Excel by defining that

if any single registered flow rate that was 1.2 times higher than the next one should the value be replaced

by the next value.

The gas analysis is also dependent on the CO2-trap; the NaOH-solution should be exchanged directly when

the colour started changing from blue to colourless. The trap often lost its colour during the night or the

weekend which resulted in an over-registration of gas production. The phenomenon was noted down and

the data from that day was replaced with the data from the other reactor with the same temperature. In

some cases both reactors of a particular temperature over-registered due to the CO2-trap. On such

occations all data from that day was neglected in further calculations. The amount of days neglected was

six, zero and 10 for the reactors in 37 ⁰C, 45 ⁰C and 55 ⁰C, respectively.

The weekly gas productions for each temperature and for the full scale process are plotted in Figure 9. In

Figure 10 the specific gas production for each week is plotted. In both cases only weeks that have values

and all the weekdays are plotted. The week that is missing some values is neglected.

0

50

100

150

200

250

300

2015-05-25 2015-05-26 2015-05-27 2015-05-28 2015-05-29 2015-05-30 2015-05-31 2015-06-01

Met

han

e ga

s fl

ow

[N

mL/

h]

Date

Methane gas flow from the lab reactors for the period 2015-05-25 to 2015-06-01

37a 37b 45a 45b 55a 55b

34

Figure 9 – Average gas production for each week in the lab scale reactors (37a and 37b – 37 ⁰C reactors, 45a and 45b – 45 ⁰C reactors, 55a and 55b – 55 ⁰C reactors) and the large scale R100 reactor (R100).

Figure 10 – Specific gas production in the lab scale reactors (37a and 37b – 37 ⁰C reactors, 45a and 45b – 45 ⁰C reactors, 55a and 55b – 55 ⁰C reactors) and the large scale R100 reactor (R100).

3.1.1.2 Methane content

The methane content of the produced gas was measured once a day, from Monday to Friday, before the

feeding procedure was initiated. Figure 11 shows the measured values of methane content for each small

scale reactor and for the large scale reactor.

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

2015-01-16 2015-02-05 2015-02-25 2015-03-17 2015-04-06 2015-04-26 2015-05-16 2015-06-05 2015-06-25Gas

pro

du

ctio

n [

Nm

3 /m

3,d

ay]

Date

Average gas production for each week

37a 37b 45a 45b 55a 55b R100

OLR increase

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0,45

2015-01-16 2015-02-05 2015-02-25 2015-03-17 2015-04-06 2015-04-26 2015-05-16 2015-06-05 2015-06-25

Gas

pro

du

ctio

n [

Nm

3/k

g VS]

Date

Specific gas production for each week

37a 37b 45a 45b 55a 55b R100

OLR increase

35

Figure 11 – Methane content measurements of the biogas produced in the lab scale reactors (37a and 37b – 37 ⁰C reactors, 45a and 45b – 45 ⁰C reactors, 55a and 55b – 55 ⁰C reactors) and the sludge in the large scale R100 reactor (R100).

3.1.1.3 pH

During the feeding process (Section 2.2.2.1), sludge from the reactors was taken out. The pH of the sludge

was measured once a day, Monday to Friday. The measured pH values of all the small scale reactors and

of the large scale reactor are plotted in Figure 12.

Figure 12 – pH measurements of the sludge in the lab scale reactors (37a and 37b – 37 ⁰C reactors, 45a and 45b – 45 ⁰C reactors, 55a and 55b – 55 ⁰C reactors) and the sludge in the large scale R100 reactor (R100).

3.1.1.4 Alkalinity

The alkalinity was measured during the lab scale digestion once a week on the digested sludge until 2015-

03-12. After that the alkalinity machine could not be calibrated, and no measurements could be

performed except for one final measurement on each reactor right before the process was shut down.

The measurements on the lab scale reactors and the full scale reactor are plotted in Figure 13.

30,00%

35,00%

40,00%

45,00%

50,00%

55,00%

60,00%

65,00%

70,00%

75,00%

2015-01-16 2015-02-05 2015-02-25 2015-03-17 2015-04-06 2015-04-26 2015-05-16 2015-06-05 2015-06-25

%M

eth

an

Date

Methane content in produced biogas

37a 37b 45a 45b 55a 55b R100

OLR increase

6,5

6,7

6,9

7,1

7,3

7,5

7,7

7,9

2015-01-16 2015-02-05 2015-02-25 2015-03-17 2015-04-06 2015-04-26 2015-05-16 2015-06-05 2015-06-25

pH

Date

pH in the digested sludge

37a 37b 45a 45b 55a 55b R100

OLR increase

36

Figure 13 – Alkalinity measurements of the sludge in the lab scale reactors (37a and 37b – 37 ⁰C reactors, 45a and 45b – 45 ⁰C reactors, 55a and 55b – 55 ⁰C reactors) and the sludge in the large scale R100 reactor (R100).

3.1.1.5 Volatile fatty acids

The concentration of volatile fatty acids was measured once a week on the digested sludge. The results

on the lab scale reactors and the full scale reactor are plotted in Figure 14.

Figure 14 – VFA measurements of the sludge in the lab scale reactors (37a and 37b – 37 ⁰C reactors, 45a and 45b – 45 ⁰C reactors, 55a and 55b – 55 ⁰C reactors) and the sludge in the large scale R100 reactor (R100).

3.1.1.6 Percentage total solids and percentage volatile solids

The percentage of total solids in the digested sludge and the percentage fixed solids of total solids (%FS)

were measured once a week. The %TS and percentage of total solids are shown in Figure 15 and Figure

16 respectively. The value on percentage volatile solids of total solids (%VS) is the fraction that is not fixed

solids.

0,4000

0,5000

0,6000

0,7000

0,8000

0,9000

1,0000

1,1000

2015-01-16 2015-02-05 2015-02-25 2015-03-17 2015-04-06 2015-04-26 2015-05-16 2015-06-05 2015-06-25

BA

/TA

Date

Ratio between bicarbonate alaklinity and total alkalinity in the digeated sludge

37a 37b 45a 45b 55a 55b R100

OLR increase

0

500

1000

1500

2000

2500

2015-01-16 2015-02-05 2015-02-25 2015-03-17 2015-04-06 2015-04-26 2015-05-16 2015-06-05 2015-06-25

VFA

[m

g HA

c/L]

Date

Concentration volatile fatty acids in the digsted sludge

37a 37b 45a 45b 55a 55b R100

OLR increase

37

Figure 15 – %TS measurements of the sludge in the lab scale reactors (37a and 37b – 37 ⁰C reactors, 45a and 45b – 45 ⁰C reactors, 55a and 55b – 55 ⁰C reactors) and the sludge in the large scale R100 reactor (R100).

Figure 16 – %FS measurements of the sludge in the lab scale reactors (37a and 37b – 37 ⁰C reactors, 45a and 45b – 45 ⁰C reactors, 55a and 55b – 55 ⁰C reactors) and the sludge in the large scale R100 reactor (R100).

3.1.2 Stability

Alkalinity, pH and concentration of volatile fatty acids in the reactor were measured as a control of stability

(Figure 12, Figure 13 resp. Figure 14, Section 3.1.1.3-3.1.1.5). Before the temperature change, the values

for all of the reactors were similar; no reactor had had any noticeable deviant measured value for pH,

volatile acid concentration or alkalinity. When the temperature was changed for reactor 45a, 45b, 55a

and 55b some instability arose. Said instability was detected by the measurement results. The first week

when the reactors were set at 55 ⁰C the largest change in all of the measurements was observed. Volatile

acids accumulated in the reactor since its methanogens were having a hard time adapting. This led to a

0,00%

1,00%

2,00%

3,00%

4,00%

5,00%

2015-01-16 2015-02-05 2015-02-25 2015-03-17 2015-04-06 2015-04-26 2015-05-16 2015-06-05 2015-06-25

%TS

Date

Percentage total solids in the digested sludge

37a 37b 45a 45b 55a 55b R100

OLR increase

15,00%

20,00%

25,00%

30,00%

35,00%

40,00%

2015-01-16 2015-02-05 2015-02-25 2015-03-17 2015-04-06 2015-04-26 2015-05-16 2015-06-05 2015-06-25

%FS

Date

Percentage fixed solids of the total solids

37a 37b 45a 45b 55a 55b R100

OLR increase

38

decrease in pH and a decrease in buffering capacity (decrease in BA/TA). The biogas production was low

during this time. After that week the reactors at 45 ⁰C had an increased accumulation of volatile acids,

which led to a low biogas production, a low pH and a low buffering ability. 2015-03-12 was the last

alkalinity measurement due to problems with the equipment.

2015-03-18 all VFA values were less than 300 gacetate/L, except the value for reactor 45b. The measurement

after that, 2015-03-27, were all values less than 300 gacetate/L, and the pH in the reactors was more or less

stable since 2015-03-09. 2015-03-14 the ratios between the bicarbonate alkalinity and the total alkalinity

were in the range 0.8-1.0. For this reason, the process was considered to be in stable operation 2015-03-

23.

The change in OLR 2015-04-24 did not affect the values of pH or VFA, nor did it affect the biogas

production in a negative way, but rather increased the methane production. For all reactors the process

was considered to be in stable operation directly.

3.2 MODELLING PART The mathematical model is built on the collected equations and constants from section 2.3 in the

modelling method part. All the equations are found in Appendix 2 – Equations to the mathematical model

and all constants are found in Appendix 3 – Constant to the mathematical model. The model determines

the profit and carbon dioxide footprint for the system with respect to OLR and digestion temperature.

The biogas usage can be divided differently between three sources.

The mathematical model have some input variables that can be varied, section 3.2.1 describes the input

for the mathematical model. Section 3.2.2 shows the output from the mathematical model for some

selected input values.

3.2.1 Input

The model is able to handle a temperature range between 37 and 55 ⁰C even though the model is most

exact for 37, 45 and 55 ⁰C. The organic loading rate is only valid for two values; 3 kgVS/(m3,day) or 3.75

kgVS/(m3,day). If the input values do not match this requirement, the output result is not usable. The

partitioning of gas at Käppala can be changed. Therefore, the partitioning between the three current

applications is set as three input variables. The methane content in the gas delivered to SL is also set as

an input variable.

Table 5 – Input variables for the mathematical model, in the green box are the input values given.

Digestion parameters Variable Value Comment Digestion temperature [⁰C] Valid in the range 37-55 ⁰C

Organic loading rate [kgVS/(m3,day)]

Valid for 3 or 3.75 kgVS/(m3,day)

39

Usage of produced biogas Variable Value Comment Sold to SL Current value 92%

- %CH4, Delivered methane gas Current value 97%

Burned in torch Current value 2%

Used for heat production Current value 6%

3.2.2 Output

The outputs from the mathematical model for the current partition of the biogas and a methane content

of 97% on the delivered gas are presented. The output data in total is presented in Appendix 4 – Output

from mathematical model for OLR 3 and 3.75 kgVS/(m3,day) and temperatures 37, 45 and 55 ⁰C. Table 6

shows a summary of the result.

Table 6 – Summary of the output result from the mathematical model calculations. CDFtotal is the total carbon dioxide footprint emitted from the system and CDFtotal

fossile is the total carbon dioxide footprint emitted from the system due to usage of fossil fuels.

Temperature [⁰C] 37 45 55

OLR [kgVS/(m3,day)]

3 3.75 3 3.75 3 3.75

Profit a year [MSEK/year]

21.07 21.43 17.86 17.69 16.83 18.50

%Profit Reference 102% 85% 84% 80% 88%

CDFtotal a year [kg/year]

117*106 118*106 106*106 105*106 106*106 114*106

%CDFtotal Reference 101% 90% 89% 91% 97%

CDFtotalfossil a year

[kg/year] -9.01*106 -9.10*106 -8.06*106 -7.98*106 -7.92*106 -8.50*106

%CDFtotalfossil Reference 101% 89% 89% 88% 94%

40

4 DISCUSSION

4.1 COMPARISON WITH FULL SCALE PROCESS In section 3.1.1 the process parameters measured during the period 2015-02-02 and 2015-06-16 are

plotted for the lab scale reactors and for the full scale R100 reactor. The differences between the lab scale

reactors, except the scale, are the temperature of four out of the six reactors, the organic loading rate.

The actual value on organic loading rate for R100 was between 2.8 and 4.0 kgVS/(m3,day), with a mean at

3.7 kgVS/(m3,day), even though Käppala endeavour to keep the value constant at 3 kgVS/(m3,day). The

comparison between the gas production (Figure 9, Section 3.1.1.1) in the lab scale and the full scale are

due to the OLR differences not sufficient for any conclusions of the process efficiency. To draw conclusions

of the process efficiency differences in the lab scale and the full scale are the specific gas production used

(Figure 10, Section 3.1.1.1). There is no significand difference between the specific gas production from

the lab scale reactor and R100 which indicates that the processes are as effective. In general, bioprocesses

are less effective in larger scale than in small scale due to poorer mixing which contradicts with the result.

The result on the methane production were registered by different gas registration types but supported

by the measurements on pH, alkalinity, VFA and %TS. Those showed that the large scale R100 reactor and

the reference lab scale reactors, 37 ⁰C reactors, have a similar pattern and are not significantly different.

This supports the scale of the reactors do not affect the processes in general. In addition to the stirring

device has R100 continuous recirculation of sludge from the bottom to the top, this factor can contribute

to that R100 has a better mixing than other large scale reactors and also more aggressive mixing than the

small scale reactors. Aggressive mixing means that the forces from the mixing contributes to that

degradation of substances in the media occurs.

The methane content of the produced gas was lower for the full scale process. Due to the fact that R100

produced as much methane as the reference reactor was the amount of total gas produced from R100

higher than for the reference reactors. R100 has a higher digestion efficiency, even though the methane

production is not higher. A contributing factor for the higher methane content in the lab scale it the fact

that the Einhorn analysis is known to somewhat overestimate methane content. However, that do not

explain that the higher digestion efficiency is supported by the fact that the value on %FS (Section 3.1.1.6)

was higher for the large scale reactor than the lab scale reactors. The value on %FS could differ between

the large scale reactor and the small scale due to different laboratory technicians and the sampling points

on the reactor were different. In full scale the point of sampling is located at the bottom of the reactor

where the concentration of inorganic material is higher due to some degree of sedimentation. The higher

carbon dioxide production can be an effect of the more aggressive mixing in the full scale, which can lead

to degradation of substrate that in turn leads to more easy availability for the microorganisms.

The transferability for the results from the lab scale investigation to anticipate process changes in the

large scale is considered to be good due to that there only was small changes in the measured parameters.

The anaerobic digestion process in R200 has not been mimicked, the process change due to changes in

temperature or OLR has not been investigated in lab scale reactors. That process is assumed to have

changed similarly to the process in R100.

41

4.1.1 Foaming

There was major foaming in the lab scale reactors that were run at 37 ⁰C while the other reactors had

insignificant foaming. Figure 17 shows the level of foam in two different reactors to represent the two

levels of foam in the reactors one hour after feeding, even though the foam layers where more or less

constant during the day. When the concentration of filamentous microorganisms is high in the sludge, the

foaming increases in the full scale reactors at Käppala, and sometimes the level of foam can reach a critical

level for the process. The level of filamentous microorganisms was not determined during this project;

however, lab scale results indicate that an increase of the temperature could decrease such foaming

problems.

Figure 17 – The level of foam in the lab scale reactor one hour after feeding. Left: one of the 37 ⁰C reactors. Right: either one of the 45 ⁰C reactors or one of the 55 ⁰C reactors.

4.1.2 Centrifuge - dewatering not effective

The dewatering of the digested sludge was not as efficient for the sludge from the lab scale reactors run

at 45°C and 55°C. Figure 18 shows the supernatant from centrifuged digested sludge from the six lab scale

reactors. The supernatant from the 37 ⁰C reactors are clearer than the other which indicates a better

dewatering. It is important for both transport and energy consumption expenses that the dewatering

stage is effective.

42

Figure 18 – Supernatant form centrifuged digested sludge, the supernatant in 1 and 6 are much clearer than in the other cups (1 and 6 – 37 ⁰C reactors, 2 and 5 – 45 ⁰C reactors, 3 and 4 – 55 ⁰C reactors).

4.1.3 Smell

The smell from the digested sludge was more pungent in the warmer reactors. The smell was worst during

the period after the processes were adapting to the new temperature. The sludge was expected to smell

bad before the system reached stable operation due to an expected increase of volatile fatty acids. In

addition, higher temperatures in the reactors also increase the evaporation of the substances that cause

the odour. The fact that the smell of the sludge from the warmer reactors did not stabilize to the level of

the reference reactors is a problem for application of a temperature increase for the anaerobic digestion

in full scale. The sludge in the full scale reactors is more or less sealed for leakage but the sludge is

transferred to the sludge dewatering and then storage in silos. Similar to the emission of greenhouse gases

from the silos, the bad smell will emit from the silos and out into the air. The smell will disperse in the

plant and to the nearby area, which can cause problems for Käppala unless additional investments are

made to control the odour.

4.2 PROCESS STABILITY The process stability for the full scale reactor is normally evaluated using six indicators (pH, alkalinity,

methane content, %VS, VFA and specific methane production) (Section 1.1.2.2), but for this study five

different conditions were measured and evaluated in order to determine the process stability. The reason

that alkalinity was not used for evaluation is that only one measurement was done during the time where

the processes were assumed to be stable. However, the standard deviation of pH, methane content,

VFA, %VS and specific methane production measurements were done for each lab scale temperature and

measurements on R100. The specific methane production is used because the methane production will

automatically change during the stable operation period when OLR is changed. The standard deviations

for the lab scale reactors were compared to the standard deviation of R100 by determining the ratio

between the standard deviations from each temperature with R100. Following yeas of in situ use, the

process in R100 has come to be considered a stable one. Figure 19 shows a plot of the relative standard

43

deviations. The standard deviations of the lab scale reactors are in general higher than the standard

deviation of R100. That means that the variations are larger in the lab scale measurements, and one

explanation for that is that the lab scale reactors were fed as a batch process (Section 2.2.2.1). In a batch

process the sludge is added in batches, which means that the access of organic matter for the

microorganisms will be different during the day. Moreover, the batches were not evenly distributed

during the week, which contributed to an uneven access of organic matter during the week. This irregular

concentration of organic matter can generate small variations in process parameters, particularly since

the measurements were not performed on the same week day every week. The variation among the lab

scale temperatures indicated that the 37 ⁰C process was more stable than the 45 ⁰C and 55 ⁰C processes

with respect to pH, methane content, and VFA. Reactor 55b had one particularly extreme VFA peak in the

stable process interval (Figure 14, Section 3.1.1.5) which contributed to the large standard deviation of

the 55 ⁰C reactors. Pursuant to specific methane production the process run at 37 ⁰C and 55 ⁰C are most

stable, nevertheless according to percentage volatile acid measurements the process preformed at 45 ⁰C

and 55 ⁰C were the most stable.

Figure 19 – The standard deviation of the lab scale measurements relative the standard deviation of measurements on R100.

Figure 8 (Section 3.1.1.1) shows the methane production rate in the lab reactors a typical week in the

stable process period. The large increase in production each day is generated by the feeding. The

production rate then decreases until the next feeding. In some cases, the production of methane for some

reason decreased and then increased again in in between two feedings. This phenomenon is seen for all

the reactors and can be due to some instability in the process. The pattern could probably be avoided if

the feeding was continuous as for R100.

Finally, the process in R100 is more stable than the process in lab scale most likely due to R100 being

operated as a continuous process whereas the processes in lab scale are run as batch processes for

practical reasons. Any changes in stability due to temperature are not that obvious. For the most

important parameters, methane content and methane production, the differences in variation are small.

This implies that the anaerobic digestion can be stable for all the tested temperatures.

0,00%

50,00%

100,00%

150,00%

200,00%

250,00%

300,00%

350,00%

400,00%

450,00%

500,00%

pH %Methane VFA %VS Specific methaneproduction

Rel

ativ

e st

and

ard

dev

iati

on

Standard deviation of measurements on the lab scale reactors relative the standard deviation of measurements on R100

37 ⁰C 45 ⁰C 55 ⁰C R100

44

4.3 SEASONAL VARIATION In this report, the only factor that varies with season in the profit calculations for the system is the

electricity consumption. The heating system, gas treatment plant and sludge dewatering consumes

electricity. Figure 20 shows the variation of total energy consumption during the year with the

contribution from each source. The gas treatment plant and the heating system have the largest impact

on the energy consumption. The variation during the year from these units have a different pattern; the

gas treatment plant has its highest values on energy consumption during the summer months (more

energy is required in the scrubbers due to decreased gas solubility in warmer water) while the heating

systems reach their highest values during the winter months (due to the incoming water being colder

then). The sum of these parameters is generally constant during the year.

Figure 20 – Energy consumption distribution during the year (Temperature: 37 ⁰C, OLR: 3 kgVS/(m3,day)).

The profit profile during the year for the six investigated combinations of temperature and organic loading

rate are similar to the profile for energy consumption during the year (Figure 21). With the knowledge

that the only difference between the change in energy consumption and the change in profit during the

year is a constant, it is assumed that the energy consumption for each case has the same pattern as its

respective profit profile. To maximize profit of the anaerobic digestion process at Käppala with respect to

seasonal variations in temperature and flow of wastewater, the process should be run at 37 ⁰C regardless

of season as it is done today. For the two 37 ⁰C cases, the profit change is the same during the year, which

means that OLR 3.37 kgVS/(m3,day) maximizes the profit regardless of month.

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

Janu

ary

Feb

ruary

March

Ap

ril

May

Jun

e

July

Au

gust

Sep

tem

ber

Octo

be

r

No

vemb

er

Dece

mb

er

Ener

gy c

on

sum

pti

on

[kW

h/m

on

th]

Month

Seasonal variation of energy consumption

Sludge dewatering

Gastreatment plan

Heating system

45

Figure 21 – The profit distribution during the year for the six investigated combinations on temperature and organic loading rate.

4.4 PROFIT The gas production was not increased by increasing the temperature but only by increasing the organic

loading rate (Figure 31, Appendix 1 – Raw data). Figure 22 shows the system’s monetary profit, with

expenses and incomes. From the figure it is obvious that the largest item in the economic balance is the

income from selling the methane gas to SL, the same item also has the largest variation with temperature

and OLR. The loss of income from SL was 3% for increasing the temperature in all cases except from

increasing both temperature to 55 ⁰C and increasing OLR to 3.75 kgVS/(m3,day) where the loss was 1.5%.

The item for electricity cost does also change with temperature even though the changes are smaller. To

overcome the increased electricity costs with temperature is the quantity of gas sold to SL needs to be

increased with 2% to run the anaerobic digestion process at 45 ⁰C and increased with 4% to run the

process at 55 ⁰C.

Käppala has a limited ability to use the produced biogas other than selling it to SL. The decrease in

expenses for Käppala when the gas is used instead of oil is small due to that only a small portion of the

gas can be used in this way. According to the model calculations it is most profitable to sell the biogas to

SL. However, the profit calculations for using the gas instead of oil are weak and to draw a more reliable

conclusion further examination is needed. The differences in transport cost between the six different

cases are insignificant.

JanuaryFebruary

MarchApril

MayJune

JulyAugust

SeptemberOctober

NovemberDecember

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8P

rofi

t [s

ek/m

on

th]

Seasonal variation of profit

46

Figure 22 – Monetary profit of the system in the six different cases described in this article. Positive values represent the monetary income to Käppala from the system of processes discussed in this article. Negative values represent monetary expenses of the system. Käppala’s profit is the difference between the system’s monetary income and expense.

The process was in lab scale stable with respect to changes in organic loading rate (Section 3.1.2). The

large scale is assumed to be stable for changes in organic loading rate as well due to that the organic

loading rate varied between 2,9 and 4,8 kgVS/(m3,day) in the period between 2015-02-02 and 2015-06-08

(Personal communication, C. Grundestam, 2015). The process is not expected to lose stability due to an

increase or decrease in the range of the testes OLRs, therefore no support can be found for the

assumption of profit loss due to an increase in OLR. The process is more sensitive to changes in

temperature (Section 3.1.2). In the laboratory investigation it was determined that the lab scale processes

were unstable between 2015-02-13 and 2015-03-25 due to the temperature change. This matches the

literature that states that it would be expected to take one month before a process reaches stability after

a temperature change has happen (Section 1.1.2.2). The loss of profit during that time is the income from

one and a third month, 2.3 MSEK, due to that the gas production is more or less repressed during that

time.

4.5 ENVIRONMENTAL ASPECTS The largest source of carbon dioxide emission from the system is combustion of the produced methane

gas, therefore the carbon dioxide footprint is larger for the system when the anaerobic digestion process

produces more biogas. In the mathematical model the combustion of biogas is partitioned between three

different areas of usage; fuel for SL-busses, heating instead of oil, and combustion in torch. The 92% of

the produced biogas is sold to SL as fuel and therefore it is the largest emission source SL, in comparison

the other emissions sources are negligible (Figure 23). As mentioned in section 4.4, the most profitable

case is when the process is run at 37 ⁰C and 3.75 kgVS/(m3,day) which gives the highest gas production,

and in turn the highest value of carbon dioxide footprint is obtained. However, even if the amount of

produced biogas stands in direct proportion to the total carbon dioxide footprint emitted from the system,

is that not the most important factor in the environmental evaluation. The usage of biogas as fuel replaces

fossil fuel, which generates a negative value on the total carbon dioxide footprint for fossil sources (Figure

24). A negative value on carbon dioxide footprint from fossil fuels is positive for the environment since it

-10

-5

0

5

10

15

20

25

30

37 ⁰C, 3 kgVS/m3,day

37 ⁰C, 3.75 kgVS/m3,day

45 ⁰C, 3 kgVS/m3,day

45 ⁰C, 3.75 kgVS/m3,day

55 ⁰C, 3 kgVS/m3,day

55 ⁰C, 3.75 kgVS/m3,day

Inco

me/

Exp

ense

s [M

sek/

year

]Monetary profit of the system

SL Oil El Transport

47

indicates that more renewable fuels are used instead of fossil fuels, bio fuels do not affect the global

warming as much as fossil fuels.

Figure 23 – Carbon dioxide footprint of the system in the six different cases described in this article. The bars represent the system’s greenhouse gas emission represented in carbon dioxide equivalents.

Figure 24 – Fossil fuel carbon dioxide footprint of the system in the six different cases described in this article. The bars represent the system’s greenhouse gas emission from fossil fuels represented in carbon dioxide equivalents.

4.6 RELIABILITY OF THE RESULTS The results from the laboratory part consist of measured values and an evaluation of stability (Section

3.1). The uncertainty in the measured value lies in the uncertainty of the measurement. Except from the

methane content and methane production analysis, all other methods are used in the daily work at

Käppala for evaluation of the full scale process. The methods are therefore carefully evaluated and tested.

This does not erase the risk for small random fault in the measurements due to human errors and sample

0

20000000

40000000

60000000

80000000

100000000

120000000

140000000

37 ⁰C, 3 kgVS/m3,day

37 ⁰C, 3.75 kgVS/m3,day

45 ⁰C, 3 kgVS/m3,day

45 ⁰C, 3.75 kgVS/m3,day

55 ⁰C, 3 kgVS/m3,day

55 ⁰C, 3.75 kgVS/m3,day

CD

F [k

g CO

2/y

ear]

Carbon dioxide footprint of the system

Silos Transport SL Oil Torch Electricity

-10000000

-8000000

-6000000

-4000000

-2000000

0

2000000

37 ⁰C, 3 kgVS/m3,day

37 ⁰C, 3.75 kgVS/m3,day

45 ⁰C, 3 kgVS/m3,day

45 ⁰C, 3.75 kgVS/m3,day

55 ⁰C, 3 kgVS/m3,day

55 ⁰C, 3.75 kgVS/m3,day

CD

F [k

g CO

2/y

ear]

Fossil fuel carbon dioxide footprint of the system

Transport SL Oil Electricity

48

errors. The analysis method that had the largest standard error was VFA. That is true for the

measurements on the lab scale reactors and the large scale reactor as well as the control. Those

measurements were only used in order to evaluate whether any value deviated a lot from the others,

which is an informative indicator for process stability. For measurement of methane content, methane

production, %TS and %VS were the relative standard deviation below 10 %. That indicates that the

variation in the measurements was low which is good when the values are used for determination of

functions and in calculations.

For the modelling part assumptions were done and functions were fitted which affect the result (Section

3.2). There are some large uncertainties in the mathematical model, firstly the fact that the values on

methane content, methane production, %TS and %VS as a function of temperature and OLR are

determined from lab scale experiments. There is a large uncertainty that the large scale system with two

reactors should react in the same way as the small scale reactors as the temperature increases. The two

large reactors are coupled in a series where the digested sludge from R100 is added with secondary sludge

to R200. Due to this, the values on %TS and %VS after R200 would probably not be the same as after R100

which is what the small scale investigation have estimated. For a better estimation of the whole process,

the small scale investigation should contain serial digestion as for the large scale. Even then, a large

uncertainty would lie in the scale difference due to that the anaerobic digestion process is a complex

process that is hard to predict.

The laboratory results are also dependent on the feeding process. The continuous feeding gives the

microorganisms a more even concentration of organic material in the large scale reactors than in the lab

reactors where the organic material was added in batches. The concentration of organic material will

differ depending on the time and the weekday (Section 2.2.2.1). The environment for the microorganisms

and their activity will therefore differ during time. Another parameter that could affect the environment

in the digester is that the feed was frozen and thawed before feeding which can lead to dead

microorganisms in the feed, new bacterial cultures have it harder to be established in the digester. The

last parameter that can affect the environment is the different stirring methods. The microbiological

culture established after that change could diverge from the microbiological culture in the lab scale

reactors. A change in the microbiological culture generates a change in operational conditions.

Additionally, the methane production has the largest impact on both the system’s monetary profit and

carbon dioxide footprint. Therefore, the estimation of the methane production is an important factor. The

gas sensors for the full scale have not historically been reliable, and have not been calibrated or verified

this gives uncertainties in the results from the gas analyses on R100 (Personal communication, M Lüdtke,

2015). In turn that contributes to uncertainties in the methane production function that is based on result

on methane gas production from R100. Further, the methane production function does not take into

account that the relationship between gas production, temperature, and OLR can change with the scale.

The function in itself is the largest approximation since the methane production only is measured at three

temperatures for each OLR. To obtain a mathematical model that is more reliable outside of the three

temperatures, a new laboratory investigation done in more different temperatures is need.

The second large estimation is the simplification of the heating system (Section 2.3.2.1). The cost of

electricity is the second largest contribution for the system’s monetary profit and the cost of electricity

that is used for heating the digesters are approximately half of the total cost. The heating system is a

complex system and the system can be run and regulated in many ways. The heating process that the

49

mathematical model is applied on is the most common way of running the system at Käppala, and the

two regulation parameters for the heating system is the most common regulation parameters. A more

exact approximation would have been possible if all the options for running the heating system were

evaluated against the temperature and flow. Also, if that evaluation was done, the system could be

optimized in terms of how to run the heating system in the most favourable way.

50

5 CONCLUSION

Käppala is a wastewater treatment plant that produces biogas in an anaerobic digestion process. The

process is dependent on the temperature and this project´s aim was to optimize the process temperature.

The conclusions are based on results from a laboratory investigation and modelling calculations.

The first goal was to determine whether it is possible to establish a stable anaerobic digestion

process in the temperature interval between mesophilic and thermophilic. From the laboratory

investigation it was determine that a stable process could be established at 45 ⁰C.

The second goal was to determine the optimum digestion temperature in the temperature

interval 37-55 ⁰C with respect to economics, environmental impact and process stability. The most

optimal temperature for Käppala is 37 ⁰C.

The last goal was to evaluate if the seasonal variation in organic loading rate and heating

requirement affects the optimal digestion temperature. The conclusion is that the optimal

temperature is independent of seasonal variation in organic loading rate and heating

requirement.

These three conclusions are drawn with the knowledge that the results are based on simplifications and

assumptions. Moreover, the conclusions are drawn within the project boundaries. Even so, the advice to

Käppala is to not change the digestion for now and as further work investigate how the process react if

the digestion temperature is lowered.

51

6 REFERENCES

Biogasföreningen, S. (2005). Nationellt Samverkansprojekt Biogas i Fordon - Förutsättningar för att utnyttja biogas från Käppalaverket som fordonsbränsle för bussar och andra fordon Projektet delfinansieras av Energimyndigheten. Stockholm.

Bioprocess Control Sweden AB. (2013). AMPTS II. Automatic Methane Potential Test System. Operation and Maintenance Manual. Version 1.6.

Boušková, A., Dohányos, M., Schmidt, J. E., & Angelidaki, I. (2005). Strategies for changing temperature from mesophilic to thermophilic conditions in anaerobic CSTR reactors treating sewage sludge. Water Research, 39, 1481–1488. doi:10.1016/j.watres.2004.12.042

Energi & Klimat rådgivning. (n.d.). Energi & Miljö. Retrieved June 2, 2015, from http://www.energiradgivningen.se/miljo-klimat/energi-miljo

Energi & Klimat rådgivningen. (n.d.). Oljepanna. Retrieved June 3, 2015, from http://www.energiradgivningen.se/varma-villan/oljepanna

Erikstam, S. (2013). Modellering av koldioxidavtrycket för Käppalaverket med en framtida processlösning utformad för skärpta reningskrav. Uppsala universitet.

Gerardi, M. (2003). The microbiology of anaerobic digesters. Vasa. New Jersey: John Wiley & Sons, Inc., Publication. doi:10.1002/0471468967

Jarvis, Å., & Schnürer, A. (2009). Mikrobiologisk handbok för biogasanläggningar. SGC Rapport, 207.

Komatsu, K., Yasui, H., Goel, R., Li, Y. Y., & Noike, T. (2011). Novel anaerobic digestion process with sludge ozonation for economically feasible power production from biogas. Water Science and Technology, 63(7), 1467–1475. doi:10.2166/wst.2011.382

Käppala. (2013). ENVIRONMENTAL REPORT 2013.

Käppala. (2015). Miljöpolicy. Retrieved March 16, 2015, from https://www.kappala.se/

Käppalaförbundet. (2013). Vattnets väg – från Mälaren till Östersjön.

Larsson, J.-E., Starberg, K., Karlsson, B., Moraeus, P., & Lindberg, A. (2005). Problems and solutions during process optimixation of the sludge treatment at wastewater treatment plants. Stockholm.

Lindorfer, H., Waltenberger, R., Köllner, K., Braun, R., & Kirchmayr, R. (2008). New data on temperature optimum and temperature changes in energy crop digesters. Bioresource Technology, 99, 7011–7019. doi:10.1016/j.biortech.2008.01.034

52

Naturvårdsverket. (2014). Förklaring av termer. Retrieved May 10, 2015, from http://www.naturvardsverket.se/Stod-i-miljoarbetet/Vagledningar/Luft-och-klimat/Berakna-utslapp-av-vaxthusgaser-och-luftfororeninga/forklaring-av-termer/

Norrman, J., Belhaj, M., & Arnell, J. (2005). Biogas som drivmedel för fordon i Västra Götaland, (December).

Stockholm läns landsting. (2014). Miljöredovisning 2013.

Svenska Elvägar AB. (2009). Lastbilarna driver hela ökningen av koldioxidutsläpp från transportsektorn. Retrieved June 8, 2015, from http://www.elvag.se/blogg/miljo/

Svenska Petroleum & Biodrivmedel Institutet. (2014). Energiinnehåll, densitet och koldioxidemission. Retrieved May 10, 2015, from http://spbi.se/blog/faktadatabas/artiklar/berakningsmodeller/

Svenskt Gasteknsikt Center. (2012). Basdata om biogas. Malmö.

Vattenfall. (2014). Livscykelanalys Vattenfalls elproduktion i Norden.

Witkiewicz, A. (2012). Mekanisk förbehandling av slam innan rötning - En erfarenhetssamanställning av försök gjorda på Käppalaverket. Stockholm.

53

APPENDIX 1 – RAW DATA

Table 7 – Rated values on the circulation pumps and the heating pump.

Pump Current [A] Power [kW] % of max power Energy consumption [kWh/month]

R100-P101 32.6 30 83% 18482.47

R100-P102 32.9 30 72% 16032.99

R200-P101 32.5 30 80% 17814.43

R200-P102 32.9 30 80% 17814.43

SB00-P151 24 22 Variable

SB00-P251 24 22 Variable

SB00-P051 24 22 70% 11430.93

SB10-P154 32.5 30 15% 3340.21

SB10-P013 7.5 7.5 62% 3451.55

SB00-K001.CUA1 79.28 46.69 Variable

SB00-K001.CUA2 76.23 44.89 Variable

SB00-K001.CUB1 64.38 37.91 Variable

SB00-K001.CUB2 64.94 38.24 Variable

Sum of constant pumps 88367.01

Figure 25 - The power for the heating pump plotted against the temperature difference between incoming sludge and digestion temperature.

0

50

100

150

200

250

300

17 19 21 23 25 27 29

Po

wer

[kW

]

ΔT [⁰C]

Heating systemPower as a function of temperature differance

54

Figure 26 – The power for the heating pump plotted against the sludge flow.

Figure 27 – The power for the heating pump plotted against the temperature difference, between incoming sludge and digestion temperature, and the sludge flow.

0

50

100

150

200

250

300

2,5 3,5 4,5 5,5 6,5 7,5 8,5 9,5 10,5

Po

wer

[kW

]

Flow [l/s]

Heating systemPower as a function of flow

y = 73.1490918371ln(x) - 900.0434720651R² = 0.8134894929

0

50

100

150

200

250

300

70 500070 1000070 1500070 2000070 2500070 3000070 3500070 4000070 4500070 5000070

Po

wer

[kW

]

ΔT4 times Q [⁰C4,l/s]

Heating pumpPower as a function of temperature differance and flow

55

Figure 28– The power for the whole heating system plotted against the temperature difference, between incoming sludge and digestion temperature, and the sludge flow.

Table 8 – Mean values of the composition of the sludge after dewatering.

Centrifuge %TS after dewatering %water after dewatering

#1 25.64% 74.36%

#2 25.93% 74.07%

#3 25.72% 74.28%

#4 25.62% 74.38%

#5 25.85% 74.15%

Mean 25.75% 74.25%

Table 9 - The gas production for each lab scale reactor for the period 2015-03-25 to 2015-04-23, OLR 3 kgVS/m3.day.

Reactor 37a (37 ⁰C)

Reactor 45a (45 ⁰C)

Reactor 55a (55 ⁰C)

Reactor 55b (55 ⁰C)

Reactor 45b (45 ⁰C)

Reactor 37b (37 ⁰C)

Monday 1614.50 1457.36 1516.11 1627.31 1489.76 1591.58

Tuesday 2043.81 2054.33 1940.60 1994.75 2048.42 1982.68

Wednesday 2149.79 2098.27 1946.79 1904.84 2075.88 2088.62

Thursday 2247.29 2010.42 1997.12 1964.98 2045.74 2208.23

Friday 2738.33 2384.01 2723.14 2666.82 2734.47 2681.63

Saturday 1141.17 983.97 840.00 837.93 1013.80 1114.03

Sunday 628.23 496.74 446.59 494.84 496.74 637.06

Mean 1794.73 1640.73 1630.05 1641.64 1700.69 1757.69

y = 63.3125549234ln(x) - 936.7811240600R² = 0.8195961522

0

50

100

150

200

250

300

70 20000070 40000070 60000070 80000070 100000070 120000070 140000070

Po

wer

[kW

]

ΔT5 times Q [⁰C5,l/s]

Heating SystemPower as a function of temperature differance and flow

56

Table 10 - The gas production for each lab scale reactor for the period 2015-04-24 to 2015-06-15, OLR 3.75 kgVS/m3.day.

Reactor 37a (37 ⁰C)

Reactor 45a (45 ⁰C)

Reactor 55a (55 ⁰C)

Reactor 55b (55 ⁰C)

Reactor 45b (45 ⁰C)

Reactor 55b (37 ⁰C)

Monday 2013.46 1695.33 2031.69 2094.64 1791.18 1914.61

Tuesday 2572.89 2133.96 2602.55 2626.35 2122.48 2532.69

Wednesday 2663.28 2477.41 2538.56 2582.56 2270.97 2621.54

Thursday 2763.74 2498.07 2648.13 2653.52 2426.90 2698.43

Friday 3265.24 3193.28 3518.59 3468.15 2930.05 3286.42

Saturday 1383.82 1518.46 1038.81 1126.45 1452.65 1415.19

Sunday 768.34 868.09 522.43 608.26 914.89 804.95

Mean 2204.40 2054.94 2128.68 2165.70 1987.02 2181.97

Figure 29 – The mean values of the methane contents for the different OLR plotted against the temperature. The fitted function for OLR 3 kgVS/(m3,day) is %CH4 = 0.0002328042*T2 – 0.0230185185*T + 1.1834523810 and for OLR 3.75 3 kgVS/(m3,day) is the fitted function %CH4 = 0,0002231481*T2 - 0,0221314815*T + 1,1698750000.

61,000%

61,500%

62,000%

62,500%

63,000%

63,500%

64,000%

64,500%

65,000%

65,500%

66,000%

30 35 40 45 50 55 60

Met

hn

ae c

on

ten

t

Temperature [⁰C]

Methane content as a function of temperature

OLR 3

OLR 3,75

57

Figure 30 – The mean values of the %VS for the different OLR plotted against the temperature. The fitted function for OLR 3 kgVS/(m3,day) is %VS = 0.0004201175*T2 - 0.0398758374*T + 1.2099601867 and for OLR 3.75 3 kgVS/(m3,day) is the fitted function %VS = 0.0002909727*T2 - 0.0271735401*T + 0.8748935280.

.

Figure 31 - The mean value of the percentage methane production for the different OLR plotted against the temperature. The fitted function for OLR 3 kgVS/(m3,day) is %Vmethane = 0.0003034409*T2 - 0.0323068593*T + 1.7799432279 and for OLR 3.75 kgVS/(m3,day) is the fitted function %Vmethane = 0.0008649754*T2 - 0.0807427612*T + 2.803309085.

0,2

0,22

0,24

0,26

0,28

0,3

0,32

30 35 40 45 50 55 60

%V

S

Temperature [⁰C]

%VS digested sludge

OLR=3

OLR=3,75

91%

92%

93%

94%

95%

96%

97%

98%

99%

100%

101%

30 35 40 45 50 55 60

%m

eth

ane

pro

du

ctio

n

Temperature [⁰C]

The methane production compare to the reference reactors

OLR 3

OLR 3,75

58

APPENDIX 2 – EQUATIONS TO THE MATHEMATICAL MODEL

𝐶𝐷𝐹 [𝑘𝑔] = 𝐺𝑊𝑃 ∗ 𝐸𝑚 [𝑘𝑔]

𝐶𝐷𝐹 [𝑘𝑔] = 𝐺𝑊𝑃 ∗ 𝐶𝐹 [𝑚3] ∗ 𝑇𝑉[𝐺𝐽/𝑚3] ∗ 𝐸𝐹 [𝑘𝑔/𝐺𝐽]

𝐸𝑇𝑜𝑡𝑎𝑙 [𝑘𝑊ℎ] = (𝐸𝐻𝑒𝑎𝑡𝑖𝑛𝑔 + 𝐸𝑈𝑝𝑔𝑟𝑎𝑑𝑖𝑛𝑔 + 𝐸𝐷𝑒𝑤𝑎𝑡𝑒𝑟𝑖𝑛𝑔) [𝑘𝑊ℎ]

𝐸𝑥𝑝𝑅𝑢𝑛𝑛𝑖𝑛𝑔[𝑆𝐸𝐾] = 𝐸𝑇𝑜𝑡𝑎𝑙 [𝑘𝑊ℎ] ∗ 𝑃𝑟𝑖𝑐𝑒𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 [𝑆𝐸𝐾/𝑘𝑊ℎ]

𝐶𝐷𝐹𝐸𝑙𝑒𝑐𝑡𝑟𝑐𝑖𝑡𝑦

𝐵𝑖𝑜 [𝑘𝑔] = 0.942 ∗ 𝐸𝑇𝑜𝑡𝑎𝑙 [𝑘𝑊ℎ] ∗ 𝐸𝑚𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 [𝑘𝑔/𝑘𝑊ℎ]

𝐶𝐷𝐹𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦𝐹𝑜𝑠𝑠𝑖𝑙 [𝑘𝑔] = 0.058 ∗ 𝐸𝑇𝑜𝑡𝑎𝑙 [𝑘𝑊ℎ] ∗ 𝐸𝑚𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 [𝑘𝑔/𝑘𝑊ℎ]

𝐸ℎ𝑒𝑎𝑡𝑖𝑛𝑔[𝐽/𝑑𝑎𝑦] = 𝑄𝑠𝑙𝑢𝑑𝑔𝑒[𝑚3/𝑑𝑎𝑦] ∗ 𝜌𝑠𝑙𝑎𝑚[𝑘𝑔/𝑚3] ∗ 𝑐𝑃[𝐽/𝑘𝑔, 𝐾] ∗ ∆𝑇 [𝐾]

𝐸ℎ𝑒𝑎𝑡𝑖𝑛𝑔[𝐽/𝑑𝑎𝑦] = 𝜌𝑠𝑙𝑎𝑚[𝑘𝑔/𝑚3] ∗ 𝑐𝑃[𝐽/𝑘𝑔, 𝐾](𝑄𝑃𝑆,𝑅100 ∗ ∆𝑇𝐷−𝑃𝑆 + 𝑄𝐸𝑆,𝑅200 ∗ ∆𝑇𝐷−𝐸𝑆) [𝐾, 𝑚3/𝑑𝑎𝑦]

𝐸 [𝑘𝑊ℎ/𝑚𝑜𝑛𝑡ℎ] =𝑃𝑟𝑎𝑡𝑒𝑑 [𝑘𝑊] ∗ 24 [ℎ] ∗ %𝑜𝑓 𝑚𝑎𝑥 ∗ 𝐷𝑎𝑦𝑠𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 𝑚𝑜𝑛𝑡ℎ [𝑑𝑎𝑦𝑠/𝑚𝑜𝑛𝑡ℎ]

𝜂

𝑃 [𝑘𝑊] = 𝑃𝑟𝑎𝑡𝑒𝑑 [𝑘𝑊] ∗ %𝑜𝑓 𝑚𝑎𝑥

𝑃 [𝑘𝑊] = 63.313 ∗ ln (∆𝑇5 [℃5

] ∗ 𝑄 [𝑙/𝑠]) − 936.78

𝑃 [𝑘𝑊] = 73.149 ∗ ln (∆𝑇4 [℃4

] ∗ 𝑄 [𝑙/𝑠]) − 900.04

∆𝑇 [℃] = 𝑇𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛[℃] − 𝑇𝑖𝑛𝑐𝑜𝑚𝑖𝑛𝑔 𝑠𝑙𝑢𝑑𝑔𝑒[℃]

𝐸 [𝑘𝑊ℎ/𝑚𝑜𝑛𝑡ℎ] =𝑃 [𝑘𝑊] ∗ 24 [ℎ] ∗ 𝐷𝑎𝑦𝑠𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 𝑚𝑜𝑛𝑡ℎ [𝑑𝑎𝑦𝑠/𝑚𝑜𝑛𝑡ℎ]

𝜂

𝑉𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝑔𝑎𝑠 [𝑚3/𝑦𝑒𝑎𝑟] = 𝑉𝐵𝑖𝑜𝑔𝑎𝑠 [𝑁𝑚3/𝑦𝑒𝑎𝑟] ∗%𝐶𝐻4,𝐵𝑖𝑜𝑔𝑎𝑠

%𝐶𝐻4,𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝑔𝑎𝑠

𝐸𝑈𝑝𝑔𝑟𝑎𝑑𝑖𝑛𝑔[𝑘𝑊ℎ/𝑦𝑒𝑎𝑟] = 𝐸𝑢𝑝𝑝𝑔𝑟𝑎𝑑𝑖𝑛𝑔 [𝑘𝑊ℎ/𝑚3] ∗ 𝑉𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝑔𝑎𝑠 [𝑚3/𝑦𝑒𝑎𝑟]

𝑉𝑑𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝑔𝑎𝑠 [𝑁𝑚3/𝑚𝑜𝑛𝑡ℎ] =𝑉𝑑𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝑔𝑎𝑠 [𝑁𝑚3/𝑦𝑒𝑎𝑟]

12

𝐸𝐷𝑒𝑤𝑎𝑡𝑒𝑟𝑖𝑛𝑔[𝑘𝑊ℎ/𝑚𝑜𝑛𝑡ℎ] = 𝐸𝐷𝑒𝑤𝑎𝑡𝑒𝑟𝑖𝑛𝑔[𝑘𝑊ℎ/𝑡𝑜𝑛𝑛𝑒] ∗ 𝑚𝑇𝑆,𝑖𝑛[𝑡𝑜𝑛𝑛𝑒/𝑚𝑜𝑛𝑡ℎ]

𝐸𝑚𝐶𝐻4[𝑁𝑚3/𝑡𝑜𝑛𝑛𝑒, ℎ] = 0.0004𝑒0.159∗𝑇 𝑆𝑖𝑙𝑜[°𝐶]

𝐶𝐷𝐹𝑆𝑖𝑙𝑜𝑠

𝐵𝑖𝑜 = 𝐶𝐷𝑊𝐶𝐻4∗ 𝑄𝐶𝐻4

[𝑁𝑚3/𝑡𝑜𝑛𝑛𝑒, ℎ] ∗ %𝑉𝑆 ∗ 𝑚𝑠𝑙𝑢𝑑𝑔𝑒,𝑠𝑖𝑙𝑜𝑠[𝑡𝑜𝑛𝑛𝑒]

𝐶𝐷𝐹𝑆𝑖𝑙𝑜𝑠

𝐵𝑖𝑜 = 𝐶𝐷𝑊𝑁2𝑂 ∗ 𝐸𝑚𝑁2𝑂[𝑘𝑔/𝑡𝑜𝑛𝑛𝑒, 𝑦𝑒𝑎𝑟] ∗ %𝑇𝑆 ∗ 𝑚𝑠𝑙𝑢𝑑𝑔𝑒,𝑠𝑖𝑙𝑜𝑠[𝑡𝑜𝑛𝑛𝑒]

𝑇𝑆𝑖𝑙𝑜 = 𝑇𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛 − ∆𝑇𝐻𝑒𝑎𝑡𝑖𝑛𝑔 𝑠𝑦𝑠𝑡𝑒𝑚

59

𝐸𝑥𝑝𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡[𝑆𝐸𝐾] = 𝑚𝐷𝑊 𝑠𝑙𝑢𝑑𝑔𝑒[𝑡𝑜𝑛𝑛𝑒] ∗ 𝑃𝑟𝑖𝑐𝑒𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡[𝑆𝐸𝐾/𝑡𝑜𝑛𝑛𝑒]

𝑉𝐷𝑖𝑒𝑠𝑒𝑙 [𝑚3] = 𝑚𝐷𝑊 𝑠𝑙𝑢𝑑𝑔𝑒[𝑡𝑜𝑛𝑛𝑒]

𝑚𝑆𝑙𝑢𝑑𝑔𝑒𝑡𝑟𝑢𝑐𝑘

[𝑡𝑜𝑛𝑛𝑒]∗ 𝑑𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡[𝑘𝑚] ∗ 𝐹𝐶𝐷𝑖𝑒𝑠𝑒𝑙,𝑡𝑟𝑢𝑐𝑘[𝑚3 𝑘𝑚⁄ ]

𝐶𝐷𝐹𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡𝐹𝑜𝑠𝑠𝑖𝑙 [𝑘𝑔] = 𝑉𝐷𝑖𝑒𝑠𝑒𝑙 [𝑚3] ∗ 0,89 ∗ 𝐸𝐹𝐷𝑖𝑒𝑠𝑒𝑙[𝐺𝐽/𝑚3] ∗ 𝑇𝑉𝐷𝑖𝑒𝑠𝑒𝑙 [𝑘𝑔/𝐺𝐽]

𝐶𝐷𝐹𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡𝐵𝑖𝑜 [𝑘𝑔] = 𝑉𝐷𝑖𝑒𝑠𝑒𝑙 [𝑚3] ∗ 0,11 ∗ 𝑇𝑉𝐹𝐴𝑀𝐸 [𝐺𝐽/𝑚3] (𝐸𝐹𝐹𝐴𝑀𝐸,𝐶𝑂2

[𝑘𝑔/𝐺𝐽] + 𝐸𝐹𝐹𝐴𝑀𝐸,𝑁2𝑂[𝑘𝑔/𝐺𝐽] )

%𝑤𝑎𝑡𝑒𝑟𝐷𝑊 𝑠𝑙𝑢𝑑𝑔𝑒 = 87.5 ∗ %𝑉𝑆 + 22.4

𝑚𝐷𝑊 𝑠𝑙𝑢𝑑𝑔𝑒[𝑡𝑜𝑛𝑛𝑒] = 30000[𝑡𝑜𝑛𝑛𝑒] ∗%𝑤𝑎𝑡𝑒𝑟𝐷𝑊 𝑠𝑙𝑢𝑑𝑔𝑒

%𝑤𝑎𝑡𝑒𝑟𝐷𝑊 𝑠𝑙𝑢𝑑𝑔𝑒,𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒

𝐼𝑛𝑐𝑜𝑚𝑒𝑆𝐿[𝑆𝐸𝐾] = 𝑉97%𝑚𝑒𝑡ℎ𝑎𝑛𝑒 [𝑚3] ∗ 𝑃𝑟𝑖𝑐𝑒𝑆𝐿[𝑆𝐸𝐾/𝑚3]

𝐶𝐷𝐹+𝑆𝐿𝐵𝑖𝑜[𝑘𝑔] = 𝑉𝑏𝑖𝑜𝑔𝑎𝑠[𝑚3] ∗ 𝑇𝑉𝑏𝑖𝑜𝑔𝑎𝑠[𝐺𝐽/𝑚3] ∗ (𝐸𝐹𝐶𝑂2,𝑏𝑖𝑜𝑔𝑎𝑠 + 𝐶𝐷𝑊𝐶𝐻4

∗ 𝐸𝐹𝐶𝐻4,𝑏𝑖𝑜𝑔𝑎𝑠 + 𝐶𝐷𝑊𝑁2𝑂

∗ 𝐸𝐹𝑁2𝑂,𝑏𝑖𝑜𝑔𝑎𝑠 )[𝑘𝑔/𝐺𝐽]

𝑉𝑑𝑖𝑒𝑠𝑒𝑙[𝑚3] = 𝑉𝑏𝑖𝑜𝑔𝑎𝑠[𝑚3] ∗𝐹𝐶𝐷𝑖𝑒𝑠𝑒𝑙,𝑏𝑢𝑠[𝑚3/𝑘𝑚]

𝐹𝐶𝐵𝑖𝑜𝑔𝑎𝑠,𝑏𝑢𝑠[𝑚3/𝑘𝑚]

𝐶𝐷𝐹−𝑆𝐿𝐹𝑜𝑠𝑠𝑖𝑙[𝑘𝑔] = −0,89 ∗ 𝑉𝑑𝑖𝑒𝑠𝑒𝑙[𝑚3] ∗ 𝑇𝑉𝑑𝑖𝑒𝑠𝑒𝑙[𝐺𝐽/𝑚3] ∗ 𝐸𝐹𝑑𝑖𝑒𝑠𝑒𝑙 [𝑘𝑔/𝐺𝐽]

𝐶𝐷𝐹−𝑆𝐿𝐵𝑖𝑜[𝑘𝑔] = −0,11 ∗ 𝑉𝑑𝑖𝑒𝑠𝑒𝑙[𝑚3] ∗ 𝑇𝑉𝐹𝐴𝑀𝐸[𝐺𝐽/𝑚3] ∗ 𝐸𝐹𝐹𝐴𝑀𝐸 [𝑘𝑔/𝐺𝐽]

𝑉𝑜𝑖𝑙[𝑚3] = 𝑉𝑏𝑖𝑜𝑔𝑎𝑠[𝑚3] ∗𝐸𝑏𝑖𝑜𝑔𝑎𝑠[𝑘𝑊ℎ/𝑚3]

𝐸𝑜𝑖𝑙[𝑘𝑊ℎ/𝑚3]

𝐼𝑛𝑐𝑜𝑚𝑒𝑆𝐿[𝑆𝐸𝐾] = 𝑉𝑜𝑖𝑙 [𝑚3] ∗ 𝑃𝑟𝑖𝑐𝑒𝑂𝑖𝑙[𝑆𝐸𝐾/𝑚3]

𝐶𝐷𝐹+𝑆𝐿𝐵𝑖𝑜[𝑘𝑔] = 𝑉𝑏𝑖𝑜𝑔𝑎𝑠[𝑚3] ∗ 𝑇𝑉𝑏𝑖𝑜𝑔𝑎𝑠[𝐺𝐽/𝑚3] ∗ 𝐸𝐹𝑏𝑖𝑜𝑔𝑎𝑠 [𝑘𝑔/𝐺𝐽]

𝐶𝐷𝐹−𝑆𝐿𝐹𝑜𝑠𝑠𝑖𝑙[𝑘𝑔] = −𝑉𝑜𝑖𝑙 [𝑚3] ∗ 𝑇𝑉𝑜𝑖𝑙 [𝐺𝐽/𝑚3] ∗ 𝐸𝐹𝑜𝑖𝑙 [𝑘𝑔/𝐺𝐽]

𝑉𝑚𝑒𝑡ℎ𝑎𝑛𝑒 [𝑁𝑚3/𝑦𝑒𝑎𝑟] = 4100000(3.03 ∗ 10−4 ∗ 𝑇𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛2 [0𝐶2] – 3.23 ∗ 10−2 ∗ 𝑇𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛[0𝐶] + 1.78)

𝑉𝑚𝑒𝑡ℎ𝑎𝑛𝑒 [𝑁𝑚3/𝑦𝑒𝑎𝑟] = 4100000(8.80 ∗ 10−4 ∗ 𝑇𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛2 [0𝐶2] – 8.21 ∗ 10−2 ∗ 𝑇𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛[0𝐶] + 2.83)

%𝐶𝐻4 = 2.33 ∗ 10−4 ∗ 𝑇𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛2 [0𝐶2] – 2.30 ∗ 10−2 ∗ 𝑇𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛[0𝐶] + 1.18

%𝐶𝐻4 = 2.23 ∗ 10−4 ∗ 𝑇𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛2 [0𝐶2] – 2.21 ∗ 10−2 ∗ 𝑇𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛[0𝐶] + 1.17

%𝑉𝑆 = 4.20 ∗ 10−4 ∗ 𝑇𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛2 [0𝐶2] – 3.99 ∗ 10−2 ∗ 𝑇𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛[0𝐶] + 1.21)

%𝑉𝑆 = 2.91 ∗ 10−4 ∗ 𝑇𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛2 [0𝐶2] – 2.72 ∗ 10−2 ∗ 𝑇𝑑𝑖𝑔𝑒𝑠𝑡𝑖𝑜𝑛[0𝐶] + 0.875)

𝑃𝑟𝑜𝑓𝑖𝑡 [𝑆𝐸𝐾] = ∑ 𝐼𝑛𝑐𝑜𝑚𝑒 [𝑆𝐸𝐾] − ∑ 𝐸𝑥𝑝 [𝑆𝐸𝐾]

𝐶𝐷𝐹𝑇𝑜𝑡𝑎𝑙 [𝑘𝑔] = ∑ 𝐶𝐷𝐹𝐹𝑜𝑠𝑠𝑖𝑙 [𝑘𝑔] + ∑ 𝐶𝐷𝐹𝐵𝑖𝑜 [𝑘𝑔]

𝐶𝐷𝐹𝑇𝑜𝑡𝑎𝑙𝐹𝑜𝑠𝑠𝑖𝑙[𝑘𝑔] = ∑ 𝐶𝐷𝐹𝐹𝑜𝑠𝑠𝑖𝑙 [𝑘𝑔]

60

APPENDIX 3 – CONSTANT TO THE MATHEMATICAL MODEL

Table 11 – Constants for the mathematical model.

Constant Abbreviation Value

Price on electricity [SEK/kWh] PriceElectricity 0.76

Mean emission of electricity [kgCO2/kWh] EmElectricity 0.08

Price of transport [SEK/tonne] PriceTransport 100

Average transport distance [km] dTransport 60

Mass of each transport [tonne] MSludge/Transport 36

Diesel consumption for truck [m3/km] FCDiesel.Truck 0.4*10-3

Price on methane gas [SEK/m3] PriceSL 7

Biogas consumption for bus [m3/km] FCBiogas.Buss 0.5

Diesel consumption for bus [m3/km] FCDiesel.Buss 0.45*10-3

Price oil [SEK/m3] PriceOil 10000

Energy content oil [kWh/m3] EOil 10000

Energy content 65% biogas [kWh/m3] EBiogas 6.5

Energy consumption for dewatering [kWh/tonneTS.in]

EDewatering 28

Nitrous oxide emission from silos [kgN2O/tonneTS.year]

EmN2O 3.33

Efficiency of the variable-frequency drive 𝜂 97%

Density of sludge [kg/m3] ρSludge 1

Temperature loss of sludge over heating system [⁰C] ΔTHeating system 22.7

Mass of sludge in silos [tonne] mSludge silo 170

Table 12 – Global warming potential values.

Greenhouse gas Chemical formula GWP

Carbon dioxide CO2 1

Methane CH4 25

Nitrous oxide N2O 298

61

Table 13 - Thermal values and emission factors.

Fuel Thermal value [GJ/m3]

Emitted substance Emission factor [kg/GJ]

Biogas

35.3*10-3

CO2 56.1

CH4 0.309

N2O 2.9

FAME

33

CO2 75.6

CH4 0

N2O 0.95buses 2.56 heavy trucks

Oil 38.16 CO2 76.2

Methane- and combustion gas 56.7 CO2 47.00

Diesel 35.78 CO2 72.01

Table 14 – Mean values for the sludge flow and temperature on incoming sludge.

Month Mean temp incoming water [⁰C]

Mean flow to R100 [l/s]

Mean flow to R200 [l/s]

Total mean flow [l/s]

January 10.63 4.82 2.84 7.66

February 9.6 4.7 2.67 7.37

March 10.36 3.88 2.53 6.41

April 11.3 3.09 2.75 5.84

May 13.28 6.57 2.75 9.32

June 15.07 5.93 2.47 8.4

July 16.11 4.73 1.86 6.59

August 17.28 5.5 1.99 7.49

September 17.23 6 2.31 8.31

October 15.69 5.81 2.49 8.3

November 14.67 5.05 2.35 7.4

December 12.77 5.22 2.47 7.69

62

Table 15 – Energy consumption for the gas upgrading system.

Month Energy consumption [kWh/m3]

January 0.38

February 0.39

March 0.38

April 0.41

May 0.45

June 0.49

July 0.54

August 0.5

September 0.48

October 0.44

November 0.43

December 0.42

Table 16 – Mean sludge flow to the dewatering.

Month Mean flow into the dewatering [l/s]

January 9.91

February 10.12

March 11.22

April 9.97

May 9.96

June 9.05

July 7.41

August 7.99

September 8.69

October 8.94

November 8.72

December 9.38

63

APPENDIX 4 – OUTPUT FROM MATHEMATICAL MODEL

Figure 32 – Monetary profit, income and expense results for temperature 37 ⁰C and OLR 3 kgVS/(m3,day).

Figure 33 – Environmental result for temperature 37 ⁰C and OLR 3 kgVS/(m3,day).

Figure 34 – Monetary profit, income and expense results for temperature 37 ⁰C and OLR 3.75 kgVS/(m3,day).

SL Oil Electricity Transport [sek] % from mean

January 356495,6297 1747884,534 0,279%

February 351089,29 1753290,873 -0,029%

March 354040,3781 1750339,785 0,139%

April 348488,5017 1755891,662 -0,178%

May 369972,3618 1734407,802 1,048%

June 360986,183 1743393,98 0,535%

July 362412,5737 1741967,59 0,617%

August 342809,7464 1761570,417 -0,502%

September 339026,8267 1765353,337 -0,718%

October 339610,2807 1764769,883 -0,684%

November 337841,4502 1766538,713 -0,785%

December 356492,5836 1747887,58 0,279%

SUMMA 27207339,1 45208,8 4219265,805 1999985,903 21033296,16

AVE. /month 2267278,26 3767,4 351605,4838 166665,4919 1752774,68

Income [sek] Expenses [sek]

EconomicsProfit

Silos Transport SL Oil Torch Electricy Transport SL Oil Electricy [kg] % from mean [kg] % form mean

January 20548,6306 34954,00978 2152,158 9784103 0,001% -750795,0574 0,003%

February 18002,2336 34441,01731 2120,572 9781012 0,033% -750826,6429 -0,001%

March 20983,1825 34724,51921 2138,028 9784294 -0,001% -750809,1874 0,001%

April 20750,6604 34215,28723 2106,674 9783521 0,007% -750840,5414 -0,003%

May 19760,1017 36326,9364 2236,69 9784772 -0,006% -750710,5247 0,015%

June 20135,5016 35500,1824 2185,786 9784270 -0,001% -750761,4289 0,008%

July 24143,9198 35685,11858 2197,173 9788475 -0,044% -750750,0422 0,009%

August 25939,6888 33758,92825 2078,575 9788226 -0,041% -750868,6399 -0,006%

September 21600,884 33378,96656 2055,181 9783484 0,007% -750892,0346 -0,010%

October 20548,6306 33387,83589 2055,727 9782441 0,018% -750891,4885 -0,010%

November 20114,791 33209,06403 2044,719 9781817 0,024% -750902,4957 -0,011%

December 20548,6306 34999,73821 2154,973 9784152 0,001% -750792,2418 0,004%

SUMMA 253076,855 3785,97 121755270,8 227847,3675 3765845,61 414581,6039 30599,14 -9052820 -13145,779 25526,26 1,17E+08 -9009840,325

AVE. /month 21089,7379 315,4975 10146272,56 18987,28063 313820,4675 34548,46699 2549,928 -754402 -1095,48159 2127,188 9784214 -750820,0271

CDF fossil [kg]CDF bio [kg]

EnvironmentalCDF total CDF fossil total [kg]

SL Oil Electricity Transport [sek] % from mean

January 356495,6354 1781219,914 0,274%

February 351089,2958 1786626,254 -0,029%

March 354040,3838 1783675,166 0,136%

April 348488,5079 1789227,042 -0,175%

May 369972,3685 1767743,181 1,028%

June 360986,1904 1776729,359 0,525%

July 362412,5818 1775302,968 0,605%

August 342809,7539 1794905,796 -0,492%

September 339026,8339 1798688,715 -0,704%

October 339610,2872 1798105,262 -0,672%

November 337841,4566 1799874,093 -0,771%

December 356492,5899 1781222,959 0,274%

SUMMA 27459296 45208,8 4219265,885 1851918,163 21433320,71

AVE. /month 2288274,66 3767,4 351605,4904 154326,5136 1786110,059

Income [sek] Expenses [sek]

EconomicsProfit

64

Figure 35 – Environmental result for temperature 37 ⁰C and OLR 3.75 kgVS/(m3,day).

Figure 36 – Monetary profit, income and expense results for temperature 45 ⁰C and OLR 3 kgVS/(m3,day).

Figure 37 – Environmental result for temperature 45 ⁰C and OLR 3 kgVS/(m3,day).

Silos Transport SL Oil Torch Electricity Transport SL Oil Electricity [kg] % from mean [kg] % form mean

January 19245,0545 35349,35669 2176,5 9869983 0,000% -757944,9786 0,004%

February 17043,0135 34813,27544 2143,493 9867212 0,028% -757977,9857 0,000%

March 19620,8407 35105,89911 2161,51 9870100 -0,002% -757959,9685 0,002%

April 19419,7632 34555,38678 2127,614 9869315 0,006% -757993,8642 -0,003%

May 18563,1604 36685,68117 2258,779 9870719 -0,008% -757862,6996 0,015%

June 18887,794 35794,63067 2203,916 9870098 -0,002% -757917,5625 0,008%

July 22354,1431 35936,06863 2212,624 9873715 -0,038% -757908,8541 0,009%

August 23907,0655 33992,29349 2092,944 9873204 -0,033% -758028,5345 -0,007%

September 20155,0088 33617,18711 2069,848 9869054 0,009% -758051,6302 -0,010%

October 19245,0545 33675,04111 2073,41 9868205 0,018% -758048,0681 -0,010%

November 18869,8841 33499,6476 2062,611 9867644 0,023% -758058,8672 -0,011%

December 19245,0545 35349,05471 2176,481 9869982 0,000% -757944,9972 0,004%

SUMMA 236555,837 3506,789 122882800,3 227847,3768 3765845,764 418373,5225 28342,73 -9136655 -13145,7796 25759,73 1,18E+08 -9095698,01

AVE. /month 19712,9864 292,2324 10240233,36 18987,2814 313820,4803 34864,46021 2361,894 -761388 -1095,48163 2146,644 9869936 -757974,8342

CDF fossil [kg]CDF bio [kg]

EnvironmentalCDF total CDF fossil total [kg]

SL Oil Electricity Transport [sek] % from mean

January 397103,6223 1482131,49 0,083%

February 385282,8381 1493952,274 -0,714%

March 394212,5304 1485022,582 -0,112%

April 387945,5462 1491289,566 -0,534%

May 413791,3846 1465443,728 1,208%

June 405841,9613 1473393,151 0,672%

July 410468,8652 1468766,247 0,984%

August 394745,3183 1484489,794 -0,076%

September 389169,2611 1490065,851 -0,452%

October 388890,4075 1490344,705 -0,471%

November 383105,272 1496129,84 -0,861%

December 399882,9016 1479352,211 0,271%

SUMMA 24354811,3 42523,5 4750439,909 1846513,419 17800381,44

AVE. /month 2029567,61 3543,625 395869,9924 153876,1182 1483365,12

Income [sek] Expenses [sek]

EconomicsProfit

Silos Transport SL Oil Torch Electricity Transport SL Oil Electricity [kg] % from mean [kg] % form mean

January 20548,6306 35349,35612 2176,5 9784523 0,000% -750770,7155 0,004%

February 18002,2336 34813,27486 2143,493 9781408 0,032% -750803,7226 0,000%

March 20983,1825 35105,89854 2161,51 9784699 -0,002% -750785,7054 0,002%

April 20750,6604 34555,38617 2127,614 9783882 0,007% -750819,6011 -0,003%

May 19760,1017 36685,6805 2258,779 9785153 -0,006% -750688,4365 0,015%

June 20135,5016 35794,62994 2203,916 9784583 0,000% -750743,2994 0,008%

July 24143,9198 35936,06783 2212,624 9788741 -0,043% -750734,591 0,009%

August 25939,6888 33992,29275 2092,944 9788474 -0,040% -750854,2714 -0,007%

September 21600,884 33617,18639 2069,848 9783737 0,008% -750877,3671 -0,010%

October 20548,6306 33675,04046 2073,41 9782746 0,018% -750873,805 -0,010%

November 20114,791 33499,64695 2062,611 9782126 0,025% -750884,6041 -0,011%

December 20548,6306 35349,05408 2176,481 9784523 0,000% -750770,7341 0,004%

SUMMA 253076,855 3785,97 121755270,8 227847,3675 3765845,61 418373,5146 30599,14 -9052820 -13145,779 25759,73 1,17E+08 -9009606,853

AVE. /month 21089,7379 315,4975 10146272,56 18987,28063 313820,4675 34864,45955 2549,928 -754402 -1095,48159 2146,644 9784549 -750800,5711

CDF fossil [kg]CDF bio [kg]

EnvironmentalCDF total CDF fossil total [kg]

65

Figure 38 – Monetary profit, income and expense results for temperature 45 ⁰C and OLR 3.75 kgVS/(m3,day).

Figure 39 – Environmental result for temperature 45 ⁰C and OLR 3.75 kgVS/(m3,day).

Figure 40 – Monetary profit, income and expense results for temperature 55 ⁰C and OLR 3 kgVS/(m3,day).

SL Oil Electricity Transport [sek] % from mean

January 394436,4902 1472624,494 0,114%

February 382545,5183 1484515,466 -0,693%

March 391545,3983 1475515,586 -0,082%

April 385067,851 1481993,133 -0,522%

May 410632,9387 1456428,046 1,212%

June 402402,7646 1464658,22 0,654%

July 406678,7301 1460382,254 0,944%

August 391235,934 1475825,05 -0,103%

September 385777,1375 1481283,847 -0,474%

October 385819,3226 1481241,662 -0,471%

November 380073,3338 1486987,651 -0,861%

December 396902,6325 1470158,352 0,281%

SUMMA 24121265,3 41659,09 4713118,052 1758192,605 17691613,76

AVE. /month 2010105,44 3471,591 392759,8376 146516,0504 1474301,147

Income [sek] Expenses [sek]

EconomicsProfit

Silos Transport SL Oil Torch Electricity Transport SL Oil Electricity [kg] % from mean [kg] % form mean

January 45329,8542 39111,49198 2408,139 8721606 0,015% -665190,0741 0,002%

February 38250,7414 37932,40823 2335,541 8713276 0,111% -665262,6716 -0,009%

March 46537,9304 38824,81739 2390,488 8722510 0,005% -665207,7249 -0,001%

April 45891,5073 38182,51744 2350,941 8721182 0,020% -665247,2721 -0,007%

May 43137,7037 40717,49771 2507,022 8721119 0,021% -665091,1905 0,016%

June 44181,3346 39901,41097 2456,775 8721296 0,019% -665141,4379 0,009%

July 55324,9403 40325,40672 2482,881 8732890 -0,114% -665115,332 0,013%

August 60317,2693 38794,13156 2388,598 8736257 -0,153% -665209,6143 -0,001%

September 48255,1719 38252,84879 2355,271 8723620 -0,008% -665242,9417 -0,006%

October 45329,8542 38257,03178 2355,528 8720699 0,026% -665242,6841 -0,006%

November 44123,758 37687,27163 2320,448 8718888 0,046% -665277,7649 -0,012%

December 45329,8542 39356,02946 2423,195 8721866 0,012% -665175,0176 0,004%

SUMMA 562009,919 3329,747 107944815,3 209957,2059 3470158,25 467342,8637 26911,83 -8025977 -12113,5963 28774,83 1,05E+08 -7982403,726

AVE. /month 46834,1599 277,4789 8995401,279 17496,43383 289179,8542 38945,23864 2242,653 -668831 -1009,46636 2397,902 8722934 -665200,3105

CDF fossil [kg]CDF bio [kg]

EnvironmentalCDF total CDF fossil total [kg]

SL Oil Electricity Transport [sek] % from mean

January 441491,7527 1397432,627 -0,117%

February 423941,7503 1414982,629 -1,374%

March 438276,2071 1400648,172 -0,347%

April 431395,7311 1407528,648 -0,840%

May 461117,4084 1377806,971 1,289%

June 453790,3567 1385134,023 0,764%

July 461429,1667 1377495,213 1,311%

August 447985,789 1390938,59 0,348%

September 440611,1966 1398313,183 -0,180%

October 439907,6361 1399016,743 -0,231%

November 430880,9277 1408043,452 -0,877%

December 446690,5908 1392233,789 0,255%

SUMMA 23947483,9 41636,16 5317518,513 1922027,514 16749574,04

AVE. /month 1995623,66 3469,68 443126,5428 160168,9595 1395797,837

Income [sek] Expenses [sek]

EconomicsProfit

66

Figure 41 – Environmental result for temperature 55 ⁰C and OLR 3 kgVS/(m3,day).

Figure 42 – Monetary profit, income and expense results for temperature 55 ⁰C and OLR 3.75 kgVS/(m3,day).

Figure 43 – Environmental result for temperature 55 ⁰C and OLR 3.75 kgVS/(m3,day).

Silos Transport SL Oil Torch Electricity Transport SL Oil Electricity [kg] % from mean [kg] % form mean

January 225927,286 43777,39274 2695,423 8847232 0,101% -659877,2653 -0,002%

February 184546,492 42037,17145 2588,276 8804004 0,589% -659984,4127 -0,018%

March 232989,068 43458,546 2675,792 8853955 0,025% -659896,897 -0,004%

April 229210,417 42776,2925 2633,784 8849452 0,076% -659938,9041 -0,011%

May 213113,12 45723,43145 2815,243 8836484 0,223% -659757,4455 0,017%

June 219213,641 44996,89642 2770,51 8841813 0,162% -659802,1791 0,010%

July 284353,336 45754,34474 2817,146 8907757 -0,582% -659755,5421 0,017%

August 313535,883 44421,3277 2735,071 8935524 -0,896% -659837,6175 0,004%

September 243027,165 43690,07865 2690,047 8864239 -0,091% -659882,6413 -0,002%

October 225927,286 43620,31507 2685,752 8847065 0,103% -659886,9367 -0,003%

November 218877,078 42725,24567 2630,641 8839065 0,193% -659942,0472 -0,011%

December 225927,286 44292,89858 2727,164 8847779 0,095% -659845,525 0,003%

SUMMA 2816648,06 3636,166 107167128 209841,653 3468248,4 527273,941 29388,38 -7968154 -12106,9294 32464,85 1,06E+08 -7918407,413

AVE. /month 234720,672 303,0138 8930593,997 17486,80442 289020,7 43939,49508 2449,032 -664013 -1008,91079 2705,404 8856197 -659867,2845

CDF fossil [kg]CDF bio [kg]

EnvironmentalCDF total CDF fossil total [kg]

SL Oil Electricity Transport [sek] % from mean

January 449589,8593 1543810,065 -0,193%

February 432252,965 1561146,96 -1,319%

March 446374,3137 1547025,611 -0,402%

April 440133,1619 1553266,763 -0,807%

May 470707,2715 1522692,653 1,177%

June 464232,652 1529167,273 0,757%

July 472937,0024 1520462,922 1,322%

August 458641,1924 1534758,732 0,394%

September 450910,566 1542489,359 -0,108%

October 449232,2482 1544167,676 -0,217%

November 440086,6804 1553313,244 -0,810%

December 455739,4627 1537660,462 0,206%

SUMMA 25702743,4 44260,75 5430837,375 1826205,09 18489961,72

AVE. /month 2141895,29 3688,396 452569,7813 152183,7575 1540830,143

Income [sek] Expenses [sek]

EconomicsProfit

Silos Transport SL Oil Torch Electricity Transport SL Oil Electricity [kg] % from mean [kg] % form mean

January 205667,187 44580,38395 2744,864 9452857 0,088% -708680,278 -0,003%

February 168185,5 42861,294 2639,018 9413550 0,503% -708786,1243 -0,018%

March 212063,572 44261,53721 2725,233 9458915 0,023% -708699,9097 -0,005%

April 208640,964 43642,67774 2687,129 9454835 0,067% -708738,0136 -0,011%

May 194060,436 46674,34208 2873,792 9443473 0,187% -708551,3506 0,016%

June 199586,135 46032,33244 2834,263 9448317 0,135% -708590,8799 0,010%

July 258588,039 46895,4375 2887,405 9508235 -0,498% -708537,7375 0,018%

August 285020,858 45477,89508 2800,125 9533163 -0,761% -708625,0172 0,005%

September 221155,828 44711,34244 2752,928 9468484 -0,078% -708672,2147 -0,001%

October 205667,187 44544,92398 2742,681 9452819 0,088% -708682,4613 -0,003%

November 199281,285 43638,06873 2686,845 9445471 0,166% -708738,2974 -0,011%

December 205667,187 45190,16567 2782,409 9453504 0,081% -708642,733 0,003%

SUMMA 2563584,18 3459,124 115022071 223069,3176 3686874,33 538510,4008 27957,49 -8552189 -12870,1068 33156,69 1,14E+08 -8503945,017

AVE. /month 213632,015 288,2603 9585172,584 18589,1098 307239,5275 44875,86673 2329,79 -712682 -1072,5089 2763,058 9461135 -708662,0848

CDF fossil [kg]CDF bio [kg]

EnvironmentalCDF total CDF fossil total [kg]