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INOM

EXAMENSARBETE BIOTEKNIK, AVANCERAD NIVÅ, 30 HP

STOCKHOLM SVERIGE 2016,

Temperature optimization of

anaerobic digestion at the Käppala Waste Water Treatment Plant

SOFIA BRAMSTEDT

KTH

SKOLAN FÖR BIOTEKNOLOGI

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www.kth.se

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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

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2

A BSTRACT

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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3

S AMMANFATTNING

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

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4

C ONTENT

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

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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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A BBREVIATIONS

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]

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1 I NTRODUCTION

1.1 B

ACKGROUND

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

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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

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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

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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

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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

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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).

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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.

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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.

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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.

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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 A

IM 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 M ETHOD

2.1 S

YSTEM 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

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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 L

ABORATORY 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).

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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

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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

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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 M

ODELLING 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).

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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).

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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

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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

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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.

References

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