Temperature optimization of anaerobic digestion at the Käppala Waste Water Treatment Plant

Transkript

1 EXAMENSARBETE INOM 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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3 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

4 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 kg VS/(m 3,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 2

5 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 kg VS/(m 3,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 3

6 CONTENT Abstract... 2 Sammanfattning... 3 Abbreviations Introduction Background Water treatment plant Anaerobic Digestion The Käppala Waste Water Treatment Plant Aim and goal Method System analysis Laboratory part Start up Reactor maintenance Modelling part Determination of total carbon footprint Energy survey Survey over the disposal of digested sludge Biogas usage survey Evaluation of lab scale parameters Concluding equations Results Laboratory part Analysis results Stability Modelling part Input Output Discussion Comparison with full scale process Foaming

7 4.1.2 Centrifuge - dewatering not effective Smell Process stability Seasonal variation Profit Environmental aspects Reliability of the results Conclusion References Appendix 1 Raw data Appendix 2 Equations to the mathematical model Appendix 3 Constant to the mathematical model Appendix 4 Output from mathematical model

8 ABBREVIATIONS SRT Solid retention time [day] HRT Hydraulic retention time [day] OLR Organic loading rate [kg VS/m 3,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] %CH 4 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 [m 3 ] TV Thermal value [GJ/ m 3 ] EF Emission factor [kg/gj] E Energy [J] Exp Expense [SEK] Q Flow [m 3 /day] ρ Density [kg/ m 3 ] c P Heating capacity [J/kg,K] T Temperature [K] P Power [W] η Efficiency R 2 Coefficient of determination V Volume [m 3 ] d Distance [km] FC Mean consumption of fuel [m 3 /km] 6

9 1 INTRODUCTION 1.1 BACKGROUND 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) 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) 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 7

10 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 8

11 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). CH 3 COOH CH 4 + CO 2 RE 1 9

12 CO 2 + H 2 CH 4 + H 2 O RE 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 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 10

13 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) 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 Psychrophilic Mesophilic Thermophilic Extremophilic Temperature range 4-25 ⁰C ⁰C ⁰C >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 11

14 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) The Käppala Waste Water Treatment Plant 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 population equivalents (p.e.), and after an expansion in 2001 the Käppala Waste Water Treatment Plant treats water from over 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). 12

15 Figure 3 Process chart over the Käppala Waste Water Treatment Plant. NH 4 NO 3 RE 3 NO 3 N 2 RE 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 m 3 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) 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. 13

16 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 Nm 3. Of the produced gas 0.1 million Nm 3 where used for heat production and 0.4 million Nm 3 was burned in the torch. The last 5.8 million Nm 3 biogas was upgraded to 3.9 million Nm 3 97% methane (Witkiewicz, 2012). A volume of methane gas corresponding to MWh was delivered to SL in 2013 (Käppala, 2013; Witkiewicz, 2012) 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. 14

17 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 T HP,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. 15

18 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 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 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 kg VS/(m 3,day) and 3.75 kg VS/(m 3,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). 16

19 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 17

20 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 ⁰C was compared to the full scale process temperature, 37 ⁰C. In the lab scale reactors, two different values for organic loading rate, 3 kg VS/(m 3,day) to 3.75 kg VS/(m 3,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). 18

21 Figure 7 Laboratory scale process set up 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 Reactor maintenance During the experiment, feeding (Section ) and analyses (Section ) 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 Feeding process To be able to mimic the full scale process at Käppala the organic loading rate (OLR) was 3 kg VS/m 3, day which gives 5.4 g VS/day. To avoid feeding the reactor on weekends the reactors were fed twice as much 19

22 on Fridays and the last portion was evenly distributed between the other days. Hence, each reactor was fed with 6.4 g VS/day of primary sludge once a day, Monday to Thursday and on Fridays with 10.8 g VS/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 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. %CH 4 = V CH 4 [ml] V Total [ml] = V CH 4 [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 (HCO 3- ) 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 V HCl [mg HCO3 /L] EQ 2 TA = 380 V HCl [mg Basic 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 g acetate/l. Acetate represents 20

23 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 (m Cup) before digested sludge from the reactors was put in them. The cups with the sludge were weighted (m Sludge) again and placed in an oven at 106 ⁰C for at least 20 hours. At last, the cups were weighted (m TS), burned again at 550 ⁰C in the ignition residue oven for two hours and then weighted (m FS) for the last time. The percentage of TS and FS were calculated as in equations 4 and 5. %TS = m TS[g] m Cup [g] m Sludge [g] m Cup [g] %FS = m FS[g] m Cup [g] m TS [g] m Cup [g] EQ 4 EQ 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 kg VS/(m 3,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 CO 2- 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). 21

24 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 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 CO 2 was measured in kilogram and then multiplied with the GWP value of that gas (EQ 6). The emission from consumption of a fuel (CF) [m 3 ] was calculated by using the thermal value (TV) [GJ/m 3 ] 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 [m 3 ] TV[GJ/m 3 ] 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) 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 ) and secondly the upgrading of the produced gas to 97% methane content (Section ). Lastly, the sludge dewatering after digestion for transport is outlined (Section ). The total energy consumption (E Total) is the sum of the energy consumptions (E Heating, E Upgrading, E Dewatering) (EQ 8). 22

25 E Total [kwh] = (E Heating + E Upgrading + E Dewatering ) [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 (Price Electricity) for determining of the total running cost (Exp Running) (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). Exp Running [sek] = E Total [kwh] Price Electricity [SEK/kWh] EQ 9 Käppala is using Nordic mix electricity that has an average greenhouse gas emission at 80 g CO 2 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). Bio CDF Electrcity [kg] = E Total [kwh] Em Electricity [kg/kwh] EQ 10 Fossil CDF Electricity [kg] = E Total [kwh] Em Electricity [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 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 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 (E Heating) is a function of the flow of sludge (Q Sludge ), the density of sludge (ρ Sludge), the specific heating capacity of the sludge (c P,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/m 3 ) 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 23

26 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). Q PS,R100 is the flow of primary sludge in to R100, ΔT D-PS is the temperature difference between the digestion temperature and the primary sludge, Q ES,R100 is the flow of secondary sludge in to R200 and ΔT D-ES is the temperature difference between the digestion temperature and the secondary sludge (Erikstam, 2013). E Heating [J/day] = Q Sludge [m 3 /day] ρ Slam [kg/m 3 ] c P [J/kg, K] T [K] EQ 12 E Heating [J/day] = ρ Slam [kg/m 3 ] c P [J/kg, K](Q PS,R100 T D PS + Q ES,R200 T D ES ) [K, m 3 /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 ). 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 ). 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 ). The rated values that have been used for the energy consumption calculations are stated in (Table 7, Appendix 1 Raw data) 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 (P Rated). 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, ) (Personal communication, C. Mikkelsen, 2015). E [kwh/month] = P Rated [kw] 24 [h] % of max Days Specific month [days/month] η EQ 14 The constant energy consumption for the circulation pumps is kwh/month and this value was added in the mathematical model as a constant 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 ). For the heating pump, the used current was logged, and a mean value of the current used for each week was determined (Acurve, Period to ). 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 to ). The effect for both the heating pump and the two circulation pumps were determined according to (EQ 15). P [kw] = P Rated [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). P Heating (Q PS,R100 T D PS + Q ES,R200 T D ES ) EQ 16 24

27 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 (R 2 ), 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] = ln ( T 5 [ 5 ] Q [l/s]) EQ 17 P [kw] = ln ( T 4 [ 4 ] Q [l/s]) EQ 18 T [ ] = T Digestion [ ] T Incoming 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 to ). 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 (EQ 20). E [kwh/month] = P [kw] 24 [h] Days Specific month [days/month] η EQ 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 (E Upgrading) is in turn dependent on the volume upgraded gas (V Delivered 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). V Delivered Gas [m 3 /year] = V Biogas [Nm 3 /year] %CH 4,Biogas %CH 4,Delivered Gas EQ 21 E Upgrading [kwh/year] = E Uppgrading [kwh/m 3 ] V Delivered Gas [m 3 /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

28 V Delivered Gas [Nm 3 /month] = V Delivered Gas [Nm 3 /year] 12 EQ 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/tonne TS,in. The electricity consumption for each month was determined with equation 24 (Personal communication, M. Medoc, 2015). E Dewatering [kwh/month] = E Dewatering [kwh/tonne] m TS,in [tonne/month] EQ 24 The mass of total solids for each month (m TS,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 to ). The value of %TS is given in section and the density in section 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 , 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 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 Em CH4 and T Silo is the mean temperature of the sludge in silos (Erikstam, 2013). Em CH4 [Nm 3 /tonne, h] = e T Silo[ C] EQ 25 The emission rate was multiplied with the mass of sludge (m Sludge,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 CO 2-equialents by using equation 26 (Erikstam, 2013). CDF Bio Silos = CDW CH4 Q CH4 [Nm 3 /tonne, h] %VS m Sludge,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 kg N2O/tonne TS,year, the carbon dioxide footprint is determined with equation 27 (Erikstam, 2013). CDF Bio Silos = CDW N2 O Em N2 O[kg/tonne, year] %TS m Sludge,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 26

29 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 to since the sludge was not transported though the last heat exchangers the months prior to that period (Acurve, Period to ). The temperature in the silo was determined by equation 28. T Silo = T Digestion T Heating 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 to ) 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 m DW Sludge (EQ 29-32). The price for transport (Price Transport) is 100 SEK/tonne, the average transport distance (d Transport) is 60 km, and each truck can transport 36 tonnes at one time (m Sludge/Truck). Diesel is used as the fuel and the mean consumption for a truck ( FC Diesel, Truck) is 0.4*10-3 m 3 /km (Personal communication, C. Bertholds, 2015) (Svenska Elvägar AB, 2009). Exp Transport [SEK] = m DW Sludge [tonne] Price Transport [SEK/tonne] EQ 29 V Diesel [m 3 ] = m DW Sludge [tonne] d [tonne] Transport [km] FC Diesel,Truck [m 3 km] m Sludge Truck EQ 30 Fossil CDF Transport [kg CO2 ] = V Diesel [m 3 ] 0,89 EF Diesel [GJ/m 3 ] TV Diesel [kg/gj] EQ 31 Bio CDF Transport [kg CO2 ] = V Diesel [m 3 ] 0,11 TV FAME [ GJ m 3] (EF FAME,CO2 [kg/gj] + EF FAME,N2 O[kg/GJ] ) EQ 32 Today, the amount of produced dewatered sludge at Käppala equals 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 (%water DW 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). %water DW Sludge = 87.5 %VS EQ 33 m DW Sludge [tonne] = 30000[tonne] %water DW Sludge %water DW Sludge,Reference EQ 34 27

30 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 to ). The mean value for the reference was 74.25% water after dewatering 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 Gas deliver to SL The upgraded gas (97% methane) is sold for 7 SEK/m 3 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 m 3 /km and the diesel consumption for a bus is 0.45*10-3 m 3 /km (Norrman et al., 2005; Stockholm läns landsting, 2014) (Personal communication, A. Thunberg, 2015). Income SL [SEK] = V 97%methane [m 3 ] Price SL [SEK/m 3 ] EQ 35 CDF Bio +SL [kg] = V Biogas [m 3 ] TV Biogas [GJ/m 3 ] (EF CO2,Biogas + CDW CH4 EF CH4,Biogas + CDW N2 O EF N2 O,Biogas )[kg/gj] V Diesel [m 3 ] = V Biogas [m 3 ] FC Diesel,Bus[m 3 /km] FC Biogas,Bus [m 3 /km] EQ 36 EQ 37 CDF Fossil SL [kg] = 0,89 V Diesel [m 3 ] TV Diesel [GJ/m 3 ] EF Diesel [kg/gj] EQ 38 CDF Bio SL [kg] = 0,11 V Diesel [m 3 ] TV FAME [GJ/m 3 ] EF FAME [kg/gj] EQ 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 SEK/m 3, the energy content is kwh/m 3. The energy content of biogas is 6.5 kwh/nm 3. 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). V Oil [m 3 ] = V Biogas [m 3 ] E Biogas[kWh/m 3 ] E Oil [kwh/m 3 ] EQ 40 Income SL [SEK] = V Oil [m 3 ] Price Oil [SEK/m 3 ] EQ 41 CDF Bio +SL [kg] = V Biogas [m 3 ] TV Biogas [GJ/m 3 ] EF Biogas [kg/gj] EQ 42 CDF Fossil SL [kg] = V Oil [m 3 ] TV Oil [GJ/m 3 ] EF Oil [kg/gj] EQ 43 28

31 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) 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 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 , until the experiment finished the OLR was increased from 3 kg VS/(m 3,day) to 3.75 kg VS/(m 3,day) but the stability was unchanged (Section 3.1.2) Methane production as function of temperature and OLR From the results on methane production in section 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 [kg VS/m 3 ] Temperature [⁰C] Mean methane production %methane production [NmL/day] % % % % % % 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 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, Nm 3 Biogas/year, and the percentage of methane in the gas, 65%, gives Nm 3 Methane/year. Equations 44 was used for OLR 3 kg VS/(m 3,day) and 45 for OLR 3.75 kg VS/(m 3,day). V Methane [Nm 3 /year] = ( T Digestion [ 0 C 2 ] T Digestion [ 0 C] ) EQ 44 V Methane [Nm 3 /year] = ( T Digestion [ 0 C 2 ] T Digestion [ 0 C] ) EQ 45 29

32 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 , one for each OLR period. Equations 46 is used for OLR 3 kg VS/(m 3,day) and 47 for OLR 3.75 kg VS/(m 3,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 [kg VS/m 3 ] Temperature [⁰C] Methane content % % % % % % %CH 4 = T Digestion [ 0 C 2 ] T Digestion [ 0 C] EQ 46 %CH 4 = T Digestion [ 0 C 2 ] T Digestion [ 0 C] EQ 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 ), 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 ). 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 kg VS/(m 3,day) and 49 for OLR 3.75 kg VS/(m 3,day). Table 4 The mean values for the %TS and %VS measurements of the lab scale investigations. OLR [kg VS/m 3 ] Temperature [⁰C] %TS %VS % 31% % 27% % 29% % 27% % 24% % 26% %VS = T Digestion [ 0 C 2 ] T Digestion [ 0 C] ) EQ 48 %VS = T Digestion [ 0 C 2 ] T Digestion [ 0 C] ) EQ 49 30

33 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 CDF Total [kg] = CDF Fossil [kg] + CDF Bio [kg] EQ 51 CDF Fossil Total [kg] = CDF Fossil [kg] EQ 52 31

34 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 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 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 , thereafter reactor 45a, 45b, 55a and 55b were fed sporadically until at what point the microorganism culture in the reactors had adapted to the new environment. At day , the processes were considered to be stable (Section 3.1.2). During the first time the organic loading rate was set to 3 kg VS/(m 3,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 kg VS/(m 3,day) 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) 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 ), methane content (Section ), ph (Section ), alkalinity (Section ), volatile organic acids concentration (Section ), percentage of total solids and percentage of volatile solids (Section ) 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. 32

35 Methane gas flow [NmL/h] Methane gas flow from the lab reactors for the period to Date 37a 37b 45a 45b 55a 55b 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 CO 2-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 CO 2-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. 33

36 Gas production [Nm 3 /kg VS ] Gas production [Nm 3 /m 3,day] Average gas production for each week 1,6 1,4 1,2 1 0,8 0,6 0,4 0,2 0 OLR increase Date 37a 37b 45a 45b 55a 55b R100 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). Specific gas production for each week 0,45 0,4 0,35 0,3 0,25 0,2 0,15 0,1 0,05 0 OLR increase Date 37a 37b 45a 45b 55a 55b 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) 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. 34

37 ph %Methan Methane content in produced biogas 75,00% 70,00% 65,00% 60,00% 55,00% 50,00% 45,00% 40,00% 35,00% 30,00% OLR increase Date 37a 37b 45a 45b 55a 55b R100 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) ph During the feeding process (Section ), 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. 7,9 7,7 7,5 7,3 7,1 6,9 ph in the digested sludge 6,7 6,5 OLR increase Date 37a 37b 45a 45b 55a 55b R100 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) Alkalinity The alkalinity was measured during the lab scale digestion once a week on the digested sludge until 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

38 VFA [mg HAc /L] BA/TA Ratio between bicarbonate alaklinity and total alkalinity in the digeated sludge 1,1000 1,0000 0,9000 0,8000 0,7000 0,6000 0,5000 0,4000 OLR increase Date 37a 37b 45a 45b 55a 55b R100 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) 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 Concentration volatile fatty acids in the digsted sludge 1500 OLR increase Date 37a 37b 45a 45b 55a 55b R100 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) 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. 36

39 %FS %TS 5,00% Percentage total solids in the digested sludge 4,00% 3,00% 2,00% 1,00% 0,00% OLR increase Date 37a 37b 45a 45b 55a 55b R100 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). Percentage fixed solids of the total solids 40,00% 35,00% 30,00% 25,00% 20,00% 15,00% OLR increase Date 37a 37b 45a 45b 55a 55b 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) 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 ). 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 37

40 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 was the last alkalinity measurement due to problems with the equipment all VFA values were less than 300 g acetate/l, except the value for reactor 45b. The measurement after that, , were all values less than 300 g acetate/l, and the ph in the reactors was more or less stable since the ratios between the bicarbonate alkalinity and the total alkalinity were in the range For this reason, the process was considered to be in stable operation The change in OLR 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 describes the input for the mathematical model. Section shows the output from the mathematical model for some selected input values 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 kg VS/(m 3,day) or 3.75 kg VS/(m 3,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] Organic loading rate [kg VS/(m 3,day)] Valid in the range ⁰C Valid for 3 or 3.75 kg VS/(m 3,day) 38

41 Usage of produced biogas Variable Value Comment Sold to SL Current value 92% - %CH 4, Delivered methane gas Current value 97% Burned in torch Current value 2% Used for heat production Current value 6% 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 kg VS/(m 3,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. CDF total is the total carbon dioxide footprint emitted from the system and CDF total fossile is the total carbon dioxide footprint emitted from the system due to usage of fossil fuels. Temperature [⁰C] OLR [kg VS/(m 3,day)] Profit a year [MSEK/year] %Profit Reference 102% 85% 84% 80% 88% CDF total a year 117* * * * * *10 6 [kg/year] %CDF total Reference 101% 90% 89% 91% 97% fossil CDF total a year -9.01* * * * * *10 6 [kg/year] fossil %CDF total Reference 101% 89% 89% 88% 94% 39

42 4 DISCUSSION 4.1 COMPARISON WITH FULL SCALE PROCESS In section the process parameters measured during the period and 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 kg VS/(m 3,day), with a mean at 3.7 kg VS/(m 3,day), even though Käppala endeavour to keep the value constant at 3 kg VS/(m 3,day). The comparison between the gas production (Figure 9, Section ) 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 ). 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 ) 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 R

43 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 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. 41

44 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) 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 ), 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 42

45 Relative standard deviation 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 ). 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 ) 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. Standard deviation of measurements on the lab scale reactors relative the standard deviation of measurements on R ,00% 450,00% 400,00% 350,00% 300,00% 250,00% 200,00% 150,00% 100,00% 50,00% 0,00% ph %Methane VFA %VS Specific methane production 37 ⁰C 45 ⁰C 55 ⁰C R100 Figure 19 The standard deviation of the lab scale measurements relative the standard deviation of measurements on R100. Figure 8 (Section ) 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. 43

46 Energy consumption [kwh/month] 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. Seasonal variation of energy consumption Sludge dewatering Gastreatment plan Heating system December November October September August July June May April March February January Month Figure 20 Energy consumption distribution during the year (Temperature: 37 ⁰C, OLR: 3 kg VS/(m 3,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 kg VS/(m 3,day) maximizes the profit regardless of month. 44

47 Profit [sek/month] Seasonal variation of profit 1,8 1,6 1,4 1,2 1 0,8 0,6 0,4 0,2 0 December November October July August September June May April March February January 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 kg VS/(m 3,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. 45