Optimization and modeling of biogas production with various substrates

University of Applied Sciences Hochschule Bremerhaven Technologie Transfer Zentrum ttz – Bremerhaven Umweltinstitut

Master Thesis Process Engineering and Energy Technology – PEET

Optimization and modeling of biogas production with various

substrates

Eng. Marcos Brito Alcayaga

Tutor: Dr. Eng. Wilfried Schütz Co-Tutor: Dr. Eng. Gerhard Schories

Bremerhaven 2006

Master Thesis PEET Hochschule Bremerhaven

Optimization and modeling of biogas production with various

substrates Developed in Technologie Transfer Zentrum ttz- Bremerhaven Germany *Cover Photo: MT-Energie (left) / Hochschule Bremerhaven (right). Second cover pictures: ttz Bremerhaven.

Marcos Brito Alcayaga 2006 2


Statement of Authenticity

Herewith I declare that the contents written in this work are original except those

that are cited from bibliography. In the Supporting Theory chapter, all topics and

definitions were taken from the bibliography declared at the end of the present

document.

Marcos A. Brito Alcayaga



Acknowledgements

This is the final step of a long

journey. It has been three years of

my life here in Bremerhaven,

Germany. I would like to dedicate

this work to all the wonderful

people I have met during my

studies, work and daily life and all

of those good friends of mine. They

are my biggest success here.

To share an important chapter of my

life and career with people from all

over the planet had made my mind

wider and my hopes bigger; Bigger

enough to think of a global, united

and unique world.

Thanks a lot to all of you!



To my grandfather Carlos Alcayaga Bacho,

who inspired me to come here.

I wish we could share all what I’ve learned here.

I’ll keep your ideas alive.



Executive Resume

This work was done as part of two current projects of the Technologie Transfer Zentrum –

Bremerhaven in its Umweltinstitut. These projects have been developed for the

improvement of the biogas generation.

The effects of different substrates for the biogas generation were analyzed using a fixed

bed semi-batch reactor. After having the reactor in steady state condition, several

variations in the feeding were tested to analyze the methanisation response. This is, to

analyze the amount and composition of the produced biogas under different conditions.

The biogas was measured by two systems, one directly in the liquid phase and the other

by analyzing the effluent gases. The system was permanently monitored for on-line

analysis and by sample testing.

After more than eight months of operation, all the data from the bioreactors and their

results is gathered together to study the implicitness of the conditions in which those

results were obtained. This can be understand by different analyzers in different ways,

due to the fact that there are several processes and reactions occurring simultaneously

and they are driven by unlike conditions of the system. In this way, conditions like the dry

matter content, nitrogen and carbon content, ammonia, chemical oxygen demand, volatile

fatty acids and pH value of the sludge in the reactor will affect in dissimilar ways the

different processes occurring from feeding to final methanisation.

Because of these parameters acting together in all the stages of the whole process, it will

depend on the scientist interpretation the conclusions he may have, within a certain range.

As it is still not perfectly known what is really happening each moment inside, specially

due to the fact of the simultaneous acting of different living creatures inside the system,

we can only base our conclusions in what we can see as result of their interactions.



INDEX

STATEMENT OF AUTHENTICITY .....................................................................3

ACKNOWLEDGEMENTS.................................................................................4

EXECUTIVE RESUME....................................................................................6

1. INTRODUCTION ......................................................................................14

1.1. Previous and related works ......................................................................................... 15

1.2. Continuation of the project.......................................................................................... 16

1.3. Objectives...................................................................................................................... 17

2. SUPPORTING THEORY............................................................................18

2.1. Biogas generation ........................................................................................................ 18

2.2. Operational Conditions ................................................................................................ 21

2.3. Common Terms ............................................................................................................ 23

2.4. Actual systems ............................................................................................................. 25 2.4.1. Balloon plants ...................................................................................................................26 2.4.2. Fixed-dome plants ............................................................................................................27 2.4.3. Floating-drum plants .........................................................................................................27 2.4.4. Biogas large scale plants..................................................................................................28

2.5. Substrates and Inhibitors ............................................................................................ 28 2.5.1. Nitrogen inhibition .............................................................................................................28 2.5.2. C/N ratio ...........................................................................................................................29 2.5.3. Substrate solids content ...................................................................................................30 2.5.4. Agitation............................................................................................................................30 2.5.5. Inhibitory factors ...............................................................................................................32 2.5.6. Gas yield...........................................................................................................................33

2.6. Mathematical models for the biogas reactor ............................................................. 35 2.6.1. Fermentation kinetics models [4] ......................................................................................35 2.6.2. Anaerobic digestion for methanogenesis models [5] ........................................................36

3. WORK STRUCTURE ................................................................................39

3.1. Work frame.................................................................................................................... 39

3.2. Work plan ...................................................................................................................... 39

4. MATERIALS AND METHODS .....................................................................41

4.1. Apparatus ...................................................................................................................... 41



4.1.1. Biogas reactor ..................................................................................................................41 4.1.2. Measuring Equipment.......................................................................................................41 4.1.3. Membranes.......................................................................................................................44

4.2. Methodology ................................................................................................................. 45 4.2.1. Determination of biogas composition................................................................................45 4.2.2. Measurement of biogas volume........................................................................................46 4.2.3. Measurement of temperature and pH value into the reactor liquid phase.........................46 4.2.4. Measurement of Chemical Oxygen demand (COD) .........................................................46 4.2.5. Estimation of Total Solid content (TS) ..............................................................................47 4.2.6. Estimation of Total Volatile Solid content (TVS) ...............................................................47 4.2.7. Lipids measurement .........................................................................................................47 4.2.8. Protein measurement .......................................................................................................47

5. EXPERIMENTS .......................................................................................48

5.1. First run, trying different substrates. Experiments B1 and B2. ............................... 48 5.1.1. Normal feeding .................................................................................................................48 5.1.2. PROBIO feeding...............................................................................................................48 5.1.3. Other feedings ..................................................................................................................49

5.2. Reloading of bioreactor. Experiments B3, K3 and K3.2............................................ 52

6. RESULTS ..............................................................................................55

6.1. On line measurements ................................................................................................. 55

6.2. Later measurements..................................................................................................... 59 6.2.1. Results of first experiments ..............................................................................................59 6.2.2. Substrate TS estimation ...................................................................................................60 6.2.3. Protein and lipids measurement on some substrates .......................................................61 6.2.4. Later measurements on sludge ........................................................................................61

7. MODELING THE PROCESS .......................................................................64

7.1. Model assumptions and description .......................................................................... 64 7.1.1. Assumptions .....................................................................................................................64 7.1.2. The gases.........................................................................................................................65 7.1.3. The mass-balance model .................................................................................................65 7.1.4. Modeling of the bacterial kinetics......................................................................................66

7.2. Model results................................................................................................................. 66

8. DISCUSSION ..........................................................................................69

8.1. Regarding Membrane measurement........................................................................... 69 8.1.1. First experiments B1 & B2 ................................................................................................69 8.1.2. Later experiments B3 & K3...............................................................................................70

8.2. Membrane design ......................................................................................................... 71



8.3. Using different substrates ........................................................................................... 71

8.4. Model results................................................................................................................. 72

9. CONCLUSIONS.......................................................................................74

10. ABBREVIATIONS ..........................................................................76

11. BIBLIOGRAPHY............................................................................77

12. ATTACHMENTS............................................................................79

12.1. Detail of reactors...................................................................................................... 79 12.1.1. Anaerobic digesters....................................................................................................80

12.2. Calculation sheets and graphic results. ................................................................ 82 12.2.1. Experiments B1 and B2..............................................................................................82 12.2.2. Experiment B3............................................................................................................91 12.2.3. Experiment K3............................................................................................................99

12.3. Experiments description. ...................................................................................... 103

12.4. Calculation of TS and TVS for reactor’s sludge and feed substrates............... 107

12.5. Data sheet used to write gas volume readings, concentrations, feedings and comments........................................................................................................................... 108

12.6. Later analysis results. ........................................................................................... 109



Table Index I. Annual manure yield and nutrient content of cow, pig and chicken

excrements; Compiled from various sources.

II. Nitrogen-content and C/N ratio data for a selection of substrates; Compiled

from various sources.

III. Limiting concentrations of various inhibitors of bio-methanisation.

IV. Effect of concentration of some cations.

V. Gas yields and methane contents for various substrates at the end of a 10-

20 day retention time at a 30°C.

VI. Description of feedings for experiments B1 & B2.

VII. Real plant v/s lab scale comparison.

VIII. Tendencies for on-line measurements during experiments.

IX. Analytic results from the reactor liquid phase obtained during AD process

under low TS conditions.

X. Correlation between variables off-line measured.

XI. Temperature and pH value. Experiment B1 and B2.

XII. Temperature and pH value. Experiment B3.

XIII. Correlations and covariance’s between measured parameters of

experiment B3.

XIV. Correlations and covariance’s between measured parameters of

experiment K3.

Figure Index

1. Three-storage biogas process.

2. Balloon plant.

3. Floating drum plant.

4. Large-scale plant.

5. Flame Ionization Detector (FID) principle.

6. Laboratory reactor “Pedrito”.

7. Infra red detector ANSYCO RA94.



8. Flame Ionization Detector FID.

9. FID measuring principle scheme.

10. Membranes after use.

11. Membrane with solutions.

12. Open reactor.

13. New propeller.

14. Membrane E modification.

15. New motor for reactor “Pedrito”.

16. Inside look of reactor “Pedrito”.

17. Results of first two experiments together measured on-line. Gas yield,

indicating methane and carbon dioxide content. Ratio CH4/CO2 with tendency

18. Analysis results after first two experiments.

19. Example of MS Excel sheet used to estimate Total TS for feedings.

20. Later analysis of VFA. Spectrograph output.

21. Later analysis of sludge including COD, TS, TVS, TNb, Ammonia and dry

feedings.

22. Variables and values utilized for model.

23. Graphical output signals of model.

Attachments

24. General scheme of the biogas pilot plant.

25. Detailed specification of the glass bioreactors used for anaerobic digestion

process.

26. Calculation of the on-line permeability. Concentration in ppm of CH4

permeated through the membrane is measured with FID, for the later

calculation of permeability. Exp. B1 + B2

27. Data sheet with volume of produced biogas, with concentrations and feedings

register. Dry matter amount and as percentage of feedings is calculated.

Experiments B1 + B2.

28. Calculation of ratio of biogas quality. Experiments B1 + B2.

29. Results of first experiment measured on-line. Permeability, ratio CH4/CO2 and

yield in L/h. Experiment B1.

30. Results of first experiment measured on-line. Gas yield, indicating methane

and carbon dioxide content. Ratio CH4/CO2 with tendency. Experiment B1.



31. Results of second experiment (after first membrane modification) measured

on-line. Gas yield, indicating methane and carbon dioxide content. Ratio

CH4/CO2 with tendency. Experiment B2.

32. Results of second experiment measured on-line. Gas yield, indicating methane

and carbon dioxide content. Ratio CH4/CO2 with tendency. Experiment B2.

33. Graphical behavior of pH v/s time, with permeation curve as reference. B1.

34. Calculation of the on-line permeability. Concentration in ppm of CH4

permeated through the membrane is measured with FID, for the later

calculation of permeability. Experiment B3.

35. Data sheet with volume of produced biogas, with concentrations and feedings

register. Dry matter amount and percentage of feedings is calculated.

Experiment K3.

36. Calculation of ratio of biogas quality. After measuring the biogas concentration

with an infrared analyzer, the concentration percentage of methane is divided

by the one of carbon dioxide to obtain the ratio. Experiment K3.

37. Permeation curve together with biogas quality ratio and produced volume is

displayed. Feedings are just showed as dash points (no units).

38. Biogas quality ratio along with feedings expressed in mass and TS%.

39. Biogas quality ratio together with volume produced in time.

40. Graphical behavior of pH v/s time, with permeation curve as reference. B3.

41. Data sheet with volume of produced biogas, concentrations and feedings

register. Dry matter amount and as percentage of feedings is calculated.

Experiment K3.

42. Concentration percentage of CH4 and CO2 and the biogas and calculation of

quality ratio.

43. Experiment K3. Gas quality ratio, liters per hour and feedings are shown

together. Feedings are expressed in grams of dry matter.

44. Experiment K3. Feedings are shown in mass and TS percentage. Along with

them, gas quality ratio and biogas yield are displayed.

45. Example sheet for calculation of TS and TVS. Samples of substrates and

reactor’s sludge were weighted and then dried at 105°C to measure dry matter;

afterwards they were heated to 550°C to estimate TVS.



46. Example sheet used to write down measurements of produced volume of

biogas, temperature pH value, concentrations, and permeation. As well as

maintenance, feedings and observations.

47. Off-line results from Experiment B3. Including COD, TVS, Dry feedings, TS,

TNb and Ammonia.

48. Off-line results from Experiment B3. Detail of COD, TNb and Ammonia,

contrasted by dry feedings.

49. Complete later analysis results from experiment B3, corresponding to reactor

“Pedrito”.

50. Complete later analysis results from experiment K3, corresponding to reactor

“Kitty”.

51. VFA later analysis, including acetic, propionic, osobutyric, butyric, valeric,

isocaproic, caproic and heptanoic acids. Kitty.

52. VFA later analysis, including acetic, propionic, osobutyric, butyric, valeric,

isocaproic, caproic and heptanoic acids. Pedrito.

53. VFA later analysis. Complete results. Every measurement was done in

duplicate, these are the average values for each measurement.



Introduction For the anaerobic biogas generation it is necessary to use as raw material organic

elements, organic waste most of the time, due to the carbon content. Carbon occurs in all

organic life. This nonmetal also has the interesting chemical property of being able to

bond with itself and a wide variety of other elements, forming nearly 10 million known

compounds. When united with oxygen it forms carbon dioxide (CO2), which is vital to plant

growth. When united with hydrogen, it forms various compounds called hydrocarbons,

which are essential to industry in the form of fossil fuels (e.g. methane – CH4). When

combined with both oxygen and hydrogen it can form many groups of compounds

including fatty acids, which are essential to life.

Anaerobic digestion is a process that, in the absence of oxygen, decomposes organic

matter. The main product is the so-called biogas – a mixture of approximately 65%

methane and 35% carbon dioxide – along with a reduced amount of bacterial biomass.

The development of anaerobic digestion technology took place at the beginning of the 19th

century; although after the Second World War biological aerobic treatments and tertiary

treatments were the main features of the incipient waste-treatment processes. However,

owing to the energy crises of the 1970’s, anaerobic digestion technology underwent

significant growth. The production of Biogas has been shown an increasing growing due

to the favorable and economical conditions in Germany as well as in many other

countries.

The use of organic waste to produce energy and compost can be an interesting way to

improve the total yield at middle scale in farms or at large scale in industry and farmers

associations. To make profit out of waste is an intelligent way to reduce costs. By using

such technologies the total waste is reduced and transformed into an energy source. This

energy can be used directly for on-site purposes as well as selling it to the electrical net,

obtaining subvention rewards.

The biological processes and biogas production knowledge is complex and, at middle

scale, there is some knowledge to manage the biogas production, but it takes time to get

the necessary experience to obtain the optimal yield from the process. Therefore, to

maximize the production of biogas, automation systems can be an attractive and also an

effective way to handle processes easier.

The present work is part of the Project BioRealSim, which is a collective work of three

partner companies with the aim to develop a real-time simulation system for biogas



production. The objective of this product is to reduce complexity and to integrate control to

the production of biogas. The three partners involved are:

ISiTEC GmbH, Bremerhaven

Hochschule Bremen, Institut für Technischen Umweltschutz, Bremen

TTZ-Bremerhaven, Umweltinstitut, Bremerhaven

As a part of this main project, the goal of this project is to research and try different

feeding conditions and substrates for a small-scale bioreactor to find optimal/worse

conditions for the reactor yield. The results of this work will be used for the design of the

later real-time simulator system. By using the results of this work, the later user of the

software will be able to know with anticipation what are the reactor responses after

different kind of feeding and the corresponding (or expected) yield as result.

For this purpose, different substrates are to be feed to the bioreactor. All of them are to be

taken from farms. Mainly, trials will be made with pig, caw, horse and chicken manure, as

well as straw. Manures are to be tried, without any treatment, in different amounts and

diets. Straw will be tried pure and milled, and also treated with an alkaline/acid treatment

with enzymes.

Another goal of the present work is o analyze the effect and influence of inhibitors to the

system. These inhibitors are sometimes contained in the same substrates, due to

chemical cleaning and disinfections processes in modern farms. For example it is

common to find cupper concentration in chicken manure, after is removed from chicken

feeders. Cooper, as heavy metal, is one of the most common inhibitors present in biogas

production processes. As well as heavy metals, other inhibitors like detergents and

antibiotics are also commonly present in farm waste. Finally, it is also necessary to

consider the inhibition produced by the nitrogen present in almost all substrates. This last

one will be related to the carbon concentration, expressed as the C/N ratio.

1.1. Previous and related works Project IMBIO: The main former project related with the present work is the IMBIO

Project. This project had as main goal to find an On-Site methane detection system for

biogas processes directly from the liquid phase of the methanogenesis. By using

membrane technologies, the project looked for proper materials and design to extract the



produced gas directly from the liquid phase in order to have quick information about the

reactor response.

The relation of this project with the present work is because of the use of the same reactor

and apparatus already set for it. Therefore, the system utilized previously for the

membrane testing will be also use for the substrate and inhibitor testing.

The results from this project are to be used in the current one. Due to the fact that it is

also necessary to measure the reactor’s response, it is optimal to use the technology

developed previously in the antecedent project.

Project PROBIO: This a parallel project to the BioRealSim one. The objective of this one

is to test treatments for substrates before they are used in biogas production. The first

experiment will be to treat a solution with 5% straw with an acid/alkaline treatment with

enzymes. The resulting product will be then also tested in the biogas reactor to study

results.

For this project, two new reactors are to be assembled. The experiments done with them

will be done and monitored for both groups, PROBIO and BioRealSim.

1.2. Continuation of the project

The two projects involved in this work are to be continued. Project Biorealsim will keep the

track of the programmed schedule, for the developing of the real time simulation software;

Project Probio, will be scale up for the experimentation of membranes in bigger scale

reactors.

Further analysis could be done with the laboratory scale reactors used for this work. For

example, it could be interesting to do more experiences with inhibitors and inhibitory

conditions.



1.3. Objectives

General: To study and understand the implications for the use of different substrate

feeding in the yield of biogas production.

Specific: To study the state of the art in biogas generation.

To understand the chemical and biological processes of biogas generation.

To do trials with different substrate and analyze results.

To measure biogas yield by different methods.

To develop an example model for the bioreactor reactions.

To develop scale bioreactor conditions to simulate real plants.



2. Supporting Theory 2.1. Biogas generation Biogas typically refers to methane produced by the fermentation of organic matter

including manure, wastewater sludge, or municipal solid waste, under anaerobic

conditions. The process is popular for treating many types of organic waste because it

provides a convenient way of turning waste into electricity, decreasing the amount of

waste to be disposed of, and of destroying disease causing pathogens which can exist in

the waste stream. The use of biogas is encouraged because methane burns with a clean

flame and produces little pollution.

Manure digestion occurs in a digester, which must be strong enough to withstand the

buildup of pressure and must provide anaerobic conditions for the bacteria inside.

Digesters are usually built near the manure source, and several are often used together to

provide a continuous gas supply. Products put into the digester are composed mainly of

carbohydrates with some lipids and proteins.

The digestion has three main stages. The first, hydrolysis, involves breaking down the

large macromolecules to sugars, amino acids, and fatty acids by bacteria under aerobic

conditions. The second stage is acetogenesis, during which acetogenic bacteria convert

sugars into short-chain acids, mainly acetic acid. The third stage is methanogenesis,

which is carried out

Figure 1. Three stages of the biogas transformation process.



by anaerobic bacteria. Here, the acids are converted into methane. Other sources of

biogas are landfills, anaerobic lagoons and sewage treatment plants.

Biogas from municipal sewage treatment plants typically contains 55% to 65% methane,

with the remainder being primarily carbon dioxide. In addition it can contain up to 0.5%

Hydrogen Sulfide, and is generally saturated with water, giving it a corrosive nature. Since

the anaerobic digestion process requires a constant temperature of up to 35-40 degrees C

(Mesophilic process) or 55-60 degrees C (Thermophilic process), part of the biogas

generated is usually used to heat the digester. The remainder can be used to generate

steam or electricity via engine generators, gas turbines, or micro-turbines. Excess gas,

due to fluctuations in the production rate, is generally flared.

Biogas from landfills tends to have lower methane percentages due to the less

homogeneous nature of the waste, as well as the lack of ability to optimize the digestion

process. Waste from food processing plants and breweries tends to have a higher

methane content.

One major benefit producing and burning biogas is that emissions are captured and

treated from the inevitable decomposition of the organic matter. Ammonia and methane

are released to the atmosphere during decomposition in landfills or during composting.

The digester accelerates decomposition and enhances methane production and capture.

Biogas can have a higher heating value in the range of 6 to 24 MJ/m³ (150 to 650 Btu/scf),

which is about half that of natural gas. The medium-heat gas can be fired in a number of

prime movers for power generation (mechanical, thermal, or electrical). The energy

produced is considered renewable energy.

Process Microbiology Consortia of microorganisms, mostly bacteria, are involved in the transformation of

complex high-molecular-weight organic compounds to methane. Furthermore, there are

synergistic interactions between the various groups of bacteria implicated in anaerobic

digestion of wastes. Although some fungi and protozoa can be found in anaerobic

digesters, bacteria are undoubtedly the dominant microorganisms. Large numbers of strict

and facultative anaerobic bacteria are involved in the hydrolysis and fermentation of

organic compounds. There are four categories of bacteria that are involved in the

transformation of complex materials into simple molecules, such as methane and carbon



dioxide. These bacterial groups operate in a synergistic relationship in as much as group

#1 has to perform its metabolic action before group #2 can take over, etc.

Group 1: Hydrolytic Bacteria Consortia of anaerobic bacteria break down complex organic molecules (proteins,

cellulose, lignin, and lipids) into soluble monomer molecules such as amino acids,

glucose, fatty acids, and glycerol. The monomers are directly available to the next group

of bacteria. Extra-cellular enzymes such as cellulases, proteases, and lipases catalyze

hydrolysis of the complex molecules. However, the hydrolytic phase is relatively slow and

can be limiting in anaerobic digestion of waste such as raw cellulolytic wastes, which

contain lignin

Group 2: Fermentative Acidogenic Bacteria Acidogenic (i.e., acid-forming) bacteria convert sugars, amino acids, and fatty acids to

organic acids (e.g., acetic, propionic, formic, lactic, butyric, or succinic acids), alcohols

and ketones (e.g., ethanol, methanol, glycerol, acetone), acetate, CO2, and H2. Acetate is

the main product of carbohydrate fermentation. The products formed vary with the type of

bacteria as well as with culture conditions (temperature, pH, redox potential).

Group 3: Acetogenic Bacteria Acetogenic bacteria convert fatty acids (e.g., propionic acid, butyric acid) and alcohols into

acetate, hydrogen, and carbon dioxide, which are used by the methanogens. This group

requires low hydrogen tensions for fatty acid conversion; and therefore a close monitoring

of hydrogen concentrations is necessary. Under relatively high H2 partial pressure, acetate

formation is reduced and the substrate is converted to propionic acid, butyric acid and

ethanol rather than methane.

Group 4: Methanogens Anaerobic digestion of organic matter in the environment releases 500-800 million tons

[453.6 - 725.75 metric tons] of methane per year into the atmosphere and this represents

0.5% of the organic matter derived from photosynthesis. The fastidious methanogenic

bacteria occur naturally in deep sediments or in the rumen of herbivores. This group is

composed of both gram-positive and gram-negative bacteria with a wide variety of

shapes. Methanogenic micro-organisms grow slowly in wastewater as well as other

wastes, and their generation times range from 2 days at 35°C [95°F] too as high as 50

days at 10°C [50°F]. About two thirds of methane is derived from acetate conversion by

methanogens. The other third is the result of carbon dioxide reduction by hydrogen.



2.2. Operational Conditions For the optimal operation of biogas generation systems, there are environmental and

operational conditions to consider, the most critical ones are:

Raw Materials Raw materials may be obtained from a variety of sources - livestock and

poultry wastes, night soil, crop residues, food-processing and paper wastes, and materials

such as aquatic weeds, water hyacinth, filamentous algae, and seaweed. Different

problems are encountered with each of these wastes with regard to collection,

transportation, processing, storage, residue utilization, and ultimate use. Residues from

the agricultural sector such as spent straw, hay, cane trash, corn and plant stubble need

to be shredded in order to facilitate their flow into the digester reactor as well as to

increase the efficiency of bacterial action. Succulent plant material yields more gas than

dried matter does, and hence materials like brush and weeds need semi-drying. The

storage of raw materials in a damp, confined space for over ten days initiates anaerobic

bacterial action that, though causing some gas loss, reduces the time for the digester to

become operational.

Influent Solids Content Production of biogas is inefficient if fermentation materials are

too dilute or too concentrated, resulting in, low biogas production and insufficient

fermentation activity, respectively. Experience has shown that the raw-material (domestic

and poultry wastes and manure) ratio to water should be 1:1, i.e., 100 kg of excrete to 100

kg of water. In the slurry, this corresponds to a total solids concentration of 8 - 11 per cent

by weight.

Loading The size of the digester depends upon the loading, which is determined by the

influent solids content, retention time, and the digester temperature. Optimum loading

rates vary with different digesters and their sites of location. Higher loading rates have

been used when the ambient temperature is high. In general, the literature is filled with a

variety of conflicting loading rates. In practice, the loading rate should be an expression of

either (a) the weight of total volatile solids (TVS) added per day per unit volume of the

digester, or (b) the weight of TVS added per day per unit weight of TVS in the digester.

The latter principle is normally used for smooth operation of the digester.

Seeding Common practice involves seeding with an adequate population of both the acid-

forming and methanogenic bacteria. Actively digesting sludge from a sewage plant

constitutes ideal "seed" material. As a general guideline, the seed material should be



twice the volume of the fresh manure slurry during the start-up phase, with a gradual

decrease in amount added over a three-week period. If the digester accumulates volatile

acids as a result of overloading, the situation can be remedied by reseeding, or by the

addition of lime or other alkali.

PH Low pH inhibits the growth of the methanogenic bacteria and gas generation and is

often the result of overloading. A successful pH range for anaerobic digestion is 6.0 - 8.0;

efficient digestion occurs at a pH near neutrality. A slightly alkaline state is an indication

that pH fluctuations are not too drastic. Low pH may be remedied by dilution or by the

addition of lime.

Temperature With a mesophilic flora, digestion proceeds best at 30 - 40 C; with

thermophiles, the optimum range is 50 - 60 C. The choice of the temperature to be used is

influenced by climatic considerations In general, there is no rule of thumb, but for optimum

process stability, the temperature should be carefully regulated within a narrow range of

the operating temperature. In warm climates, with no freezing temperatures, digesters

may be operated without added heat. As a safety measure, it is common practice either to

bury the digesters in the ground on account of the advantageous insulating properties of

the soil, or to use a greenhouse covering. Heating requirements and, consequently, costs,

can be minimized through the use of natural materials such as leaves, sawdust, straw,

etc., which are composted in batches in a separate compartment around the digester.

Nutrients The maintenance of optimum microbiological activity in the digester is crucial to

gas generation and consequently is related to nutrient availability. Two of the most

important nutrients are carbon and nitrogen and a critical factor for raw material choice is

the overall C/N ratio.

Domestic sewage and animal and poultry wastes are examples of N-rich materials that

provide nutrients for the growth and multiplication of the anaerobic organisms. On the

other hand, N-poor materials like green grass, corn stubble, etc., are rich in carbohydrate

substances that are essential for gas production. Excess availability of nitrogen leads to

the formation of NH3, the concentration of which inhibits further growth. Ammonia toxicity

can be remedied by low loading or by dilution. In practice, it is important to maintain, by

weight, a C/N ratio close to 30:1 for achieving an optimum rate of digestion. Combining

materials low in carbon with those that are high in nitrogen, and vice versa can judiciously

manipulate the C/N ratio.



Toxic Materials Wastes and biodegradable residue are often accompanied by a variety of

pollutants that could inhibit anaerobic digestion. Potential toxicity due to ammonia can be

corrected by remedying the C/N ratio of manure through the addition of shredded bagasse

or straw, or by dilution. Common toxic substances are the soluble salts of copper, zinc,

nickel, mercury, and chromium. On the other hand, salts of sodium, potassium, calcium,

and magnesium may be stimulatory or toxic in action, both manifestations being

associated with the cation rather than the anionic portion of the salt. Pesticides and

synthetic detergents may also be troublesome to the process.

Stirring When solid materials not well shredded are present in the digester, gas

generation may be impeded by the formation of a scum that is comprised of these low-

density solids that are enmeshed in a filamentous matrix. In time the scum hardens,

disrupting the digestion process and causing stratification. Agitation can be done either

mechanically with a plunger or by means of rotational spraying of fresh influent. Agitation,

normally required for bath digesters, ensures exposure of new surfaces to bacterial action,

prevents viscid stratification and slow-down of bacterial activity, and promotes uniform

dispersion of the influent materials throughout the fermentation liquor, thereby

accelerating digestion.

Retention Time Other factors such as temperature, dilution, loading rate, etc., influence

retention time. At high temperature bio-digestion occurs faster, reducing the time

requirement. A normal period for the digestion of dung would be two to four weeks.

2.3. Common Terms There are several terms, which are used to describe any bioconversion process, and it's

parameters:

Total Solids (TS) All organic matter contains some water. The human body is

approximately 70% water. Total Solids (TS) is a measure of the actual solid content of a

substance. Only portions of the solid material are actually bio-converted. Weighing a

sample, oven-drying it to remove all moisture, and then re-weighing the dried sample

determine TS. TS% is determined by dividing the "dry" weight by the "wet" weight. The

same human body is therefore 30% TS.

Volatile Solids (VS) Volatile Solids (VS) is a measure of the solids (portion of TS) which

are actually available for bioconversion. VS are determined by "burning" the dried TS



sample, which removes the "volatile" component. What remains is non-volatile (see NVS

below). The sample is weighed again to determine this "ash" weight, which is subtracted

from TS to determine VS. VS% is found by dividing VS by TS.

Non-Volatile Solids (NVS) Non-Volatile Solids (NVS) is what remains in a sample after

removing the VS in a furnace. NVS (mostly minerals in ash form) are not bio convertible.

NVS% is determined by dividing NVS by TS.

Hydraulic, Solids, Micro-organism Retention Time(s) (HRT, SRT, MRT) Retention

Time(s) refers to how long a given material is kept (retained) in the system. The units are

days. Hydraulic Retention Time (HRT) measures the length of time that liquid remains in

the system. HRT is determined by dividing system volume by feedstock volume. Solids

Retention Time (SRT) is the length of time that feedstock solids remain in the system. An

up flow Solids Reactor (USR) retains the solids longer than the liquid (SRT>HRT).

Microorganism Retention Time (MRT) is the length of time that the anaerobic bacteria

(microorganisms) remain in the system. Longer MRT's, which can be achieved by using a

growth matrix, promote increased system stability while simultaneously reducing nutrient

requirements (see below).

Organic Loading Rate (kg VS/m3-day) Organic Loading Rate is a measure of the

organic material (VS), per Bio converter volume, added to the system on a daily basis.

The units are kg VS/m3-day. The value is determined during engineering. For a given

system size, higher organic loading rates generally result in lower bioconversion

efficiency. Any value greater than 3.3 kg VS/m3-day is considered high-rate

bioconversion.

Methane Yield (m3 CH4 / kg VS added) Methane Yield is a measure of the quantity of

methane produced from the VS, which are added to the system. The units are m3 CH4 /

kg VS added. The value is dependent upon the type and digestibility of the feedstock and

the retention time in the system. It is also affected by the condition of the fermentation

(raw gas quality). 1 kg VS 100% bio converted into 100% methane would yield 1.4 m3.

More typically, 1 kg VS is 70% bio converted into 65% methane, yielding 0.4 m3.

Methane Production Rate (m3/m3-day) Methane Production Rate is a measure of the

quantity of methane, per Bio Converter volume, generated by the system on a daily basis.

The units are m3/m3-day. A value of 1 m3/m3-day is reasonable. Methane production



rates are proportional to the sulphur required for bioconversion, because more H2S is

carried away during vigorous gassing.

Volatile Acids Concentration Volatile acids are measured to determine the equivalent

buffering capacity, which may be needed for bioconversion to proceed. The relative

concentration of volatile acids affects the overall pH. If the volatile acids concentration

exceeds the ability of the bicarbonate alkalinity to maintain the pH above 6.5, then the

fermentation turns acid and methane formation ceases.

Bicarbonate Alkalinity (CaCO3, mg/l) Bicarbonate Alkalinity is a parameter that provides

an estimate of the buffering capacity of fermentation. The units are mg/litter, expressed as

CaCO3. Bicarbonate alkalinity is usually derived from the solubility of carbon dioxide,

which results from the bioconversion of organic wastes. During bioconversion, acids are

formed as intermediary compounds. To the degree sufficient bicarbonate alkalinity is

present, high loading rates of solids to the Bio Converter can occur without the need to

make pH adjustments.

Chemical Oxygen Demand (COD, mg/l) Chemical Oxygen Demand (COD) is a

parameter, which provides an estimate of the quantity of organic material in a sample.

The units are mg/l. The value returned is dependent upon the sample being tested.

Samples of feedstock may measure 100,000+ mg/l, while filtrate samples are generally

around 2000 mg/l. The test itself is an EPA-approved method, which provides faster more

repeatable results than the more common Biological Oxygen Demand (BOD) test.

2.4. Actual systems

The history of biogas utilization shows independent developments in various developing

and industrialized countries. The European biogas-history and that of Germany in

particular, as well as developments in Asian countries form the background of German

efforts and programs to promote biogas technology worldwide.

Normally, the biogas produced by a digester can be used as it is, just in the same way as

any other combustible gas. But it is possible that a further treatment or conditioning is

necessary, for example, to reduce the hydrogen-sulfide content in the gas. When biogas

is mixed with air at a ratio of 1:20, a highly explosive gas forms. Leaking gas pipes in



enclosed spaces constitute, therefore, a hazard. However, there have been no reports of

dangerous explosions caused by biogas so far.

A first overview of the physical appearance of different types of biogas plants describes

the three main types of simple biogas plants, namely balloon plants, fixed-dome plants

and floating-drum plants.

Today, the highest degree of market maturity can be found in the area of municipal sludge

treatment, industrial wastewater purification and treatment of agricultural wastes. The use

of the technology in municipal wastewater treatment is currently experiencing an upswing

in Asia (India in particular) and Latin America. Anaerobic treatment of municipal organic

waste is experiencing a boom in Northern Europe. Agricultural biogas plants in developing

countries are usually promoted on a large scale in connection with energy and

environmental issues, and are installed particularly where water pollution through liquid

manure from agriculture is most severe.

2.4.1. Balloon plants The balloon plant consists of a digester bag (e.g. PVC) in the upper part of which the gas

is stored. The inlet and outlet are attached directly to the plastic skin of the balloon. The

gas pressure is achieved through the elasticity of the balloon and by added weights

placed on the balloon.

Advantages are low cost, ease of

transportation, low construction sophistication,

high digester temperatures, uncomplicated

cleaning, emptying and maintenance.

Disadvantages can be the relatively short life

span, high susceptibility to damage, little

creation of local employment and, therefore,

limited self-help potential. A variation of the

balloon plant is the channel-type digester, which is usually covered with plastic sheeting

and a sunshade. Balloon plants can be

recommended wherever the balloon skin is not

likely to be damaged and where the

temperature is even and high.

Figure 2. Example of Balloon plant.



2.4.2. Fixed-dome plants The fixed-dome plant consists of a digester with a fixed, non-movable gas holder, which

sits on top of the digester. When gas production starts, the slurry is displaced into the

compensation tank. Gas pressure increases with the volume of gas stored and the height

difference between the slurry level in the digester and the slurry level in the compensation

tank.

Advantages are the relatively low construction costs, the absence of moving parts and

rusting steel parts. If well constructed, fixed dome plants have a long life span. The

underground construction saves space and protects the digester from temperature

changes. The construction provides opportunities for skilled local employment.

Disadvantages are mainly the frequent problems with the gas-tightness of the brickwork

gas holder (a small crack in the upper brickwork can cause heavy losses of biogas).

Fixed-dome plants are, therefore, recommended only where experienced biogas

technicians can supervise construction. The gas pressure fluctuates substantially

depending on the volume of the stored gas. Even though the underground construction

buffers temperature extremes, digester temperatures are generally low.

2.4.3. Floating-drum plants

Figure 3. Water-Jacket plant with external guide frame. 1 Mixing pit, 11 Fill pipe, 2 Digester, 3 Gasholder, 31 guide frame, 4 Slurry store, 5 Gas pipe. Source: Sasse, 1984.

Floating-drum plants consist of an

underground digester and a moving

gas-holder. The gasholder floats either

directly on the fermentation slurry or in

a water jacket of its own.

The gas is collected in the gas drum,

which rises or moves down, according

to the amount of gas stored. The gas

drum is prevented from tilting by a

guiding frame. If the drum floats in a

water jacket, it cannot get stuck, even

in substrate with high solid content.

Advantages are the simple, easily understood operation - the volume of stored gas is

directly visible. The gas pressure is constant, determined by the weight of the gasholder.



The construction is relatively easy; construction mistakes do not lead to major problems

in operation and gas yield.

Disadvantages are high material costs of the steel drum, the susceptibility of steel parts

to corrosion. Because of this, floating drum plants have a shorter life span than fixed-

dome plants and regular maintenance costs for the painting of the drum.

2.4.4. Biogas large scale plants Actually, there are also large

scale plants to produce biogas

from several sources of organic

waste. Especially in developed

countries, these plants are

constructed including two stage

fermenters and large-scale

storage silos for the substrates. Figure 4. Example of large scale plant. Snertige Energy Plant in Denmark. Source: MCON BIO, Inc.

2.5. Substrates and Inhibitors

In principle, for the porpoises of gasification, all organic materials can ferment or be

digested. However, only homogenous and liquid substrates can be considered for simple

biogas plants: faeces and urine from cattle, pigs and possibly from poultry and the

wastewater from toilets. When the plant is filled the excrement has to be diluted with about

the same quantity of liquid, if possible, the urine should be used. Waste and wastewater

from food-processing industries are only suitable for simple plants if they are homogenous

and in liquid form. The maximum of gas-production from a given amount of raw material

depends on the type of substrate.

2.5.1. Nitrogen inhibition All substrates contain nitrogen. Table II lists the nitrogen content of various organic

substances and the C/N ratio. For higher pH values, even a relatively low nitrogen

concentration may inhibit the process of fermentation. Noticeable inhibition occurs at a

nitrogen concentration of roughly 1700 mg ammonium-nitrogen (NH4-N) per liter

substrate. Nonetheless, given enough time, the methanogens are capable of adapting to

NH4-N concentrations in the range of 5000 - 7000 mg/l substrate, the main prerequisite



being that the ammonia level (NH3) does not exceed 200 - 300 mg NH3-N per liter

substrate. The rate of ammonia dissociation in water depends on the process temperature

and ph value of the substrate slurry.

Table I. Annual manure yield and nutrient content of cow, pig and chicken excrements; compiled from various sources.

2.5.2. C/N ratio Microorganisms need both nitrogen and carbon for assimilation into their cell structures.

Various experiments have shown that the metabolic activity of methanogenic bacteria can

be optimized at a C/N ratio of approximately 8-20, whereby the optimum point varies from

case to case, depending on the nature of the substrate. Table II shows some typical

Nitrogen contents, as well as C/N ratios for common substrates.



2.5.3. Substrate solids content The mobility of the methanogens within the substrate is gradually impaired by an

increasing solids content and the biogas yield may suffer as a result. However, reports of

relatively high biogas yields from landfill material with a high solids content may be found

in recent literature. No generally valid guidelines can be offered with regard to specific

biogas production for any particular solids percentage.

2.5.4. Agitation Many substrates and various modes of fermentation require some sort of substrate

agitation or mixing in order to maintain process stability within the digester. The most

important objectives of agitation are:

• Removal of the metabolites produced by the methanogens (gas)

• Mixing of fresh substrate and bacterial population (inoculation)

• Preclusion of scum formation and sedimentation

• Avoidance of pronounced temperature gradients within the digester

• Provision of a uniform bacterial population density

• Prevention of the formation of dead spaces that would reduce the effective

digester volume.

Table II. Nitrogen-content and C/N- ratio data for a selection of substrates, compiled from various sources. Pig manure is remarked with a red ellipse.



In selecting or designing a suitable means of agitation, the following points should be

considered:

1. The process involves a symbiotic relationship between various strains of bacteria, i.e.

the metabolite from one species can serve as nutrient for the next species, etc. Whenever

the bacterial community is disrupted, the process of fermentation will remain more or less

unproductive until an equivalent new community is formed. Consequently, excessive or

too frequent mixing is usually detrimental to the process. Slow stirring is better than rapid

agitation.

2. A thin layer of scum must not necessarily have an adverse effect on the process. For

systems in which the digester is completely filled with substrate, so that any scum always

remains sufficiently wet, there is little or no danger that the extraction of gas could be

impeded by the scum.

3. Some types of biogas systems can function well without any mechanical agitation at all.

Such systems are usually operated either on substrates with such a high solid content,



that no stratification occurs, or on substrates consisting primarily of solute substances.

Since the results of agitation and mixing are highly dependent on the substrate in use, it is

not possible to achieve a sufficiently uniform comparative evaluation of various mixing

systems and/or intensity levels. Thus, each such system can only be designed on the

basis of empirical data.

2.5.5. Inhibitory factors The presence of heavy metals, antibiotics (Bacitracin, Flavomycin, Lasalocid, Monensin,

Spiramycin, etc.) and detergents used in livestock husbandry can have an inhibitory effect

on the process of bio-methanation. Table III lists the limit concentrations (mg/l) for various

inhibitors.

Table III. Limiting concentrations for various inhibitors of biomethanisation

The antibiotics utilized in the cattle productions reach the excrements but they usually do

not mainly affect the digestion due to the dilution with non-toxic materials. The

methanogenic are sensitive to antibiotics that affect the protein or lipids synthesis, they

are already affecting the function of the citoplasmatic membrane.

Source: Biogas Digest – Vol 1 Pag.16

The concentration of ammonic nitrogen must be lower than 1,5 g/L; if bigger, as it

happens normally with poultry droppings, becomes toxic. However is an absorber, its rise

can even not allow the digestion process. Also salts like zinc, cupper and nickel are toxic,

although the last one is necessary in very low concentrations. The salts from alkaline

elements can be stimulators or inhibitors, depending on the concentration, as shown in

table IV.

The chlorate disinfectants are very toxic even at low concentrations (<1 mg/L) but the are

rapidly absorbed by the inactivated solids. The majority of synthetic detergents are easily

degraded but if the concentration is bigger than 20 mg/L they can affect the digestion. The

compounds of ammonic quaternary are persistent and toxic at low concentrations (1



mg/L). The chlorate solvents and derivative are toxic at concentrations of 1 mg/L. The

toxicity of the sulfates demonstrate to major concentrations (>1 mg/L) and the complete

inhibition occurs above 4,5 g/L.

Table IV. Effect of concentration of some cations.

[g/L] Stimulant Inhibitor Very

Inhibitor

Sodium 0,1 – 0,2 3,5 – 5,5 8,0

Potassium 0,2 – 0,4 2,5 – 4,5 12,0

Calcium 0,1 – 0,2 2,5 – 4,5 8,0

Magnesium 0,075 –

0,15 1,0 – 1,5 3,0

Source: Microbiología Agrícola. Chap.5 Pag.10 – Leonor Carrillo 2003

2.5.6. Gas yield Finally, the gas yield will depend on the already mentioned factors and their combination.

Nevertheless, for each kind of substrate there will be different results for the yield of the

reactor. Table V shows some expected gas yield under same conditions of retention time

and temperature for common substrates utilized in agricultural farms.



Table V. Gas yields and methane contents for various substrates at the end of a 10-20 day retention time at a process temperature of roughly 30°C



2.6. Mathematical models for the biogas reactor

Despite the many models to describe anaerobic processes, there are still no exact

simulation algorithms for such complex systems. However, the models describing with

detail all the processes responsible for anaerobic digestion are generally difficult to use for

control purposes (Bastin and Dochain, 1990). Also we have to consider that those models

are not based on sufficient range of operation conditions. In practice, the technologies

often aim at increasing the contact surface between the biological phase and the organic

matter in order to improve the process efficiency (Bernard et. al., 2001).

Despite the lack of phenomenological knowledge, the complexity of the process, its non

linear nature, and the lack of necessary sensors explain why most of the existing models

are generally only rough approximations that have not been validated with a large set of

data. (Bernard et. al., 2001)

The next are examples of the models typically used to simulate anaerobic processes.

Their equations come from mass balances of solid, bacteria, substrate and biomass

concentrations, as well as partial pressures. The model developed for this work, is based

on this same principles, but it is a simplification of a more complex model1, which

considers methanogenic and acidogenic bacteria.

2.6.1. Fermentation kinetics models [4]

The first step before the acidification and later methane formation is the fermentation of

the biomass; fermentation is generally modeled by kinetic equations giving the time

evolutions for biomass, substrate, and product concentrations. Although these equations

can be solved analytically in simple cases if substrate/product inhibition and biomass

death are included, they are typically solved numerically.

According to a recent publication2, analytical treatment of the kinetic equations is also

possible. This models can also include cell death and an arbitrary number of inhibitions, in

which constant yield needs not to be assumed. These equations can be solved in phase

space by considering the biomass concentration as function of the substrate concentration

instead of time.

1 Bernard et. Al. (2001) 2 Mathieu Bouville, Fermentation kinetics including product and substrate inhibitions plus biomass death: a mathematical analysis. Institute of Materials Research and Engineering, Singapore (Dated: October 9, 2005)



Several models have been proposed to describe the kinetics of fermentation, giving the

time evolutions for microbial mass X, substrate S, and product P. Kinetic equations are

generally of the form:

(1)

(2)

where m is the maintenance coefficient, µ is the maximum specific growth rate and K1 is

the Monod’s constant. Equation (2), originally proposed by Monod3, can be modified to

account for product inhibition, substrate inhibition, and biomass death.

The biomass yield, -dX/dS, is often assumed to be constant. In that case, X – X0 = Y (S-

S0) where X0 and S0 are the initial values of X and S. Equation (2) then gives:

(3)

However, biomass yield is not necessarily constant4, in such cases the time dependences

of S and X are not obtained analytically. Instead of that, equations (1) and (2) are typically

solved numerically. However, since monitoring of the biomass concentration can be used

to follow the evolution of fermentations an explicit expression linking microbial mass (X)

and substrate (S) could be very useful.

2.6.2. Anaerobic digestion for methanogenesis models [5]

Anaerobic bioconversion of organic waste is a multi-step process of series and parallel

reactions in which several key groups of bacteria take part. To maintain an anaerobic

treatment system that will stabilize organic waste efficiently, the non-methanogenic and

methanogenic bacteria must be in a state of dynamic equilibrium. Anaerobic digestion

systems are rather complex processes that unfortunately often suffer from instability. In

order to be able to design and operate anaerobic digestion systems, appropriate

mathematical models needs to be developed.

3 J. Monod, Recherche sur la croissance des cultures bactériennes (Hermann, 1941) 4 Thatipamala et al. for instance found that it decreased from 0.16 to 0.03 g/L when ethanol increased from 0 to 107 g/L.



The new version of <Methane> model presented in the Supplement [5] of the State

University of Moscow (2000), based on an earlier development, assumes in the model

that an initial complex substrate X 1 is a mixture of proteins (P), lipids (L) and

carbohydrates (C). Hydrolysis, Acidogenesis and Methanogenesis induced by different

groups of microorganisms were described. The following groups of variables and

equations are included in the model:

a. Suspended solid concentrations (Xk, k=1,2,3)

(4)

where:

XFk are the influent concentrations of components of suspended solids

qf is the feed flow rate

qBX is the discharge rate of excess-suspended solids including biomass

ρXk are the rates of transformation of the components of suspended solids (expressed as

a product of the functions describing the temperature dependence, mechanism of

substrate limitation and inhibition

V is the volume of the liquid phase

b. Active biomass concentration (Bi , i=1,2,….,10)

(5)

where:

BFi are the influent concentrations of active bacteria

ρBi are the growth rates of various subpopulations

c. Dissolved substrate concentrations (Sj , j=1,2,….,13)

(6)

where:

SFj are the influent concentrations of soluble substrates

ρSj are the rates of transformation of the components of soluble substrate

TRSj are the rates of mass exchange between gaseous and liquid phases



d. Partial gas pressures (pl l = 1,…,5)

(7)

where:

R is the universal gas constant

T is the temperature (K)

Vg is the volume of gas phase

PT is the total gas pressure

The advantages of this model for simulation of complex processes was described in

several papers. The main feature of the <Methane> model is the flexible way which allows

to select initially the appropriate rate functions, temperature dependence and inhibition

impact.



3. Work structure 3.1. Work frame

This research starts with the understanding and learning of the actual systems already

installed in the Institute, as well as the necessary equipment for the operation and

measurement of results. The installed equipments correspond to the ones previously

utilized for the IMBIO Project. For the aims of this following project, the same equipment

will be utilized, as well as the same bioreactor. Modifications will be done during the

develop of the BioRealSim Project, according to the new necessities.

The first goal of research is to investigate in the literature and internet publications about

the state of the art regarding biogas production. Focus in specially in substrate handling

and properties, a group of specific substrates coming from common farms will be focused

as target of investigation. This work contemplates to search in literature the properties of

these substrates and to compare them between different sources. Later on, to do

experiments to compare and obtain own results of these parameters.

It is also included in this work, the develop of a scaled reactor using bioreactor mass and

feeding from a real big-scale plant. This is to achieve real results in terms of yield and

operation. Once this is working, it will be possible to analyze the implicitness of abnormal

or critical situations that may occur in reality. In this case inhibitor factors are to be tested,

in order to learn how to avoid them in the real plants.

The results of the biogas reactor under different circumstances will be used for the

development of an example model for the methanogenesis. This will be a simplified model

to simulate the effect of different substrates and their effect on the biogas production yield.

3.2. Work plan

The work for the present project has been divided into two main parts, the first one is the

literature research and second, experimental experiences. For the first two months of

work, vast literature investigation was planned including internet publications and

documents publicized from diverse sources. The investigation has as objective to focus

mainly in the biogas principles and substrate utilization. Since the objective of this work is

to analyze the implications of the use of different substrates, it is important to have as a

reference enough information about the materials used as substrates for the biogas



generation normally in industry. This includes the main characteristics of them as well as

the proper use and mixture of them.

During the research period, it is also considered the understanding of the previous

system. This includes the operation of all the apparatus and the operation of the biogas

reactor itself. Once the actual procedures and techniques are understand, it is possible to

do changes or improvements for the present project in order to tailor the system to the

new requirements. Once the system is optimally running, it will be possible to experiment

with new trials.

The experiments start with the normal feeding and data taking of the system. Gradually,

the feeding substrates are to be changed to analyze the response of the reactor to them.

In this first phase, several substrates will be tasted in the same reactor to see how the

system reacts to several changes of conditions of carbon, protein, fiber and dry matter

content. The measured data will be constantly recorded and stored for analysis.

A second phase of the work will be the start up of a new experiment, with a different inner

content and substrate feeding. This time the idea will be to emulate a real plant, by

copying their feeding substrates and conditions. Once they are reproduced, it will be

possible to use the bioreactor as a mother source for other smaller reactors where

individual trials are to be achieved. In this way, the original content will be not affected and

experiments with harmful or inhibitor substrates will be also possible.



4. Materials and methods

4.1. Apparatus For the development of the experiments, two reactors were used. Both reactors were

similar in shape and characteristics but different only in size. One (considered “main one”)

has a total volume of 60 liters; the other, 20 liters. For more detail, see attachment 1.

4.1.1. Biogas reactor The main reactor consisted in a two-pieced 60-liter glass reactor with 5 inlets and one

outlet. The reactor had also a cooling/heating jacket for temperature control. In the top of

the reactor, the inlets where used as follows:

1. Stirrer inlet (driven by a Janke

& Kunkel RE16 motor)

2. Feeding inlet with ball-valve

3. Membrane inlet and outlet

4. Gas outlet

5. pH and temperature sensors

inlet

The main reactor was filled with 40 to

42 liters of a mixture of activated

sludge from water treatment plant

and the incoming substrates (i.e. pig

manure). The volume of the content

remains constant, because every

time the reactor was feed, the same

volume (at the same time) was

extracted. Figure 6. Laboratory reactor 1 („Pedrito“)

4.1.2. Measuring Equipment a) Gaseous phase From the top of the reactor the produced gas, by its own pressure is taken off, to be then

analyzed and measured. First, the gas goes through a humidity filter to protect the further

equipment from corrosion. Then the gas is analyzed with an infrared analyzer, to be then

the volume measured by a drum-type gas-meter.



Infrared Gas Analyzer model ANSYCO GA94 Once the gas is driven off (without using the internal pump of the apparatus) and filtered,

goes through the top inlet to measurement chamber where an infrared beam is projected,

via sapphire windows, through the gas sample.

Three detectors sense the beam: one for Methane, one for

Carbon Dioxide and one for compensation. A microprocessor

calculates the amount of infrared light absorbed at different

wavelengths and determines the various gas concentration levels

present.

Figure 7. ANSYCO GA94

Readings are shown on a liquid crystal display as a percentage of gas concentration by

volume. Methane concentrations may also be expressed in terms of a percentage of the

Lower Explosive Limit. Oxygen concentration is measured by the Galvanic Cell Principle.

For our purposes, the concentration of CH4, CO2 and O2 where measured.

A data logging facility allows an unattended unit to automatically obtain readings. The

analyzer was set to take measurements every half an hour. In addition, the unit has the

ability to measure atmospheric pressure, to make automatic corrections to measured gas

concentrations and to measure the relative vacuum in gas extraction pipelines. Additional

gases can be measured by using one of nine plug-in Gas Pods. The additional gas

reading automatically appears on the Gas Analyzer screen.

Drum -type Gasometer, Ritter TG 05 mod. 5-8 To apparatus was considered to measure the production of biogas. After the

concentrations of the biogas where analyzed, this equipment measured the produced

volume. Afterwards, the gas was thrown away through an exhaust pipe. The readings

from this equipment must be read manually. Therefore several times a day, the lectures of

the apparatus where copied into a paper form.

b) Liquid phase From the liquid phase of the reactor different measurements were done. One output was

the methane concentration of the gas permeated through the membranes immerged into

the liquid, by using a flame ionization detector (FID); a pH and temperature electronic

probe did the other measurements. The readings of both systems were stored in a

personal computer for further analyzes.



PH & temperature probe WTW Multilab 340i This electronic device was used to measure online the temperature and pH in the liquid

phase of the reactor. The data is automatically collected and stored in a database,

Multilab® Pilot. The pH value was maintained, without practically any intervention, at

values around 6,9 to 7,1; the temperature was kept around 36°C.

Flame Ionization Detector Sick Maihak Model 3005 The analyzer operates according to the principle of

comparison. The unknown sample gas concentration

is compared with the known concentration of the

calibration gas. The physically measured quantity is

converted into an electrical signal by means of a so-

called flame ionization detector (FID). Here an

electrical field is connected to a pure hydrogen flame,

which burns while being supplied with hydrocarbon-

free air. If organic compounds are fed to this flame to

the sample gas, a measurable ionization current is

produced. The signal is proportional to the number of

carbon atoms, which are fed to the flame and are not

pre-oxidized. The oxidized carbon atoms are only

partially registered. Carbon monoxides and carbon

dioxide are not registered. The FID is extremely

sensitive with a large dynamic range; its only

disadvantage is that it destroys the sample.

Figure 8. FID Sick Maihak 3005. Red cylinder with hydrogen; Gray cylinder with calibration gas propane.

Driving potential source

Electrode

o-+ -

+

Combustion air Ions

Flame Burner

FID amplifier and monitor

Combustion gas Sample gas

Figure 9. FID measuring principle scheme.



The apparatus was monitored by using special software from ttz-Umweltinstitut called

LIDES. The software stores the measurements every minute and displays a real time

diagram of the methane concentration. This equipment can detect from 1 to 100.000 ppm

and it has 5 different ranges of measurement, depending on the desirable scale. The

calibration gas used was propane, considering a concentration of 800 ppm. The FID

equipment has its own pump, extracting the gas at a pressure of 200 mbar.

4.1.3. Membranes The first membrane utilized was the model E, developed for the IMBIO project. The

membrane model E showed the best performance in terms of stability and quality of

measurements during the project. At the beginning of BioRealSim Project, the same

membrane was utilized. Afterwards some modifications were made to avoid strangles until

finally, it was cut and modified mechanically to enhance its performance. This membrane

consisted into a 1,22 meter long silicone pipe (Rotilabo®) with a external diameter of 0,7

cm, a wall thickness of 0,1 cm and an active surface of 269 cm2.

Figure 10. Membranes after

operation.

After four months of

operation the membrane E

was taken out for

maintenance. It was

notorious that there where

strangles across the

membrane that difficult the

gas flow (left picture).

Biofilm was also present

across the whole membrane

(right picture).

For the membrane technology developed in IMBIO Project, there are many other

possibilities or configurations to set membranes inside reactors. In our case, there is one

primordial condition, which is to design a membrane that can fit the entrance of the reactor

to be replaced or cleaned. This made the membrane design specially complicated for the

development of our experiments, since the membrane should not strangle at all. Some of

the solutions ideated are shown in picture 11.



Figure 11. Membrane solutions to avoid strangles. In the left picture the membrane model E was rolled over a sponge rubber with a weight in the lower part; In the upper right picture, another membrane composed by several thinner hoses in parallel; The lower right picture shows membrane E rolled over a plastic wire-net with a weight attached in the lower part. These were the first attempts to avoid strangles after the finalization of the previous project Imbio. Later on, a new solution was found with better results. Results of the last modification are showed in chapter 8.2

4.2. Methodology

4.2.1. Determination of biogas composition The proportion between the gases produced by anaerobic digestion especially CH4 and

CO2, provide important information about the process evolution and the biogas quality.

Hence not only the biogas volume is measured but also the biogas composition.

The biogas collected in the headspace of the reactor goes out to an infrared gas analyser

(Ansyco GA94) which measures the proportion of CH4, CO2, O2 in the biogas produced.

The measurements are expressed as percentage whit an accuracy of 0,1 %. The



frequency of measurements has been changed according the necessities of the

experiment.

4.2.2. Measurement of biogas volume After measured the biogas composition, the produced volume was counted by a gas flow-

meter, one for each bioreactor drum-type (Ritter TG 05 Mod. 5-8). The measurements

have been registered manually with a frequency between 1-4 measurements/day.

The produced volume of biogas is not environmentally significant and the use of it is not

considered into the aims of this project. Therefore the biogas is not burned and neither

cleaned up of SH2 nor NH3.

After measurement with the gas flow-meter, the biogas is eliminated through the building’s

exhaust gases system.

4.2.3. Measurement of temperature and pH value into the reactor liquid phase A probe containing an electrode is placed into the reactor liquid phase in order to obtain

on-line measurements of pH and temperature. The probe is coupled with a portable

register (WTW multi 340i) and the data are automatically collected (each 1 - 30 minutes

depending the necessities of the experiment) and stored by a software database (Multilab

Pilot).

4.2.4. Measurement of Chemical Oxygen demand (COD) Different measurements have been undertaken on the liquid phase of the AD bioreactors.

The samples have been obtained at the same time when feeding ensuring to be

representative of the physical-chemical state of the reactor at the moment of feeding.

Each sample has been obtained from 1 L volume of sludge and aliquots of 200 ml have

been frozen until being analyzed. All the shown values have been obtained at least as

average from duplicates or triplicates. The measurement have been conducted by

triplicates of each sample and processed at room temperature.

The determination of COD has been done on the supernatant obtained by dilution of the

suspended sample and sedimentation of the particulate over night at 4 °C (or by

centrifugation). The obtained supernatant has been analyzed using the kit LANGE; LCK

014 for COD determination.

The determinations on the substrates have been in the beginning conducted by similar

way putting special attention to ensure the sample homogeneity. Using this method, only

the dissolved fraction of COD has been determined. Therefore, for the later experiments



B3 and K3, the samples were analyzed by drying and pulverizing the samples at 105°C

and the added directly in Kuvette test-probes to be then analyzed with specter-

photography.

4.2.5. Estimation of Total Solid content (TS) Total Solid content has been determined by weighing the sample, oven-drying it at 105 °C

to remove all moisture during 8 h or until constant weight of the dried sample. The

percentual difference between the wet and the dry sample is the TS %.

4.2.6. Estimation of Total Volatile Solid content (TVS) The TVS is a measure of the solids (portion of TS), which are actually available for

bioconversion. VS are determined by "burning" the dried TS sample at 550 °C, which

removes the "volatile" component (organic matter). What remains is non-volatile. The

sample is weighed again to determine this "ash" weight, which is subtracted from TS to

determine VS. VS% is found by dividing VS by TS.

4.2.7. Lipids measurement The lipid content of the substrates was measured with the methodology designed for meat

in the food industry5. The method consists into a dissolution of fats in boiling HCl during

1,5 Hrs.; later, the water content is filtered and the remaining solids rinsed with water until

pH value is around 7. Afterwards the solids remaining are dried and thereafter fats are

extracted by distillation with petroleum ether. In the end the fat content remains pure in the

glass which is weighted to determine the lipid percentage.

4.2.8. Protein measurement To estimate the protein content of the substrates, a method designed for food industry

was used, known as Kjeldahl6 Method7. The methodology is based on the fact that

proteins are proportional to the nitrogen content. First, samples are cooked in boiling

sulphuric acid for 2 hours in a Kjeldahl tube with a catalyst (Kjeltab) to increase boiling

point. Afterwards the liquid is distillated and the nitrogen content is estimated by titration.

Thus, the protein content will be 6,258 times the content of nitrogen. En resume, the key

steps for the assay are (a) sample digestion, (b) neutralization, (c) distillation and trapping

of ammonia, and (d) titration with standard acid.

5 „Bestimung des Gesamtfettgehaltes in Fleish und Fleisherzeugnissen“ §35 LMBG / L06.00-6 6 Johan Kieldahl (1876 Denmark). Method published originally in 1883. 7 „Bestimung des Gesamteiweißgehalt in Fleish und Fleisherzeugnissen“ §35 LMBG / L06.00-7 8 Frequently, Fk is given a default value of 6,25 or 5,7 for animal and plant proteins, which are assumed to have an average N content of 16% and 17,5%, respectively. - Food Protein Analysis, R.K. Owus-Aspenten.1:9-10



5. Experiments

5.1. First run, trying different substrates. Experiments B1 and B2. 5.1.1. Normal feeding This corresponds to the same diet that the reactor has been receiving during the previous

months. It consists into two main substrates. The first one is pig manure (PM), normally a

dilution of 25% in water; the second one consists into a milled straw solution. The normal

feeding was done once per week. The utilized pig manure was found to be, with the feed

concentration, with a total solid content (TS) of around 4,7%.

The concentrations for these feedings were done as follows:

Pig Manure: 252 gr. PM + water = 1 liter solution

Straw solution: 250 gr. Milled Straw + water = 1 liter solution

The feedings are always done adding one-liter solution to the reactor and taking out one

liter at the same time. In this way the internal pressure of the reactor remains constant.

Since the gas produced is extracted by its own pressure (no pump is used), to maintain a

relative constant gas flow no pressure is added or extracted from the gaseous phase.

However, the internal pressure is not constant. Because the internal friction forces of the

rotameter must be overcome by the internal pressure to let the gas go out, the outgoing

gas flows are not constant.

The Chemical Oxygen Demand (COD) for the pig manure feed was found to be 13.365

[mg/l O2], following the LCK514 procedure, measured with a photo-spectrometer; The

Nitrogen content, by following the LCK338 procedure, was found to be 375 [mg/l TNb].

Considering both measurements, we can estimate a C/N ratio for the pig manure

substrate of 35,75.

5.1.2. PROBIO feeding The products obtained from the reactors build for the PROBIO project will be tested also

in the biogas reactor. These products will come from a straw solution (10%) previously

pretreated with enzymes.

The test consisted in an enzymatic pretreatment for a straw solution with an alkaline/acid

procedure for the digestion of the straw. The test was done by adding 20 liters of straw



solution (10% mass) to the reactor, this one with similar characteristics to the one used for

the biogas generation, then, enzymes were added from a substrate called Celluclast (700

EGU/g9). The enzymes are in charge of breaking the bonds (β1-4) of glucose that form

the cellulose molecule of straw. Celluclast can be used whenever the aim is to break

down cellulosic materials into fermentable sugars, the reduction of viscosity of soluble

cellulosic substrates, or the increase in yield of valuable products of plant origin.

The steps done were:

1. Filling the reactor with 20 liters of straw solution

2. Adding of 200 gr. of NaOH

3. Wait 5 days while stirring (250 rpm) the solution

4. Adding of 20 liters with a HCl solution (1%)

5. Stabilize pH value to 4,8

6. Adding of 500 ml with a Celluclast solution (10%); ~20 FPU

7. Take samples every hour during five hours.

After the test was done, with all the samples taken, a muster of five liters was taken and

storage to try the resulting product later on in the biogas reactor.

5.1.3. Other feedings As the reactor was about to be stopped and cleaned during Christmas vacations, some

tests were done with new substrates. This was the finish o a fist phase for the

experiments. These tests were done without the infrared gas-analyzer, thus the content of

the produced gas was unknown. The membrane was taken out and it was rolled as a

bovine to enhance its performance, storing measurements as a new experiment (B2).

These late feedings were done during December 2005.

After been feeding the reactor with pig manure regularly once a week, the biogas

production increases its quality, reaching levels of 58% methane. Nevertheless, the

permeation through the membrane reduces constantly down to 5% of its initial

permeation. To enhance the biogas production volume by adding lipids to the reactor’s

content, a first test was done with pig lard. Afterwards, the reactor was feed with food

waste coming from the university’s kitchen.

a) Pig lard feeding

9 The Cellulytic activity may be measured in endoglucanase units (EGU), determined at pH 6.0 with carboxymethyl cellulose (CMC) as substrate.



At the end of November, the reactor was feed with normal lard buy in supermarket. The

substrate was prepared with 250 gr lard (melted). The solution was prepared this time

also with some waste solution from the same reactor, to prepare an emulsion easier to

assimilate for the reactor. The feed solution was:

+ 300 mL lard

+ 200 mL water

+ 500 mL reactor waste

= 1000 mL Solution

Observations: There were no notorious reactions in the reactor at the beginning. During

the next days the gas production increase constantly to a peak level 8 times higher than

before, after one week.

b) Waste food After the test with lard, the next substrate to be tested was waste food collected in the

university’s cantina. The substrate contains waste from 5 different days, which was

grinded and then mixed all, to have an homogeneous and representative sample of waste

food. Visually, the waste contains at least:

• Potatoes (cooked and fried)

• Rice

• Salad

• Cooked Coffee

• Bread

• Beans

• Cabbage

• Sauces

The feedings were done during one week on Monday, Wednesday, Thursday and Friday.

The idea behind this “every day feeding” is to see the implicitness of a constant feeding.

Every time the reactor is feeding with a volume of substrate, the same amount (normally 1

lt) is taken out. If feedings are too close with each other, there is a washing effect in which

the bacteria population is taken out of the reactor together with the reactor waste. This

effect is to be also analyzed. As this experiment was started two weeks before shooting

off the reactor, the tests are aimed to find ways to decrease the reactor yield.



The total dry matter (TS) of the substrate was measured by the standard process of

105°C until no further weight losses are detected. As result was determinate that TS =

25,40%

The defined protocol of feeding (kind of substrates, amount, frequency and the proportion

substrate-tap water in each feeding) is described in table VI. Due the different periodicity

of feeding during this experiment, the HRT during this experiment is not easily to be

estimated.

nº

time of feeding (days)

Kind of substrate use for feeding

dry substrate

(g) TS (%)

2 1,70 Straw 100,43 92,99 3 6,68 Straw 100,43 92,99 4 8,95 Pig manure 59,14 23,65 5 15,92 Pig manure 59,14 23,65

6 21,71 Enzymatic digested Straw (EDS) 2,67 0,27

7 27,77 Enzymatic digested Straw (EDS) 2,67 0,27

8 28,74 Pig manure 59,61 23,65 9 35,79 Pig manure 59,14 23,65

10 42,82 Lard 250,00 100,0

0 11 49,73 Pig manure + EDS 61,14 6,11 12 54,90 Food waste 139,15 27,83 13 56,72 Food waste 139,15 27,83 14 57,76 Food waste 139,15 27,83 15 58,70 Food waste 139,15 27,83 16 60,02 Food waste 139,15 27,83 17 63,90 Olive Mill Waste water 41,80 2,09 18 64,92 Olive Mill Waste water 41,80 2,09

Table VI. Feeding schedule during the experiment of AD under

low TS conditions. Detail of the kind of substrate, percentage of

solids of each substrate and dry amount added for feeding. All the

substrates have been added in 1L suspension with tap water

except lard which was suspended up to 0,5 L.

The feedings have been done manually trying to introduce in the reactor equal volume of

input than out-put removed simultaneously, in order to keep the volumetric and barometric

conditions into the system. Hence each sample of digested sludge removed from the

reactor when feeding, represents the conditions of the digester liquid phase at that time.

Except the feeding with lard (number 10) which has been suspended in 0,5L of tap water

all others feedings have been suspended in 1L of the liquid.



In order to evaluate the possible effect of intoxication of a substrate on the biological

population present in the reactor, the two last feedings of the experiment (17 and 18) have

been done with Olive Mill Waste water, with an important content of phenols10 (379,5 mg/L

SD 4,95), and a short interval between them.

A suspension of enzymatic digested straw (14 g/L % of Glucose), has been obtained by

alkaline treatment (NaOH 1% during 5 days) and enzymatic treatment (Celluclast ®) after

neutralization.

5.2. Reloading of bioreactor. Experiments B3, K3 and K3.2.

After four months of operation, the reactor was shutdown for maintenance and some re-

adjustments to its configuration. The main purpose was to simulate a real 500 kW reactor

with the same content and feeding. For this purpose, a sample from a bioreactor from the

company MT-Energie (partners of Project IMBIO) was taken, as well as samples of the

substrates feed to this plant. The idea of this new experiment is to be closer to the reality

of this kind of plants and to perform sub-experiments in little reactors with active samples

taken from our 60-liter reactor “Pedrito”.

For the simulation of the conditions of the real plant, the following aspects were

considered:

• C / N / P / S ratio of a properly working biology is 600 / 15 / 5 / 1

• Manure content can be from 100% manure to 0% manure. Average mixture for

usual NAWARO plants is about 30t corn / 10t manure

• Standard feed range for 500kW power output can be estimated between 25 and

30t corn silage per day, next to 10m3 manure

• Retention time for the substrate is app. 40days per digester. Since there are two

stages, the substrate retains 80days in the first two stages. Afterwards the product

is stored in the third vessel until the plant operator (farmer) deploys it to his land.

Comparing the characteristics of the real and laboratory reactors we have that:

500kW Real Plant Laboratory Scale

Volume 2078 + 2493 = 4571m3 45 liters = 0,045m3

Retention time 40 + 40 = 80 days 80 days

10 EPA has a lifetime health advisory for adults for phenol in drinking water of 4 milligrams per liter (4 mg/L)



Feeding 30t Corn + 10t Manure 300g Corn + 100g Manure

Table VII. Real plant v/s laboratory scale

To emulate the same feeding quantity and ratio of the 500kW plant installed in Rockstedt

by MT-Energie, it will be necessary to feed 300 gr. of corn plus 100 gr. of manure. There

is almost a direct size relation between the real and the laboratory reactors of 1:10-5, this

is valid for the volume as well as for the feeding amounts.

The feed corn substrate is to be taken from the same company that runs the 500kW plant

without any dilution, to emulate as best as possible the reality. The pig manure to

complement the feeding will be the same used for the previous experimental phase of this

project, which remains freeze since recollection.

The new configuration for the reactor, for this second experimental phase considers:

• New propeller with downward propulsion. With a lower double wing (twisted

downwards) and an upper double wing (straight) for surface movement. See

Pic.13.

• The E model membrane was cut into two parts to set them in parallel in a vertical

position. See Pic. 14.

Figure 12. Open reactor Picture 13. New Propeller Picture 14. Membrane E modification.



• Inlet and outlet valves were changed to bigger ones (1¼ inch).

• The Teflon disk where the stirrer is mounted was made again, because the old one

was not straight anymore in the entrance hole. For the new propeller, with larger

wings, it was needed a vertical position for the propeller axis.

• The motor was also changed for a more powerful one with rpm visualization

(230W). See Pic. 15.

• A new configuration for the inside net of the reactor was developed to protect

better the membranes. This one was designed with a floor net, to avoid contact of

the membrane with the lower propeller. See Pic. 16.

Picture 15. New motor with rotational speed regulation and visualization.

Picture 16. Modified protection chamber for the membrane. The wire-net protects the membrane from the propeller. Due to the turbulences, the propeller sometimes reaches the hose breaking it up.



6. Results 6.1. On line measurements

After the first four months of operation, whose were the continuation of the previous

experiments done for the IMBIO Project, a collection of more than 70.000 data were taken

during 65 days of operation. This data includes the measurement of:

• Membrane permeability [l/h_bar_m2] (every 10 minutes)

• Volume of produced biogas [l/h] (4 times/day)

• Quality of biogas (CH4/CO2 concentrations) [%] (every 30 minutes)

• Temperature inside reactor [°C] (every 5 minutes)

• PH value inside reactor (every 5 minutes)

The data was collected in MS Excel sheets and then shown in graphics to be then

compared and understand easily.

As main results, it was obtained the behavior of the membrane detection and biogas yield

in time, for different conditions of feeding. The obtained curves of data show the

tendencies of the parameters measured, been some of them variable and some also

constant. These tendencies can be resumed as:

Average value Standard Dev. Tendency

M. Permeability [L/h bar m2] 0,203 0,132 Decreasing

Vol. Biogas [L/hr] 0,63 0,679 Variable

CH4 / CO2 1,40 0,545 Increasing

Temperature [°C] 36,09 0,183 Constant

PH value 7,00 0,066 Variable

Table VIII. Tendencies for on-line measurements, including average value and standard deviation.

Although some result are easily to see, it is a conclusion of this first run experiment that it

is actually difficult to obtain scientific statements from the shown results. This is because

there are no enough trials with each substrate, being the feedings to inconstant. It is not

evident the influence of each of the substrates in the resulting yield of the reactor. Some

substrates are more difficult to be fermented or digested.



The following are the results obtained after 1.500 hours of continuous operation,

separated in two stages. The first one, called B1 (BioRealSim #1), with 1.200 operative

hours using a membrane model E (single hose), with weekly feedings using different

substrates; the second one, B2, was tested during 355 operative hours until the reactor

was turned off. The second part was different because of the disposition of the

membrane, this time it was rolled over a gum-sponge to avoid strangles in the hose.

The results are presented as graphs, been each one:

• CH4 permeability: This graph shows the results obtained from the membrane,

which permeates methane through a silicon hose. The concentration of permeated

gas, taken from the liquid phase of the reactor, is then measured with the FID in

ppm units. These measurements are then used to estimate the permeability,

related to the area and pressure of the membrane. It is also shown the feeding

dates (red dots) and the volume produced per hour along with biogas quality

(CH4/CO2 ratio), as references.

• Volume and quality of produced gas: This graph shows the volume per hour in

a bigger scale to see in detail the liters of biogas produced every hour. The green

dots represent the total volume of biogas per hour; meanwhile the blue and pink

lines represent this amount divided into methane and carbon dioxide proportions,

respectively. Thus, the blue line represents the liters produced per hour of pure

methane. Furthermore, it is shown a blue cloud of points whose are the ratio of

methane over carbon dioxide (biogas quality).

These results are all presented as attachments in chapter 14.2. Picture 17 shows, as

example, the results from experiments B1 and B2 together.



feed

ing

num

ber

Feeding substrates

dry

subs

trat

e (g

)

CO

D (g

/l)

TS (%

)

TVS

(%)

TN (m

ol/L

)

Am

mon

ium

(m

ol/L

)

2 Straw 100,43

3 Straw 100,43

4 Pig manure 59,14 4,167 3,164 79,492 0,087 0,076

5 Pig manure 59,14 4,450 3,078 73,269 0,091 0,083

6 Enzymatic digested

Straw (EDS) 2,67

7 Enzymatic digested

Straw (EDS) 2,67

8 Pig manure 59,61 4,250 2,741 72,810 0,089 0,083

9 Pig manure 59,14 4,283 2,876 72,039 0,088 0,083

10 Lard 250,00 4,375 2,643 71,950 0,098 0,087

11 Pig manure + EDS 61,14 5,142 2,717 73,491 0,097 0,084

12 Food waste 139,15 5,525 2,786 70,379 0,093 0,084

13 Food waste 139,15 5,025 2,995 71,025 0,090

14 Food waste 139,15

15 Food waste 139,15 5,433 2,971 72,281 0,098 0,075

16 Food waste 139,15

17 Olive Mill Waste water 41,80

18 Olive Mill Waste water 41,80 4,892 2,927 72,573 0,093 0,067

Middle value 90,217 4,754 2,890 72,931 0,093 0,080

Standard deviation 62,455 0,512 0,168 2,493 0,004 0,006 Table IX. Analytic results from the reactor liquid phase obtained during AD process

under low TS conditions. Grey columns refer to the kind and amount substrates

added. The feeding number is coincident with the sampling number. The middle

values and the standard deviations of the obtained results give an idea about the

variation of each parameter according the variation of the added substrate throughout

the experiment.



Picture 17. Results of first two experiments together measured on-line. Gas yield, indicating methane and carbon dioxide content. Ratio CH4/CO2 with tendency.



6.2. Later measurements Besides the on line measurements, samples were taken for later analysis. These samples

were taken from the reactor, after each feeding. In this way is possible to measure the

condition of the reactor in terms of dry matter content (TS), chemical oxygen demand

(COD), ammonium and nitrogen content (TN), as well as volatile fatty acids (VFA) and

total volatile solids (TVS).

Since every substrate has a different solid content and different COD, the ones from the

sludge inside the reactor are also changing with time after changing substrates. This can

be a critical factor for the yield of the reactor and therefore it is necessary to be measured.

6.2.1. Results of first experiments

After 70 days running, the reactor did not show significant changes in its dry content and

COD. Although the five different substrates used, with dry matter contents from less than

Picture 18. Analysis results after first two experiments.



1% to 100%, the dry matter of the sludge was not affected in proportional ranges. This

can be understood by the short periods of feeding for each of the substrates.

6.2.2. Substrate TS estimation One of the most changing parameters during the experiments done in the essays is the

TS from feedings. Vegetable substrates like corn silage are drying constantly11 and

therefore their diminishing water content must be considered when feeding bioreactors.

This effect however, is because of laboratory conditions and does not occur normally in

outside farm-storage silages. Consequently, the amount of straw, corn silage and manure

added to feedings must be controlled by measuring in weekly basis the dry content of

these substrates.

For the calculation of the total dry content of feedings, the total solids of each of the

substrates of the mixture must be considered, as well as the water added. The formula to

control total TS used is:

WATERSi

SiSiFEED MASSMASS

TSMASSTS

+=

∑∑ *

Then a simple MS Excel sheet can be used by working with Solver, to estimate the

adequate amounts of each substrate to reach a desirable total TS for the feed. For

example, to have a 27% of total TS using corn silage and pig manure, when the TScorn =

88% and TSPM = 26%, we need to add water to reach the original TS of both substrates

combined.

In the example sheet, we

have a dryed corn silage wit

88% TS. So, by giving a total

wanted TS and total

desirable mass, it is

posssible to estimate

amounts of substrates and

water.

Picture 19. MS Excel sheet use to calculate total TS in feedings

11 When receiving corn silage from farms, the TS is around 26%. After one week the TS can be 55%, and after two months 88%. This is, by storing the silage in a plastic open barrel inside the laboratory at room temperature.



6.2.3. Protein and lipids measurement on some substrates

the laboratories of TTZ-Bionord, the substrates utilized for the experiment 3 (B3 & K3)

ORN SILAGE

In

where analyzed to estimate their protein and Lipids content. To do this, methodologies

normally applied for food industry were used. These methods are described in this chapter

above. The results obtained were:

C

86,92%

s 0g robe => 2,43%12

ccording to the literature13, the protein content for corn silage is determinate by

IG MANURE

TS =

Lipid = 2,11 g/10 p

Proteins = 7,44 g/100g probe => 8,56%

Nitrogen = 1,19 g/100g probe => 1,37%

A

combustion method as 7,34%, and the nitrogen content as 1,175 g/kg.

P

28,9%

probe => 8,44%

ccording to the literature, the nitrogen content for pig manure is 3,8%14. Thus, the

.2.4. Later measurements on sludge

he first parameter measured was the concentration of volatile fatty acids (VFA) from a

resumed in fig. 20. Complete results in chapter 12.6.

TS =

Lipids = 2,44 g/100g

Proteins = 5,91 g/100g probe => 20,45%

Nitrogen = 0,95 g/100g probe => 3,27%

A

Protein content (by factor 6,25) will be 23,75%

6 T

collection of samples from both reactors. VFAs indicate the metabolic state of the obligate

hydrogen producing acetogens and the acetoclastic methanogens, which are the most

delicate microbial groups. Therefore, the concentrations of individual volatile acids

(especially acetic, propionic and butyric acid) can be considered as the best control

parameters in the liquid phase (Buyukkamaci Et. Al. 2003). An example of these results is

12 The probes were made with dried substrates. Therefore, the results are shown for a 100% TS and in the right side, the calculated ones for the measured TS. 13 Food Protein Analysis. R. K. Owusu-Apenten – 1:34 Table 15 14 See chapter 3.5.1, p.23



Fig. 20 Example of sludge VFA analysis with spectrograph. Measured in Lab. Complete results are d

isplayed in figure 51 and 52 in chapter 12.6.

easurement of COD, TNb, Ammonia, TS and TVS. Results are displayed in fig. 21

Fig. 21. Later sludge analysis with COD, TVS, TS, TNb, Ammonia and dry feedings. Measured in Lab.

For the secondary reactor (exp. K3), a complete sample analysis was carried out for the

m



Having a deeper look into these results, if we graph only COD, TNb and ammonia

together, it is possible to note a correlation between them (Fig. 21b). The statistical values

for these correlations are presented in table X.

Fig. 21b. Later analysis results with only COD, TNb and ammonia displayed with coincident scales.

Table X. Variables correlations.

Complete off-line analysis results are in chapter 12.6.



7. Modeling the process The following model is b chapter 2.6. Even

though these, this is sim

empts to model anaerobic processes during the last three

decades (Bernard, 2001). Starting from Haldane model (1968, growth inhibition to

mphasize process instability) and then the improvement of the representation of the

genesis and methanogenesis (Hill

nd Barth, 1977), there has been a remarkable improvement up to the later ADM1 model

] of grater complexity and effectiveness.

.1. Model assumptions and description

.1.1. Assumptions The model complexity is directly related to the choice of the number of considered

bacterial populations involved in the anaerobic digestion process. To simplify the system

we can assume two main groups of characteristic homogeneous bacterial populations,

and that the AD can be described by a two-stage process. In the first step (acidogenesis),

the acidogenic bacteria (X1) consume the organic substrate (S1) and produce CO2 and

volatile fatty acids (S2). The population of the methanogenic bacteria (X2) uses, in a

second step, the VFAs as substrate for growth, and produce CO2 and CH4.

Based on hydrodynamic tests, we can assume that the reactor behaves like a perfectly

mixed tank, and that the biomass is uniformly distributed within the reactor.

Biological reaction pathways

The acidogenic and methanogenic bacteria intervene in the two following biological

reactions:

• Acidogenesis (with reaction rate r1 = m1 X1):

K1 S1 X1 + k2 S2 + k4 CO2 (1)

• Methanisation (with reaction rate r2 = m2 X2):

K3 S2 X2 + k5 CO2 + k6 CH4 (2)

S1 represents the organic substrate (and its concentration) characterized by its COD (g/L).

The total concentration of VFA is denoted S2 (mmole/L). In the sequel, we assume that S2,

ased on mass balance equations as descript in

plification of a third model for wastewater treatment [15].

There have been several att

e

process considering solubilization of organics, acido

a

[6

7

7



which is mainly composed of acetate, propionate and butyrate, basically behaves like pure

cetate. It is important to note that the total COD is composed of S and S ; m and m

gases

M 6 2 2 (3)

2 in the total inorganic carbon

ompartment into account. The molar CO2 flow rate qC can be computed using Henry’s

.1.3. The mass-balance model

a 1 2 1 2

(d—1) represent the specific growth rates of acidogenesis and methanisation, respectively.

7.1.2. The We assume that the gas out flow is mainly composed of CO2 and CH4. Because of the

very low solubility of methane, the concentration of dissolved methane is neglected and

the produced methane is assumed to go directly out of the fermenter with a molar flow

rate qM proportional to the reaction rate of methanogenesis:

q = k m X

For the outflow rate of CO2, we must take the storage of CO

c

law:

qC = kL a (CO2 – KH PC) (4)

with KLa being the liquid-gas transfer coefficient, KH being Henry’s constant and PC being

the CO2 partial pressure. Although it is possible to estimate PC and the CO2 concentration

afterwards, for simplification these values are assumed constant.

7From the considerations of reactions (1) and (2) we obtain the following mass-balance

model:

111 [

dt= ]XaDmdX

− (5)

222 ][ XaDm

dtdX

−= (6)

111111dS )( XmkSSD

dt in −−= (7)

22312222 kXmkSSDdS

−+−= )( Xmdt in

(8)



S1in (g COD/L) and S2in (mmole/L) are the influent concentrations od S1 and S2,

respectively.

ider the following models for

acterial kinetics:

cidogenic bacteria: Monod type kinetics for the growth of acidogenic bacteria; that is:

7.1.4. Modeling of the bacterial kinetics According to Bernard et. Al., the modeling of biological kinetics is a difficult task for which

a systematic methodology is still lacking. For the sake of model simplicity and in line with

other works on anaerobic digestion modeling, we shall cons

b

A

11

1)( SmSm = (9) max11SKS +

alf-saturation constant

ssociated with the substrate S1.

where m1max is the maximum bacterial growth rate and KS1 is the h

a

Methanogenic bacteria: In order to emphasize the possible VFA accumulation, it is

possible to consider Haldane kinetics for the methanogenesis:

2

22

max222 )(SKS

mSm++

= (10)

22

2

IS K

S

and KS2 and KI2 are

the saturation and inhibition constants associated with the substrate S2, respectively.

7.2. Model results

he model was implemented by using MS Excel, by discrete iterations for the first 5 days

of operation, been each iteration 12 hours operation. The constants k1, k2, k3, k4, k5, kLa,

ka, kb, KS1 and KS2 were taken from the publication of the original model15. Constants Vliq,

1, S2, S1in, qin and qout were taken from the measurements of this work. The rest of

constants were chosen as tuning variables.

where m2max is the maximum bacterial growth rate without inhibition

T

S

15 Bernard et. al. : Anaerobic Digestion Modeling



Fig. 22. Variables and values utilized for the operation of the model.




Fig 23. Graphical results of the model’s output. The third graphic result shows the real v/s imulated results for methane flow rate. s



provement of the biological process has been produced over the time, even the amount

8. Discussion

8.1. Regarding Membrane measurement The membrane chosen to be used as measurement system for the liquid phase

corresponds to the one that show better results during the previous IMBIO project. This

membrane, however, was not used for long periods (i.e. more than four weeks). During

the beginning of the BioRealSim project, one of the first aims was to test the chosen

membrane for a longer period under different substrate feedings, to analyze its behavior

after more working hours.

8.1.1. First experiments B1 & B2 The permeation through the membrane walls decays in the beginning a 50% in a period of

two weeks. This rapid change is due to the formation of a biofilm around the membrane

that difficult the permeation it self. Afterwards, the decay in the membrane permeation is

smaller. The problem is that this decay does not reach a steady state condition, therefore

the use of the membrane is not yet a proper way to measure gas yield. Because of the

diminishing capacity of the membrane to permeate, the measured permeability will be

smaller even at constant yield.

After more than 1200 working hours, the membrane permeation in the first experiment

(B1) was reaching a level considered to low for yield analysis. The membrane was taken

out and showed that this was cause by struggles in the hose in several points, making the

gas flow difficult. To avoid this situation and improve the performance of the membrane, a

proper container for the membrane gauge should be designed. The design must avoid

struggling and also allow as much of active surface to be in-touch with the liquid phase.

Even the different kind substrate used for feeding and the amount of solids added for each

feeding, no significant variation of the solid content 2,89 % and organic matter 72,93 %

has been observed during the experiment.

From day 37 to 63 was not possible to measure de biogas composition due technical

problems. Nerveless, is possible to observe a clear general tendency to increase the

methane proportion in the produced biogas throughout the process reaching values up to

64% of methane and 31% of carbon dioxide. This observation allow to affirm that an

im

and quality of feeding have been keep the same (e.g. feedings 4, 5, 8, 9).


Feedings with dry straw, even considering the high content of solids of this substrate,

oes not show important changes of biogas yield and methane ratios comparing with

d) of the media by the production of acidic compounds.

an interesting peak of biogas volume flow, with a

.1.2. Later experiments B3 & K3

oncerning reactor B, the membrane showed a better result compared with the previous

ous phase. Nevertheless, it is not expected to

ach a correlation better than 0,8; because the bubbles in the liquid phase as well as the

d

other sources of feeding.

Although there were no measures of biogas quality due technical problems during

feedings with food waste, it was possible to detect important peaks in the biogas yield

characterized by a fast increase of the produced volume. Furthermore, a fast decay after

some hours was also seen, probably due to a bigger bio-availability of the substrates

(most of them cooked), comparing with other substrates and the consequent fast

acidification (also measure

The feeding with pig lard produced

considerable delay (more than 5 days). This delay can be explained by the phenomena of

low bio-availability of a hydrophobic substrate. Even the high energetic content of lipids,

the biodegradation of them requires the generation of emulsifiers (biologic surfactants).

Due that most of the used substrates have a low content of lipids (most of them

carbohydrates) the generation of surfactants requires an additional use of energy by the

microbial population and a period of adaptation, evident in this point of feeding.

8In these later experiments, two reactors were used: B (“Pedrito”) and K (“Kitty”). Reactor

B kept the configuration described in chapter 8.2, meanwhile the second reactor (20 liters

of sludge) was arranged to emulate the other (42 sludge liters) and been used for

substrate experiments. The main reactor B was feed with the same substrate mix during

the whole experiment in order to analyze response and membrane performance.

C

experiments, because of a non-decaying performance in the measurement. Although the

read measurements were almost 10 times smaller, they showed a better correlation (only

visual) with the measured gas from the gase

re

micro bubbles inside the membrane walls have a behavior not perfectly comparable with

the gas released in the gaseous phase. This means that it is expected to have a delay in

performance between both phases and this is precisely the reason why to use a

membrane and measure directly in the liquid phase on-line.



Reactor K was feed also with a constant substrate mix, equivalent to reactor B, consisting

of 75% corn silage and 25% pig manure. Because of the drying of the silage, the amount

was periodically revisited to maintain the resulting TS. During the first 32 days the feed TS

as 15%, then 34 days with 24% and finally, the reactor was feed with a different

of the

embrane in the liquid phase, a good contact between membrane and environment and a

he design with which the membrane is located into the reactor is the third factor for a

rhaps the most critical one, because it can make the

roll the

embrane around a solid ring, avoiding contact between ring and hose. Nevertheless, a

ut also as a later result from

revious feedings. Normally, the bacteria inside the sludge need periods of time to get

w

substrate with a TS between 0,30 and 3,15%.

8.2. Membrane design The way the membrane is incorporated to the reactor is a decisive factor for the

permeability capacity of it. The main aspects to consider are: a good distribution

m

robust design to avoid struggling and/or breaking. A good distribution means that the

membrane should be able to permeate a representative muster of gas and therefore must

be put in a place where the distribution of gas is homogeneous. The second factor, deals

specially with the liquid content of the reactor. At lower solid concentration, the contact

between gas bubbles and the membrane is more homogeneous, because the bubbles

can rise easier and thus, a better measurement is possible.

T

good measurement. This factor is pe

difference between a good measurement and nothing. As already explained, struggles in

the hose can be decisive when measuring the methane content permeated through the

membrane. Furthermore, the supporting device (where the membrane is allocated) should

allow a good –or perfect- contact between liquid phase and membrane surface. An

alternative tried in the second phase of the experiments of BioRealSim was to

m

procedure to determine if there is membrane struggling inside the reactor could facilitate

better working conditions. An alternative is to inflate the hoses of the membrane inside the

reactor to make sure that there are no flux problems. This must be however tested.

8.3. Using different substrates After been more than 70 days trying different diets for the bioreactor, it was possible to

note that it is uncertain the amount of time to digest it completely by the bacteria inside the

sludge. Therefore, once the substrate is modified or changed, the reaction of the reactor

can be understood as a direct result of the modification b

p



used to each substrate and in some cases this time is much longer with some substrates

like, for example, long chain molecules of oils and greases, as we experienced with lard.

Usually, in industrial biogas plants, there are no yearly-fix feeding diets. The feedings are

usually coming from the seasonal production and the proportions of substrates are also

ot fixed. Manures are often used for Biogas production, but also as stabilizers of pH and

es can be responsible for rapid formation of volatile acids,

hich decreases de pH. This can be overcome by the use of nitrogen rich waste such as

5,7 – 6,0 (Zoetemeyer et al.,

982).

n

nitrogen content. Some substrat

w

poultry excreta.

Some residues should be pre-treated before they can be successfully treated with

anaerobic digestion process (Gamal-El-Din et al., 1984). Pre-treatment could include pH

adjustment to 7.0 and nutrient addition, particularly nitrogen. Reducing the loading rate

may also improve the biogas production. Also two-phase anaerobic digestion could be

suitable for treating these residues. A pre-treatment reactor can be suitable for example

for soluble carbohydrates containing wastewaters, where it was found that the optimum

pH for the separate acidogenesis of them is in the range of

1

By using the Pre-Enzymatic Treatment of straw and Celluclast enzymes, the yield in the

bioreactors can be improved rapidly. Because the concentration of sugars the first step of

the process, the Hydrolysis, is overcome by the already present sugar in the substrate.

This brings a faster digestion for the fermentation bacteria whose can produce at once

hydrogen and carbon dioxide, whose can be later converted to acetates and finally

methane. Despite the effectiveness of this substrate, one must consider the total solids

content of it (0,3 and 3,15% in our case). By feeding only with such a substrate, the

resulting TS of the bioreactor yield will decay until too low levels (<9%), which will make

the biogas yield decay in the middle term.

8.4. Model results

After several attempts with a higher complexity in the modeling of the anaerobic process

and reactions of the laboratory bioreactors, it was found to be more accurate to simplify

assumptions and to model the start-up of the process.



The original model used as reference for the here presented was more complex in terms

of calculations. Considering, among the presented equations, an ion balance for alkalinity

nd a calculation for the carbon partial pressure and carbon dioxide concentration were

h off-line measurements and later analysis for the sludge of the

ioreactors. However, these steady states (whose are not totally flat) do not represent

ond graph shows how the organic substrate decreases to the increasing bacteria

opulation, which consumes the substrate faster after every feeding. This same bacteria

acids measured in laboratory was 2,25g/L,

onsidering acetic, propionic, butyric and valeric acids.

a

also included. Thus, as output in the variable vector were also included the total alkalinity

and the total inorganic carbon.

The results presented graphically are accurate in terms of the steady state values reached

after five days of operation of the system. They represent truthfully the real values

obtained throug

b

exactly what in reality happens, because there will be always variations in the response

due to the characteristics of this bio-system, and the characteristics do not remain

constant. Despite this, the obtained results represent what the normal values in average

are.

It is possible to see in the graphical results that there is a constant proportion between

acidogenic and methanogenic bacteria, where the first ones are about 2,2 times the other.

The sec

p

growing, and specially the one of acidogenic ones, causes simultaneously a VFA

increasing concentration.

Considering the assumption of the characteristics for fatty acids (acetate, butyrate and

propionate) and a model behavior based on pure acetate, we can compare the reached

value after 5 days with the one measured on the bioreactor sludge. For the model, the

reached value is 37,92mmole/L, which can be expressed too as 2,28g/L as an

approximation by considering only acetic acid molecular mass (60,05g/mol). In the order

hand, the sum of the main volatile fatty

c



9. Conclusions For the optimal operation of a biogas reactor, it is recommended to follow the described

parameters in chapters 3.2 and 3.5. These were collected from updated literature and

Germany, as well as other countries. The right temperature, pH

ese last parameters to be measured in laboratories could be estimated and

sts of operation.

values fewer than six in the substrate when feeding. One

he new substrate with the old one when changing. In this way the

n simulated, there are relations

found and used for the modeling of such processes. There were strong

correlations especially between ammonia with COD (0,97) and nitrogen content (0,92).

Since the ammonia measuring test is the simplest and fastest method, these relations

could be used to estimate COD and nitrogen variations by measuring only ammonia

content in the sludge.

The maximal yield obtained from the main reactor in a sustainable basis was around 1l

(±0,1) with 65% methane content. This was possible with regular feeding of corn silage

and pig manure (3:1), 25% TS and 180 days HRT. This yield can be associated

actual running plants in

value and dry matter content have to be controlled constantly. Gas concentration should

also be controlled as frequently as possible to take adequate measurements in case of

anomalies. Other parameters, such as volatile fatty acids content (VFA), ammonia,

nitrogen and chemical oxygen demand (COD) should, ideally, be controlled as well.

However, this analysis can be done online, but limited by economics (sensors) or time

(development of soft-sensors) and therefore require proper laboratory equipment, not

always available. With the aid of a proper system, using simulation software and sofl-

sensors, th

therefore reduce the co

When switching from one substrate to another, it is recommended to consider to keep

constant parameters for the next feed such as TVS and total COD content, for maintaining

a constant (or increasing) biogas yield. This will avoid drastic changes in terms of total

TVS and total COD content inside the reactor, keeping the system stable. It is also

important to avoid always pH

good measure is to mix t

anaerobic bacteria in the sludge, especially the methanogenic communities (more

sensible to changes) can be adapted to the new feeding. Acidogenic bacteria are not as

sensitive as the methanogenic types. This can be seen when there is an abrupt change in

feeding, the pH value becomes more acid (lower) and the CO2 percentage in the biogas

increases.

Among the parameters chosen to be controlled, and later o

that can be



theoretically with a power yield of 180 Wh/d, considering 7,5 kWh/m³CH4 (Boxer –

fodienst). Despite this, the reactor was not working at maximum capacity. In fact, by

he dry feeding from 100 to 160g, it was possible to obtain yield average-peaks

and a description of the yield

inhibition or dying of bacteria. Nevertheless, useful approximations

h the previous range of concentrations.

In

increasing t

of 2,3 liters/hour, but this was not done in a regular basis due to the large feed storage

needed.

For the measurement of COD on substrates with a higher TS content than 3%, a proper

methodology must still be developed. The actual measurement systems, based on the

use of kuvette tests are appropriate for liquid substrates and sludge but not completely

appropriate for higher solid contents.

For the modeling of anaerobic processes, to be accurate, a higher level of complexity is

needed. Hydrolysis process must also be considered to describe in a more complete way,

how the system behaves. There are some phenomena not considered in such models,

like the bio-availability of the substrates to the bacteria

consequences due to

for these kinds of bioprocesses can be found by taking the correct assumptions, to be

later clear with the interpretation of the output results.

The major disturbances when changing feeding are always related to changes in the COD

and TVS feed amount (more than 20% difference). This can be understood by the later

analysis probes that have shown drastic changes in the VFA concentrations (see chapter

12.6) when the feed was changed in terms of TVS content. By changing the feeding from

41g to 3g dry matter (0,5 and 1L solutions), the TVS% of sludge decrease in 65%,

simultaneously the VFA concentration rose, along with an of the acetic acid content from

30 to 130ppm. Nevertheless, after several feedings with low TVS content the VFA

became stable again wit



10. Abbreviations

Letters Meaning

AD Anaerobic Digestion

COD Chemical Oxygen Demand

DM Dry Matter

FID Flame Ionization Detector

HF Hollow Fiber

HRT Hydraulic Retention Time

IR Infra Red detector

MDS Methane Detection System

MRT Micro-organism retention time

NVS Non volatile solids

PM Pig Manure

SRT Solids retention time

TN Total Nitrogen content

TS Total Solids

TTZ Technologie Transfer Zentrum

TVS Total Volatile Solids

VFA Volatile Fatty Acids

VOCs Volatile Organic Compounds

VS Organic Material



11. Bibliography

lume I: Biogas Basics Biogas Digest – Information and y

[2] TZ Volum cation and Product Development Biogas Digest

– rmation and Appropriate Technology [3] U*, Rodrígu rolleghem PA. IWA Conference

on ENVIRONMENTAL BIOTECHNOLOGY Application of the IWA ADM1 m l to simulat namics using a concise set of practical measurem A cations in th ecember 9th – 10th, 2003 Kuala Lumpur

sia. [4] Mata- Alvarez, J. 2003

ipal Solid W ondon UK. [5] uren, A. A Chinese Biogas Manual. Intermediate Technology

ations Ltd., [6] ne DJ, Kelle lyuzhnyl SV, Pavlsostathis SG, Rozzi

A, Sandera WTM, Slegris The IWA Anaerobic D stion Model ce and Technology, Vol 45 N^10 pp

[7] u-Aspenten is, Quantitative Effects on

Processing. The Pennsylvania State University [8] Bouville M. Fermentation kinetics including product and substrate

ions plus matical analysis. Institute of als Resear pore 117602 (Dated: Oct. 9,

2005) http://arxiv.org/PS_cache/q-bio/pdf/0509/0509014.pdf

[1] Isat / GTZ Vo

Advisory Service on Appropriate Technolog

Isat / G e II: Appli Info Advisory Service on

Zaher ez J, Franco A, and Van

ode e anaerobic digester dyents Advancement on Water and Wastewater

e Tropics, DppliMalay

Biomethanization of Organic Fraction ofMunic astes IWA Publishing. L

Van BPublic 1979.

Batsto r J, Angelidaki I, Kat H, and Vavillin VA.

(ADM1). Water Scienige

Owus RK. Food Protein Analys

inhibitMateri

biomass death: a mathech and Engineering, Singa

[9] Vavilin V.A., Ya. Lokshina L., Rytov. Vestnik S.V. Moskovskogo

Universiteta. Khimiya. 2000. Vol.41, No.6. The <Methane> simulation model as the first generic user-friend model of anaerobic digestion. http://www.chem.msu.su/eng/journals/vmgu/00add/22.pdf

[10] Carrillo L. ( 2003) Microbiología Agrícola. Charter 5 – Rumen y Biogas [11] Gamal-El-Din H, El-Bassel A, and El-Badry M. Faculty of Agriculture, Cairo

University, Fayum, Egypt. Biogas production fromn some organic wastes

[12] Zoetemeyer R.J, Van den Heuvel JC, and Cohen A. pH Influence of acidogenic dissimilation of glucose in an anaerobic digester. 1982.

[13] Hutnan M. et al. Anaerobic Treatment of Wheat Stillage. Chem.

Biochemical Engineering Q. 17(3) 233-241 (2003)



[14] Buyukkamaci N, Filibeli A. Volatile fatty acid formation in an anaerobic hybrid reactor. Process Biochemestry 39 (2004) 1491-1494

tification for an Anaerobic

ki/Biogas

[15] Bernard O, Hadj-Sadok Z, Dochain D, Genovesi A, Steyer JP. Dynamical

Model Development and Parameter IdenWastewater Treatment Process. Biotechnology and Bioengineering 75-4 (2001) 424-438

[16] Bastin G, Dochain D. On-line estimation and adaptive control of

bioreactors. Amsterdam: Elsevier (1990)

Websites

[17] http://en.wikipedia.org/wi

[18] http://www.unu.edu/unupress/unupbooks/80434e/80434E0k.htm

[19] http://www.mconbio.com/index.html

http://www.bioconverter.com[20]

[21] http://www.united-tech.com/wd-anaerobicdigestion.html

[22] http://www.seilnacht.com/referate/biogas01.htm#1.



12. Attachments

MIXER

Out-let and Sampling

pH METER TEMPERATURE FLOW METER

42-45 L

Gas Flow out

PC DATA

COLLECTOR

Feeding Inlet

IR GAS

ANALYSER

LABORATORY

• COD • VFA • TS • TVS • TN • Ammonia

• CH4 • CO2 • O2 • pH • temperature

WATER HEATING SYSTEM

Major Bioreactor

12 –15 L

Minor Bioreactor

Out-let and Sampling

12.1. Detail of reactors

gas pilot plant is composed by the anaerobic digester and the peripheral devices

sure different parameters of the AD

The bio

mea process. In Fig. 23 are represented two

easu em according the necessities of the

Fig. 24. General scheme of the biogas pilot plant.

to

bioreactors but not always they operate in parallel. The peripheral devices to undertake

rements have been alternatively adapted to thm

experiment.



12.1.1. Anaerobic digesters epending of the necessities of the experiment, the AD plant have been operated using a

e Fig. 24 for the detailed description of them.

he specifications form the customer QVF are the following:

D

major of a minor digester. Se

T

Reactor Major Minor

Upper part DVZ50/12 DVZ30/12

Lower part VZA450/2 VZA12/2

Major bioreactor (main one)

he major digester corresponds to a QVF 60 L glass reactor with an external glass jacket

for thermostatisation.

It has five holes on the top (for with the same size and a central bigger) and one hole at

the bottom of the reactor. Each hole has been used to fix different devices. On the top are

fixed the system for stirring, a pH-temperature probe, membrane connectors, an inlet for

feeding the process and biogas outlet. The emptying and sampling of the reactor have

been done from the bottom.

The water and gas-tightness of the reactor has been tested up to 0,75 bars after placing

all the peripheral devices.

Minor bioreactor (secondary one) The minor bioreactor is a 20 L glass reactor (total capacity) provided by QVF, with an

external glass jacket for thermostatisation.

It has five holes on the top and one at the bottom of the reactor (all with the same size).

Each hole has been used to fix different devices. On the top are fixed the system for

stirring, a pH-temperature probe, membrane connectors, an inlet for feeding the process

and biogas outlet. The emptying and sampling of the reactor have been done from the

bottom.

T



Fig. 25. Detailed specification of the glass bioreactors used for anaerobic digestion process.

A

I

B

C

J

D

E

G

H

F I

Upper

K

Side view view

L

UPPEPROPEL

R LER

LOWER PROPELLER

M

DESCRIPTION OF THE MAJOR AND MINOR REACTORS

O

Dimensions of the reactors in cm

A B C D E F G H I J K L M N

Major reactor 90,5 90 53,5 30 32 78,5 4 8,6 20 45 10 3,5 3,5 37

Minor

reactor 71 55 31 25 28 43,5 4 4 13 15 8 2 3 40



12.2. Calculation sheets and graphic results. 2.2.1. Experiments B1 and B2 1

Fig. 26. Calculation of the on-line permeability. Concentration in ppm of CH4 permeated through the membrane is measured with FID, for the later calculation of permeability. Exp. B1 + B2



Fig. 27. Data sheet with volume of produced biogas, with concentrations and feedings register. This data was manually collected in a paper which was transcribed later. Dry matter amount and as percentage of feedings is calculated. Experiments B1 + B2.



ig. 28. Calculation of ratio of biogas quality. After measuring the biogas concentration with an F

infra red analyzer, the concentration percentage of methane is divided by the one of carbon dioxide to obtain the ratio. Experiments B1 + B2.



Table XI. Temperature and pH value were constantly measured inside the bioreactor. Measurements were recorded every 10 minutes. Unfortunately the sensors need to be cleaned and calibrated in a regular basis and after this, the measurements vary. Afterwards, the measurements were done once a week, with an external device to check accuracy. Experiment B1+ B2




Fig. 29. Results of first experiment measured on-line. Permeability, ratio CH4/CO2 and yield in L/h. Experiment B1.



Fig. 30. carbon dioxide content. Ratio CH4/CO2 with tendency. Experiment B1.

Results of first experiment measured on-line. Gas yield, indicating methane and



Fig. 31. Results of second experiment (after first membrane modification) measured online. Gas yield, indicating methane and carbon dioxide content. Rat

-io CH4/CO2 with

tendency. Experiment B2.



Fig. 32. Results of second experiment measured on-line. Gas yield, indicating methane and carbon dioxide content. Ratio CH4/CO2 with tendency. Experiment B2.



Fig. 33. Graphical behavior of pH v/s time, with permeation curve as reference. B1.



Fig.34. Calculation of the on-line permeability. Concentration in ppm of CH4 permeated through the membrane is measured with FID, for the later calculation of permeability. Experiment B3.

12.2.2. Experiment B3


Fig. 35. Data sheet with volume of produced biogas, with concentrations and feedings register. Dry matter amount and percentage of feedings is calculated. Experiment K3.




Fig n . 36. Calculation of ratio of biogas quality. After measuring the biogas concentration with ainfrared analyzer, the concentration percentage of methane is divided by the one of carbon dioxide to obtain the ratio. Experiment K3.


Table XII. Temperature and pH value were constantly measured inside the bioreactor. Measurements were recorded every 10 minutes. Unfortunately the sensors need to be cleaned and calibrated in a regular basis and after this, the measurements vary. Afterwards, the measurements were done once a week, with an external device to check accuracy. Experiment B3.



Fig. 37. Permeation curve together with biogas quality ratio and produced volume is displayed. Feedings are just showed as dash points (no units).




Fig. 38. Biogas quality ratio along with feedings expressed in mass and TS%.


Fig. 39. Biogas quality ratio together with volume produced in time.




Fig. 40. Graphical behavior of pH v/s time, with permeation curve as reference. B3.



2.2.3. Experiment K3

register. Dry atter amount and as percentage of feedings is calculated. Experiment K3.

1

ig. 41. Data sheet with volume of produced biogas, concentrations and feedingsFm


Experiment K3.

2.

2 and the

ion of

gs,

Figure 4Concentration percentage of CH4 and CO

biogas and calculatquality ratio. Oxygen was also sometimesdetected, especially after feedinwhen someair got inside the reactor.




Fig. 43. Experiment K3. Gas quality ratio, liters per hour and feedings Feedings are expressed in grams of dry matter.

are shown together.



Fig. 44. Experiment K3. Feedings are shown in mass and TS percentage. Along with them, gas ality ratio and biogas yield are displayed. qu



2.3. Experiments description.

1

B1. MEMBRANE E I

Period 11.10.2005 18:00 – 01.12.2005 10:20 (~ 55 days)

Description The main objective was to try a membrane for a long period to analyse the

accumulation of biofilm in the membrane and the decrease in permeability of it.

Different substrates were tried, as first experiment for BioRealSim, to analyse their

yield and effect in the biogas generation. The reactor Pedrito was feed with: pig

manure, straw, pre-treated and not pre-treated straw and pig lard.

Membrane E-I consisted into a silicon hose (OD 0,7cm; ID 0,5cm; length 122,1cm;

thickness 0,1cm) rolled and cached it with a hanging weight.

Results The membrane showed a clear tendency to decrease its permeability and did not

reach a stable measuring level. Unfortunately, the equipment for the permeability

measurement was send for maintenance during the second half of the experiment and

it was not possible to reach 25 days of measurements for it.

A constant increase in the biogas quality CH4/CO2 was detected. Also an increase in

the gas volume was detected after the feeding of pig lard.

The hose shown strangles when taken off the reactor.

Conclusions Feeding with Pig Manure shown the best and fastest yield for the reactor.

Pig lard feeding was slower in reaction but with a yield a least three times bigger than

pig manure.

No sustain stabilization in the membrane’s permeability was detected.

Strangles might cause wrong or poor measurements of permeability.

B2. MEMBRANE E-II

Period 1.12.2005 12:00 – 21.12.05 11:50 (~ 20 days)

Description The experiment was a continuation of the previous. Only the membrane hose was

improved for better measurements. The improvement consisted into roll the hose

across a flexible foam-gummy round, to avoid strangles.


The reactor was feed with food waste and alpechín (waste from olive oil production).

ibit the reaction by overfeeding, but the result was,

increases 200%. However, there is

ation about the biogas quality, because the gas �nalyzer (Ansyco) was on

Conclusions

yield after daily

ue to the fact

s affect the gas flow and therefore the measurements.

In the beginning the idea was to inh

on contrary, a higher yield in the case of food waste.

ResultsDuring the daily feeding with food waste, the yield

no inform

maintenance.

After 15 days, the permeability showed a constant behaviour.

The food waste, mainly composed of cooked food, is easily digestible for the reactor.

Therefore, there was no inhibition but, on the contrary, increase of

basis feeding.

Avoiding strangle in the membrane must be consider as a high priority, d

that strangle

B 1+2. MEMBRANE E2

Period

Debetter understanding of the

er, chemical

the analysis done with the samples taken during each feeding.

Corange of ±16%

onstant.

variation of ±6,5% until the feeding of lard, afterwards increase

and remain during the food waste daily feeding.

11.10.2005 18:00 – 21.12.05 11:50 (~ 75 days)

scription These are the experiments B1 and B2 put together for a

whole experiment.

Results Here there is also a graph showing the later measurements of dry mat

oxygen demand, as well as nitrogen and ammonium concentrations. These

correspond to

nclusions Ammonium, nitrogen and total solids levels remain between a

c

COD remain with a

15%

B3. PEDRITO RELOADED MEMBRANE E-III

Per

DeThe reactor “pedrito” was reloaded completely with a new sludge coming from a real

500kW bioreactor from the company MT-Energie. The objective of this experiment is

iod 09.02.2005 11:52 –

scription



to emulate a real plant with the same sludge and substrates for feeding and, in this

way, do further tests without affecting a real plant.

The feeding was brought from the same company. It consists of corn sewage.

ame membrane used before for B1 and B2 was now modified in its form. The

Ree

has been at constant ranges of operation. In opposite to the previous

Co

ess reason of this

ter one is due to its straight shape that gives no chance for strangles.

The s

hose was cut into two pieces and putted in parallel with two Y-connectors. This new

membrane is 6cm shorter than the previous ones.

sults The membrane has shown much better results in its performance, since th

permeation

membranes, the E-III did not show a decay in its permeation curves due to strangles

or bio-film formation.

nclusions It is recommended to use during the following experiment this arrangement for the

membrane because of its improved performance. The main succ

la

B4. Small reactor (kitty) feed from the major (Pedrito)

Per0 – 31/05 1700

Deptation containing 10L of sludge coming from the major at 37 °C

w mixing (around 25 rpm), the small reactor has been feed with around 12 L

ive is to feed kitty with different substrate but using always the same

Re manure constantly. The feed TS

ad been changing in time due to the corn drying. Nevertheless, smaller amounts of

tal TS of them. Despite this, it is calculated

the feedings have been increasing their TS from 21 to 27% during the first two

feedings in industrial scale, taking as reference MT-Energie, are around

Co

iod 06/03 113

scription After 5 days of ada

and lo

more from the major. The revolutions of the small have been fixed in 125 rpm. The

measurement conducted on it will be the biogas total volume, CH4/CO2 ratio and

chemical measurements on the sludge.

The object

inoculums coming form Pedrito. The proportion inoculums/feeding will be always the

same ()

sults The reactor has been feeding with corn silage and pig

h

corn mass had been feed to maintain the to

that

months of operation.

Since the

27% TS, the feedings for the lab-scale reactors will be sustain at 27%.

nclusions



For the maintenance of the desirable TS, the substrate must be controlled

permanently, because there is a constant drying and fermentation of the corn silage.

rocesses of a real size plant. The

of corn, for example, are much greater for the size of 20 litters in compare with

The conditions of the real size plants are not reproducible in a small laboratory scale

reactor. Due to differences in design, stirring and specially, proportionality, it is not

possible to emulate exactly the mechanism and p

grains

their proportion with 2500 m3. This causes a totally different mechanical situation for

the bacteria and for the internal reactions of the process, whose are similar but not the

same with the ones observed in the bigger bioreactors.



12.

4. Calculation of TS and TVS for reactor’s sludge and feed substrates.

Fig. 45. Example sheet for calculation of TS and TVS. Samples of substrates and reactor’s sludge were weighted and then dried at 105°C to measure dry matter, afterwards they were heated to 550°C to estimate TVS.



12.5. Data sheet used to write gas volume readings, concentrations,

feedings and comments.

Fig. 46. Example sheet used to write down measurements of produced volume of biogas,

mperature pH value, concentrations, permeation. As well as maintenance, feedings and bservations.

teo



12.6. Later analysis results.

Fig. 47. Off-line results from Experiment B3. Including COD, TVS, Dry feedings, TS, TNb and Ammonia.

Fig. 48. Off-line results from Experiment B3. Detail of COD, TNb and Ammonia, contrasted by dry feedings.



Fig. 49. Complete later analysis results from experiment B3, correspondin


g to reactor “Pedrito”.

Table XIII. Correlat and covariance’s between measured paramet f experiment B3.

ionsers o


Fig. 50. Complete later analysis results from experiment K3, corresponding to reactor “Kitty”.

Table XIV. Correlations and covariance’s between measured parameters of experiment K3.




Fig. 51 and 52. VFA later analysis, including acetic, propionic, osobutyric, butyric, valeric, isocaproic, caproic and heptanoic acids.


Fig 53. VFA later analysis. Complete results. Every measurement was done in duplicate, these are the average values for each measurement.


Master Thesis PEET Hochschule Bremerhaven Master Thesis PEET Hochschule Bremerhaven



Documents

Optimization and modeling of biogas production with various substrates