Monitoring the anaerobic digestion process

Transcription

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27.11.10 17:55
Monitoring the anaerobic digestion process
by
Harry Michael Falk
file:///Users/harry/Dropbox/PhD/proposal/Logo_JacobsUniversity.svg
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A thesis submitted in partial fulfilment
of the requirements for the degree
Doctor of Philosophy
in Biochemical Engineering
Approved, Thesis Committee
...................................................................................................................
Prof. Dr. Dr. h.c. Roland Benz
...................................................................................................................
Prof. Dr. Volker C. Hass
...................................................................................................................
Prof. Dr. Laurenz Thomsen
...................................................................................................................
Prof. Dr. Mathias Winterhalter
Date of Defense: December 13, 2011
School of Engineering and Science
Declaration
I hereby declare that my thesis entitled “Monitoring the anaerobic digestion process”
is the result of my own work. I did not receive any help or support from commercial
consultants. All sources and/or materials applied are listed and specified in the thesis.
Furthermore, I verify that this thesis has not yet been submitted as part of another examination process neither in identical nor in similar form.
Bremen, September 17, 2012
Harry Falk
2
Abstract
In the anaerobic digestion process, microorganism produce methane and carbon dioxide from organic substrates, either organic waste or renewable primary products. Being
a versatile biofuel, biogas can be combusted in a combined heat and power plant to
produce electricity and heat or, after purification, fed directly into the natural gas grid
as biomethane. Due to the Renewable Energy Sources Act introduced in 1991 in Germany, this process became economically advantageous and led to a boom of biogas
plants being built in Germany.
A major problem of operating a biogas plant is to monitor an unstable process over
time. Parameters like pH or redox potential do not necessarily suffice to estimate the
degree of fermentation. At present, the preferred indication parameter are the concentrations of process intermediates, particularly short chain volatile fatty acids. They
can be quantified with different gas or liquid chromatographic methods, which requires
in-depth knowledge and expensive hardware and is usually carried out by specialized
laboratories. Periodically, the digestate is sampled and sent in for analysis. Knowing
the absolute concentrations of the different volatile fatty acids can only give a hint about
the current fermentation status, though what would be more meaningful would be elucidating the dynamics of generation and degradation of the respective short chain volatile
fatty acids.
A new online technique using attenuated total-reflectance Fourier-transformed infrared
spectroscopy (ATR-MIR-FTIR) was developed, which allows an online monitoring of the
concentrations of the different volatile fatty acids in situ. This can give an insight into
the dynamics of the anaerobic digestion process. It was adapted to a laboratory scale
one-stage biogas plant fed with typically renewable primary products to simulate an agricultural biogas plant. Chemometric models were developed using spiked samples and
samples from a real fermentation for acetic, propionic, iso-butyric, butyric, iso-valeric
and valeric acid. The methods were evaluated by monitoring the startup phase of the
anaerobic digestion of ground wheat in a 10 l continuous-stirred tank reactor. Sample preparation, recording and analysis of IR-spectra of digestate were fully automated.
Predictions of the absolute concentration for acetic and propionic acid were reasonable,
the existence of other volatile fatty acids could be detected. The developed anaerobic
sensor system is able to determine their concentration dynamics and can thereby help
to utilize unused potential in biogas plants.
Another ascending problem are the substrates being used for the production of biogas. Renewable primary products are in direct rivalry with the agricultural and food
industry. For a sustainable future, other biomass sources have to be made accessible for energy production. In contrast to energy crops, especially waste products can
be used for energy generation without any concerns. The biogas potential of potential substrates is estimated with parallel running batch experiments. In the process, the
minor gas flow rate of these lab-scale fermentations has to be monitored closely and
accurately. Especially for this purpose, an easy to build and maintain automated biogas
meter was developed to measure the biogas flow in anaerobic digestion experiments.
The flow meter is built upon the open-source Arduino platform, and can therefore be
easily enhanced or adapted to other environments. Recordings are sent via ethernet to
a MySQL database, making the data widely accessible. By combining nine fermenters
into an array, triplicate batch experiments according to VDI 4630 with negative control,
positive control and the test substrate are monitored effortlessly.
Concluding, this thesis focuses on optimizing the biogas production with the development of novel measurement instrumentation. The already well-established process of
industrial digestion of energy crops is made transparent with an advanced online measurement method for the volatile fatty acids. Methane potential of novel biodegradable
substrates can be monitored with the affordable low-cost gasUino biogas meter.
4
Contents
1. Introduction
1.1. Energy supply
8
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
1.2. Biogas as a renewable energy source . . . . . . . . . . . . . . . . . . . .
8
1.3. EEG 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4. Basics of anaerobic digestion . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.4.1. Hydrolysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.4.2. Acidogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.4.3. Acetogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.4.4. Methanogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.5. Process parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.5.1. Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.5.2. pH value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.5.3. Volatile Fatty Acids (VFA) . . . . . . . . . . . . . . . . . . . . . . . 18
1.5.4. Total Solids and Volatile solids . . . . . . . . . . . . . . . . . . . . 19
1.5.5. Biogas potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.5.6. Organic loading rate (OLR) . . . . . . . . . . . . . . . . . . . . . . 20
1.5.7. Hydraulic Retention Time (HRT) . . . . . . . . . . . . . . . . . . . 21
1.6. Process variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.7. Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2. Materials and Methods
25
2.1. Biogas yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2. Total solids and volatile solids . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.3. Sample preparation and analysis . . . . . . . . . . . . . . . . . . . . . . . 27
2.4. High Performance Liquid Chromatography (HPLC) . . . . . . . . . . . . . 27
2.4.1. Theory
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.4.2. Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5
Contents
2.5. IR Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.5.1. Infrared radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.5.2. Michelson Interferometer . . . . . . . . . . . . . . . . . . . . . . . 30
2.5.3. Attenuated total reflectance . . . . . . . . . . . . . . . . . . . . . . 33
2.5.4. Quantitative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3. Online monitoring of the volatile fatty acid dynamics with MIR spectroscopy 35
3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2. Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2.1. Lab-scale biogas plant . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2.2. Analytical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2.3. Controlling software . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2.4. PLS method development and validation approach . . . . . . . . . 40
3.3. Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.3.1. FTIR-MIR spectra of the digestate . . . . . . . . . . . . . . . . . . 42
3.3.2. Chemometric analyses
. . . . . . . . . . . . . . . . . . . . . . . . 44
3.3.3. Test of the developed methods . . . . . . . . . . . . . . . . . . . . 47
4. gasUino - biogas flow meter
51
4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.2. Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.2.1. Flow meter design . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.2.2. Electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.2.3. Database & Web interface . . . . . . . . . . . . . . . . . . . . . . . 55
4.2.4. Fermenter bottle . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2.5. batchLab fermenter array . . . . . . . . . . . . . . . . . . . . . . . 57
4.2.6. Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.3. Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5. Conclusion
62
List of Tables
64
List of Figures
65
6
Contents
Bibliography
68
A. Appendix
75
A.1. Arduino sketch sourcecode . . . . . . . . . . . . . . . . . . . . . . . . . . 75
A.2. SQL statements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
A.3. LabVIEW Virtual Instruments . . . . . . . . . . . . . . . . . . . . . . . . . 79
7
1. Introduction
1.1. Energy supply
The industrialized society is almost exclusively based on the use of fossil energy carriers. Coal, oil and natural gas are the main boosters of innovation in the last 100 years.
Since the 1950s, also nuclear power is used as a source of energy, but the difficulty
of an ultimate disposal of the nuclear waste and recent nuclear disaster of Fukushima,
Japan leads to rethinking the safety and environmental impact. In Germany, all remaining nuclear power plants will go off-grid in 2022 [25]. Unfortunately, all fossil fuels are
limited too and in the near future, all natural gas reservoirs and oil fields will have been
exploited. Furthermore, the release of carbon dioxide into the atmosphere accelerates
global warming and a catastrophic climate change (see 1.1). A transition to renewable, CO2 - neutral energies is not only necessary, but the only way for sustainabil-
ity. The energy market of the future will be a mixture of all available energy sources
- wind energy, geothermal heat, hydro power, solar energy and heat and energy from
biomass, through different processes like combustion, thermochemical transformation
(carbonization, biochar), physical-chemical transformation and biochemical transformation to ethanol or biogas. Especially the field of anaerobic digestion has a great potential
for further improvements in the future [33].
1.2. Biogas as a renewable energy source
Biogas is a mixture of methane (CH4 : 55 - 70 %), carbon dioxide (CO2 : 30 - 45 %) and
trace gases such as ammonia (NH3 ) and hydrogen sulfide (H2 S).The energy content is
between 6 - 6.5 kWh · m-3 , which equals 0.6 - 0.65 L of oil · m-3 [33]. A symbiosis of
various bacteria degrade organic matter under anaerobic conditions to the energetically
interesting final product methane. The first systematic studies were conducted in 1770
by the physicist Alessandro Volta. He recognized the combustibility of marsh gas. Faraday later identified the combustible part as a hydrocarbon, but only in 1821, Avogadro
8
1. Introduction
concentrations.pdf 2006-08-23 16:37
C
M
Y
CM
MY
CY
CMY
K
Figure 1.1.: Past and future CO2 concentrations. Since pre-industrial times, the atmospheric concentration of greenhouse gases has grown significantly. Carbon
dioxide concentration has increased by about 31 %, methane concentration
by about 150 %, and nitrous oxide concentration by about 16 % [77]. The
present level of carbon dioxide concentration (around 375 parts per million)
is the highest for 420 000 years, and probably the highest for the past 20
million years. [56]
9
Fachverband Biogas e.V.
Angerbrunnenstraße 12
85356 Freising
Telefon +49(0)81 61/98 46 60
Telefax +49(0)81 61/98 46 70
E-Mail [email protected]
Biogas Segment
Statistics 2010
1. Introduction
Development of the number of biogas plants and the total installed electric output
in megawatt [MW] (as of 06/2011)
7.000
2500
5.905
6.000
Number of biogas plants
2.291
Installed electric output (MW)
2000
4.984
1.893
5.000
3.891
3.711
3.500
4.000
1500
1.377
1.271
3.000
2.000
1.000
1.100
2.680
450
274 370
139 159 186
617
850
2.050
1.750
1.600
1.300
1.050
50
65
182
256
333
1000
650
500
390
0
0
Figure 1.2.: Development
of the
number
of biogas plants
the total installed electric
Biogas
Sector
Statistics
at aand
Glance
output in megawatt [MW] (as of 06/2011) in Germany [19]
End of 2010
Forecast for 2011
Number of plants
5.905 (45)
7.000 (60)
(of these feeding biomethane)
managed to determine
the chemical formula
for methane [61].
The agricultural use of
Installed electric output
biogas fermenters
began
in the second half
of the 20th century, 2.728
but many biogas plants
2.291
in MW
were decommissioned
only a short time due to defective function and the boom of
Net electricity after
production
cheap oil.
in MWh per annum
14,8 Mio.
17,8 Mio.
Homes supplied with
4,2 Mio.
5,1 Mio.
A renaissance
resulted
as a consequence
of the oil crisis 1972.
A change in thinking
biogas-based
electricity
took place for
alternatives
to fossil fuels and and a dependency on foreign countries was
Proportion
of electricity
2,46
3,1
consumption in %
questioned. The
biogas process was further developed in the following years. EspeTurnover in
Germany
5,1the
Mrd.amendment of the
5,9 Mrd.
cially the Electricity
Feed
Act inof€ 1990 and
Renewable Energies
Jobs 2004, and the development
39.100
44.500 plants with better
Act (EEG) in 2000 and
of cogeneration
in %
10
10
efficiency had aExport
greatrate
impact
of the number
of biogas plants. This
led to an exponenSource: Fachverband Biogas e.V.
tial increase in biogas plants in Germany to about 6000 plants
in the last 20 years (see
figure 1.2). Also in developing countries like China or India the utilization of biogas as
an energy source has been established. Thousands of mini-scale biogas plants of various capacities are supplying a great number of households with electricity and gas for
cooking purpose [43].
10
1. Introduction
1.3. EEG 2012
The latest version of the "Erneuerbare Energien Gesetz (EEG)" in Germany, which translates to “Renewable Energy Sources Act”, becomes effective in January 2012 and includes great changes for biomass based renewable energy. Paragraph 1, clause 1 of
the EEG lists the following points as the main goals [38]:
1. Sustainable development of energy supply
2. Reducing the economical costs of energy supply considering long-term external
effects
3. Protection of nature and environment
4. Reduction of conflicts over fossil energy resources
5. Promote the development of technologies for the production of electricity from renewable energies
The future plan is to increase the share of the renewable energies in the total energy
market to 35 % until 2020, 50 % until 2030, 65 % until 2040 and 80 % until 2050 and to
integrate this amount of electricity into the electric supply.
In previous versions of the EEG, a fixed amount was paid for the kWh, depending
on the plant size (between 7.79 and 11.67 cent). Furthermore, different bonuses were
added, which increased the compensation. Most importantly was the "Nachwachsende
Rohstoffe (NaWaRo) Bonus, which added a compensation between 7 cent and 11 cent
per kWh when exclusively using non-processed renewable raw materials (e.g. grain,
oil, wood, waste of landscape management). The usage of the waste heat was also
rewarded monetarily with 3 cent per kWh, if the heat use was listed in the EEG (KWK
bonus). Other bonuses were the formaldehyde-bonus, a compensation of 1 cent per
kWh, if a certain concentration of formaldehyde in the exhaust gas was complied with.
A compensation supplement of 2 cent per kWh was added for the use of innovative
technologies, such as biogas upgrade to natural gas quality (technology bonus). Unfortunately, this led to an increasing demand of primary renewable products, which directly
competed with the food industry and arable land. With the new EEG in 2012, the bonus
system vanishes and is replaced with a more efficient compensation system based on
11
1. Introduction
3. Zu §§ 27, 27a und 27b EEG: Vergütungen für Strom aus Biomasse
3.1. Vergütungsstruktur für Strom aus Biomasse
Vergütung für
Biogasanlagen (ohne Bioabfall) und Festbrennstoffanlagen
Bemessungs
leistung
Grundvergütung
Einsatzstoff- Einsatzstoff- GasaufbereitungsBonus
vergütungs vergütungs
klasse
(§ 27c Abs.2)
klasse
II 3)
I 2)
[kW el ]
Bioabfallvergärungsanlagen 5)
(§ 27a)
Kleine
GülleAnlagen
(§ 27b)
[ct/kWh]
75 4)
6)
25
700 Nm³/h: 3
150
500
14,3
12,3
6
750
11
5
5.000
11
4
20.000
6
-
8
1.000 Nm³/h: 2
16
1.400 Nm³/h: 1
8 / 6 4)
14
-
2)
3)
4)
5)
Über 500 kW bis 5.000 kW nur 2,5 ct/kWh für Strom aus Rinde und Waldrestholz.
Nur für ausgewählte, ökologisch wünschenswerte Einsatzstoffe.
Über 500 kW bis 5.000 kW nur 6 ct/kWh für Strom aus Gülle (nur Nr. 3, 9, 11 bis 15 der Anlage 3 BiomasseV).
Gilt ausschließlich für Biogasanlagen, die bestimmte Bioabfälle (nach § 27a Abs. 1) vergären und unmittelbar mit einer
Einrichtung zur Nachrotte der festen Gärrückstände verbunden sind. Die nachgerotteten Gärrückstände müssen stofflich
verwertet werden. Die Vergütung ist nur mit dem Gasaufbereitungs-Bonus kombinierbar.
6) Sonderkategorie für Gülle-Biogasanlagen bis 75 kW installierter Leistung am Standort der Biogaserzeugungsanlage, nicht
kombinierbar (d.h. keine zusätzliche Grund- oder Einsatzstoffvergütung bzw. Gasaufbereitungsbonus).
3.2. Grundvergütung für Anlagen zur Erzeugung von Strom aus Biomasse*
Degression 7): 2,0 %; Vergütungszeitraum 20 Jahre
Figure 1.3.:
Compensation
system
EEG 2012 [26] I oder II
Ohne einsatzstoffbezogene
Zusatzvergütungen
nach
Einsatzstoffvergütungsklasse
5 MW el - 20
MW el
the
and the methane potential of certain substrates (biomass,
which can be
in ct/kWh
2012 The sole
14,30
11,00 to only 60 6,00
fermented).
use of corn12,30
was also prohibited
% by weight as a coun2013
14,01
12,05
10,78
5,88
termeasure
Furthermore,
the basic
2014 to monocultures.
13,73
11,81
10,56 compensation
5,76 was increased, but
2015
13,46
11,58
10,35
5,65
the usage of the waste heat is a requisite. Small manure plants up
to 75 kW or digestion
2016
13,19
11,35
10,15
5,53
plants with
organic waste
received a separate
compensation
scheme between
2017 municipal
12,93
11,12
9,94
5,42
2018
12,67
10,90
9,74
5,32
16 cent and 25 cent per kWh. Figure 1.3 presents an overview of the new compensation
2019
12,41
10,68
9,55
5,21
2020Overall, the
structure.
positive idea
of creating a9,36
new dynamic on
12,17
10,46
5,10the substrate market
2021
11,92
10,26
9,17
5,00
Jahr der
Inbetriebamount
nahme
bis 150 kW el
in ct/kWh
500 kW el - 5 MW el
in ct/kWh
150 - 500 kW el
in ct/kWh
by widening the field of* Im
possible
substrate eligible for subsidization is being pursued.
Sinne der Verordnung über die Erzeugung von Strom aus Biomasse
in der crops
ab 1. Januar
geltenden Fassung
This will hopefully (Biomasseverordnung
help to shift the-BiomasseV)
use of food
as 2012
an exclusive
substrate to more
organic
waste biomass
(seeder
figure
1.4).
Gasaufbereitungsbonus
(§ 27c,instead
Abs. 2) unterliegen
Degression
von 2,0 % (§ 20, Abs. 2, Nr. 5).
7) Die Grundvergütung (§ 27, Abs. 1), die Vergütung für Bioabfallvergärungsanlagen (§ 27a), kleine Gülleanlagen (§ 27b) und der
6
12
1. Introduction
Utilisable energy potential of Biogas
Agricultural residues
13.7 PJ/a
Energy crops
(on 2 mio. hectares)
236 PJ/a
FigureUtilizable
3: Utilisableenergy
energy potential
(Hartmann/Kaltsschmitt,
2002, reworked
by FNR)
Figure 1.4.:
potential
(Hartmann/Kaltsschmitt,
2002,
reworked by
FNR) [54]
stainable production in European agriincreased utilisation of plant raw mateculture and forestry. One of the Federal
rials and energy sources is however that
Ministry of Agriculture’s (BMELV) main
they are produced and used sustainably.
1.4. Basics
of anaerobic
funding domains is to test these approaSustainability,
as defineddigestion
in the 1987
ches through research projects and to
Brundtland Report, means meeting the
further develop
them.
Some
of the stra-in which
needs
of
the
present
generation
without
The anaerobic fermentation is a biological process
involving
many
substeps,
tegies that are being pursued are:
compromising the ability of future genethe organic
carbon
is converted
to its1).most
form,
the most
2 , and
• Increasing
theCO
species
diversity
usedreduced
rations
to meet
their own needs
Su- oxidized
in
energy
crop
production;
stainability
therefore
has
an
environform, CH4 . The process of biogas production can be divided in four stages, whereas
• Breeding new varieties;
mental, an economic and a social dimenthe first sion.
and When
the second
as the
third•and
stage
are linked
closely with
Newfourth
production
methods
using lower
applied as
to well
renewable
raw
of pesticidesdifferent
and fertilisers
as may be
materials,
this means
utilisatieach other.
Depending
onthat
thetheir
degradability
ofdoses
the substrate,
stages
well as year-round vegetative cover on
on needs to strike a balance between
responsible
their limitednecessary,
decomposition
whatfor
is economically
such as [33].fields;
• The use of especially efficient converhigh and guaranteed biomass yields,
sion processes;
and what nature can be expected to tole1.4.1. Hydrolysis
• The recycling of residues as fertiliser.
rate. The social component refers among
other things to people’s working condiThe BMELV’s
task is to fundbacterial
research in
tions,
new
incomesubstrates
opportunities
a
In this first
step
organic
areand
disassembled
by extracellular
enzymes
an appropriate and consistent manner
share of value-added processes. There
into oligomers and monomers performed by facultative or obligatorily anaerobic bacterial
so as to develop the most suitable meare many different approaches to su-
such as Clostridium, Bacillus and Pseudomonas. Carbohydrate cleavage by cellulases,
xylanases or amylases result in simple sugars and normally takes place within a few
6
hours; proteins are degraded by peptidases to single amino acids and (oligo-)peptides,
and energy-rich and energy-rich lipids are decomposed by lipases into glycerol and fatty
acids, normally within a few days. The biodegradation of other macro molecules like
lignocellulose or lignin is very slow and incompletely [33].
13
1. Introduction
Table 1.1.: Examples of fermentation processes from glucose [72]
Product
Reaction
Acetic acid
C6 H12 O6 + 2 H2 O ! 2 CH3 COOH + 4 H2 + 2 CO2
Acetic, prop. acid C6 H12 O6 ! 4 CH3 CH2 COOH + 2 CH3 COOH + 2 CO2 + 2 H2 O
Butyric Acid
C6 H12 O6 ! CH3 CH2 CH2 COOH + 2 CO2 + 2 H2
Lactate
C6 H12 O6 ! 2 CH3 CHOHCOOH
Ethanol
C6 H12 O6 ! 2 CH3 CH2 OH + 2 CO2
1.4.2. Acidogenesis
The second phase of the first stage is the acidogenesis, named after the formation
of volatile fatty acids. Monomers, the end products of the hydrolysis step, are taken
up by different facultative and obligatorily anaerobic bacteria such as Bifidobacterium
spp. Selenomonas spp. and Flavobacterium spp. and further degraded to acetic acid,
propionic acid, butyric acid, alcohols, hydrogen and carbon dioxide. The formation of
the end products is related with the partial hydrogen pressure; if the H2 partial pressure
is increasing, fewer reduced compounds like acetate are formed [33].
1.4.3. Acetogenesis
In this step long chain fatty acids are reduced to acetic acid (C2 ) and hydrogen (H2 )
by acid-forming bacteria like Acetobacterium spp., Sporomusa spp. and Ruminococ-
cus spp. Under standard conditions, these biochemical reaction are endergonic (see
table 1.2). For the degradation propionic acid,
G0 = 76.2 kJ/mol are needed. At very
low concentrations of H2 , however, the acetate formation by oxidation of the long chain
fatty acids is thermodynamically possible. The problem, that acetogenic bacteria are
obligatory H2 producers on the other hand is solved by living with the methanogenic
bacteria syntrophically, a process termed "interspecies hydrogen transfer" [24]. The
methanogenic bacteria remove the H2 , which is formed by the acetogenic bacteria and
therefore lowering the hydrogen partial pressure [33]. Figure 1.5 presents the influence
of the hydrogen partial pressure on the free energy the bacteria can gain in the acetogenesis and methane formation from carbon dioxide and hydrogen. Clearly, all the
reactions are thermodynamically favorable only in a small window.
14
1. Introduction
0
endergonic
exergonic
Butyrate
–1
Methane
2
logp H (bar)
–2
–3
–4
ideal hydrogen
concentration
Propionate
–5
–6
–7
–8
80
40
0
–40
∆ G at pH7;25
–80
–120
–160
°C(kJ/Reaction)
Figure 1.5.: Thermodynamic window of the degradation of the volatile fatty acids [33]
Table 1.2.: VFA degradation during acetogenesis; G0 ’; T = 25 °C, pH 7, pH2 10-5 atm,
pCH4 0.7 atm, c(VFA) 1 mM, HCO3 - 0.1 mM[21, 72]
Substrate
Reaction
G0
G0 ’
[kJ/mol]
[kJ/mol]
Propionate
C3 + 2 H2 O ! CH3 COOH + 3 H2 + CO2
+76.2
-14.6
iso-Butyrate iC4 + 2 H2 O ! 2 C2 + 2 H2
+48.4
-25.9
Butyrate
nC4 + 2 H2 O ! 2 C2 + 2 H2
+48.4
-25.9
iso-Valerate iC5 + 2 H2 O + CO2 ! 3 C2 + H2
+20.2
-36.8
Valerate
nC5 + 2 H2 O ! C3 + C2
+48.8
-25.9
15
1. Introduction
Substrate
type
CO2 type
Table 1.3.: Methanogenic degradation [33]
Chemical reaction
4 H2 + HCO3 - + H+ ! CH4 + 3 H2 O
CO2 + 4 H2 ! CH4 + 2 H2 O
CO2
4 HCOO- + 2 H2 O + H+ ! CH4 + 3 HCOOAcetate
CH3 COO- + H2 O ! CH4 + HCO3
Methyl type
4 CH3 OH ! 3 CH4 + HCOO3 - + H+ + H2 O
Methyl type
CH3 OH + H2 ! CH4 + H2 O
e.g. Methyl type:2 CH3 CH2 CH2 OH + CO2 ! CH4 + 2 CH3 COOH
ethanol
Gf
[kJ/mol]
-135.4
-131.0
-130.4
-30.9
-314.3
-113
-116.3
Methanogenic
species
All species
Many species
Some species
One species
1.4.4. Methanogenesis
The final step of reducing the organic intermediates to methane is the methanogenesis, an exergonic reaction, which only takes place under strictly anaerobic conditions.
The responsible bacteria all belong to the archaea family, such as Methanococci spp.,
Methanobacteria spp. and Methanomicrobia spp. Central to the anaerobic fermentation
and methane generation this methanogenic bacteria are very sensitive to all sort of process disturbances, especially pH-fluctuations and oxygen. Due to their very low growth
rate, the whole process of anaerobic digestion is optimized for these bacteria. Not all
methanogenic species can use all available substrates. These can be divided into three
major groups and their methane forming reactions can be found in table 1.3:
• CO2 type: CO2 , HCOO• Methyl-type: CH3 OH, CH3 NH3 , (CH3 )2 NH2 + ,(CH3 )3 NH+ , CH3 SH, (CH3 )2 S
• Acetate type: CH3 COOThe energy yield for the bacteria is varying with the biochemical reactions. The direct
reduction of CO2 and H2 gains up to - 136 kJ / mol and can be done by all methanogenic
species. In contrast, the comproportionation of acetate only yields - 31 kJ / mol. Interestingly, only 27 % - 30 % of the methane arises from the reduction pathway, while 70
% are generated by the combined reduction and oxidation of acetate [33].
16
1. Introduction
1.5. Process parameters
The previous section clarified, that the different bacteria have a different optimum for
different process parameters. The first stage of hydrolysis and acidogenesis has a pH
optimum of 3 - 4 and can also have aerobic conditions, in contrast to methanogenesis,
which has to be strictly anaerobic. It is important to inhibit fluctuations like rapid substrate
changes or temperature shifts, because this can lead to a deficit of the gas production.
With a two-stage plant, it is possible to optimize the two stages of the biochemical reactions. In a one-stage plant, the process must be optimized for the methanogenic
bacteria, because of their low growth rate and higher sensitivity to environmental factors [33]. The following sections will give a short overview of the different parameters
that can be either set to an optimal range or should be monitored closely for an early
detection of problems during the biochemical breakdown of the biomass to methane.
1.5.1. Temperature
For every biological process, the temperature is one of the most important factors. Dependent on the microorganisms optimum, different temperature levels are used for the
fermentation process:
1. psyrchophilic at a temperature range between 15 °C and 25 °C
2. mesophilic at a temperature range between 25 °C and 45 °C
3. thermophilic at a temperature range between 45 °C and 70 °C
At a higher temperature, the biogas yield is increased (thermophilic microorganisms),
but meanwhile the susceptibility to errors is also heightened. If the process temperature
is low, the biogas yield will decrease as well. Most biogas plants will therefore be run
at a mesophilic temperature range with temperatures around 37 °C to 40 °C to ensure
a balanced compromise between biogas yield and process stability. In two-stage systems, the first hydrolysis step and the methane digester can be operated at different
temperatures, although this varies individually depending on the used substrate.
17
1. Introduction
1.5.2. pH value
The pH optimum of the methanogenic bacteria is at pH = 6.7 - 7.5. If it decreases to
pH < 6.5, a positive feedback leads to a further decrease, because the activity of the
methanogenic bacteria is inhibited and thus the volatile fatty acids in the process cannot
be oxidized. With rising concentrations, the pH sinks even more and the process will
come to a halt. In the fermentation process, however, two buffer systems ensure a
pH in the optimal range. A too strong acidification is avoided by the carbon dioxide
/ hydrogen carbonate / carbonate buffer system, which is created by the equilibrium
between dissolved carbon dioxide and hydrocarbonic acid (pKa = 6.35):
CO2 $ H2 CO3 $ H+ + HCO3 - $ 2 H+ + 2 CO3 2At pH 4, the buffer equilibrium is shifted to free carbon dioxide, at pH 13 all carbon
dioxide is bound as carbonate in the system. For monitoring the process, a rise of the
carbon dioxide percentage in the biogas can be an indicator for a process disturbance
[33]. A second system, the ammonia-ammonium buffer inhibits a too weak acidification
(pKa = 9.25):
NH3 + H2 O $ NH4 + + OHNH3 + H+ $ NH4 +
Although these buffer systems equilibrate the pH, both can still be overloaded with
e.g. a too high organic load of easy degradable carbohydrates (starch powder, potato
wastewater), which can lead to a rapid increase of the volatile fatty acids.
1.5.3. Volatile Fatty Acids (VFA)
Besides the hydrogen concentration, the concentrations of the different volatile fatty
acids serve as one of the best process indicators. They are either products of degradation steps from the hydrolysis and acidogenesis or serve as substrate for the acetogenesis and methanation. As the different stages are linked and in equilibrium, the concentration of the volatile fatty acids is naturally low. With a change of the environmental
conditions (pH drop, inhibition of the degradation, substrate overload, temperature instabilities), the bacteria can be inhibited and the concentrations can increase. In the
literature, different rules and inhibition limits can be found for the concentrations. As a
18
1. Introduction
Table 1.4.: Overview of TS and VS percentages of common substrates [10]
Substrate
TS [%]
VS [%]
Methane yield [L / kg VS]
Municipal organic waste
60 - 75
30 - 70
300 - 900
Fat (grease separator)
2 - 70
77 - 99
1300
Crop
84 - 88
95
600 - 800
Maize
40 - 42
95 - 97
390 - 400
Cattle manure
6 - 12
68 - 85
150 - 400
Green cut
12 - 42
87 - 93
450 - 750
rule of thumb, especially the increase of propionic acid and other short chain fatty acids
can be problematic, because they themselves inhibit the degradation even more. But
biochemical reactions are dependent on the temperature, the inoculum and especially
the used substrates, every biogas plant can exhibit a unique acid spectrum. This should
be monitored closely to detect any changes in the dynamics (see chapter 3).
1.5.4. Total Solids and Volatile solids
Substrates can have a great variation of their water content and the ratio of organic
to inorganic fraction. Especially from liquid biomass like manure to corn silage, big
differences can be observed (see table 1.4). It is important to estimate the contingents
to establish a stable organic loading rate and hence continuous gas production in the
anaerobic digestion process. The estimation is done with standard methods, see section
2.2.
1.5.5. Biogas potential
For the estimation of the degradation efficiency of the substrates, the maximum biogas yield has to be identified. If the substrate composition and the organic content is
known, the ideal biogas yield, according to Buswell and Mueller [28] can be calculated
as (simplified):
Cc Hh Oo ! ( c2 + h8 - o4 ) CH4
This formula can only be an approximation, because the volatile solids have to be
separated into easy-degradable (carbohydrates, protein, lipids) and hard-degradable
fractions (lignin, cellulose), known as FOM (content of fermentable organic matter) [78]:
19
1. Introduction
FOM = VS - VSnon-degradable
To estimate the objective biogas potential of the used substrates, batch fermentations
have to be carried out. The VDI guideline 4630 [75] serves as the standard protocol for
comparable batch fermentation studies. At least nine batch fermenters are needed for
valid results (see also chapter 4):
• a triplet of the sludge, without substrate, as the negative control to estimate the
residual gas potential,
• a triplet with a defined, 100 % degradable substrate as the positive control to estimate the activity of the sludge and to eliminate inhibition effects,
• a triplet of the substrate tested.
The amount of VS [%] of the inoculum sludge should be < 2 % and the ratio of of
V S substrate
 0.5
V S inoculum sludge
An experiment is run for 28 days and the recorded biogas volume is furthermore corrected to standard conditions (T = 0 °C; p = 1013 hPa).
1.5.6. Organic loading rate (OLR)
The organic loading rate refers to the daily feeding amount of fermentable biomass (VS),
based on the digester volume. In a mesophilic operation, values between 3.5 and 5 kg
VS · m-3 d-1 have proven to be successful [74]. Most of the biogas plants still run under
load to minimize the possibility of process errors and therefore exhibit a lot of unused
potential. It could be shown, that doubling the OLR from 2.11 to 4.25 kg VS m-3 d-1
can also double the plant capacity of 500 kW to 1000 kW without building any additional
fermenters [49].
OLR[
g
V S Substrate [g]
]=
L·d
V Fermenter [L] · d
20
Desulphurization
1. Introduction
CHP
Headspace
Silage
Heat storage
Hydrolysis
Plant
Measurement
Manure
Control Center
Gas
Substrate
Digestate
Data
Figure 1.6.: Two-stage agricultural biogas plant
1.5.7. Hydraulic Retention Time (HRT)
Closely linked to the volumetric loading rate is the hydraulic retention time (HRT). It
specifies the statistically average residence time of the substrate in the digester:
HRT [d] =
V Fermenter [L]
V Feed [ Ld ]
In conjunction with the digester temperature, the residence time is the decisive factor
for the degree of conversion of biomass into biogas. With short residence times, only
the easily degradable substances are methanized. At longer residence times of 20 or
more days, also difficult degradable substrates can be converted to biogas. Hydraulic
retention times of 30 to 40 days have been proven to be satisfactory [10, 74].
21
1. Introduction
1.6. Process variants
The digestion process only needs an anaerobic atmosphere, a stable temperature,
degradable biomass and a suitable bacteria inoculum. Therefore, specifically engineered for the used substrate, different plant designs have been established. Primary
renewable products usually get co-fermented with manure in a continuously stirred tank
reactor in a one stage or two stage system (see figure 1.6). The first and the second
stage are separated, providing optimal conditions for hydrolysis / acidogenesis (pH < 4)
and acetogenesis / methanogenesis (T = 40 °C, pH 6.5 - 7.5). The methane fermenter
is tempered and stirrers inhibit temperature drifts as well as administer the substrate
evenly across the active volume. Generated biogas is stored under a membrane ceiling
prior to desulfurization and combustion to generate electricity and heat. Being nutrient
rich, the digestate is stored in a post-digestion tank and used to fertilize the soil.
22
1. Introduction
1.7. Aim
In continuously operated industrialized plants, the different time constants for hydrolysis, acidogenesis, acetogenesis and methanogenesis can lead to low organic loading
rates and long hydraulic retention times for plant operation to ensure a stable digestion
process. This consequently leads to underloaded biogas plants with capacious fermentation tanks, which may exhibit a optimization potential. As a plant operator, informed
decisions need to be made, wether to increase organic loading rates or decrease hydraulic retention times while maintaining a stable bioprocess. The intermediates in the
breakdown from macromolecule to methane and carbon dioxide are the volatile fatty
acids and generally favored as process indicators. If the digestion is balanced, the
generated volatile fatty acids are catabolized instantly in the direction of methane. Conversely, increasing concentrations of these intermediates can be an indicator for process
inhibition e.g. overfeeding, shortened retention time, ammonia inhibition due to protein
rich substrates or presence of antibiotics in the process. Chromatographic methods are
used to determine the concentrations of the volatile fatty acids on an irregular basis,
which can only give a snap-shot of the current process status. Furthermore, every biogas plant exhibits a certain profile of volatile fatty acids in a stable process, depending
on the bacteria involved and the substrate mixture fed.
The anaerobic digestion of biomass to biogas is a complex process, with a lot of different biological, chemical, and physical reactions involved. It is difficult to narrow down
the complex interactions between substrate and inoculum to one or two absolute parameters. Depending on the composition of the substrate, the velocity-determining step
of the biochemical reactions will vary. Although the major components of all biomasses
are carbohydrates, proteins and lipids, their chemical structures exhibit enormous variations and subsequently some substrates are easily degradable by microorganisms (e.g.
starch), while others (e.g. lignin) are more difficult to catabolize and take time to be
completely degraded. This makes it difficult to estimate the methane potential of novel
biomass or waste products, purely based on a composition analysis. For meaningful
results, standardized triplicate batch experiments of substrates are conducted and the
resulting biogas yield is monitored closely. These tests are usually limited by high priced
equipment to monitor minor gas flows.
23
1. Introduction
The common denominator and a possible solution for both problems is the design
and development of novel measurement instrumentation. Real time information about
the concentration dynamics of the volatile fatty acids in continuous stirred tank reactors
would enable plant operators to better understand the "black-box" process of anaerobic
digestion and help to exploit unused fermentation potential. Furthermore, a good online measurement of the intermediates can help to improve algorithms for mathematical
models of the process. An online sensor system, based on IR-spectroscopy was developed, which is able to evaluate the concentration dynamics of the individual volatile
fatty acids. An infrared spectrometer was connected to an anaerobic digester and spectra of the digestate were recorded periodically. With reference analysis of the volatile
fatty acids concentration, multivariate calibration allows to develop different chemometric models to predict the concentration dynamics in situ (see chapter 3).
For the investigation of the methane potential of putative new substrates, a low-cost
automated biogas flow meter was developed upon the open-hardware Arduino platform.
Accompanying, a batch fermenter array with an integrated stirrer for three triplicate assays was designed and constructed. This enables upscaling of batch fermentation tests
with comparatively minor monetary investments (see chapter 4).
Both studies were done under the premise of developing new measurement instrumentation for the biogas process. Materials, instruments and common analytical methods used throughout the projects are introduced in the next chapter.
24
The chemicals used in the experiments are summarized in 2.1, materials used are shown
in 2.2
2.1. Biogas yield
The produced biogas was measured with the gasUino, an in-house developed biogas
flow meter. It was calibrated beforehand and the final biogas yield was corrected to
standard conditions (T = 273.15 K, p = 1013 hPa) automatically. A detailed description
is presented in chapter 4.
2.2. Total solids and volatile solids
The determination of total solids of the sludge and the substrates is done according to
DIN 12880 [30]. Samples are dried at 105 °C until a constant weight is achieved for 24
hours. To discern the portion of solid mass attributed to ash content from non-volatile
organic content, the samples are furthermore ignited in a furnace at 550 °C until constant
weight or for 24 hours, according to DIN 12879 [30].
Product name
Microcrystalline cellulose
Acetic Acid
Propionic Acid
Iso-butyric Acid
Butyric Acid
Iso-valeric Acid
Valeric Acid
Table 2.1.: Chemicals
Manufacturer
Avicel PH-10, Sigma Aldrich
Carl Roth GmbH & Co. KG
Merck
Fluka
AppliChem
Fluka
Fluka
25
Order Number
11365
3738.1
8.00605.0100
58360
A2582
59850
94530
Instrument
Annealing furnace
Centrifuge
pH meter
pH electrode
Thermostate
HPLC system
Autosampler
UV Detector
RI Detector
HPLC Column
Guard-column
Compartment drier
Micro scale
Micro filter
Fermenter
Fermenter
Communication Unit
Gas collecting bags
Multi-channel Peristaltic Pump
Peristaltic Pump
Peristaltic Pump
Multiple Socket Outlet
Spectrometer
pH electrode
Redox potential electrode
Table 2.2.: Equipment
Model name
Brennofen U15
Mikroliter 2041
pH 523
Inlab Micro
RM 6t
2707
2489
2414
Aminex HPX-87-H
Micro-Guard Cation-H
R 160P-* D1
Minisart RC 25
Biostat MD
Biostat B
Micro MFCS
50L, PETP/AL/PE
Ismatec IPC
XX 8000230
Ecoline VC
SIS-PMS
Tensor 27
405-DPAS-SC-K8S
PT4805-DPA-SC
26
Manufacturer
Uhlig, Germany
Hettich, Germany
WTW, Germany
Mettler Toledo, Switzerland
Lauda, Germany
Waters, US
Waters, US
Waters, US
Waters, US
Bio-Rad Laboratories, US
Bio-Rad Laboratories, US
Heraeus
Sartorius GmbH, Germany
Sartorius GmbH
Braun AG, Germany
Braun AG, Germany
Braun AG, Germany
Tesseraux, Germany
Ismatec, Germany
Millipore, US
Ismatec, Germany
Gembird, Germany
Bruker Optics, Germany
2.3. Sample preparation and analysis
Sludge samples were extracted with a syringe and centrifuged at 13 000 rpm for 10 min
to pelletize the solids. The supernatant was further filtered with a 0.45 µm micro filter
(Minisart RC 25, Sartorius GmbH) into a chromatographic sample vial and stored at -20
°C.
2.4. High Performance Liquid Chromatography (HPLC)
2.4.1. Theory
Liquid chromatography was discovered and named by the russian botanist Mikhail Semenovich Tswett at the beginning of the 20th century. With a column of calcium carbonate, he was able to separate chlorophyll a and b amongst other extracts from leave
extracts [18]. Subsequently, other methods were invented like liquid-liquid chromatography or gas-liquid chromatography. A big leap forward was in the 1970s by Horváth et
al. with the invention of High Pressure Liquid Chromatography (HPLC) systems [45]. A
schematic overview of a isocratic HPLC System is given in figure 2.1. Separation of the
mixture of compounds is achieved by moving a mobile phase (aceto nitril, methanol or
water) through a densely packed column (stationary phase) with high pressure. After
the injection of the sample, the analytes adsorp to the column and depending on the
interactions, get eluted at specific retention times. With different chemical structures, a
suitable detection mechanism is needed. Common methods are UV/Vis (for substances
that absorp at a certain wavelength) and Refractive Index detection (e.g. for sugars). As
a result, a chromatogram is recorded, where the different analytes are represented as
peaks. For the quantitation, the peaks get integrated and with the help of a calibration
curve, their concentrations can be estimated.
2.4.2. Protocol
The samples were analyzed with a HPLC setup consisting of a binary HPLC Pump and
an auto sampler (Model 2707, Waters, MA). To detect the volatile fatty acids, two successively connected detectors were used (UV/Vis detector 2489 at 210 nm and Refractive
Index Detector 2414). An Aminex HPX-87-H column (Bio-Rad Laboratories, Richmond,
27
Chromatogram
HPLC Column
Packing Material
Computer
Data Station
Injector
Auto Sampler
Solvent
(Mobile Phase)
Sample
Pump
Solvent Manager
Solvent Delivery System
Detector
Waste
Figure 2.1.: Overview of a HPLC System (modified [14])
CA) combined with a suitable guard-column (Bio-Rad Micro-Guard Cation-H) was used
as stationary phase. The column temperature was 40 °C. 5 mM sulfuric acid with a flow
rate of 0.6 ml·min-1 served as mobile phase. The injection volume was 20 µl and the
sample run time 60 minutes. For calibration, an external standard with varying concentrations for the different VFA was prepared (acetic acid 0 - 4 g/l, propionic acid 0 - 3 g/l,
butyric, iso-butyric acid, butyric acid, iso-valeric acid and valeric acid 0-1 g/l). Integration and quantification of the recorded chromatograms were performed with the Breeze2
software package (Waters, MA).
2.5. IR Spectroscopy
The infrared (IR) spectroscopy is a physical method of analysis which is used for the
quantitative determination of substances. In general, it is the absorption measurement
of different IR frequencies by a sample positioned in the IR beam. It can also be used for
structure analysis, because different functional groups of molecules absorb character-
28
istic frequencies. For the quantitative analysis, the relationship between the absorption
of light and concentration of the substances is important, which is described in BeerLambert law:
E =
with
✓
lg
I
I0
◆
=c ·↵ ·l
• E = Extinction
• I: Intensity of transmitted light
• I0 : Intensity of irradiated light
• c: Concentration of the analyte in the solution
• ↵ : Molar extinction coefficient
• l: Path length of light
The concentration of the absorbing substance is therefore directly proportional to the
extinction, if the layer thickness and wavelength are kept constant. In practice, the relationship between the two variables is described by calibrating. Each component of a
mixture can thus quantitatively be determined, if a sufficiently intense absorption band
can be found, which is not disturbed by the other analytes or by the solvent mixture.
Using the multivariate calibration approach, also complicated multi-component analysis
by specifying a broader spectral range are is possible.
2.5.1. Infrared radiation
In the electromagnetic spectrum, infrared light takes its place between visible light and
microwaves [0.78 - 1000 µm]. Like any electromagnetic wave, it is characterized by
its wavelength
[nm] and oscillation frequency f [Hz]. Of great importance in IR-
spectroscopy is the wavenumber ⌫¯, that corresponds to the reciprocal of the wavelength
with the unit cm-1 and is therefore directly proportional to the frequency as well as the
energy of the absorption. A IR spectra is usually presented with the absorption intensity
29
on the y-axis and the wavenumber on the x-axis. (see figure 3.4a)
⌫¯ =
1
1 · 104
=
[cm]
[µm]
The stored energy in the wave can cause a transfer of energy - the energy of the
wave can be absorbed by the molecular system and transferred into another energy
form, e.g. thermal energy. However, this can only happen if it leads to a change of the
dipole moment of the specific atom group. Therefore, diatomic molecules with identical
atoms are IR inactive because they have no dipole moment. If a molecule is composed
of different atoms, it can always interact with the infrared light, because wave motion
leads to an anti-symmetrical shift of charge center and hence creates a dipole moment
[42]. Dependent on the geometry of the molecule, different translational, vibrational,
and rotational movements are possible. Upon irradiation with infrared light, the bonds
in the molecules start to vibrate, which the major types of molecular vibrations being
stretching and bending: Figure 2.2b presents the vibrational possibilities and their specific wavenumbers for these of a -CH2 group [62, 63]. Different absorption bands are
therefore characteristic for different bonds (C-C, C-O, C-N, C=C, C=O, ...). Functional
groups that have a strong dipole also show strong absorptions in the IR spectra. The
specific wavenumbers and corresponding bands for the analysis of volatile fatty acids
are discussed in chapter 3.
2.5.2. Michelson Interferometer
An interferometer uses the effect of interference, the addition or subtraction of the amplitudes of superimposed waves to generate different wavelengths simultaneously. In
figure 2.3, the effect of interference is exemplary presented with two superimposed sine
waves with the same frequency and the same phase or 180° out of phase. If the waves
are in phase, constructive interference produces a higher amplitude (A1 + A2 = 2A.
Destructive interference occurs if the waves are out of phase by 180° and cancel themselves out.
In the Michelson interferometer, main part of a Fourier-Transformed Infrared (FTIR)
spectrometer (see figure 2.2), a wave is superimposed with itself to create an interferogram. Its general functionality is, that light is emitted from a light source and divided
by a semi-transparent mirror into two beams. One half is reflected at the beam splitter
30
Figure 3.3: Three of six degrees of freedom of a two atom molecule: two rotational about x and z axis and one vibration along the y axis.
dµ
!= 0
dx
(3.2)
There exist molecules having a dipole moment, but not fulfilling the above con-
2.dition,
Materials
and Methods
for example
the symmetric stretch of a CO2 molecule. Such molecules are
either infrared inactive, or some of their modes, which do not fulfil the above condition, are not visible in an infrared spectrum. Examples of some basic infrared
active modes, are 21
depicted in Figure 3.4.
3.1.1 Infrared Absorption
C
z
H
Rotation
C
H
Symmetric
Stretch
H
C
H
H
Asymmetric
Stretch
H
Scissors
x
n
atio
Vibr
y
H C
C
Rotation
H
H
Rocking
(a) Three of six degrees of freedom of a two atom
H
H C
H
Symmetric
Bend
dµ
!= 0
dx
main classes of infrared induced vibrations [34, 19].
(3.2)
There exist molecules having a dipole moment, but not fulfilling the above condition, for example the symmetric stretch of a CO2 molecule. Such molecules are
either infrared inactive, or some of their modes, which do not fulfil the above con-
Mirror
dition, are not visible in an infrared spectrum. Examples of some
basic infrared
active modes, are depicted in Figure 3.4.
C
H
Symmetric
Stretch
H
Rocking
C
H
H
Asymmetric
Stretch
H C
C
H
H
x1
C
H
half-shivered
mirror
Scissors
IR coherent
light source
H
H
Symmetric
Bend
H C
H
Asymmetric
Bend
(b) Major vibrational modes for a nonlinear group,
Figure 3.3: molecule:
Three of six two
degrees
of freedom
of a x
two
atom
molecule:
twoExamples
rotarotational
about
and
z axis
and
CH4 [47].
Figure
3.4:
of
tional about xone
andvibration
z axis andalong
one vibration
along [47].
the y axis.
the y axis.
H
H
H
y
x2
H
Asymmetric
Bend
Mirror
Figure 3.4: Examples of main classes of infrared induced vibrations [34, 19].
Detector
Figure 2.2.: Schematic setup of a Michelson Interferometer [79]
31
(a) constructive interference
(b) destructive interference
Figure 2.3.: Interference of superimposed sinus waves
to a rigidly mounted mirror and reflected back to the beam splitter, hence traveling the
way of 2x1 As the beam-splitter is semi-transparent, the other portion gets transmitted to
the second mirror and reflected likewise, hence passing the way of 2x2 . The key factor
now is, that the second mirror is not mounted firmly but is movable, whereby the light
actually passes a range of 2 · (x2 + y). Both partial beams get combined again at the
beam-splitter and interference is occurring before striking the detector. This interference
equals the distant of 2y. Constructive interference for a particular wavelength
b
is ob-
tained exactly, if the path difference 2y is an integer multiple of this wavelength. For
the other wavelengths from the broadband IR source destructive interference can be
observed. Each position of the movable mirror thus corresponds to a certain wave number ⌫¯b . Before impinging on the detector, the modulated beam passes the sample. The
registered signal of the detector is the interferogram, the intensity I(x) of the IR radiation
as a function of the position of the movable mirror [42]. Fourier transformation converts
this interferogram from the time domain into one spectral point on the frequency domain
[63]. The ratio of this spectrum and a reference spectrum yields the desired spectrum
with wavenumber on the x-axis, absorbance on the y-axis. A major advantage of FTIR
spectroscopy is, that all emitted IR frequencies impinge on the detector simultaneously,
thus enhancing the signal to noise ratio. This makes rapid measurements possible,
which depend only on the time the mirror takes for moving across a particular path [42].
32
Sample
n2
n1
dp
n2
Figure 2.4.: Schematic representation of the attenuated total reflectance. n1 : Refractive Index of the crystal; n2 : Refractive Index of the sample; n2 < n1 ; dp =
Penetration depth, [42]
2.5.3. Attenuated total reflectance
The attenuated total reflectance (ATR) method makes it possible to record IR-spectra
from solid or intransparent samples. For the measurement, the samples only have to
stay in contact with the surface of the ATR crystal, which has to have a very high refractive index. On the boundary surface of two media with different refractive indices,
the total reflected radiation creates an evanescent wave, which can penetrate the sample of about one wavelength (dp , c.f. fig. 2.4). The intensity of the wave is reduced
(attenuated) by the sample in regions of the IR-spectrum, where the sample absorbs.
Zinc selenide, thallium bromide-thallium iodide (KRS-5) or germanium are examples for
crystals with a high refractive index [62].
2.5.4. Quantitative Analysis
To extract the concentration of the samples from the recorded spectrum, a calibration
has to be done, which matches the varying spectra structure to a known concentration. In the classical univariate calibration, which is based on the Beer Lambert law
(see 2.5), only one spectral data point is used. In contrast, the multivariate calibration
uses the whole spectral structure for the calibration. This makes it possible to simultaneously estimate the concentration of different analytes in the sample mixture [31]. For
the calibration, the method of Partial Least Squares (PLS) Regression, implemented in
the QUANT2 Software Package (Bruker Optics, Germany) was used. For good PLS
prediction models, the calibration set has to fulfill some principal requirements [47] :
33
• the data set has to include all expected components which can lead to a variation
in the spectra
• for the analytes, the sample concentration should span the range of interest
• the background signal should be the same for all samples; for biotechnologically
produced products it is best to use spectra directly out of the process
• the variation of the concentrations of the different analytes should be independent
of each other
To test the calibration method developed for accuracy, each model is assessed using a validation. Two validation methods are commonly used: the cross-validation and
the test-set validation. In the cross-validation a sample is obtained prior to creating a
model from the test set. The concentration of this sample is not known and therefore
independent. With the remaining samples, a method is developed and used to predict
the concentration of the prior skipped sample. Now, for this sample the reference concentration is known (e.g. analyzed with HPLC) as well as the predicted concentration.
Subsequently, the sample is re-added to the test set and another sample is removed
until all samples have been determined successively. To determine the accuracy now,
the prediction error Root Mean Square of Cross Validation (RMSECV) is calculated. For
a good model, this prediction error is low. The external test set validation is similar, but
two real sets are formed: one for the calibration, and one as the test-set for the validation. Other variables which have to be taken into account are R2 of the prediction versus
the reference analyses as well as the number of the internal latent variables, which the
chemometric model is based on. Ideal are low numbers for the prediction errors and the
internal latent variables and a high value for the correlation [31].
This section about IR spectroscopy was to give a very short overview of the principles
and methods the next chapter is based upon. Detailed secondary literature can be found
in the bibliography [31, 47, 62, 63].
34
3. On-line mid infrared monitoring of the dynamics of the
individual volatile fatty acids in the continuous
fermentation of biogas
3.1. Introduction
The degradation of biomass and the formation of methane is a complex biochemical process, which can be separated into four main phases: hydrolysis, acidogenesis, acetogenesis and methanation. A kinetic uncoupling between the syntrophic bacteria of acid producers and consumers in these different fermentation steps can be observed by an accumulation of the different volatile fatty acids [70]. Therefore, concentration and composition of the VFA are the best parameters to reflect the metabolic state of the biochemicalprocess [3, 23, 57]. Offline determinations of the VFAs are usually performed with chromatographic methods, including gas chromatography (GC) [1], Headspace GC [32] or
high performance liquid chromatography (HPLC) [29]. On- and offline titrimetric methods
can achieve a low cost and quick result of a VFA sum parameter, but ignore the individual composition of the specific VFA [48]. Pind et al. [57] concluded that measurements
of all-individual VFAs are important for control purposes. The dynamics and the history
should always be evaluated in close relationship to the conversion of other VFAs and the
history of the reactor process. Few online methods for GC [20, 58, 64] and HPLC [81]
have been applied as well. However, problems with sample preparation and biofouling
make these methods not applicable for use in the field. FTIR-spectroscopy has been
proven to be a real alternative without facing equivalent problems. This method is widely
used in pharmaceutical, food, medical and bioprocess applications [46, 60]. In the field
of anaerobic digestion Near Infrared Spectroscopy (NIR, 0.78 - 3 µm) was shown to be
able to evaluate VFA content in glycerol-boosted anaerobic digestion processes [44] and
in a hydrogen-producing bioreactor [80]. MIR-spectroscopy (Mid infrared spectroscopy,
3- 50 µm ) was already shown to be suitable for monitoring volatile fatty acid content,
chemical oxygen demand, alkalinity, sulfate, ammonia and nitrate concentration in in-
35
3. Online monitoring of the volatile fatty acid dynamics with MIR spectroscopy
dustrial wastewater treatment [68, 69]. In aerobic batch composting studies, FTIR spectroscopy was used to evaluate the different decomposition stages of bio waste [41, 65].
The aim of this work is the development of an online continuous sensor system based on
MIR-ATR-FTIR-spectroscopy for measuring the individual concentrations of the different
volatile fatty acids in the anaerobic fermentation of biogas for highly heterogeneous and
viscous bio-slurries.
3.2. Materials and Methods
3.2.1. Lab-scale biogas plant
An anaerobic continuous stirred-tank reactor (CSTR) with an active sludge volume of
10 l was operated at 40 °C and 75 rpm (Biostat MD, Braun, Melsungen). The inoculum sludge was taken from an operating biogas plant. The hydraulic retention time
(HRT) was 42 d with a varying organic loading rate between 0 and 4 g l-1 d-1 VS. Different parameters (pH, redox potential) were monitored by electrodes (Mettler Toledo,
Greifensee, Switzerland), the biogas flow was measured with a gasUino (see chapter
4). Sampling and feeding of the reactor were done manually once a day. The substrates used in the experiments were artificial substrates (corn starch powder and 10 %
peptone) and ground wheat, respectively.
3.2.2. Analytical methods
To estimate the concentrations of the volatile fatty acids, the samples were centrifuged
and the supernatant was filtered and stored at -20 °C (see section 2.3). The protocol for
the chromatographic analysis with HPLC can be found in section 2.4.2.
MIR-ATR-FTIR equipment and data analysis
The spectra were recorded with a TENSOR 27 (Bruker Optics, Ettlingen, Germany)
FTIR spectrometer with an ATR-Cell (ZnSe) at room temperature (25 °C). Wavenumbers from 2800 cm-1 to 900 cm-1 were scanned at a resolution of 4 cm-1 . A background
measurement with fresh water was done prior to measuring a digestate sample. The
typical stable pH of the digestate samples is between 7 - 8, so no adjustment to a given
36
pH was done. Before sample-collection, the suspended solids were removed from the
liquid phase by a raw mesh filter to achieve a homogeneous probe. This was either done
manually for the calibration set or with a rotating filter unit for the continuous fermentation experiments (see figure 3.5). Each reference and sludge sample was scanned 256
times to get a well-balanced average spectra and to minimize temperature drifts or shifts
due to sedimentation of solid particles. Both, sample and blank spectra were collected
in absorbance mode. Partial least square (PLS-2) algorithm was used to create multivariate calibration models for acetic acid, propionic acid, iso-butyric acid, butyric acid,
iso-valeric acid and valeric acid with the QUANT2 software package (Bruker Optics,
Ettlingen). A separate model was set up for each chemical component. Principles of
chemometrics and multivariate calibration are described elsewhere [51].
Process cycle of FTIR spectra recording during the continuous fermentation
experiments
The process of sludge extraction, spectra recording, and processing during the fermentation experiments was automated (c.f. 3.1). An acrylic glass chamber with two inputs
and one output was constructed and mounted on top of the ZnSe crystal, see 3.2. The
homogenous sludge filtrate is extracted from the inside of a rotating filter unit attached to
the stirrer axis (modified, according to [58]). The filter has a pore size of approximately
1 mm2 and the rotation shearing force removes solids, which accumulate on the surface
of the filter. A process cycle included the following steps (c.f. figure 3.1):
1. The acrylic glass chamber is flushed with fresh water by a peristaltic pump (2)
(Millipore, USA)
2. A background spectrum of the water is recorded immediately
3. A multi-channel peristaltic pump (1) (Ismatec, Wertheim) transfers approximately
20 ml of the digestate from the filter unit to the flow chamber. The filling level of
the fermenter is leveled out with fresh water, which results in a HRT of 42 days
4. IR-spectrum of the digestate is recorded and processed.
5. The flow chamber is rinsed again and the cycle restarts after two hours.
37
Figure 3.1.: Piping and instrumentation scheme of the anaerobic digester and the IRspectrometer. A sample is generated by a rotating filter unit mounted on the
stirrer axis. Pump 1 transfers the digestate to the measurement chamber,
fitted on top of the ZnSe crystal. Simultaneously, fresh water is transferred
to the fermenter to balance the filling level. After the spectra recording is
finished, crystal and chamber are cleaned with fresh water via pump 2.
38
(a) Top View
(b) Side View
Figure 3.2.: Acrylic glass chamber mounted on top of the ZnSe crystal
Twelve spectra per day are recorded. It was chosen to flush the acrylic glass chamber
before and after each recording of the sludge sample to ensure a minimized biofouling.
Furthermore, the ZnSe crystal was periodically cleaned with paper tissue.
3.2.3. Controlling software
The biogas lab plant is automated with the software LabVIEW 8.5 (National Instruments,
Austin, TX). Different available or developed Virtual Instruments (VI) are used. Biostat
MD and Biostat B are connected to the PC via a MFCS (RS-422) to the serial port. They
can be addressed individually by defining their address in the input string, transmitted
to the MFCS (1: Biostat MD, 2: Biostat B, see A.5, A.4). The answer string is separated
by colons and converted to the respective values (temperature, jacket temperature, stirrer rpm, pH and redox potential). A gasUino was connected to a second serial port,
which returns a comma-separated answer string with the number of clicks, the room
temperature and the barometric pressure (see A.3d). After gathering all the data from
the different devices, an INSERT string was assembled and the data was saved in a
MySQL database with the help of LabSQL [7] (see A.7), as well as plotted on the front
panel (c.f. A.2). An independent block structure was developed to transfer the sludge
to the ZnSe crystal, record the IR spectra, and rinse the crystal (see A.3). Therefore,
one inlet of the acrylic glass chamber was connected to the fermenter with a peristaltic
pump, the second one to a water tank. Both pumps have to be set to on for cleaning
39
the crystal, because the high velocity of the cleaning led to a leak of digestate into the
sample chamber after stopping the cleaning pump. To inhibit this effect, the revolution of the digestate pump was set to counter clockwise with a little flow rate during the
cleaning. It could be controlled with a VI provided by the manufacturer [15] and was
therefore connected to a third serial port. The cleaning pump had no interface for the
PC and was controlled with a USB power socket (SIS-PMS, Gembird) by incorporating
a command line program (SisPmCtlWin, [12]) into another VI (c.f. A.6). The OPUS Software Package and LabVIEW both support the protocol Dynamic Data Exchange (DDE)
for interprocess communication. This allows LabVIEW to send commands to OPUS, if
both software packages are running simultaneously (fig. A.8). The VI set the protocol
file for the spectra recording and the developed QUANT2 methods as input parameters,
which resulted in the estimated concentrations of the volatile fatty acids as the output
parameters. These were furthermore saved to the database and presented as plots on
the frontpanel.
3.2.4. PLS method development and validation approach
The approach of developing and validating the different PLS methods to predict the
concentration of the VFA is illustrated in figure 3.3. For the method development (see
figure 3.3a), spiked samples of digestate with varying concentrations of the VFA and the
samples from an anaerobic digestion, where a systematic overloading occurred, were
used. The IR-spectra were recorded and HPLC measurements were done as reference
analyzes. In total 147 samples of digestate were collected.
Calibration set - part A (see figure 3.3a)
Similar to Udén et al. (2009) [73], which estimated the VFA concentration in rumen
samples by MIR-FTIR, a calibration set with spiked samples was used to develop the
PLS methods for the different VFA. Therefore, the feeding of the biogas plant was halted
until the concentrations of the VFA had decreased below the detection level of the HPLC.
This VFA-free digestate was then used as the matrix for the spiked samples. Analytical
grade acids were added to 5 ml of digestate in different concentrations, according to a
developed scheme, where the variations of the concentrations of VFA were independent
of each other (R2 of acetic acid vs. propionic acid low, same for all other combinations
40
CSTR lab-scale
Biogas plant 10 L
+ VFA in different
concentrations
spiked samples
FTIR
record
spectra
HPLC
check
prepared
samples
Calibration Set Part A
CSTR lab-scale
Biogas plant 10 L
stepwise increase of
OLR
OLR
concentrations of
VFA increase
FTIR
automated spectra recording
every 2h, 12 per day
feedback loop
+- feeding
VFA-free digestate
b) 40 °C, HRT 42d
feedback loop
+- feeding
a) feeding halted
CSTR lab-scale
Biogas plant 10 L
Prediction of the
VFA concentrations
with developed methods
rotating filter /
pump
FTIR
automated spectra
recording
HPLC
every 2h,
1 sample
12 per day
per day
HPLC
reference analysis:
1 sample
per day
Calibration Set Part B
(a) Two-step method development with the manually(b) Test-set validation with a second continuous ferprepared calibration set (a) and the gathering of
mentation experiment
real process spectra (b).
Figure 3.3.: Flow chart diagram of the chosen approach developing and validating the
different methods
41
OLR
FTIR
automated spectra recording
every 2h, 12 per day
Prediction of the
VFA concentrations
with developed methods
from 1.1
HPLC
reference analysis:
1 sample
per day
of volatile fatty acids). MIR-spectra were recorded and HPLC controls were prepared,
respectively.
Calibration set - part B (c.f. fig. 3.3b)
In addition to the manually prepared sample set, a continuous fermentation was set up
to obtain the process dynamics of the VFA in a real bioprocess. The recordings of the
spectra were automated and one spectrum per day as well as the corresponding HPLC
results were added to the calibration set. Based on the calibration set, PLS methods to
predict the VFA concentrations in the digestate of an anaerobic digestion were developed.
Test of the methods
The developed PLS prediction methods were then tested with a second continuous
anaerobic digestion experiment. Here, the recorded dynamics of the volatile fatty acids
served as a decision criterion when to increase the organic loading rate: If the feeding
rate was raised and a reaction of the VFA could be observed, the feeding rate was not
further increased until the system was successfully adapted to the new organic loading
rate. If no reaction of the VFA could be monitored, the timespan between the increases
of the OLR was abbreviated.
3.3. Results and Discussion
3.3.1. FTIR-MIR spectra of the digestate
IR-spectra taken at various time points (day 6, day 16, day 26, day 37, day 47,day 57)
during the continuous fermentation (c.f. 3.5) experiment are presented in figure 3.4a.
The main absorbance region was found between 1800 cm-1 and 900 cm-1 , which represents the absorption region for early decomposition products like aldehydes, ketones,
esters and short chain carboxylic acids. A typical IR band is present at 1640 cm-1 ,
which reflects the aromatic C=C bond and C=O group absorption of amides (Amid I
band) or carboxylates. A second band of protein origin, an indicator for protein rich
components, the Amid II band is located around 1570 and 1540 cm-1 , due to the N-H
42
(a) Relevant bands and spectra at different points in time of the continuous fermentation experiment (day 6, day 16, day 26, day 37, day 47, day 57). Spectra are shifted for clarity on the
y-axis.
(b) Spectra of the digestate at different points in time, z-axis = time
43
in-plane bend vibration and can be assigned to secondary amides [41, 65, 66]. The
shoulder at 1425 cm-1 is due to the COO- stretch of carboxylates and the C-O stretch
of carbonyls. Another characteristic region for the VFA is between 1265 cm-1 and 1240
cm-1 , where the C-O vibration can be observed. A significant peak can be seen between
1070 cm-1 and 1000 cm-1 . This region is assigned to the C-O stretching of polysaccharides and polysaccharide-like substances, Si-O of silicate impurities and clay minerals
possibly in a complex with humic acids [41]. Figure 3.4b presents spectra from an actual fermentation in a 3D environment, where the change of the spectra over time and
the increasing peak at 1551 cm-1 is evident. The heterogeneous and complex matrix
of the sludge led to an elevated background noise. For method development, all spectra were smoothed and pre-processed with the first or second derivative to remove this
background signal [46].
3.3.2. Chemometric analyses
The combined calibration set formed the basis for the chemometric analyses to circumvent two prior observed problems (data not shown): developed models only based on
the artificial calibration set lacked the ability to predict the absolute concentrations of
the different VFA in a real process in a satisfying range. If only spectra from an anaerobic digestion were used for the model development, it was not possible to differentiate
the signals of the different VFA, because the correlation amongst each other (R2 ) was
too high. The models, which had the best agreement between a low number of internal
latent variables and a low root mean square prediction error (RMSPE), were chosen. Table 3.1 summarizes the properties for each chemometric model and table 3.2 presents
the frequency ranges used in the methods.
Acetic acid is one of the direct precursors for the methanogenesis. In contrast to the
other VFA, an increased concentration of acetic acid (< 3000 mg/l) is not inhibiting the
biochemical degradation processes at a pH > 7.5 [33]. Therefore, an equal distribution
across the concentration range up to 3 g/l can be observed. The predictability is good,
with a R2 of 0.94 and a RMSPE of 0.16 g/l. Inhibition of the process coincides with
the increase of the propionic acid concentration [17, 22, 34, 59], making it a perfect
indicator for reactor imbalances. The developed method spans a concentration range
of 0 - 3 g/l with a RMSPE of 0.26 g/l and R2 of 0.88, with equal distributed values across
the concentration range. These standard errors of predictions are consistent with the
44
Figure 3.4.: Regression plots from the PLS-Models for the different volatile fatty acids.
PLS predicted concentrations were plotted against the HPLC reference
analyses. All spectra were smoothed and pre-processed with the first or
second derivative and different frequency ranges were chosen, respectively
(cf. 3.1,3.2).
45
1st derivative
2nd derivative
1st derivative
1st derivative
1st derivative
1st derivative
0 - 2.75
0 - 3.33
0 - 0.26
0 - 0.33
0 - 0.25
0 - 0.26
7
7
2
6
3
6
0.94
0.88
0.83
0.75
0.59
0.9
RMSPE [g/l]
R2
internal latent variables
69
66
41
44
39
44
Concentration range [g/l]
71
67
42
44
40
44
Data pretreatment
No. test spectra
Acetic acid
Propionic acid
Iso-butyric acid
Butyric acid
Iso-valeric acid
Valeric acid
No. calibration spectra
Component
Table 3.1.: Summary of the properties of the developed methods for the different
volatile fatty acids. 147 samples were collected.
0.156
0.235
0.0318
0.0472
0.0496
0.0191
Table 3.2.: Frequency ranges [cm-1 ] of the different developed methods.
Compound
Frequency Ranges
Acetic acid
1801-1641
1589 - 1535
1429 - 1375
1323 - 1269
Propionic acid
1801 - 1747
1535 - 1269
Iso-butyric acid 1811 - 1622
1496 - 1433
1371 - 1182
Butyric acid
1492 - 1178
Iso-valeric acid 1716 - 1550
1389 - 1346
Valeric acid
1717 - 1661
1390.6 - 1335 12823 - 1173
46
findings of other studies with NIR (acetic acid: 0.28 - 0.57 g/l, propionic acid: 0.53 g/l) or
MIR (acetic acid: 0.1 - 0.9 g/l) application [46]. Similar to propionic acid, the appearance
or increasing concentrations of butyric acid, valeric acid and their iso-forms are also
a sign of process disturbances [3, 52]. Their ability to inhibit the process is inverse
to their concentration (inhibition at concentrations of 50 mg/l undissociated fatty acids
without prior adaption for iso-butyric and iso-valeric acid [33]). It was therefore decided
to use a smaller concentration range from 0 to a maximum of 300 mg/l to account this
factor. All models exhibit a clustering of values around 0 g/l, due to their absence in
the normal fermentation process. Samples with increased concentration are due to the
calibration set part A and did also appear after the process was significantly overloaded.
This shows the difficulty to create valid models for these higher chain fatty acids by
monitoring a real process alone. The R2 of these methods are 0.83, 0.75, 0.59, 0.90 for
iso-butyrate,butyrate, iso-valerate, and valerate, respectively.
3.3.3. Test of the developed methods
In the next step, the performance of the developed methods was evaluated in a second
continuous fermentation. In this experiment, the HPLC analysis served as a reference
and was not included in the chemometric model. This approach of using two independent methods for estimating the VFA content was chosen, to evaluate if the models were
able to predict the concentrations reliably. In contrast to the first continuous fermentation
experiment, the PLS predicted concentrations of the VFA were also used as a decision
criterion when to increase the organic loading rate. Figure 3.5 presents the reactions
of pH, biogas generation and the volatile fatty acid content. The feeding started at an
organic loading rate of 0.5 g l-1 d-1 at day 4 with a mixture of starch powder and peptone.
Due to the rapid drop in pH and the increase of the organic acid concentrations, the
feeding was stopped at day 11 until the concentrations of the volatile fatty acids had
recovered. It was restarted day 17 with ground wheat as substrate at the same organic
loading rate of 0.5 g l-1 d-1 . On day 24, the OLR was raised to 1 g l-1 d-1 . These increments led to increased levels of acetate, butyrate and valerate. Therefore, the OLR was
kept steady until day 37 (1 g l-1 d-1 ), where all acids had been degraded again and afterwards increased to 2 g l-1 d-1 on day 37, furthermore to 3 g l-1 d-1 on day 42 and then to 4
g l-1 d-1 on day 48. This rapid perturbation induced reactions of all monitored volatile fatty
acids, especially the concentrations of butyrate and valerate increased. The absolute
47
2.5
2
1.5
1
0.5
0
2.5
2
1.5
1
0.5
0
0.75
0.75
0.5
0.5
0.25
0.25
pH
Acetic acid
1.0
2.0
3.0
4.0
3.0
7.6
7.4
7.2
Propionic acid
0.5
pH
L
0
0.2
0.2
0.1
0.1
g/l
Iso-butyric acid
0
0
0.2
0.2
0.1
0.1
g/l
Butyric acid
0
0
0
0.2
0.2
0.1
0.1
g/l
Iso-valeric acid
g/l
7
700
600
500
400
300
200
100
0
0.5
g/l
8
7.8
L
0
0
10
20
30
40
50
60
70
0
Time [d]
Figure 3.5.: Methods did not include the corresponding HPLC results. a) straight line:
pH, dashed line: cumulated biogas volume b) - e) dots: PLS-predicted concentrations. line: smoothed average of PLS-predicted values, points: HPLC
reference analysis. Only prediction results are plotted, which passed the
internal QUANT2 validity test. The shaded areas highlight the increasing
organic loading rate.
48
reference concentration of the HPLC results is not matched by the PLS prediction. For
acetate, a maximum deviance of about 0.5 g/l can be observed. Similar behavior can
be seen for the other VFA. Comparing the PLS predicted values with the reference, propionate also shows a deviance of about 0.175 g/l. The first increase of propionate level
from day 4 to day 13 fits the prediction well; the complete degradation is not matched
precisely. The changing dynamics from day 50 to 70, however, are recognized. The
dynamics of iso-butyrate and iso-valerate get detected in the same manner. As soon as
they appear during the fermentation process,from day 30 to day 35 and again beginning
from day 50, they can be detected via MIR spectroscopy. Butyrate seems not to be detectable in this experiment. All the spectra from day 0 to day 52 failed the internal validity
tests of the QUANT2 software package and are therefore not plotted. Additionally the
evaluation of the gathered chromatograms caused problems for butyrate, because no
defined peak could be integrated. Therefore, neither of the two methods served valid
concentration values, but the HPLC, as well as the chemometric model gives a hint about
the existence of butyrate after day 45. The pH dropped from an initial value great than
pH 8 to 7.3 +/- 0.2 and was stabilizing after 30 d in the optimal range between 6.7 and
7.5 [33], but did not show any reactions to the introduced perturbations due to the carbonate and ammonia buffer systems in the reactor. The experiment demonstrates the
problem of controlling the anaerobic fermentation based on the pH and clearly shows
the importance of monitoring the dynamics of the volatile fatty acids closely. The developed FTIR sensor makes it possible to monitor the dynamics of the volatile fatty acids.
In this experiment, this information was already used to decide the best point in time
to increase the OLR. After increasing the OLR from 0.5 g l-1 d-1 to 1 g l-1 d-1 on day 24,
especially acetic acid, but also iso-butyric and iso-valeric acid exhibited an increase in
their concentrations. Therefore, the next increase to 2.0 g l-1 d-1 happened only after the
VFA were almost completely degraded again (day 38). This strategy also explains the
following short time between the next to steps, to 3.0 g l-1 d-1 and 4.0 g l-1 d-1 . Only the
reference analysis shows elevated levels of butyric acid, which hints at a process failure
- but this was not detectable with the anaerobic sensor system. The concentrations of
the higher chain fatty acids were stable, which served as an indicator for raising the
OLR. Clearly, the short time of only one week between the increase of the OLR had a
major impact on the bioprocesses, where usually hydraulic retention times great than
30 days can be found. Just 2 days after the last step to 4.0 g l-1 d-1 , the concentrations
49
of all volatile fatty acids increased again, the biogas production slowed down and the
process did not recover afterwards. The results indicate, that a successful control algorithm for the anaerobic digestion has to be based on all available parameters. Even if
the dynamics of the volatile fatty acids are the best indicator for process failures at the
moment, they cannot be used out of context for decisions regarding alterations of the
biogas fermentation process.
50
4. gasUino - a low-cost biogas flow meter based on the
open-source electronics prototyping platform Arduino
4.1. Introduction
For a sustainable future, the energy supply has to shift from using fossil fuels
to renewable energies. The transformation of biomass to biogas or bioethanol
contributes to a mixture of renewable energies like solar power, wind power or
geothermal energy. To maximize the biogas output and degradation of the organic
substrates, their biogas yield potential has
to be identified. This can have a great
variation in quality between different substrates like corn, animal manure or organic wastes depending on the composition of carbohydrates, protein and lipids as
well as the structure of the biomass. The
estimation is usually carried out by triplicate batch fermentation tests in laboratory scale according to VDI 4630 [75]. Al-
though the preparation of the tests is sim- Figure 4.1.: gasUino with controlling unit in
the front
ple, the correct measurement of the produced biogas can be time consuming and
error-prone. The easiest method, the water displacement principle with a graduated
cylinder has to be maintained regularly and can have a great variability in the measurements. A special glass apparatus (Eudiometer) is the standard method for estimating
the biogas potential of municipal waste. Unfortunately, this is also operated manually. To
51
address these problems, researchers started developing laboratory biogas flow meters,
based on different designs, which can handle the minor gas flows and are able to record
the gathered data to a personal computer [2, 36, 39, 50, 53, 55, 67, 71]. The electronic
hardware needed for these devices is normally an in-house development of the different
institutes, which makes it difficult to reproduce for other working groups and lab environments. Therefore, we developed the gasUino - a low cost biogas meter based on the
open-source electronics platform Arduino, which addresses these problems. The biogas flow meter is based on the liquid displacement principle like a U-shaped manometer,
with the main focus of making it easy to assemble and operate, affordable and robust.
According to the open hardware spirit, it will be released under an open license.
4.2. Materials and Methods
A complete overview of the used materials is given in the Appendix, see table A.1 and
table A.2.
4.2.1. Flow meter design
The cell for the measurement of the biogas volume is formed by an U-shaped tube (see
figure 4.4a). Two serological pipettes (Stripette, 25 ml, Corning, MA) are connected by a
PVC tube and filled with a sealing liquid of 75 % NaCl (pH 2) to enhance the conductivity
and decrease the gas solubility according to Walker et al. [76]. A 3/2 way rocker solenoid
valve (Typ 6606, 12V, bürkert, Ingelfingen, Germany) connects the U-shaped tube with
the anaerobic digester and a gas collecting bag (PETP/AL/PE, 20 l, tesseraux, Bürstadt,
Germany). In between bioreactor and valve, a gas-washing bottle filled with silica gel
removes the water vapor. Two physically separated open graphite electrodes are used
as contacts for the switch-like detection mechanism:
1. The valve is not activated and the gas can freely flow from the digester to the Ushaped tube. Due to the developing pressure of the biogas, the liquid column is
lifted until the sensor gets in contact with the sealing liquid.
2. Bridging the contacts triggers a 10 second activation of the valve ("click"), which
inverts the inlet and outlet of the valve. The weight of the liquid column pushes the
biogas into the gas-collecting bag, until the liquid column is balanced again.
52
3. The valve switches back to the start position and the next cycle begins.
Depending on the filling level of the sealing liquid in the pipets, the corresponding
volume of a click is determined by pushing air into the system with a graduated syringe.
The calibration volume is calculated by
V calib =
V number of clicks
clicks
The sensor (temperature and air pressure) values and clicks get sent to a web server in
a preset interval where the standard volume (0 °C, 1013 hPa) is calculated by
Vstandar d [ml] =
Vcalib [ml] · clicks · pambient [hP a] · 273.15[K])
1013[hP a] · (Tambient [ C] + 273.15[K])
and stored in a database for further analyzing and plotting. All components of the biogas
flow meter are embedded in a custom designed laser-cut case of acrylic glass (see A.9,
Fig. 4.2).
4.2.2. Electronics
The gasUino biogas flow meter is built upon the Arduino - an open source electronics
prototyping platform based on flexible, easy-to-use hardware and software [4]. Different
circuit boards (shields) are available and can be used to extend the possibilities of the
Arduino, by means of stacking them together. For ethernet access, the ethernet shield
[5] was incorporated. For the simultaneous operation of nine gasUinos, two separate
circuit boards were designed and connected with a 40-Pin ribbon cable:
1. The connection shield (see figure 4.3a) provides the power supply (12 V), which
drives the solenoid rocker valves and supplies 9 V to the Arduino via a voltage regulator (LM340, STMicroelectronics, Geneva, Switzerland). Up to nine gasUinos can
be connected to the board via terminal blocks. 10k⌦ pull-up resistors are used for
the sensing mechanism; NPN transistors (TIP102, ON Semiconductors, Phoenix,
AZ, US) switch the valves.
2. A LCD Display (162C, Displaytech, Hong Kong), a real time clock (DS1307, Maxim,
Sunnyvale, CA, US), the environmental sensors for temperature (LM35, National
53
(a) Picture of the assembled gasUino (without electronics)
(b) Exploded View
Figure 4.2.: gasUino
54
(a) batchLab connection shield. Nine flow meters can(b) batchLab mega. A developed shield stacked bebe connected.
tween the Arduino mega and the Ethernet shield.
Incorporates a temperature and pressure sensor,
a LCD Display and tactile buttons.
Figure 4.3.: Board layouts for the developed shields. Corresponding circuit diagrams
can be found in the Appendix, see A.11 and A.10.
Semiconductor, Santa Clara, CA, US) and pressure (MPX4115, Freescale Semiconductor, Austin, TX, US) and three freely programmable tactile switches are
soldered on this shield, which is stacked between the Arduino Mega 2560 and the
Ethernet shield (see figure 4.3).
4.2.3. Database & Web interface
The data acquisition and storage is based on standard technologies. A web application, developed with the PHP framework CodeIgniter [9] is running on an Apache2
web server [6] and MySQL [11] as the relational database. It can be accessed at
http://biogas.jacobs-university.de (see fig. 4.4b). In a given interval, the Arduino connects to the server and submits the recorded values with a HTTP-GET command. On
the server, a PHP [8] script parses and validates the request and stores the data with
a timestamp in the database. With the help of PHP and Gnuplot [40], the data can be
interactively retrieved, plotted and exported into a comma separated values file (CSV)
for further data processing. For the calculation of the correct standard volume, the calibrated volume for each gasUino is also stored in the database and accessible via the
web interface.
55





(a) U-shaped tube, sensor head and 3/2 way rocker
valve
(b) Web frontend for the gasUino / batchLab
Figure 4.4.
56
The detailed and commented gasUino sketch can be found in the Appendix under
section A.1 as well as the SQL statements to create the table structure (A.1). The source
code of the PHP application would go beyond the scope of the thesis and is therefore
available online for further studying, see [13].
4.2.4. Fermenter bottle
The fermenter is composed of a 2 L glass bottle with a gas-washing bottle fixture. One
gas outlet is connected via the gas washing bottle with silica gel to the flow meter. The
second outlet forms the inlet for the flexible stirring mechanism. A Norprene Tubing is
routed trough the glass tube into the active sludge. It is a very flexible but still strong
material to overcome the small radius of the 90º angle (c.f 4.5b). For achieve homogenous conditions via stirring, the end of the tube was folded like an 8 and secured with
cable straps (see fig. 4.5a). Due to the flexibility, the stirrer can be removed with the
bottle lid for feeding and cleaning.
4.2.5. batchLab fermenter array
A heating bath tank with 10 mm wall thickness was built with acrylic glass. The tank can
host a thermostat and nine fermenter bottles. On the backside, nine 12V DC motors
with 60 rpm are mounted, which power the flexible stirrer inside the bottle. On the front
side, a hole was kept to feed the biogas outlet tube through the wall. Floating plastic
balls (Ritter Chemie, Ritterhude, Germany) on the surface reduce the evaporation of the
water.
The flexible stirrer is connected to the motor with a cutting ring fitting. A flexible stirrer
axis, which also allows a quick dismantling is formed by three hooks. The motors are
powered by a small PCB with terminal blocks and a 12 V connector (c.f. fig. 4.5) and
are rotating counter-clockwise to ensure a gas tight connection to the fermenter.
4.2.6. Performance
The biogas flow meter setup was tested in a stress test. It was connected to a membrane
pump with a high steady flow of air (90 l / d) and the clicks were recorded for 15 days.
57
(a) Fermenter array heating bath tank, 10 mm acrylic
glass
58
(b) Detail of the mounted motor and the flexible stirring system








 


 
 
 
 


 
(a) Fermenter array heating bath tank
(b) batchLab fermenter array
(a) Circuit diagram
(b) Board layout
Figure 4.5.: Stirrer Power
59
4.3. Results and Discussion
The detection mechanism of the gasUino dictates, that the calibrated volume must not
change over time, otherwise the error of the measurement will increase with every click.
Although the used sealing liquid of 75% NaCl has a even higher boiling point of 104.9
°C compared to the standard sealing liquid of 0.5 M sulfuric acid [35], evaporation could
be observed, resulting in a slight increase of the graduated volume and therefore in a
smaller overall biogas yield. This error correlates to the elapsed time as well as the
number of detected clicks, and hence the initially calibrated volume. Considering an
overall idealized biogas yield of carbohydrates of 0.746 l / g [27], after 28 days, the
detected biogas volume for 10 g of microcrystalline cellulose (MCC, positive control) is
7.46 l. With a calibrated volume of 5 ml, 1492 clicks should be detectable during the
experiment duration. In the worst case (5.6 ml as the actual volume) this would result in
only 1332 clicks, which would equal 6.66 l of biogas and and an underestimation of 10
%. For a calibrated volume of 20 ml, however, the error will decrease (20 ml = 373 clicks,
20.6 ml = 362 clicks; deviation of 3 %). As the loss of sealing liquid is time dependent
and the highest activity of biomass degradation takes place in the first 10 - 14 days, this
error will even be smaller in a real application. For a comparative study with triplicates
according to the VDI 4630 guideline, it is suggested to calibrate the flow meters with
a volume greater than 15 ml and furthermore, use the same volume in all parallel flow
meters.
A stress test over a period of 15 days was done to estimate this error development
(see figure 4.6). With a steady flow of around 90 l / day, more than 220,000 clicks could
be detected during the experiment time. If the flow of the membrane pump is constant
and without evaporation, a straight line was expected. For this, an extrapolation of the
first two days was plotted as the reference. Evaporation or other loss of the sealing
liquid leads to an increase of the volume, and therefore an expanded time between the
clicks, which leads to a truncation of the line. This effect can be seen starting at day
8, after 100,000 clicks have been registered. As mentioned previously, this does not
resemble a real fermentation experiment, where dependent on the volume a maximum
of 2,000 clicks can be estimated. Overall, the deviance is small. Another fact which has
to be taken into consideration is the kinetics of an actual batch fermentation. In figure
4.6b, the biogas development of microcrystalline cellulose, which served as the positive
60
2 105
900
Measurement
First 2 days extrapolated
2 105
800
1 105
700
5
600
1 105
500
clicks
ml/VS
1 10
A
B
C
D
E
F
G
8 10
4
400
6 10
4
300
4 104
200
4
100
2 10
0 100
0
0
2
4
6
8
time [d]
10
12
(a) Stress test
14
0
2
4
6
8
time [d]
10
12
14
(b) Exemplary results of digestion of microcrystalline
cellulose, positive control in batch fermentation
tests
Figure 4.6.: Stress test of the biogas flow meter and the result of the positive controls
with mcc in different batch setups
control in all batch fermentations (A - G) is presented. All curves express a sigmoidal
form, the greatest changes in the slope can be observed in the first 15 days, where the
evaporation as an error source is irrelevant. On a technical side, the stress test also
proved that the sensing and valve switching mechanism is robust.
The gasUino was furthermore tested in actual fermentation experiments. All biogas
volume recordings in this thesis were done with different versions of the biogas counter.
It was successfully implemented into a continuous operated biogas fermenter (see chapter 3), a dry batch fermentation setup [37] and standard batch methane potential tests
with different novel substrates [16].
61
5. Conclusion
The aim of this work was to develop methods and instruments to decipher the "blackbox" process of anaerobic digestion. This problem was approached from two different
directions. As presented in chapter 3, an online sensor system for the volatile fatty acids
in the digestate was developed. It confirmed, that ATR-MIR-FTIR-spectroscopy proves
to be a good method to monitor the process of anaerobic digestion. The developed PLS
models can predict the dynamics of the different volatile acids and their concentration
in a satisfying concentration range. The fully automatic evaluation makes it possible
to use the system as an alarm to detect arising problems of the bioprocess in an early
stage. It can give time to counter steer possible process breakdowns. Likewise, the
technology may optimize biogas plants by utilizing unused potentials, due to e.g. low
organic loading rates. This is especially interesting for plants operated in thermophilic
mode, because a higher process temperature also accelerates biochemical reactions.
A continuing study of the application of the sensor at a full scale biogas plant digesting
municipal organic waste is carried out at the moment. Despite the obvious advantages
for biogas plant operators, it can also help to improve the available mathematical models for the anaerobic digestion process. Integrated in a biogas simulator, these could
be used to train plant personnel, prior operating the real plant. In current models, the
volatile fatty acids are summarized in one variable, which gets calculated from other input values (feed, pH, gas production). The possibility to measure the concentration of
the volatile fatty acids in detail may vastly aid in the optimization of the prediction capabilities of these adapted models. A very simple calculation to estimate the performance
of a fermentation is the Buswell Equation. Based on the different fractions of lipids, proteins and carbohydrates, an idealized maximum biogas volume can be calculated. As
different substrates exhibit different chemical structures with different degradation constants, a real fermentation has to be performed to really estimate the biogas potential
of substrates. In the second project (chapter 4), a biogas meter was developed upon
the open hardware platform Arduino, which makes it possible to monitor these little gas
flows in laboratory environments. It is not specialized to only monitor the volume flow
62
5. Conclusion
in biogas potential tests or continuous setups. Other applications can be the estimation
of the residual gas potential of municipal waste before storing it in landfills, or monitoring the carbon dioxide volume of the anaerobic phase of ethanol production due to its
adjustable resolution. In contrast to other commercially available products, it is open
sourced and low-cost. Two major advantages can be achieved with this approach. It
can enable workgroups in the field of anaerobic digestion to upscale their biogas potential tests, due to the low price, and the availability of source code and design files makes
it easily possible to adapt the equipment to the specific laboratory needs. In conclusion,
this work shows that for the optimization of the anaerobic digestion process, a lot more
parameters and their interaction have to be monitored more closely, although this does
not necessarily require high-tech equipment to do so.
63
List of Tables
1.1. Examples of fermentation processes from glucose [72] . . . . . . . . . . . 14
1.2. VFA degradation during acetogenesis;
G0 ’; T = 25 °C, pH 7, pH2 10-5
atm, pCH4 0.7 atm, c(VFA) 1 mM, HCO3 - 0.1 mM[21, 72] . . . . . . . . . 15
1.3. Methanogenic degradation [33] . . . . . . . . . . . . . . . . . . . . . . . . 16
1.4. Overview of TS and VS percentages of common substrates [10] . . . . . 19
2.1. Chemicals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2. Equipment
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.1. Summary of the properties of the developed methods for the different
volatile fatty acids. 147 samples were collected.
3.2. Frequency ranges
[cm-1 ]
. . . . . . . . . . . . . . 46
of the different developed methods. . . . . . . . 46
A.1. batchLab materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
A.2. batchLab electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
64
List of Figures
1.1. Past and future CO2 concentrations. Since pre-industrial times, the atmospheric concentration of greenhouse gases has grown significantly. Car-
bon dioxide concentration has increased by about 31 %, methane concentration by about 150 %, and nitrous oxide concentration by about 16 %
[77]. The present level of carbon dioxide concentration (around 375 parts
per million) is the highest for 420 000 years, and probably the highest for
the past 20 million years. [56] . . . . . . . . . . . . . . . . . . . . . . . . .
9
1.2. Development of the number of biogas plants and the total installed electric
output in megawatt [MW] (as of 06/2011) in Germany [19] . . . . . . . . . 10
1.3. Compensation system EEG 2012 [26] . . . . . . . . . . . . . . . . . . . . 12
1.4. Utilizable energy potential (Hartmann/Kaltsschmitt, 2002, reworked by
FNR) [54] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.5. Thermodynamic window of the degradation of the volatile fatty acids [33] . 15
1.6. Two-stage agricultural biogas plant . . . . . . . . . . . . . . . . . . . . . . 21
2.1. Overview of a HPLC System (modified [14]) . . . . . . . . . . . . . . . . . 28
2.2. Schematic setup of a Michelson Interferometer [79] . . . . . . . . . . . . . 31
2.3. Interference of superimposed sinus waves . . . . . . . . . . . . . . . . . . 32
2.4. Schematic representation of the attenuated total reflectance. n1 : Refractive Index of the crystal; n2 : Refractive Index of the sample; n2 < n1 ; dp =
Penetration depth, [42] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
65
List of Figures
3.1. Piping and instrumentation scheme of the anaerobic digester and the IRspectrometer. A sample is generated by a rotating filter unit mounted
on the stirrer axis. Pump 1 transfers the digestate to the measurement
chamber, fitted on top of the ZnSe crystal. Simultaneously, fresh water is
transferred to the fermenter to balance the filling level. After the spectra
recording is finished, crystal and chamber are cleaned with fresh water
via pump 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.2. Acrylic glass chamber mounted on top of the ZnSe crystal . . . . . . . . . 39
3.3. Flow chart diagram of the chosen approach developing and validating the
different methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.4. Regression plots from the PLS-Models for the different volatile fatty acids.
PLS predicted concentrations were plotted against the HPLC reference
analyses. All spectra were smoothed and pre-processed with the first or
second derivative and different frequency ranges were chosen, respectively (cf. 3.1,3.2). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.5. Methods did not include the corresponding HPLC results. a) straight line:
pH, dashed line: cumulated biogas volume b) - e) dots: PLS-predicted
concentrations. line: smoothed average of PLS-predicted values, points:
HPLC reference analysis. Only prediction results are plotted, which passed
the internal QUANT2 validity test. The shaded areas highlight the increasing organic loading rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.1. gasUino with controlling unit in the front . . . . . . . . . . . . . . . . . . . 51
4.2. gasUino . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.3. Board layouts for the developed shields. Corresponding circuit diagrams
can be found in the Appendix, see A.11 and A.10. . . . . . . . . . . . . . 55
4.4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.5. Stirrer Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.6. Stress test of the biogas flow meter and the result of the positive controls
with mcc in different batch setups . . . . . . . . . . . . . . . . . . . . . . . 61
A.1. Main block diagram
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
A.2. Frontpanel A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
A.3. Frontpanel B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
66
List of Figures
A.3. Loop for continuous data retrieval from Biostat MD, B and gasUino . . . . 84
A.3. Loop for automatic sample measurement with the FTIR . . . . . . . . . . 86
A.4. Data retrieval from Biostat B . . . . . . . . . . . . . . . . . . . . . . . . . . 87
A.5. Data retrieval from Biostat MD
. . . . . . . . . . . . . . . . . . . . . . . . 88
A.6. Control of the USB Power Socket . . . . . . . . . . . . . . . . . . . . . . . 88
A.7. Assembly of the SQL input string . . . . . . . . . . . . . . . . . . . . . . . 89
A.8. Control of the OPUS Software; Measuring and Analyzing of the IR-spectra 90
A.9. Laser-cut holder for the gasUino, 5 mm acrylic glass; Dimensioning in mm. 92
A.10.Circuit diagram: batchLab connection shield . . . . . . . . . . . . . . . . . 93
A.11.Circuit diagram: batchLab connection shield . . . . . . . . . . . . . . . . . 94
67
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74
A. Appendix
A.1. Arduino sketch sourcecode
#include <Ethernet.h>
#include <EthernetDHCP.h>
#define interval 10000
//modify = noOfCounters
//Ethernet//
int noOfCounters = 9;
byte mac[] = {
int counter[9];
0x90, 0xA2, 0xDA, 0x00, 0x35, 0x9D }; //printed on Ethernet Shield
byte server[] = {
//Sensors
212,201,46,122 }; // biogas.jacobs-university.de
int gasPins[]={
49,45,25,24,28,32,40,44,48};
const char* ip_to_str(const uint8_t*);
//Voltage for sensors
//LCD//
int voltagePins[]={
#include <LiquidCrystal.h>
41,22,33};
// initialize the library with the numbers of the interface pins
LiquidCrystal lcd(12, 11, 9, 8, 7, 6);
char buf[6];
long unsigned displayTime;
#define lcdInterval 3000
Client client(server, 80);
//DS1307//
#include <WProgram.h> // clock
#include <Wire.h> //clock
#include <DS1307.h>
int dateTime[6];
//Sensors//
#define refVoltage 5
#define tempPin 10
#define pressurePin 8
float pressure;
float temperature;
//Transistor pins
int transistors[]={
47,29,23,26,30,34,42,46,27};
//Transistor on/off
int OnOff[]={
0,0,0,0,0,0,0,0,0};
long unsigned milliDelay[9]={
1,1,1,1,1,1,1,1,1};
//buttons
int buttons[3]={
14,15,16};
void setup() {
EthernetDHCP.begin(mac, 1);
Serial.begin(9600);
delay(2000);
RTC.stop();
RTC.set(DS1307_SEC,00);
RTC.set(DS1307_MIN,46);
RTC.set(DS1307_HR,9);
RTC.set(DS1307_DOW,3);
RTC.set(DS1307_DATE,20);
RTC.set(DS1307_MTH,7);
RTC.set(DS1307_YR,11);
RTC.start();
//token=which batchlab/gasUino array
#define token "batchLabB"
// Serial
String serialString;
long lastConnectionTime = 0;
boolean lastConnected = false;
#define postingInterval 60000
#define deltaTInterval 1000
//set the voltagePins to Output and HIGH = 5V
for (int i=0; i<3; i++) {
pinMode(buttons[i], INPUT);
pinMode(voltagePins[i], OUTPUT);
digitalWrite(voltagePins[i], HIGH);
}
long unsigned last_tm[9];
long unsigned delta_Interval =0;
75
A. Appendix
//init LCD
lcd.begin(16,2);
lcd.print("batchLab mega 2.0");
delay(500);
lcd.scrollDisplayLeft();
delay(500);
displayTime = millis();
milliDelay[i]=millis();
switch_transistor(i);//
increase_counter(i);
if (digitalRead(gasPins[i]) == LOW) {
OnOff[i]=1;
last_tm[i]=millis();
}
}
}
void loop() {
static DhcpState prevState = DhcpStateNone;
static unsigned long prevTime = 0;
updateDisplay();
DhcpState state = EthernetDHCP.poll();
if(!client.connected() && (millis() - lastConnectionTime > postingInterval)) {
updateDB();
}
lastConnected = client.connected();
if (prevState != state) {
Serial.println();
}
switch (state) {
case DhcpStateDiscovering:
Serial.print("Discovering servers.");
break;
case DhcpStateRequesting:
Serial.print("Requesting lease.");
break;
case DhcpStateRenewing:
Serial.print("Renewing lease.");
break;
case DhcpStateLeased:
{
const byte* ipAddr = EthernetDHCP.ipAddress();
const byte* gatewayAddr = EthernetDHCP.gatewayIpAddress();
const byte* dnsAddr = EthernetDHCP.dnsIpAddress();
lcd.clear();
lcd.setCursor(0,0);
lcd.print("My IP address is ");
lcd.setCursor(0,1);
lcd.println(ip_to_str(ipAddr));
delay(2000);
lcd.setCursor(0,0);
lcd.print("Gateway IP Adress: ");
lcd.setCursor(0,1);
lcd.println(ip_to_str(gatewayAddr));
delay(2000);
Serial.print("DNS IP address is ");
lcd.setCursor(0,0);
lcd.print("DNS IP Adress: ");
lcd.setCursor(0,1);
lcd.println(ip_to_str(dnsAddr));
delay(2000);
break;
}
void reset_gas(int i){
if (digitalRead(buttons[0]) == HIGH) {
counter[i]=0;
}
}
void switch_transistor(int i){
if (OnOff[i] == 1 && millis() - last_tm[i] <= interval){
digitalWrite(transistors[i], HIGH);
}
else if (OnOff[i] == 1 && millis() - last_tm[i] >= interval){
digitalWrite(transistors[i], LOW);
OnOff[i]= 0;
milliDelay[i]= 0;
}
}
void increase_counter(int i){
if (milliDelay[i] == 0){
counter[i]++;
}
}
float get_temperature() {
int span = 25;
int averageLM35 = 0;
float temperature = 0;
for (int i = 0; i < span; i++) {
//loop to get average of 20 readings
averageLM35 = averageLM35 + analogRead(tempPin);
}
averageLM35 = averageLM35 / span;
//convert the analog data to a temperature
temperature = (refVoltage * averageLM35 * 100.0)/1024.0;
return temperature;
}
}
}
prevState = state;
float get_pressure() {
float pressure;
pressure = ((analogRead(pressurePin)/1024.0 + 0.095))/0.009;
return pressure;
}
for (int i=0; i<noOfCounters; i++) {
void updateDisplay(){
76
A. Appendix
int dateTime[6]={
RTC.get(DS1307_YR,true),
RTC.get(DS1307_MTH,false),
RTC.get(DS1307_DATE,false),
RTC.get(DS1307_HR,false),
RTC.get(DS1307_MIN,false),
RTC.get(DS1307_SEC,false)
};
else if (millis() - displayTime >
(lcdInterval*4) && millis() - displayTime < (lcdInterval*5)) {
lcd.clear();
lcd.setCursor(0,0);
lcd.print("gUo 4: ");
lcd.print(counter[3]);
lcd.setCursor(0,1);
}
// Loop to rotate date, sensors and gasUino results on display
if (millis() - displayTime < lcdInterval){
lcd.clear();
lcd.setCursor(0,0);
if (dateTime[3] < 10) {
lcd.print("0");
}
lcd.print(dateTime[3]);
lcd.print(":");
lcd.print("0");
}
lcd.print(":");
lcd.print("0");
}
lcd.clear();
lcd.setCursor(0,0);
lcd.setCursor(0,1);
}
// lcd.clear();
lcd.setCursor(0,0);
lcd.setCursor(0,1);
}
lcd.setCursor(0,1);
lcd.print(get_temperature());
lcd.write(223);
lcd.print("C");
lcd.print(" ");
lcd.print((get_pressure() * 10));
lcd.print(" hPa");
lcd.setCursor(0,0);
}
(lcdInterval) && millis() - displayTime < (lcdInterval*2)) {
lcd.clear();
lcd.setCursor(0,0);
lcd.setCursor(0,1);
}
else if (millis() - displayTime
(lcdInterval*2) && millis() lcd.clear();
lcd.setCursor(0,0);
lcd.setCursor(0,1);
}
lcd.setCursor(0,0);
lcd.setCursor(0,1);
}
>
displayTime < (lcdInterval*3)) {
lcd.setCursor(0,0);
lcd.setCursor(0,1);
}
lcd.setCursor(0,0);
lcd.setCursor(0,1);
}
lcd.setCursor(0,0);
lcd.setCursor(0,1);
}
else {
displayTime = millis();
}
>
>
>
}
>
int process_values(){
displayTime < (lcdInterval*4)) { char ch = ’0’;
if (Serial.available() > 0) {
char ch = Serial.read();
if (ch == ’v’) //pv = process values
{
Serial.print(get_temperature());
Serial.print(",");
Serial.print(get_pressure()*10);
77
A. Appendix
Serial.print(",");
client.print(counter[5]);
client.print("&f7=");
client.print(" ");
client.println("HTTP/1.0\r\n");
client.println("HOST: 212.201.46.122");
lastConnectionTime = millis();
client.stop();
for (int i=0; i<noOfCounters; i++) {
Serial.print(counter[i]);
Serial.print(",");
}
Serial.println("");
}
}
}
const char* ip_to_str(const uint8_t* ipAddr)
{
}
static char buf[16];
else {
sprintf(buf, "%d.%d.%d.%d\0", ipAddr[0], ipAddr[1], ipAddr[2], ipAddr[3]);
lcd.println("connection failed");
return buf;
}
}
}
void updateDB (){
if (client.connect()) {
client.print("GET /index.php/batchlab/insert?&
key=qwertzui123456!&token=");
client.print(token);
client.print("&temperature=");
client.print(get_temperature());
client.print("&pressure=");
client.print(get_pressure()*10);
A.2. SQL statements
-- ---------------------------------------------------------- Table structure for table
--
‘configuration‘
CREATE TABLE IF NOT EXISTS ‘configuration‘ (
‘id‘ int(11) NOT NULL AUTO_INCREMENT,
‘token_id‘ char(12) COLLATE utf8_unicode_ci NOT NULL,
‘fermenter_id‘ int(11) NOT NULL,
‘experiment_id‘ varchar(255) COLLATE utf8_unicode_ci NOT NULL,
‘volume_calib‘ float NOT NULL,
‘description‘ tinytext COLLATE utf8_unicode_ci NOT NULL,
PRIMARY KEY (‘id‘)
) ENGINE=MyISAM DEFAULT CHARSET=utf8 COLLATE=utf8_unicode_ci AUTO_INCREMENT=40 ;
-- --------------------------------------------------------
78
A. Appendix
--- Table structure for table
--
‘sensor‘
CREATE TABLE IF NOT EXISTS ‘sensor‘ (
‘timestamp‘ timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
‘token_id‘ char(12) COLLATE utf8_unicode_ci NOT NULL,
‘temperature‘ float NOT NULL,
‘pressure‘ float NOT NULL,
--
‘users‘
CREATE TABLE IF NOT EXISTS ‘users‘ (
‘email‘ varchar(255) COLLATE utf8_unicode_ci NOT NULL,
‘password‘ varchar(255) COLLATE utf8_unicode_ci NOT NULL,
‘salt‘ varchar(255) COLLATE utf8_unicode_ci NOT NULL,
‘hash‘ varchar(255) COLLATE utf8_unicode_ci NOT NULL,
--
‘volume‘
CREATE TABLE IF NOT EXISTS ‘volume‘ (
‘sensor_id‘ int(255) NOT NULL,
‘timestamp‘ timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
‘fermenter_id‘ int(11) NOT NULL,
‘experiment_id‘ varchar(255) COLLATE utf8_unicode_ci NOT NULL,
‘token_id‘ varchar(255) COLLATE utf8_unicode_ci NOT NULL,
‘clicks‘ int(11) NOT NULL,
‘volume_nl‘ float NOT NULL,
A.3. LabVIEW Virtual Instruments
79
A. Appendix
Figure A.1.: Main block diagram
80
A. Appendix
Figure A.2.:81
Frontpanel A
A. Appendix
Figure A.3.: Frontpanel B
82
A. Appendix
(a) Request data from Biostat MD (c.f. A.5)
(b) 500 ms wait
83
(c) Retrieve data from Biostat B (c.f. A.4)
A. Appendix
(d) Get data from gasUino and correct to standard conditions
(e) Insert the data in the SQL-Database
84
(f) Update the plots on the front panel (c.f. A.2)
Figure A.3.: Loop for continuous data retrieval from Biostat MD, B and gasUino
A. Appendix
(a) Reverse the multichannel pump, set
speed to 50
(b) Start the cleaning of the crystal by starting the cleaning pump, setting power
socket to ON
(c) Start multichannel pump, wait for 20
sec
(d) Stop the cleaning pump, setting power
socket to OFF
(e) Stop the multichannel pump
(f) Wait until the next measurement begins
85
(g) Start the cleaning of the crystal by starting the cleaning pump, setting power
socket to ON
(h) Start multichannel pump, wait for 20
sec
A. Appendix
(i) Stop the cleaning pump, setting power
socket to OFF
(j) Stop the multichannel pump
(k) Start the background measurement
(l) Set the multichannel pump to a slower
velocity and change direction
(m) Transfer a sample from the fermenter
onto the crystal, start the multichannel
pump for 3 minutes
(n) Stop the multichannel pump
86
(o) Start the IR-measurement and analyzing of the spectra (c.f. A.8)
(p) Update the plots on front panel A (c.f.
A.2)
Figure A.3.: Loop for automatic sample measurement with the FTIR
A. Appendix
Figure A.4.: Data retrieval from Biostat B
87
A. Appendix
Figure A.5.: Data retrieval from Biostat MD
Figure A.6.: Control of the USB Power Socket
88
A. Appendix
Figure A.7.: Assembly of the SQL input string
89
A. Appendix
Figure A.8.: Control of the OPUS Software; Measuring and Analyzing of the IR-spectra
90
Name
Acrylic Glass Tank Custom designed
CostarÆ Stripette-Pipette
Flask, 2000ml
Floating plastic balls
Gas collection bags, 20l
Gas wash bottle
Gas wash bottle
gasUino Acrylic Glass Housing
Lab-Ring, open
Norprene Tube 3.2x1.6
PVC Tubing 3mm x 1mm
Reducing Adapter, 3-5 / 6-10 mm, 55 mm length
Silicagel, colour indicator orange
Silicon Tube 4mm x 1.5mm
Slotted cheesehead steel screw M2.5x20mm
Spacer 8x3mm round
Stirring Motor 12V / 60 rpm
Thermostate Julabo ED
Zinc plated steel hexagon full nut, M2.5
Sinker
Stirrer
Tubes for gasUino
Connecting Fermenter / Gas collecting bag
Gas drying
Input for second electrode
Mounting PCB and Stirring Motors
Stirrer
Waterbath Heating
Gas drying
Function
Heating bath and Stirrer Mound
U-Tube
Fermenter
reduce evaporation
Manufacturer
CD Thalau
Corning
Simax
Ritter Chemie
tesseraux
Lenz Laborglasinstrumente
Lenz Laborglasinstrumente
formulor
Kleinfeld Labortechnik
Tygon
DEUTSCH & NEUMANN
Brand
Kraemer and Martin
DEUTSCH & NEUMANN
RS Components
RS Components
Philips
Julabo
RS Components
Table A.1.: batchLab materials
Distributor
CD Thalau
Omnilab
Omnilab
Omnilab
tesseraux
Omnilab
Omnilab
formulor
Omnilab
Carl Roth
Omnilab
Omnilab
Omnilab
Omnilab
RS Components
RS Components
RS compontents
Omnilab
RS Components
5424053
AH62.1
5205358
9207336
5042552
5205266
546-6146
102-6205
336-315
5422645
560-287
Order Number
11/025
5380583
5072336
5003390
35005000600
6073489 / 5530058
6331669/5530158
A. Appendix
91
A. Appendix







Figure A.9.: Laser-cut holder for the gasUino, 5 mm acrylic glass; Dimensioning in mm.
92
Figure A.10.: Circuit diagram: batchLab connection shield
A. Appendix
93
A. Appendix
Figure A.11.: Circuit diagram: batchLab connection shield
94
Name
16x2 reflective STN yel/grn, 80x36
3310Y conductive plastic pot,10K lin 9mm
34 way IDC strain relief
34way polarised skt w/o strain relief
36way header, 5.7mm, 7.6mm,size3
3mm Easy Break Terminal Strip
4 conductor screened cable, 30m
6x6mm tactile switch, 5mm H 1.6N
Arduino Ethernet Shield
Arduino Mega 2560
Arduino Stackable Header Kit
batchlab connection shield 4.3a
batchlab mega 4.3
batchlab stirrer power 4.5
Cable 2/20
Cable 2/20
Capacitor 1uF
Carbon Resistor, 0.25W ,5%, 10k
Carbon Resistor, 0.25W ,5%, 1k
Ceramic Capacitor 470 pF
Cover Nut, M3
CR2032 Lithium Coin Cell 3V
Crystal 32.768KHz 3x8mm
Cutting ring fitting, 6mm -> 4mm
Diode 1N4004-E3
IC LM35 DZ
Intos Patchcable Cat 5e 10m
Magnetic Valve 3/2 way, 12V, Typ 6606
MPX5114A
NPN darlington transistor, TIP102G 8A
PCB mount DC power socket 2.1mm 1A 12V
Power Supply 12V, 2.08A, 25W
Radial Ceramic capacitor 330nF 50Vdc
Radial Z5U ceramic cap, 100nF 50V 5mm
Real time clock, DS1307 56B RAM DIP8
Ribbon Cable 34way
Slotted cheesehead steel screw M2.5x20mm
Slotted cheesehead steel screw M3x16mm
Spacer 8x3mm round
Surface mount PCB coin cell holder,20mm
Terminal Block MKDS 1/12-3.81
Terminal Block MKDS 1/6-3.81
Threaded Bar M3
Voltage Regulator 9V 2A TO220
Zinc plated steel hexagon full nut, M2.5
Zinc plated steel hexagon full nut, M3
95
Mount PCB
12V –> 9V
Mounting PCB and Display
Battery Holder
Connecting the wires
Voltage Regulator
Voltage Regulator
Clock
Pressure Sensor
Switching Valves
Temperature Sensor
LAN
Switch
Ethernet Connectivity
Central Processing Board
Connecting PCBs
PCB
PCB
PCB
Sensor
Sensor
for pressure sensor
Sensor
for Transistors
for pressure sensor
Mount PCB
Buffer for Clock
Clock
Connecting Stirrer to Motor
Connecting PCBs
Function
Display
Adjusting LCD Brightness
Vishay
National Semiconductor
Intos
bürkert
Freescale
ON Semiconductor
RS Components
Mean Well
Murata
Murata
Maxim
Speedbloc
RS Components
RS Components
RS Components
Keystone
Phoenix Contact
Phoenix Contact
Graupner
STMicroelectronics
RS Components
RS Components
Alpha Wire
Alpha Wire
RS Components
RS Components
RS Components
Epcos
Toolcraft
RS Components
Fox Electronics
Brand
Displaytech
Bourns
TE Connectivity
TE Connectivity
RS Components
RS Components
Alpha Wire
TE Connectivity
Arduino.cc
Arduino.cc
Sparkfun
Distributor
RS Components
RS Components
RS Components
RS Components
RS Components
RS Components
RS Components
RS Components
TinkerSoup
RS Components
TinkerSoup
Q-Print electronics
Q-Print electronics
Q-Print electronics
RS Components
RS Components
RS Components
RS Components
RS Components
RS Components
Conrad Electronics
RS Components
RS Components
Rauh Hydraulik
RS Components
RS Components
amazon
bürkert
RS Components
RS Components
RS Components
RS Components
RS Components
RS Components
RS Components
RS Components
RS Components
RS Components
RS Components
RS Components
RS Components
RS Components
Conrad Electronics
RS Components
RS Components
RS Components
Table A.2.: batchLab electronics
111-9183
111-9183
521-2315*
707-7745
707-7666
211-4729*
521643-62
597-201
547-6985
170272
628-9029
533-5907
B000AGETB0
137-782
2509678637*
545-0494
448-382
721-2269
721-5278
652-9995
540-2726
289-9931
546-6146
560-782
102-6205
219-7954
648-7891
220-4377
261494-62
686-9751
560-287
560-293
Order Number
532-6408
522-0625
454-2390
454-2413
251-8339
716-7346
111-9048
479-1413
131 / DEV-09026
715-4084
370 / PRT-10007
A. Appendix
Nomenclature
⌫¯
Wavenumber [cm-1 ]
Wavelength
µl
mikro Liter
AL
Aluminum
Arduino Arduino is a tool for making computers that can sense and control more of
the physical world than your desktop computer. It is an open-source physical
computing platform based on a simple micro controller board, and a development
environment for writing software for the board [4]
ATR
Attenuated Total Reflectance
batchLab Nine fermenters and gasUinos combined in one array
C2
Acetic acid
C3
Propionic acid
CH4
Methane
CHP Combined heat- and power plant
click
The sensors of the gasUino get in contact with the sealing NaCl solution. The
conductivity is sensed and the valve is activated, which reverts inlet and outlet.
The weight of the liquid column pushes the biogas into the gasbag, until the liquid
column is balanced again.
CO2
Carbon dioxide
CSTR Continuous Stirred Tank Reactor
96
A. Appendix
CSV
Comma separated values; A file format used as a portable representation of a
database. Each line is one entry or record; the fields in the record are separated
by commas. This format is often used to import data into spreadsheet software.
DDE Dynamic Data Exchange
EEG Erneuerbare Energien Gesetz - Renewable Energy Law
Ethernet A system for connecting a number of computer systems to form a local area
network, with protocols to control the passing of information and to avoid simultaneous transmission by two or more systems
FOM Content of fermentable organic matter
FTIR Fourier-Transformed Infra-Red
gasUino biogas flow meter based on the open-source electronics prototyping platform
Arduino
GC
Gas chromatography
H2 S
Hydrogen sulfide
HRT
Hydraulic Retention Time [d]
HTTP Hypertext Transfer (or Transport) Protocol, the data transfer protocol used on the
World Wide Web
HTTP-GET Requests a representation of the specified resource
iC4
Iso-butyric acid
iC5
Iso-valeric acid
KWK Kraft-Wärme-Kopplung
MIR
Mid Infra Red
NaCl Sodium Chloride
NaWaRo Nachwachsende Rohstoffe - primary renewable products
97
A. Appendix
nC4
Butyric acid
nC5
Valeric acid
NH3
Ammonia
OLR
Organic Loading Rate [g VS l-1 d-1 ]
PE
Polyethylen
PETP Polyethylene terephthalate
pH
potentia Hydrogenii
PHP
Hypertext Preprocessor
PLS
Partial Least Squares
PVC
Poly Vinyl Chloride
RMSECV Root Mean Square of Cross Validation
RMSPE Root Mean Square Prediction Error
Shield Shields are boards that can be plugged on top of the Arduino PCB extending
its capabilities. The different shields follow the same philosophy as the original
toolkit: they are easy to mount, and cheap to produce.
Sketch A sketch is the name that Arduino uses for a program. It’s the unit of code that
is uploaded to and run on an Arduino board
SQL
Structured Query Language
Substrate Biomass, which can be digested to biogas
substrate biomass, which can be fermented
TS
Total solids (after drying at 105 °C for 24 h)
U-shaped tube Two serological pipettes are connected by a PVC tube
VDI
Verein Deutscher Ingenieure
98
A. Appendix
VFA
Volatile Fatty Acids
VI
Virtual Instrument
VS
Volatile solids (after combustion at 550 °C for 24 h)
ZnSe Zinc Selenide
99
Acknowledgements
I would like to thank ...
... Prof. Dr. Dr. hc. Roland Benz for giving me the opportunity to do my PhD thesis
under his kind supervision and for all the support throughout the last years.
all remaining members of my PhD committee, Prof. Dr.-Ing. Volker C. Hass, Prof.
Dr. Laurenz Thomsen and Prof. Dr. Mathias Winterhalter.
... my diploma students Tobias Dörr and Yann Barbot for the exceptional collaboration.
... Dr. Peter Reichling and Dr. Christian Andersen for introducing me into the scientific
work and their enduring interest in "our" work.
... the technicians in Würzburg, Albert Gessner, Marcus Behringer and Willi Bauer,
the technicians from the Bremen, Maik Dreßel, Michael Hofbauer, Stefan Baltrusch, Rene Popp and CD Thalau.
... my former colleagues in Würzburg and our current workgroup in Bremen.
... the Internet, especially all developers and makers behind the Arduino Platform.
... my family and friends.
... my Grandmother.
... Sebastian.
100

Monitoring the anaerobic digestion process

Transcription

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