New Measuring Methods for Commercial Scale
Biogas Plants
Marcel Pohl, Jan Postel
Large Scale Bioenergy Lab 2 workshop, 29.01.2018, Sankelmark Academy
The Deutsches Biomasseforschungszentrum
2
As per: summer 2015
Pictures: DBFZ / Jan Gutzeit
Applied scientific research at the DBFZ
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Research biogas plant Combustion lab Fuel conditioning lab
Fuel technical centre Engine test bed Analytical lab
What we can do for you
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Applied R&D troughout the value chain
National and international networking across the whole research landscape
Creation of scientifically sound decision-making aids for policy-makers,
businesses and other institutions
In short: we are the leading institute in bioenergy research
Ownership and decision-making structure
5
MINISTRIES IN THE DBFZ SUPERVISORY BOARD
BMEL: Federal Ministry of Food and Agriculture
BMBF: Federal Ministry of Education and Research
BMUB: Federal Ministry for the Environment, Nature Conservation,
Building and Nuclear Safety
BMVI: Federal Ministry of Transport and Digital Infrastructure
BMWi: Federal Ministry of Economics and Energy
SMUL: Saxon Ministry of the Environment and Agriculture
Steps towards smart bioenergy
6Picture: DBFZ according to Daniela Thrän (Ed.): "Smart Bioenergy", Springer 2015
The research focus areas at the DBFZ
7
Paths from biomass into the energy system
8Copyright: DBFZ, 2012
9
Focus: waste and residues
151.1 Mio. t DM Theoretical potential
- 43.1 Mio. t DM Not usable (Restriction)
- 09.7 Mio. t DM Unclear data
= 98.4 Mio. t DM Technical potential
- 29.7 Mio. t DM Material use
- 26.9 Mio. t DM Energetic use
- 07.3 Mio. t DM Material or energetic use
- 03.5 Mio. t DM Unclear usage
= 30.9 Mio. t TS Unused potential
(Discrepancies due to rounding)
Biomass potentials from Waste and residues
And their actual use – Status quo in Germany
77 Single biomasses have been considered
Time references are not uniform
Source: Brosowski et al. 2015
Current challenges for biogas technology
● Flexibilisation, on-demand energy supply
● High efficiency of conversion processes
● Utilisation of currently un(der)used potential
• Adaptation of process technologies to „new“ substrate streams
● High utilization ratios of the energy provided (electricity and heat)
● Reduction of emissions and losses (GWPCH4 = 86 * GWPCO2)
● Coupling of material and energetic use of biomass11
Run times of back up flares
12 (Source: DBFZ operator‘s survey, 2015)
• Run times define losses
• If not automatically triggered emissions climate-relevant
class
Avg. gas storage filling level at normal operation, upper and lower limit [%]
Flare events [count/yr]
Run times, median [h/yr]
Ga
s s
tora
ge
fillin
g le
vel [%
]
Fla
re e
ven
ts [
co
un
t/yr
]
an
d
Ru
n t
ime
s,
me
dia
n [
h/yr
]
Pressure relief valves
13
From operator‘s statements, unverified. Volumetric flows remain unclear.(Source: DBFZ operator‘s survey, 2015)
Relative abundance [%]
Cla
ss [
kW
el]
> once a week
once a week
once a month
once a quarter
once a year
Pressure relief valve release events
14
(Source: DBFZ operator‘s survey, 2015), n = 31
No gas utilization
Weather conditions
CHP downtime
Flare failure
Substrate change
No knowledge
others
Efficiency - approach
What data is required to evaluate a biogas plant?
1. Mass balance of in- and output
2. Energy balance of the biogas plant
3. Data of the plant performance and reliability of the equipment (hours/year)
4. Normative-actual value comparison
What is needed for the mass/energy balances and the reliability data?
1. Characterization of substrates
2. Analysis and evaluation of process characteristics & plant concept
3. Theoretical energy output as basis for the biogas plant evaluation
4. Assessment and monitoring of losses during the fermentation process
5. Possibilities for system optimization / performance improvements
15
16
Energy balancing of an
AD plant
System boundary
Error analysis – data acquisition on site
17
Measuring system Data collection Source of error Suitable for balancing?
Constant weighing of solids
input
automatic Load cells Yes
manual Load cells
Transcription error
Yes
Singular weighing +
consequent counting of
volumetric flow
manual Load cells
Transcription error
Filling level
Properties of substrate
Doubtful
Solids input
Measuring system Data collection Source of error Suitable for balancing?
Flow meters
Weighing of volumes
automatic Counter Yes
manual Counter
Transcription error
Yes
Counting of solid volumes manual Filling level Doubtful
No measurement No
Digestate amount
System boundaries
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Substrate
provision
Biogas
production
Biogas
conversion
Biogas plant
Gross energy
Net energy
Usable energy
Heat use
Power grid
© DBFZ, 2016
Mean fuel efficiency based energy balanceApplying an energy industry`s assessment standard
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Efficiency assessmentFoTS-related gross energy yields
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0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
0,4 0,5 0,6 0,7 0,8 0,9 1,0 1,1 1,2
Arb
eit
sau
snu
tzu
ng
[-]
Kapazitätszahl [-] (FoTS-Bezug)
BGA06BGA 05
BGA 09
BGA 01
BGA 04
BGA 03
BGA 07
BGA 08
BGA 02
BGA 10
𝐾 =𝑃𝑁 + 𝑄 𝑁Σ 𝑚 𝑖 ∙ 𝐻𝑆,𝑖
𝑛𝐴
=𝑊
𝑒𝑙,𝑏𝑟𝑢𝑡𝑡𝑜
+𝑄𝑏𝑟𝑢𝑡𝑡𝑜
(𝑃𝑁
+𝑄 𝑁
)∙𝑇
𝑁
Insufficient
biogas utilizationPlant not used to
capacity / over-built
Desired domain
Uti
liza
tio
n f
acto
r o
f m
axi
mu
m c
ap
acit
y [-
]
Capacity figure [-]
Technical appraisal based on mean fuel
efficiency ( 𝜔)
21
Uti
liza
tio
n f
acto
r o
f m
axi
mu
m c
ap
acit
y [-
]
Capacity figure [-]
© DBFZ, 2016
Relative changes of energy yields
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© DBFZ, 2016
Re
lati
ve c
ha
nge
Gross energy yield Net energy yield Mean fuel efficiency
Mechanical pre-
treatment,
stirring replaced
Installation of
secondary
fermenter
Frequent
substrate changes
equalized
Direct utilization to
grid injection
Technical appraisal based on mean fuel
efficiency ( 𝜔) – sensitivity analysis
23
0,00
0,20
0,40
0,60
0,80
1,00
+100%+80%+60%+40%+20%0%-20%-40%-60%-80%-100%
Bre
nn
sto
ffau
snu
tzu
ngs
grad
[-]
ParametervariationSubstratleistung Gasleistung EigenstrombedarfEigenwärmebedarf Nutzwärmemenge
© DBFZ, 2016
me
an
fu
el e
ffic
ien
cy
[-]
50 % net heat utlization
parameter variation
Substrate input
Parasitic load (heat)
Parasitic load (electricity)Gas utilization
Usable heat
AD plant balancing - conclusion
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• Mass balance as a critical point
• Actual mode of operation and key figures of AD plants often poorly measured
• Analytical (lab) errors insignificant in comparison to errors made on site
• Recommended measuring technology for upgrading
• Load cells for solids streams
• Measurement of digestate amount
• Gas quantity measurement at standard conditions (CHP and flare)
• Count of pressure relief valve events
• Influence of substrate by different degrees of fermentability
• Plant or operating state comparison based on fermentable oDM
Substrate independent balancing
Research project: BiogasFingerprint
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Objectives:
1) Establishing a quasi online cytometric method for monitoring microbial
communities (in a plug-flow digester)
2) Examining spatial distribution of microorganisms
3) Monitoring of the stability of microbial consortia during changes in
temperature, OLR or substrate composition
4) Utilization of interrelationships to evaluate process stability and
process flexibilisation
SEITE 26
Exemplified workflow
Flow cytometric measurement
The working hypothesis
Abiotic parameters
Process parameter
Feeding
o Substrate
o Organic loading
o Regime
Mixing
Temperature
Reactor design
Methane yield
FOS/TAC value
Gas composition
pH-value
org. dry matter in
sludge [%]
VFA range
Established control circuit
“Community” Sensor Feedback loop
Microbial community
Biosensor for VFA measurement in
anaerobic digestion
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• Receptor: Geobacter sp. dominated biofilm
• Transducer: Three electrode arrangement
• Signal Processing: Potentiostat / adapted switching circuit
• Signal: I / µA
Re
ce
pto
rTransducer/
Signal Processing
Signal
CH3COOH + 10 H2O
2 CO2 + 8 H3O+
8 e-
Underlying principle
29
H+ Reduktion,
z.B. zu H2
e-
e-
e-
reduzierter Analyt
oxidierter Analyt + H+
Anode mit Biofilm (Rezeptor) Kathode
e- e-Strommessung
e-
e-
e-
Measurement range
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Measurement range:
0.5 – 5 mmol L-1 acetate
Measurement resolution:
> 0.25 < 1 mmol L-1 acetate
J. Kretzschmar, L.F.M. Rosa, J. Zosel, M. Mertig, J. Liebetrau, F. Harnisch, A microbial biosensor platform for in-line quantification of acetate in anaerobic digestion: potential and challenges, Chem. Eng. Technol., 39(4),
637–642, 2016
Figure: Biofilm current response on changing acetate concentrations,
data from CA and CV measurements, the boxes represent the middle
50% of each data set, whiskers indicate min and max, n=12
Proof of concept
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Figure: Biofilm current response within a semi continuous biogas
process, 10 L CSTR with maize silage and cow manure, HRT: 30 d,
OLR: 4 gVS (L d)-1, vfa measurement with GC-FID
0 200 400 600 800 1000 1200 14000
100
200
300
400
500
600
c /
mg
L-1
t / min
acetic acid (GC-FID)
sensor signal
0.02
0.04
0.06
0.08
0.10
j /
mA
cm
-2
Do promise:
• Reliable early warning indicators for process failure
• Fast response on changing process parameters (-> flexibilisation)
• Cheap alternatives to chemical analyses
But do need:
• A thorough check for cross-sensitivities
• Greater lifespans
• Higher measurement ranges
32
Lab methods…
Footer
Process modelling and control
© DBFZ
33
Mauky, Eric; Weinrich, Sören; Nägele, Hans-Joachim; Jacobi, Hans-
Fabian; Liebetrau, Jan; Nelles, Michael (2016): Model Predictive Control
for Demand-Driven Biogas Production in Full Scale. In: Chemical
Engineering & Technology39 (4), S. 652–664.
ManBio - Development of technical measures for
improving gas management on biogas plants
Research project ManBio 03KB094A
Background
• Control and monitoring of demand driven biogas
plants necessary
• Need for consumption and production data of the
biogas plus gas storage filling level
• Detectability of the gas storage filling level
• Operational emissions
Aims
• Minimizing gas losses
• Forecast of gas storage filling level
• higher capacity utilisation
• Development of gas extraction
strategies
• technical improvement of the storage
filling level measurement
• Development of integrated systems for
coupling the gas storage with the gas
production and the conversion units
Fermenter with gas storage
All pictures: DBFZ 35
Measurement methods
Remote sensing (overall plant emissions measurement)
On-Site (single source measurement)
Inverse dispersion modelling in combination with tunable diode laser absorption spectroscopy
Operational methane emissions on biogas
plants – pressure relief valves (PRV)
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• Main digester: sole PRV equipped with flow sensor and
temperature probe
• Digestate storage: 2 PRVs equipped with a thermocouple each
Operational methane emissions on biogas
plants – pressure relief valves
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0
10
20
30
40
50
0
30
60
90
120
150
15.08.2016 00:00 15.08.2016 06:00 15.08.2016 12:00 15.08.2016 18:00 16.08.2016 00:00
Te
mp
era
tur
in
C
Em
issi
on
sra
te
in m
3C
H4
h-1
Datum/Zeit
Emissionsrate ÜUDS Temperatur ÜUDS Lufttemperatur
Operational methane emissions on biogas
plants – reasons for PRV events
38
• Changing personnel
• Ambient temperature / sun exposure
• Outage of conversion units
• Lack of knowledge on the working principle of (combined) gas
storages
• High gas storage filling levels during normal operation
DBFZ Deutsches
Biomasseforschungszentrum
gemeinnützige GmbHTorgauer Straße 116
D-04347 Leipzig
Phone: +49 (0)341 2434-112
E-Mail: [email protected]
www.dbfz.de
Thank you for your attention!
Many thanks to Jörg Kretzschmar, Torsten Reinelt, and Johannes Lambrecht
for their contributions!
Contact person
Dipl.-Ing. Marcel Pohl
Group leader „biogas technology“
Biochemical conversion department
Tel.: +49 (0)341 2434-471
E-Mail: [email protected]
Fotos: DBFZ, Jan Gutzeit, DREWAG/Peter Schubert (Titelfolie, rechts), Pixabay / CC0 Public Domain
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