Sandra Esteves, University of South Wales

12/6/2013
Importance of Process Monitoring in
Optimising Biogas Production
BIOGAS13 Congress
4th December 2013 - St Pölten , Austria
Dr. Sandra Esteves
[email protected]
© University of South Wales
Anaerobic Digestion
Waste and Wastewater Treatment
Bio Energy Systems
Monitoring and Control
Environmental Analysis
Biohydrogen Systems
Bioelectrochemical Devices
Hydrogen Energy
The
Hydrogen
Centre
Advanced Nanomaterials
Bioplastics Production
Biogas upgrading and utilisation
1
12/6/2013
Wales Centre of Excellence for Anaerobic Digestion
•
•
•
Established in 2008 with financial support from
WG and ERDF
Expand knowledge and expertise for a rapid and
successful deployment of AD
The Centre acts as a process development platform
and delivers:
– Industrial focus R&D
– feasibility studies
– feedstock and digestate analysis
– system monitoring, diagnostics and optimisation
– analytical method development
– development of new of improved
products/processes – funding available to SMEs
– regulatory and policy development support
– awareness raising and training events
www.walesadcentre.org.uk
1. Establishing the current
situation and strategies for
actions
2. Encouraging and facilitating
new AD and biomethane
plants
3. Developing positive
environment for biomethane
production
4. Quality management for
efficient operation and
increased gas yields
5. Communication
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12/6/2013
Full-Scale Plants
Optimisation
Increase methane production in full-scale AD
plants through the deployment of
monitoring and management strategies
2011-2013
Made improvements (almost doubled biogas)
at Insource Energy AD Plant in Rogerstone
treating food wastes
2012 - 2013
Evaluating the scope for
improvements at Cardiff
Sewage Treatment Plant
Investigations
are continuing
© University of South Wales
Full-Scale Plants
Optimisation
Increase methane production in full-scale AD
plants through the deployment of
monitoring and management strategies
2012-2013
Evaluating the scope for improvement at the
Wrexham AD plant - animal slurries
Investigations
are continuing
2011 - 2013
Evaluating the scope for
improvements at Thornton and
Leyland Plants - Organic Fraction of
Municipal Solid Wastes
© University of South Wales
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12/6/2013
Monitoring Matrices
TS
VS
COD
C:H:N:P:K:S ratios
Trace Elements
Organic Nitrogen and
Ammonium
Carbohydrates, Proteins
and Lipids
Metals (including light
and heavy)
Temperature
pH and Alkalinity
Pathogens
Biocides
Biogas or Methane
Potential
Particle Size
Organic and Hydraulic
Loading Rates
Retention time
TS and VS
C:N ratio
Organic Nitrogen and
Ammonium
Metal Ions (Na, Ca, K, Mg)
pH/ Buffering Capacity
Temperature
Redox Potential (ORP)
VFAs and longer chain
fatty acids
Macro and Micronutrients
Biogas Flowrate and
Composition (CH4, CO2,
O2, NH3, H2S and H2)
Dissolved Hydrogen
Microbial Enzyme Activity
& Populations
TS
VS
COD and Biochemical
Oxygen Demand (BOD)
pH
N, P, K, Na, Ca, Mg and S
content
Pathogens
BMP
VFAs
Physical contaminants
(glass / plastic, etc)
Potential toxic elements
or inhibitors to plants,
animals and microbial
receptors (e.g. heavy
metals)
Biogas and Biomethane
Flow Rate
Gas content in terms of
CH4, CO2, O2, H2S, H2O and
NH3
Other content –
particulates, siloxanes,
volatile organics,
mercaptans, oxygen and
halogens
Calorific value and
Wobbe Index
Microbial agents
Esteves et al. (2012)
Monitoring
Review and Guide
For the Optimisation of
AD and Biomethane Plants
Esteves et al. (2012)
Deliverable of
IEE Biomethane Regions Project
www.walesadcentre.org.uk/News.aspx
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12/6/2013
Monitoring AD systems
for Control.....
Feedstocks
digestates
biogas
In-situ monitor
Ex-situ analysis
In-line or in-loop monitor
Digester
Data received on-line
(in real-time or past data)
Data received/input off-line
External lab analysis
On-site analysis
Data fluxes
Sample fluxes
Esteves et al. (2012)
Near Infrared Spectroscopy In
Feedstock/Bioreactor Performance
Monitoring (Sewage Sludge)
Absorbance
2.0
Primary/Secondary
Ratios
1.8
100/0
70/30
40/60
20/80
0/100
1.6
3.9
1.4
1
2
3
4
5
6
7
8
3.7
1.2
g.L
-1
3.5
Total Solids
3.3
1.0
12000 10000 8000
Wavenumber (cm
6000
)
-1
3.1
4000
2.5
g.L-1
Volatile Solids
2.3
2.1
4000
3500
mg.L-1
3000
Bicarbonate Alkalinity
2500
2000
1500
1600
1400
1200
1000
mg.L-1
800
Volatile Fatty Acids
600
400
200
0
-200
-400
5
9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77
Data Point
Reed et al. (2011)
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12/6/2013
Tracking Digester
Performance (Sewage Sludge)
(Reed et al, 2011)
Process Monitoring with
PCA (Sewage Sludge)
Model created using “steadystate” spectra
Model applied to new spectra
T2 scores monitored as basis of
alarm
Halved HRT (OC 7,8)
Feed Disturbance
(OC 2)
Reed et al. (2013)
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12/6/2013
Use of FT-NIR for
Measuring Solids
Content, BMP .....
Reed et al. In preparation
R&D and Optimisation at Full
Scale AD plants
Rogerstone - food waste
500 kWe
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12/6/2013
Flow chart of Full-scale AD plant
Food Wastes
MECHANICAL
SEPARATION
Plastics
Removed for
recycling
DAF Sludge
HEAT
PASTEURISATION
Waste Potato
Pasteurised
Slurry
RF BROOKES
HEAT
Volume 3090 m3
Load
30-50 m3 /d
HRT
60-100 d
ANAEROBIC
DIGESTION
Biogas
CHP
ELECTRICITY
Electricity
DEWATERING
Solid fraction
Applied to
land
Liquid effluent
© University of South Wales
Variation in the chemical parameters of
the digester
Acetate
Propionate
© University of South Wales
8
12/6/2013
Quantitative PCR (qPCR)
Sample
DNA extraction
Selection of primers
& probes
Amplification
Yu Y, Lee C, Kim J & Hwang S (2005).
Group- Specific Primer and Probe Sets to
detect Methanogenic Communities Using
Quantitative Real-Time Polymerase Chain
Reaction. Biotechnology and
Bioengineering 89: 670-679.
Quantification
© University of South Wales
Microbial Populations Present in
the Sewage Sludge Seed
Microbial Target Group
Number of gene copies ml-1
Total eubacteria
Methanosaetaceae
Methanosarcinaceae
Methanococcales
3.8 x 1010
4.6 x 108
n.d
n.d
Methanomicrobiales
2.6 x106
Methanobacteriales
3.2 x 106
n.d not detected (< 200 gene copies ml-1)
© University of South Wales
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12/6/2013
1200
MST
1,00E+09
1000
Propionate
800
8,00E+08
600
6,00E+08
seed
4,00E+08
400
2,00E+08
200
VFAs (mg/l)
Methanosaetaceae (gene copies /ml)
1,20E+09
0
0,00E+00
0
40
80
120
Time (d)
160
200
240
© University of South Wales
2500
MST
Acetate
1,00E+09
2000
Propionate
8,00E+08
1500
6,00E+08
1000
4,00E+08
VFAs (mg/l)
Methanosaetaceae (gene copies /ml)
1,20E+09
500
2,00E+08
0
0,00E+00
0
40
80
120
Time (d)
160
200
240
© University of South Wales
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12/6/2013
2500
MST
Acetate
1,00E+09
2000
Propionate
8,00E+08
1500
6,00E+08
1000
4,00E+08
VFAs (mg/l)
Methanosaetaceae (gene copies /ml)
1,20E+09
500
2,00E+08
0
0,00E+00
0
40
80
120
Time (d)
160
200
240
© University of South Wales
Acetate
Propionate
Eubacteria
Methanosaetaceae
Methanobacteriales
Methanomicrobiales
Methanosarcinaceae
© University of South Wales
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12/6/2013
Characteristics of Methanosarcina &
Methanosaeta sp.
Parameter
μmax (d−1)
Ks (mg COD L−1)
NH4+ (mg L−1)
Na+ (mg L−1)
pH-range
pH-shock
Temperature range (°C)
Methanosaeta
0.20
10–50
<3000
<10,000
6.5–8.5
<0.5
7–65
Acetate concentration (mg L−1)
Methanosarcina
0.60
200–280
<7000
<18,000
5–8
0.8–1
1–70
<3000
<15,000
De Vrieze et al., 2012
Ammonium inhibition
5,0E+08
NH4
MST
MBT
2.400
2.200
4,5E+08
4,0E+08
3,5E+08
2.000
3,0E+08
1.800
2,5E+08
1.600
2,0E+08
1,5E+08
1.400
1,0E+08
1.200
5,0E+07
1.000
0,0E+00
300
330
360
390 420 450
Time (days)
480
Methanogens (gene copies / ml)
Ammonium concentration (mg /l)
2.600
510
© University of South Wales
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12/6/2013
1,6E+11
1,2E+11
8,0E+10
4,0E+10
0,0E+00
3,0E+07
1200
1000
3,0E+06
800
3,0E+05
600
400
MMB
MBT
Propionic
acid
Propionate
3,0E+04
VFA (mg / l)
Methanogens (gene copies ml-1)
Eubacteria
Propionate & Lithotrophic
Methanogens
2,0E+11
200
3,0E+03
0
140
170
200
230
260
Time (days)
290
320
© University of South Wales
R&D and Optimisation at
Full Scale AD plants
Cardiff STWs
TH Pretreatment
4.5 MW
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12/6/2013
AD of Sewage Sludges
• >70% VS red. for primary sludges
• Secondary sludge is more difficult to
digest than primary
– 30-45% VS destruction
– Much of the organics are within the
extracellular polymers and encased within the
cell wall
– The cell is protected from lysis by a semi-rigid
structure of the cell wall (glycan and peptide
strands are cross-linked)
– Hydrolysis is the limiting step for secondary
sludge digestion
Microscope image (x100) of
stained pre-thermal hydrolysis SAS sample
Microscope image (x100) of
stained post-thermal hydrolysis SAS sample
~ 5 µm
~ 5 µm
University of Glamorgan, Feb 2013
University of Glamorgan, Feb 2013
TH and Acid Pretreatment of Mixed
Sludges
Type of
Sludge
Untreated
pH2
Thermal
Hydrolysis
Biogas
ml/g VS
added
346
295
%CH4
End of trial
472
63
59
56
TH had a greater
effect when sludge
have a high
proportion of WAS,
with up to 47%
increased in biogas
yield
OLR ~ 3.5 kg/m3.d
and HRT of 12 days
Devlin et al. In preparation
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12/6/2013
Pretreatments Impact on
Methanogens
Lab. Experiments (Mixed Sludges)
TH resulted in higher level of methanogens, Methanosaeta
(2-fold), Methanosarcina (6-fold) and Methanomicrobium
(0.6 fold) compared to untreated
Devlin et al. In preparation
Not detected
Differences in Microbial Populations in
Digesters Operating on Mixed Sludges and
Secondary Sludges Thermally Hydrolysed
<150 mg/l VFAs
<1300 mg/l ammonium
300 - 500 mg/l VFAs
~3600 mg/l ammonium
Oliveira et al. In preparation
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12/6/2013
Characteristics of Methanosarcina
& Methanosaeta sp.
Parameter
μmax (d−1)
Ks (mg COD L−1)
NH4+ (mg L−1)
Na+ (mg L−1)
pH-range
pH-shock
Temperature range (°C)
Acetate concentration (mg L−1)
Methanosaeta
0.20
10–50
<3000
<10,000
6.5–8.5
<0.5
7–65
Methanosarcina
0.60
200–280
<7000
<18,000
5–8
0.8–1
1–70
<3000
<15,000
De Vrieze et al., 2012
Differences in Secondary Digestions of
Digestate and Mixture of Digestates
Mixture - More than double than the separate digestates; 20% of current Plant recovery;
Why:
Dilution of Inhibition? Population Profile, Diversity and Activity? Others?
© University of South Wales
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12/6/2013
Ammonia Removal
for Cardiff STWs
Effect of pH at constant temperature: digestate
100
90
80
70
60
50
40
30
20
10
0
pH 8
pH 10
24h
48h
Time (hours)
Potential consumption of alkali
(for pH 10-10.5):
249-299 kg NaOH / Ton TS
100
5,0
90
4,5
80
4,0
70
3,5
60
3,0
50
2,5
40
30
2,0
1,5
20
1,0
10
0,5
0
0,0
0
10
20
30
40
% removed at 60 C pH10
[NH4+] (g/kg FM)
6h
NH4+ removal (%)
% removed at 60 C pH 8
[NH4+] at pH 8 and 60 C
[NH4+] at pH 10 and 60 C
50
Time (hours)
© University of South Wales
Ammonia Removal for
Cardiff STWs
Effect of pH at constant temperature: Thermally hydrolysed WAS
At constant temperature of 60o C
100
70
1,00
0,90
60
80
70
6h
60
24h
48h
50
40
30
20
0,80
[NH4+] (g/kg FM)
NH4+ removal (%)
90
% NH4+ removal
NH4+ removal (%)
At constant temperature of 60o C
50
0,70
40
0,60
30
0,40
0,50
0,30
20
0,20
10
10
0,10
0
0
0,00
0
pH 10
20
pH 10.5
40
pH 10 at 60 C
pH 10.5 at 60 C
[NH4+] at pH 10
and 60 C
[NH4+] at pH 10.5
and 60 C
60
Time(h)
Potential consumption of alkali (for pH 10 or 10.5):
68-80 kg NaOH /Ton TS
© University of South Wales
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12/6/2013
R&D and Optimisation at
Full Scale AD plants
Wrexham AD Plant for Animal Slurries
160 kWe
ADBA (Anaerobic Digester & Biogas Association)
2012 Award for the “Best Integration of AD into a
Farming Business”
35
© University of South Wales
Microbes and Energy Loss
Due to De-gritting
Removal of grit and inerts from feedstocks or digesters is essential
However digester degritting strategies should be evaluated and
optimised to avoid important losses
½ microbial culture lost; de-gritting occurs ~ 3 weeks
Immediate reduction of conversion but afterwards improved
performance
Esteves et al. In
preparation
18
12/6/2013
R&D and Optimisation
at Full Scale AD plants
Thornton and
Leyland OFMSW
2*1 MW
Enzyme Enhanced VFA and
Biogas Production
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12/6/2013
Soluble COD Released To
70000 Percolate Liquor
water control
water control
0.03% Cellulase N11/12
0.03% Cellulase N11/12
0.03% Cellulase N11/12
0.1% Cellulase N11/12
0.1% Cellulase N11/12
0.1% Cellulase N11/12
0.3% Cellulase N11/12
0.3% Cellulase N11/12
0.3% Cellulase N11/12
1% Cellulase N11/12
1% Cellulase N11/12
0.3% Protease N11/11
0.3% Celluclast
60000
sCOD (mg/l)
50000
40000
30000
20000
10000
0
0
20
40
Time (h)
60
80
Williams et al. (2011)
Methane Yield From Batch Tests
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12/6/2013
VFAs in Percolate (Full Scale)
Double solubilisation or organics to
be digested instead of composted
Oliveira et al. In preparation
Conclusions
• Routine monitoring of microbial populations and VFAs provide
valuable insights into the digestion process and can be used to
predict digester stability and manage performance
• Microbial consortia is important to the outcome of digester
performance
• Early warning of instability can be provided by measuring the
actual workers ‘bacteria and archae’
• Control actions e.g. reduction of OLR and the timing for the
addition of trace elements and alkalinity based on microbial
abundance and diversity allowed maintenance of digester
stability, which led to an allowed increased load and resulting
additional biogas production
42
© University of South Wales
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12/6/2013
Conclusions
• Feedstock characteristics and pre-treatments influence
digester population and performance
• Inhibition such as from ammonia may need to be
reduced in order to increase digester performance
• De-gritting is essential to maintain a digester, which
takes in inert material operational in the long run, but
de-gritting strategies can be optimised based on
microbial measurements
• Solubilisation of organics via the use of enzymes in a
pre-leaching process can increase biogas production
43
© University of South Wales
Acknowledgments
Dr. Julie Williams, Dr. Des Devlin, Ivo Oliveira, Dr, James Reed, Ikechukwu
Tolefe, Fergal Hegarty, Dr. Gregg Williams, Prof. Richard Dinsdale, and Prof.
Alan Guwy
The sole responsibility for the content of this document lies with the authors. It does not necessarily reflect the funders opinion.
Neither the authors or the funders are responsible for any use that may be made of the information contained therein.
© University of South Wales
22