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TSEC Biosys
TSEC Biosys
TSEC-BIOSYS:
The potential for hydrogen-enriched biogas
production from crops: Scenarios in the UK
www.tsec-biosys.co.uk
Bharat K.V. Penumathsa, Manuel Vargas, Sandra Esteves,
Richard Dinsdale, Alan J. Guwy, Jorge Rodríguez, Giuliano C. Premier
Sustainable Environment Research Centre, University of Glamorgan, Wales, UK
Biomass role in the UK energy futures
The Royal Society, London: 28th & 29th July 2009
Anaerobic Digestion
Biohydrogen
Microbial Electrolysis
Biological Fuel Cells
Bioenergy
Hydrogen Research Centre
Wastewater
Treatment
Research Centre
WWTRU
Environmental Monitoring
Hydrogen Energy Systems
Waste Treatment
Environmental Analysis
Hydrogen Storage
Contribution of UOG to TSEC-Biosys - Overview
Topic 1.3: Modelling of novel bioenergy conversion routes and their potential
 Model new technologies and systems for bioenergy
• Modelling fermentative biohydrogen systems
Penumathsa, B.K.V., Premier, G.C., Kyazze, G., Dinsdale, R., Guwy, A.J, Esteves, S., Rodríguez and J. (2008) ADM1 can be applied to
continuous biohydrogen production using a variable stoichiometry approach. Water Research 42(16), 4379-4385.
• Modelling anaerobic hydrolysis and two stage (H2/CH4) system
Penumathsa, B.K.V., Vargas, M., Premier, G.C., Dinsdale, R., Guwy, A.J., Rodríguez and J. (2008) Modelling studies of a
two-stage continuous fermentative hydrogen and methane system with biomass as substrate. 13th European Biosolids
and Organic Resources Conference. Lowe, P. (ed), Aqua Enviro, Manchester, Manchester, UK.
• Alternative approach
to modelling anaerobic processes
Jorge Rodríguez; Giuliano C Premier; Alan J Guwy; Richard Dinsdale; Robbert Kleerebezem, Metabolic models to investigate
energy limited microbial ecosystems, 1st IWA/WEF Watewater Treatment Modelling Seminar, Mont-Sainte-Anne, Quebec, Canada,
1-3 June 2008. Paper has also been accepted in Journal. Water Science and Technology.
 Assess the prospects of new technologies and configurations for the production of electricity
and transport fuels based on technical, economic and environmental considerations
Patterson, Tim, Dinsdale, Richard, Esteves and Sandra (2008) Review of Energy Balances and Emissions Associated with
Biomass-Based Transport Fuels Relevant to the United Kingdom Context. Energy & Fuels 22(5), 3506-3512.
 Contributions to other themes (Themes 1.2 and 3)
• Implementation of AD in UK-MARKAL (development of strategy and input data generation).
• An assembled database of 230 feedstock samples, corresponding to ~ 80 different feedstocks.
Anaerobic digestion model No. 1 (ADM1)
- Model structure
•
Solids solubilisation represented as a two step (non-biological) process of
disintegration and hydrolysis (mainly implemented for sludge)
•
Model uses 7 biochemical processes: acidogenesis from sugars, amino
acids, and LCFA; acetogenesis from propionate, butyrate (includes valerate);
aceticlastic methanogenesis; and hydrogenotrophic methanogenesis
•
Uses fixed-stoichiometry for all its embedded biochemical reactions
•
Physicochemical processes implemented by modelling acid-base equilibria
•
pH is represented via dynamic states for cations and anions
•
Inhibition due to pH, H2 and NH4 are incorporated
•
First order kinetics to represent disintegration, hydrolysis and decay
processes, while Monod-type expressions for uptake, growth, and inhibition
ADM1 conversion processes
CH4
CO2
H2O
H2
gas
composites
death/decay
proteins
Biochemical
gas
inerts
carbohydrates
lipids
liquid
NH4
+
aminoacids mono
saccharides
NH3
HAc, HPr, HBu, HVal, CO2, NH3,LCFA
H2
HAc
CO2
Ac -, Pr -, Bu -, Val -, HCO3 -, NH4 +,LCFA-
HCO3 -
CH4
growth
gas
microorganisms
H2O
from A. Puñal with
frompermission
A. Puñal
Physicochemical/Transfer
Implementation of Lactate metabolism
Distribution fractions of converted substrate COD into fermentation products based on estimated pseudo
steady state values for each experimental condition. An increasing COD imbalance is observed at the
higher substrate and acids concentration conditions, attributed to an unmeasured product, which is
assumed to be lactate in this study.
Yields of products and biomass
[molX(Prod)/molGlu(Catab)]
Variable stoichiometry
fh2_su
(molH2/molGlu)
fac_su
(molAc/molGlu)
flac_su [calc]
(molLac/molGlu)
1.8
1.6
1.4
Yxs
(molX/molGlu)
fbu_su
(molBu/molGlu)
1.2
1.0
0.8
0.6
0.4
0.2
0.0
0.00
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
Concentration of undissociated acids (molAH/L)
Variation of products and biomass yields with total concentration of un-dissociated volatile fatty acids. The
values were manually selected from pseudo steady conditions at each experimental condition. (Ysu is the biomass
yield on sugar and fpr_su is the catabolic product “pr” yield from sugar). Note that the lactate yield is calculated to
close the COD balance.
Partial Peterson Matrix of stoichiometric coefficients of the products from glucose fermentation.
Sugar
Uptake
Ssu
-1
Slac
Sbu
(1-Ysu) fla,su (1-Ysu) fbu,su
Spro
Sac
Sh2
Xsu
(1-Ysu)fpro,su
(1-Ysu) fac,su
(1-Ysu) fh2,su
Ysu
Simulation studies
Experimental vs. simulation data show the total gas production rates
(top) and the hydrogen production rate (bottom) using the modified
and the original versions of the ADM1. Simulation data for an initial 20
g/L influent substrate concentration are also shown (dotted lines).
Experimental vs. simulation data showing the acetate, propionate,
butyrate and lactate concentrations predicted by the original and the
modified ADM1 suggested in this work. Propionate is only predicted
by the standard ADM1 while lactate only by the modified ADM1.
Simulation data for an initial 20 g/L of influent substrate
concentration with the modified model are also shown (dotted lines).
Conclusions (Biohydrogen modelling)
•
Extends ADM1 applicability to non-methanogenic anaerobic systems.
•
Good dynamic predictions of a continuous biohydrogen reactor over a wide
range of influent substrate concentrations.
•
Successful application of variable stoichiometry as a function of
undissociated acidic products to represent product distribution.
•
Model was able to depict the pattern of systematic inhibition and recovery of
the system at the highest loading rates.
•
Accurate simulation of pH required to achieve good simulation.
Penumathsa, B.K.V., Premier, G.C., Kyazze, G., Dinsdale, R., Guwy, A.J, Esteves, S.,
Rodríguez and J. (2008) ADM1 can be applied to continuous biohydrogen production
using a variable stoichiometry approach. Water Research 42(16), 4379-4385.
Two-stage anaerobic systems - Advantages
•
Allows selection and separation of trophic bacterial groups, providing
optimal conditions for their enrichment.
•
Physically segregates the acid forming
methanogenic bacteria (methanogenesis).
•
Maximum loading rates and higher elimination (twice that of a single
stage process) of chemical oxygen demand (COD).
•
Increased process stability and digestibility.
•
Two-stage biohydrogen and methane system is reported to give
greater conversion efficiency than anaerobic digestion alone
(Hawkes et al., 2007).
•
Used in different treatment scenarios e.g. sewage sludge, dairy
waste water, instant coffee, food and agro-industrial waste.
(acidogenesis)
and
Modelling two stage H2/CH4 system with
particulate feed- Overview
•
A mathematical model has been developed to represent a
mesophilic two-stage continuous biohydrogen/methane system
(CSTR/UAF).
•
Widely applied IWA Anaerobic Digestion Model No.1 (ADM1) is
used as the base model.
•
Wheatfeed, was selected as the substrate for this study.
•
Anaerobic hydrolysis model to represent particulate degradation.
•
Other modifications have been implemented to incorporate
degradation of intermediates (lactate metabolism).
•
Variable stoichiometry approach has been used for carbohydrate
metabolism to represent accurate distribution of products.
•
Simulation studies are used to understand the performance and
dynamics of the two stage system.
Two-stage anaerobic systems
– A Process configuration
CO2 sensor
H2 sensor
Gas flow meter
CH4 sensor
pH controller
Gas flow meter
CO2 sensor
Biogas
Antifoam
Feed
Antifoam
NaOH
Packing
material
Redox probe
pH probe
Effluent
Impeller
UAF Reactor
H2 Reactor
NaHCO3
Recirculation line
pH probe
Anaerobic hydrolysis modelling
(ADM1 modifications)
• An additional expression (developed from Valentini et al. 1997)
implemented to model disintegration of slow degrading constituent of
wheatfeed.
r = k0 * e-(d/d0) * Xbs
where d =(6*Xbs/π*N*ρp) is particle diameter (mm); k0 (0.08 h-1); and d0 original particle diameter (2 mm).
Xbs is biosolids concentration (mol/L); ρp is density of biosolids (mol/L); N is number of particles per unit
volume. Xbs and N are new state variables.
• An additional first order expression implemented to model hydrolysis of
slow degrading constituent (cellulose) of wheatfeed.
r = khyd,ce * Xce
Modelling anaerobic hydrolysis
Wheat
Feed
Dead
biomass
Disintegration
r = kdis * XC
r = k0 * e-(d/d0) * Xbs
Particulate
slow degradable
matter (cellulose)
(first order kinetics)
Particulate
fast degradable matter
(starch; hemicellulose; lipids;
proteins)
Inerts
Hydrolysis
r = khyd,ce * Xce
r = khyd,ch,pr,li * Xch,pr,li
(first order kinetics)
(first order kinetics)
New
implementation
New model
framework for
H2-CH4 reactor system
Old
implementation
System operational parameters
•
The biohydrogen reactor is completely mixed and has a total volume of
11 L (operating volume of 10L). A constant HRT of 12 h is maintained
throughout the operating period.
•
For methane reactor a constant HRT of 2 days was maintained.
•
pH is controlled in the biohydrogen reactor between 5.2 and 5.3 using
NaOH, while in the methane rector it is maintained above pH 6.5 using
continuous sodium bicarbonate (NaHCO3) addition.
•
Batch simulations have been performed on single stage process with
inlet biosolids concentration (Xbs) of 0.5 mol/L and number of particles
(N) of 13322.3 L-1.
•
Continuous simulations has been performed on a two stage
biohydrogen (CSTR) and methanogenic (UAF) reactor system with
dynamic step changes in inlet biosolids concentration of 0.5 mol/L, 0.7
mol/L, 1 mol/L, 1.5 mol/L, 2 mol/L and 3 mol/L progressively.
Simulation studies – Single stage batch
Model
simulation
results
illustrating the biosolids (Xbs6)
substrate degradation into two
assumed
intermediate
hydrolysis products namely
starch carbohydrates (Xch fast degradable) and cellulose
(Xce - slower degradable)
•
Exponential degradation of biosolid concentration over time.
•
Sharp decrease in biosolid concentration leads to increase in
cellulose concentration to its maximum.
•
The concentration curves of slow and fast degrading particulates
show difference in their rate of hydrolysis.
Simulation studies – Single stage batch
Model simulation results indicating gas
concentrations.
Sh2-gas – hydrogen concentration
Sch4-gas – methane concentration
Sco2-gas – CO2 concentration
•
Non presence of hydrogenotrophic methanogens leads to initial
production of H2.
•
CH4 production reaches peak concentration (at pH-7) as the H2
production ceases.
Simulation studies
(a) Single stage (b) Two-stage continuous
(a)
(a) Model simulation results indicating the particle diameter.
(b) Model simulation results indicating pH control in a two
stage reactor system.
•
The particle size is directly
proportional function of biosolid
concentration.
•
pH is controlled in H2 reactor
between 5.2-5.3 by addition of
NaOH.
•
pH
in
CH4
reactor
is
maintained above 6.5 using
continuous
dosage
of
NaHCO3.
(b)
Simulation studies – Two-stage continuous
Model simulation results indicating gas
production rates.
H2 - refers to biohydrogen reactor
CH4 - refers to methane reactor
•
Operating H2 reactor in the pH range 5.2-5.3 could inhibit the growth
of methanogens.
•
Similarly, CH4 reactor operated above pH 6.5 and near to 7 does not
support H2 production.
Simulation studies - Two stage continuous
Model simulation results indicating
biomass concentrations.
H2 - refers to biohydrogen reactor
CH4 - refers to methane reactor
H2 influent - refers to influent
concentration of bio-solid
•
Step wise increase in biosolid in H2 reactor (due to low HRT) can
lead to washout.
•
Concentration of cellulose in CH4 reactor is higher even with less
biosolids compared to H2 reactor.
•
Conversion of biosolids to cellulose is low in both reactors –
attributed to disintegration expression and its associated kinetic
parameters.
Conclusions (two stage modelling)
•
The analysis of simulation results support the modifications adopted
in the ADM1 structure.
•
The results show that the modified ADM1 consisting of bio-solid
hydrolysis model (intermediate degradation species and a particle
size dependent kinetics) could be applied to simulate a two stage
anaerobic reactor system with biosolids as feed.
•
Results show qualitative description of reported dynamic behaviour in
a similar two stage system.
•
Hydrolysis kinetic parameters:
- Highly sensitive to the whole system behaviour.
- Must to be determined experimentally for good quantitative
description of system dynamics.
Penumathsa, B.K.V., Vargas, M., Premier, G.C., Dinsdale, R., Guwy, A.J., Rodríguez
and J. (2008) Modelling studies of a two-stage continuous fermentative hydrogen and
methane system with biomass as substrate. 13th European Biosolids and Organic
Resources Conference. Lowe, P. (ed), Aqua Enviro, Manchester, Manchester, UK.
Transport biofuels using energy crops (UK context)
•
Three transport biofuels (biomethane, biodiesel, bioethanol) produced from
crops were compared (UK context).
•
Comparison is based on energy balance, waste/co-products, and exhaust
emissions
•
Biomethane has a more favourable energy balance for the production of
transport fuel than biodiesel or bioethanol
•
Exhaust emissions (CO, CO2 and particulates) from biomethane are
generally either lower than or comparable to emissions from biodiesel and
bioethanol
•
Biodiesel performs the least well out of the biofuels considered
•
Lack of established distribution network and the requirement to convert
vehicles are significant barriers to use biogas
Patterson, Tim, Dinsdale, Richard, Esteves and Sandra (2008) Review of Energy Balances
and Emissions Associated with Biomass-Based Transport Fuels Relevant to the United
Kingdom Context. Energy & Fuels 22(5), 3506-3512.
Transport biofuels using energy crops (UK context)
Biofuels, Production Methods, and Source Crops Considered
Fuel
Biodiesel
Bioethanol
Biomethane
production method considered
extraction of plant oil followed by transesterification to
biodiesel
hydrolysis of sugars followed by fermentation and
distillation
anaerobic digestion of carbohydrates
crop considered
rape seed
wheat grain
sugar beet (roots only)
rye grass
sugar beet (whole crop)
forage maize
Net Energy Associated with Biofuels from Energy Crops
Fuel
crop
Biodiesel
rape seed
Bioethanol
Biomethane
wheat grain
sugar beet
(roots only)
rye grass
sugar beet
(whole crop)
forage maize
Gross energy
produced (MJ/ha)
50 125
Total energy
losses (MJ/ha)
25 940
Net energy
balance (MJ/ha)
24 185
67 501
131 240
38 908
53976
28 593
77264
114164
172640
20997
43850
93167
128790
288544
51533
237011
Transport biofuels using energy crops (UK context)
Potential Contribution of Biomethane to Total U.K. Transport Fuel Demand and
Biofuels Directive Target
crop
energy/ha
(MJ)
U.K. set
aside
area (ha)
biofuel
energy
available
(MJ)
contribution to percent of total
2020 target of petrol and
10%
diesel energy
demand
grass
sugar
beet
maize
93 167
128 790
559 000
559 000
5.2 × 1010 28%
7.2 × 1010 40%
237 011
559 000
1.3 × 1011 72%
area
required for
100% of
petrol and
diesel
energy (ha)
percent of
U.K. land
area
required to
meet 100%
demand
2.87
3.98
2.1 × 107
1.5 × 107
80%
58%
7.18
8.2 × 106
32%
Theoretical Energy Output from Biohydrogen and Methane Production
crop
perennial rye
grass
energy output energy output total gross
net energy
from H2 (MJ/ha) from CH4
energy output output (MJ/ha)
(MJ/ha)
(MJ/ha)
3140
115 759
118 899
114 189
sugar beet
18 853
112 017
130 871
112 624
forage maize
13 429
125 723
139 152
121 522
Biomass availability for AD in UK
(Data for MARKAL modelling)
Year of
Available
Gas
Resource's Description availability tonnage
factor
(start year) tDM/yr
(m3/tDM)
Total CH4
(m3/yr)
Resource
PJ/yr
cost
(£/tDM)
Annual
resource
cost (£/yr)
Annual
resource cost
(£/PJ)
Organic Fraction of MSW
Sewage sludge
2006
2004
8424000
340000
330
195
2779920000 110.08
66300000
2.63
0.00
0.00
0.00
0.00
0.00
0.00
Animal slurry (wet and
dry combined)
2005
3998400
130
519792000
20.58
0.00
0.00
0.00
Commercial industrial
waste (food waste)
2003
6295000
330
2077350000
82.26
0.00
0.00
0.00
Sugar Beet
2007
10478000
400
4191200000 165.97
119.05
1247380952
7515632.52
Forage Maize
2007
12939000
330
4269870000 169.09
57.00
737523000
4361799.82
Fodder beet
2007
9534000
468
4461912000 176.69
107.50
1024905000
5800526.63
Rye grass
2007
9534000
320
3050880000 120.81
39.00
371826000
3077651.52
Sweet sorghum
2007
16684500
400
6673800000 264.28
57.00
951016500
3598484.85
2006
960000
272
95.00
91200000
8819815.81
Energy crops (wet)
Industrial by product
Wheat feed
261120000
10.34
Technology cost estimation (AD)
(Data for MARKAL modelling)
Energy in
(PJ/tDM)
Energy out
excluding process
heat (PJ/tDM)
Net energy
(PJ/tDM)
Efficiency (%)
Capital cost
£/(PJ/Yr)
Organic fraction of MSW (OFMSW)
4.44312E-06
0.000013068
8.62488E-06
49.25
15681596
Sewage Sludge
2.62548E-06
0.000007722
5.09652E-06
49.25
26538086
Animal slurry (wet/dry)
1.75032E-06
0.000005148
3.39768E-06
49.25
39807129
commercial industrial waste
(food waste)
4.44312E-06
0.000013068
8.62488E-06
49.25
15681596
6.78922E-06
0.00001584
9.05078E-06
40.00
12937317
5.37101E-06
0.000013068
7.69699E-06
41.74
15681596
7.60451E-06
1.85328E-05
1.09283E-05
41.81
11057536
4.64491E-06
0.000012672
8.02709E-06
46.35
16171646
6.13662E-06
0.00001584
9.70338E-06
44.15
12937317
3.66221E-06
1.07712E-05
7.10899E-06
49.25
19025466
Resources
Sugar beet
Forage maize
Fodder beet
Rye grass
Sweet sorghum
Wheat Feed
Evaluation of energy crops for fermentative
H2/CH4 production in UK
Data used for the calculation of hydrogen and methane production
Biomass
Carbohydrate for H2
Crop yield tdm
production as
(ha−1)
% of dm
Holo-cellulose for
CH4 production as %
of dm
H2 yield mol
mol−1 hexose
converted
Barley
4.5
55.1 starch
13
1.9
Flax
5.5
Not found
81
—
Fodder beet
14
63.9 WSC
21.75a
1.7
Forage maize
19
31 starch
36
1.9
Hemp
7
5.5 soluble sugars
82.3
1.7
Miscanthus
13.5
Not found
71
0.7
Oats
4.7
53.5 starch
6.1
1.9
Perennial rye grass
14
25.3 soluble sugars
57.5
0.7
Potato
3.4
86 starch
Not found
1.9
Reed canary grass
7.5
Not found
50
—
Sugar beet
13
67.35 soluble sugars
21.75
1.7
Sweet sorghum
24.5
43 soluble sugars
47.44
1.7
Switch grass
9.2
11.2 (starch and soluble
67.6
sugars)
1.9
Wheat (whole plant)
14
10.5 starch
1.9
47
Evaluation of energy crops for fermentative
H2/CH4 production in UK
Calculated gross and net energy output per year
Biomass
Energy output from
H2 (MJ ha−1)
Energy output
from CH4
(MJ ha−1)
Total gross
energy output
(MJ ha−1)
Net energy
output
(MJ ha−1)
Barley
5653
29,522
35,175
15,613
Flax
0
45,441
45,441
36,785
Fodder beet
19,263
116,046
135,309
117,063
Forage maize
13,429
125,723
139,152
121,522
Hemp
829
62,419
63,248
45,618
Miscanthus
0
97,767
97,767
91,533
Oats
5733
26,812
32,545
17,451
Perennial rye grass
3140
115,759
118,899
114,189
Potato
7259
27,737
35,037
−13,163
Reed canary grass
0
38,250
38,250
34,168
Sugar beet
18,853
112,017
130,871
112,624
Sweet sorghum
22,685
219,642
242,327
223,928
Switch grass
2338
73,180
75,519
69,190
Wheat (whole crop)
3351
81,081
84,432
62,538
Martinez-Perez, N., Cherryman, S. J., Premier, G. C., Dinsdale, R. M., Hawkes, D. L., Hawkes,
F. R., Kyazze, G., and Guwy, A. J. (2007). The potential for hydrogen-enriched biogas
production from crops: Scenarios in the UK. Biomass and Bioenergy, 31(2-3), 95-104.
General view of the pilot plant installed at IBERS
Future work
•
Utilisation of arable crops as substrates (feed) for fermentative energy
generation (e.g. sweet sorghum)
•
Utilisation of waste and co-products (e.g. municipal, agro) streams as
substrates for energy generation
•
Landfill mining
•
Look at possibilities for Co-digestion of substrates to maximise yield
•
Hydrolysis modelling
•
Non-empirical modelling
•
Model parameters estimation
Thank you for your attention!
TSEC Biosys
TSEC Biosys
www.tsec-biosys.ac.uk