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: 28 th & 29 th July 2009

Hydrogen Research Centre
Wastewater
Treatment
Research Centre
WWTRU
Anaerobic Digestion
Biohydrogen
Microbial Electrolysis
Biological Fuel Cells
Bioenergy
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 (H
2
/CH
4
) 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, H
2 and NH
4 are incorporated
•
First order kinetics to represent disintegration, hydrolysis and decay processes, while Monod-type expressions for uptake, growth, and inhibition

ADM1 conversion processes death/decay
CH
4
CO
2
H
2
O composites inerts
H
2 proteins carbohydrates lipids
NH
4
+ aminoacids
NH
3 mono
HAc, HPr, HBu, HVal, CO
2
, NH
3
,LCFA
HAc H
2
CO
2
CH
4 growth microorganisms gas gas gas liquid
Ac , Pr , Bu , Val , HCO
3
, NH
4
+ ,LCFA -
HCO
3
-
H
2
O
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.

Variable stoichiometry
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
0.00
fh2_su
(molH2/molGlu) fac_su
(molAc/molGlu) flac_su [calc]
(molLac/molGlu)
Yxs
(molX/molGlu) fbu_su
(molBu/molGlu)
0.02
0.04
0.06
0.08
0.10
0.12
0.14
Concentration of undissociated acids (mol
AH
/L)
0.16
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. (Y su yield on sugar and f pr_su is the biomass 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
S su
S lac
-1 (1-Y su
) f la,su
S bu
(1-Y su
) f bu,su
S pro
(1-Y su
)f pro,su
S ac
(1-Y su
) f ac,su
S h2
(1-Y su
) f h2,su
X su
Y su

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 (acidogenesis) and 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.

Modelling two stage H
2
/CH
4 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
Feed
CO
2
sensor
Gas flow meter pH controller
Antifoam
NaOH
H
2
sensor
CH
4
sensor
Gas flow meter
CO
2
sensor
Biogas
Antifoam
Packing material
Redox probe pH probe
Impeller
H
2
Reactor
NaHCO
3
Effluent
UAF Reactor
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 = k
0
* e -(d/d
0
) * X bs where d =(6*X bs
/ π*N*ρ p
) is particle diameter (mm); k
X bs is biosolids concentration (mol/L); ρ p volume. X bs and N are new state variables.
0
(0.08 h-1); and d
0 original particle diameter (2 mm).
is density of biosolids (mol/L); N is number of particles per unit
•
An additional first order expression implemented to model hydrolysis of slow degrading constituent (cellulose) of wheatfeed.
r = k hyd,ce
* X ce

Modelling anaerobic hydrolysis
Wheat
Feed
Dead biomass
Disintegration r = k
0
* e -(d/d
0
) * X bs
Particulate slow degradable matter (cellulose) r = k dis
* X
C
(first order kinetics)
Particulate fast degradable matter
(starch; hemicellulose; lipids; proteins)
Inerts
Hydrolysis r = k hyd,ce
* X ce
(first order kinetics) r = k hyd,ch,pr,li
* X ch,pr,li
(first order kinetics)
New implementation
New model
H
2 framework for
-CH
4 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 (NaHCO
3
) addition.
•
Batch simulations have been performed on single stage process with inlet biosolids concentration (X bs
(N) of 13322.3 L-1.
) of 0.5 mol/L and number of particles
•
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 H
2
.
•
CH
4 production reaches peak concentration (at pH-7) as the H production ceases.
2

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.
(b)
•
The particle size is directly proportional function of biosolid concentration.
• pH is controlled in H
2 reactor between 5.2-5.3 by addition of
NaOH.
• pH in CH
4 reactor is maintained above 6.5 using continuous dosage of
NaHCO
3
.

Simulation studies – Two-stage continuous
Model simulation results indicating gas production rates.
H2 - refers to biohydrogen reactor
CH4 - refers to methane reactor
•
Operating H
2 reactor in the pH range 5.2-5.3 could inhibit the growth of methanogens.
•
Similarly, CH support H
2
4 reactor operated above pH 6.5 and near to 7 does not 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 H
2 lead to washout.
reactor (due to low HRT) can
•
Concentration of cellulose in CH
4 biosolids compared to H
2 reactor.
reactor is higher even with less
•
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 production method considered extraction of plant oil followed by transesterification to
Bioethanol biodiesel hydrolysis of sugars followed by fermentation and distillation
Biomethane 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
Bioethanol
Biomethane rape seed wheat grain sugar beet
(roots only) rye grass sugar beet
(whole crop) forage maize
Gross energy produced (MJ/ha)
50 125
67 501
131 240
114164
172640
288544
Total energy losses (MJ/ha)
25 940
38 908
53976
Net energy balance (MJ/ha)
24 185
28 593
77264
20997
43850
51533
93167
128790
237011

Transport biofuels using energy crops (UK context) crop
Potential Contribution of Biomethane to Total U.K. Transport Fuel Demand and
Biofuels Directive Target energy/ha
(MJ)
U.K. set aside area (ha) biofuel energy available
(MJ) contribution to
2020 target of
10% percent of total petrol and diesel energy demand area required for
100% of petrol and diesel energy (ha) percent of
U.K. land area required to meet 100% demand grass 93 167 sugar beet
128 790 maize 237 011
559 000 5.2 × 10 10 28%
559 000 7.2 × 10 10 40%
559 000 1.3 × 10 11 72%
2.87
3.98
7.18
2.1 × 10 7
1.5 × 10 7
8.2 × 10 6
80%
58%
32%
Theoretical Energy Output from Biohydrogen and Methane Production crop energy output from H
2
(MJ/ha) energy output from CH
4
(MJ/ha) total gross energy output
(MJ/ha) net energy output (MJ/ha) perennial rye grass
3140 115 759 118 899 114 189 sugar beet forage maize
18 853
13 429
112 017
125 723
130 871
139 152
112 624
121 522

Biomass availability for AD in UK
(Data for MARKAL modelling)
Resource's Description
Year of availability
(start year)
Available Gas
Total CH4
(m
3
/yr)
PJ/yr
Resource cost
(£/tDM)
Annual resource cost
(£/yr)
Annual resource cost
(£/PJ)
Organic Fraction of MSW
Sewage sludge
Animal slurry (wet and dry combined)
Commercial industrial waste (food waste)
Energy crops (wet)
Sugar Beet
Forage Maize
Fodder beet
Rye grass
Sweet sorghum
Industrial by product
Wheat feed
2006
2004
2005
2003
2007
2007
2007
2007
2007
2006
8424000
340000
3998400
6295000
10478000
12939000
9534000
9534000
16684500
960000
330
195
130
330
400
330
468
320
400
272
2779920000 110.08
0.00
66300000 2.63
0.00
519792000 20.58
2077350000
4191200000 165.97
119.05
4269870000 169.09
57.00
4461912000 176.69
107.50
3050880000 120.81
39.00
6673800000 264.28
261120000
82.26
10.34
0.00
0.00
57.00
95.00
0.00
0.00
0.00
0.00
1247380952
737523000
1024905000
371826000
951016500
91200000
0.00
0.00
0.00
0.00
7515632.52
4361799.82
5800526.63
3077651.52
3598484.85
8819815.81

Technology cost estimation (AD)
(Data for MARKAL modelling)
Resources
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
Sewage Sludge
Animal slurry (wet/dry) commercial industrial waste
(food waste)
Sugar beet
2.62548E-06
1.75032E-06
4.44312E-06
6.78922E-06
Forage maize
Fodder beet
Rye grass
5.37101E-06
7.60451E-06
Sweet sorghum
Wheat Feed
4.64491E-06
6.13662E-06
3.66221E-06
0.000013068
0.000007722
0.000005148
0.000013068
0.00001584
0.000013068
1.85328E-05
0.000012672
0.00001584
1.07712E-05
8.62488E-06
5.09652E-06
3.39768E-06
8.62488E-06
9.05078E-06
7.69699E-06
1.09283E-05
8.02709E-06
9.70338E-06
7.10899E-06
49.25
49.25
49.25
49.25
40.00
41.74
41.81
46.35
44.15
49.25
15681596
26538086
39807129
15681596
12937317
15681596
11057536
16171646
12937317
19025466

2
4
Biomass
Barley
Flax
Fodder beet
Forage maize
Hemp
Miscanthus
Oats
Perennial rye grass
Potato
Reed canary grass
Sugar beet
Sweet sorghum
Switch grass
Data used for the calculation of hydrogen and methane production
Wheat (whole plant)
3.4
7.5
13
24.5
Crop yield tdm
(ha −1 )
Carbohydrate for H
2 production as
% of dm
4.5
5.5
14
19
7
13.5
4.7
14
9.2
14
55.1 starch
Not found
63.9 WSC
31 starch
5.5 soluble sugars
Not found
53.5 starch
25.3 soluble sugars
86 starch
Not found
67.35 soluble sugars
43 soluble sugars
11.2 (starch and soluble sugars)
67.6
10.5 starch 47
82.3
71
6.1
57.5
Holo-cellulose for
CH
4 production as % of dm
H
2 yield mol mol
−1 hexose converted
13 1.9
— 81
21.75
a
36
1.7
1.9
1.7
0.7
Not found
50
21.75
47.44
1.9
0.7
1.9
—
1.7
1.7
1.9
1.9

2
4
Calculated gross and net energy output per year
Biomass
Barley
Energy output from
H
2
(MJ ha
−1
)
5653
Energy output from CH
4
(MJ ha −1 )
29,522
Flax
Fodder beet
Forage maize
0
19,263
13,429
Hemp
Miscanthus
829
0
Oats 5733
Perennial rye grass 3140
Potato 7259
Reed canary grass 0
Sugar beet
Sweet sorghum
18,853
22,685
Switch grass 2338
Wheat (whole crop) 3351
45,441
116,046
125,723
62,419
97,767
26,812
115,759
27,737
38,250
112,017
219,642
73,180
81,081
Total gross energy output
(MJ ha −1 )
35,175
Net energy output
(MJ ha −1 )
15,613
45,441
135,309
139,152
63,248
97,767
32,545
118,899
35,037
38,250
130,871
242,327
75,519
84,432
36,785
117,063
121,522
45,618
91,533
17,451
114,189
−13,163
34,168
112,624
223,928
69,190
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