TSEC-BIOSYS:The potential for hydrogen-enriched biogas production from crops: Scenarios in the UK

Bharat K.V. Penumathsa, Manuel Vargas, Sandra Esteves,Richard Dinsdale, Alan J. Guwy, Jorge Rodríguez, Giuliano C. Premier

TSEC BiosysTSEC Biosys

TSEC BiosysTSEC Biosys

Biomass role in the UK energy futures The Royal Society, London: 28th & 29th July 2009

www.tsec-biosys.co.uk

Sustainable Environment Research Centre, University of Glamorgan, Wales, UK

Hydrogen Energy Systems

Biohydrogen

Biological Fuel Cells

Bioenergy

Anaerobic Digestion

Waste Treatment

Environmental Monitoring

Hydrogen Research Centre

Wastewater Treatment

Research Centre

WWTRU

Microbial Electrolysis

Hydrogen Storage

Environmental Analysis

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) systemPenumathsa, 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 processesJorge 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 considerationsPatterson, 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

from A. Puñal with permission

liquid

gas

Bio

chem

ical

Physicochemical/Transfer

gas

CH4

CO2H2O

H2

HAc, HPr, HBu, HVal, CO2, NH3,LCFA

HCO3 -

gas H2O

CH4

death/decay

CO2HAc H2

NH3

Ac -, Pr -, Bu -, Val -, HCO3 -, NH4

+,LCFA-

NH4 +

proteins carbohydrateslipids

inerts

composites

growth

microorganisms

aminoacids monosaccharides

from A. Puñal

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.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16Concentration of undissociated acids (molAH/L)

Yie

lds

of

pro

du

cts

an

d b

iom

as

s

[mo

l X(P

rod

)/mo

lGlu (

Ca

tab

)]

fh2_su(molH2/molGlu)

Yxs (molX/molGlu)

fac_su(molAc/molGlu)

fbu_su(molBu/molGlu)

flac_su [calc](molLac/molGlu)

Variable stoichiometry

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.

Sugar Uptake

Ssu Slac Sbu Spro Sac Sh2 Xsu

-1 (1-Ysu) fla,su (1-Ysu) fbu,su (1-Ysu)fpro,su (1-Ysu) fac,su (1-Ysu) fh2,su Ysu

Partial Peterson Matrix of stoichiometric coefficients of the products from glucose fermentation.

Simulation studies

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).

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).

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.

• 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.

Two-stage anaerobic systems - Advantages

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

Biogas Antifoam

Effluent

pH probe Recirculation line

Gas flow meter CO2 sensor CH4 sensor

NaHCO3

UAF Reactor

NaOH Antifoam

Feed

Redox probe

pH controller

H2 Reactor

CO2 sensor Gas flow meter H2 sensor

pH probe

Impeller

Packing material

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

New model framework for

H 2-CH 4 reactor system

Inerts

Particulatefast degradable matter

(starch; hemicellulose; lipids; proteins)

Particulateslow degradable matter (cellulose)

Disintegration

Wheat Feed

Dead biomass

r = kdis * XC (first order kinetics)

r = k0 * e-(d/d

0) * Xbs

Hydrolysis

r = khyd,ce * Xce (first order kinetics)

r = khyd,ch,pr,li * Xch,pr,li (first order kinetics)

Newimplementation

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 concentrationSch4-gas – methane concentrationSco2-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)

(b)

(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.

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.

•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.

Conclusions (two stage modelling)

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)

Fuel production method considered crop consideredBiodiesel extraction of plant oil followed by transesterification to

biodieselrape seed

Bioethanol hydrolysis of sugars followed by fermentation and distillation

wheat grainsugar beet (roots only)

Biomethane anaerobic digestion of carbohydrates rye grasssugar beet (whole crop)forage maize

Biofuels, Production Methods, and Source Crops Considered

Fuel crop Gross energy produced (MJ/ha)

Total energy losses (MJ/ha)

Net energy balance (MJ/ha)

Biodiesel rape seed 50 125 25 940 24 185

Bioethanol

wheat grainsugar beet (roots only)

67 501 38 908 28 593

131 240 53976 77264

Biomethane rye grass 114164 20997 93167

sugar beet(whole crop)

172640 43850 128790

forage maize 288544 51533 237011

Net Energy Associated with Biofuels from Energy Crops

crop 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 93 167 559 000 5.2 × 1010 28% 2.87 2.1 × 107 80%

sugar beet

128 790 559 000 7.2 × 1010 40% 3.98 1.5 × 107 58%

maize 237 011 559 000 1.3 × 1011 72% 7.18 8.2 × 106 32%

Transport biofuels using energy crops (UK context)

crop energy output from H2 (MJ/ha)

energy output from CH4 (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 18 853 112 017 130 871 112 624

forage maize 13 429 125 723 139 152 121 522

Theoretical Energy Output from Biohydrogen and Methane Production

Potential Contribution of Biomethane to Total U.K. Transport Fuel Demand and Biofuels Directive Target

Resource's Description

Year of availability

(start year)

Available tonnage tDM/yr

Gas factor

(m3/tDM)

Total CH4 (m3/yr)

PJ/yrResource

cost (£/tDM)

Annual resource cost (£/yr)

Annual resource

cost (£/PJ)

Organic Fraction of MSW 2006 8424000 330 2779920000 110.08 0.00 0.00 0.00

Sewage sludge 2004 340000 195 66300000 2.63 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

Energy crops (wet)  

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

Industrial by product  

Wheat feed 2006 960000 272 261120000 10.34 95.00 91200000 8819815.81

Biomass availability for AD in UK (Data for MARKAL modelling)

ResourcesEnergy 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

Sugar beet6.78922E-06 0.00001584 9.05078E-06 40.00 12937317

Forage maize5.37101E-06 0.000013068 7.69699E-06 41.74 15681596

Fodder beet7.60451E-06 1.85328E-05 1.09283E-05 41.81 11057536

Rye grass4.64491E-06 0.000012672 8.02709E-06 46.35 16171646

Sweet sorghum6.13662E-06 0.00001584 9.70338E-06 44.15 12937317

Wheat Feed3.66221E-06 1.07712E-05 7.10899E-06 49.25 19025466

Technology cost estimation (AD) (Data for MARKAL modelling)

BiomassCrop yield tdm (ha−1)

Carbohydrate for H2

production as % 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 9.211.2 (starch and soluble sugars)

67.6 1.9

Wheat (whole plant) 14 10.5 starch 47 1.9

Data used for the calculation of hydrogen and methane production

Evaluation of energy crops for fermentative H2/CH4 production in UK

Evaluation of energy crops for fermentative H2/CH4 production in UK

BiomassEnergy 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

Calculated gross and net energy output per year

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!

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