Presentation is loading. Please wait.

Presentation is loading. Please wait.

TSEC-BIOSYS: The potential for hydrogen-enriched biogas production from crops: Scenarios in the UK Bharat K.V. Penumathsa, Manuel Vargas, Sandra Esteves,

Similar presentations


Presentation on theme: "TSEC-BIOSYS: The potential for hydrogen-enriched biogas production from crops: Scenarios in the UK Bharat K.V. Penumathsa, Manuel Vargas, Sandra Esteves,"— Presentation transcript:

1 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 Biomass role in the UK energy futures The Royal Society, London: 28 th & 29 th July 2009 www.tsec-biosys.co.uk Sustainable Environment Research Centre, University of Glamorgan, Wales, UK

2 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

3 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.

4 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

5 ADM1 conversion processes from A. Puñal with permission liquid gas Biochemical Physicochemical/Transfer gas CH 4 CO 2 H2OH2O H2H2 HAc, HPr, HBu, HVal, CO 2, NH 3,LCFA HCO 3 - gas H2OH2O CH 4 death/decay CO 2 HAc H2H2 NH 3 Ac -, Pr -, Bu -, Val -, HCO 3 -, NH 4 +,LCFA - NH 4 + proteins carbohydrates lipids inerts composites growth microorganisms aminoacids mono saccharides from A. Puñal

6 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.

7 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. (Y su is the biomass yield on sugar and f pr_su is the catabolic product “pr” yield from sugar). Note that the lactate yield is calculated to close the COD balance. Sugar Uptake S su S lac S bu S pro S ac S h2 X su (1-Y su ) f la,su (1-Y su ) f bu,su (1-Y su )f pro,su (1-Y su ) f ac,su (1-Y su ) f h2,su Y su Partial Peterson Matrix of stoichiometric coefficients of the products from glucose fermentation.

8 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).

9 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.

10 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

11 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.

12 Two-stage anaerobic systems – A Process configuration Packing material

13 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 0 (0.08 h-1); and d 0 original particle diameter (2 mm). X bs is biosolids concentration (mol/L); ρ p is density of biosolids (mol/L); N is number of particles per unit volume. X bs and N are new state variables. An additional first order expression implemented to model hydrolysis of slow degrading constituent (cellulose) of wheatfeed. r = k hyd,ce * X ce

14 Modelling anaerobic hydrolysis New model framework for H 2 -CH 4 reactor system Inerts Particulate fast degradable matter (starch; hemicellulose; lipids; proteins) Particulate slow degradable matter (cellulose) Disintegration Wheat Feed Dead biomass r = k dis * X C (first order kinetics) r = k 0 * e -(d/d 0 ) * X bs 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 Old implementation

15 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 ) 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.

16 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.

17 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 2 production ceases.

18 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 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.

19 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 4 reactor operated above pH 6.5 and near to 7 does not support H 2 production.

20 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 reactor (due to low HRT) can lead to washout. Concentration of cellulose in CH 4 reactor is higher even with less biosolids compared to H 2 reactor. Conversion of biosolids to cellulose is low in both reactors – attributed to disintegration expression and its associated kinetic parameters.

21 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.

22 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.

23 Transport biofuels using energy crops (UK context) Fuelproduction method consideredcrop considered Biodiesel extraction of plant oil followed by transesterification to biodiesel rape seed Bioethanol hydrolysis of sugars followed by fermentation and distillation wheat grain sugar beet (roots only) Biomethaneanaerobic digestion of carbohydratesrye grass sugar beet (whole crop) forage maize Biofuels, Production Methods, and Source Crops Considered Fuelcrop Gross energy produced (MJ/ha) Total energy losses (MJ/ha) Net energy balance (MJ/ha) Biodiesel rape seed50 12525 94024 185 Bioethanol wheat grain sugar beet (roots only) 67 50138 90828 593 131 2405397677264 Biomethanerye grass1141642099793167 sugar beet (whole crop) 17264043850128790 forage maize28854451533237011 Net Energy Associated with Biofuels from Energy Crops

24 cropenergy/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 grass93 167559 0005.2 × 10 10 28%2.872.1 × 10 7 80% sugar beet 128 790559 0007.2 × 10 10 40%3.981.5 × 10 7 58% maize237 011559 0001.3 × 10 11 72%7.188.2 × 10 6 32% Transport biofuels using energy crops (UK context) cropenergy 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 759118 899114 189 sugar beet 18 853 112 017130 871112 624 forage maize 13 429 125 723139 152121 522 Theoretical Energy Output from Biohydrogen and Methane Production Potential Contribution of Biomethane to Total U.K. Transport Fuel Demand and Biofuels Directive Target

25 Resource's Description Year of availability (start year) Available tonnage tDM/yr Gas factor (m 3 /tDM) Total CH4 (m 3 /yr) PJ/yr Resource cost (£/tDM) Annual resource cost (£/yr) Annual resource cost (£/PJ) Organic Fraction of MSW200684240003302779920000110.080.00 Sewage sludge2004340000195663000002.630.00 Animal slurry (wet and dry combined)2005399840013051979200020.580.00 Commercial industrial waste (food waste)20036295000330207735000082.260.00 Energy crops (wet) Sugar Beet2007104780004004191200000165.97119.0512473809527515632.52 Forage Maize2007129390003304269870000169.0957.007375230004361799.82 Fodder beet200795340004684461912000176.69107.5010249050005800526.63 Rye grass200795340003203050880000120.8139.003718260003077651.52 Sweet sorghum2007166845004006673800000264.2857.009510165003598484.85 Industrial by product Wheat feed200696000027226112000010.3495.00912000008819815.81 Biomass availability for AD in UK (Data for MARKAL modelling)

26 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-060.0000130688.62488E-0649.2515681596 Sewage Sludge2.62548E-060.0000077225.09652E-0649.2526538086 Animal slurry (wet/dry)1.75032E-060.0000051483.39768E-0649.2539807129 commercial industrial waste (food waste) 4.44312E-060.0000130688.62488E-0649.2515681596 Sugar beet 6.78922E-060.000015849.05078E-0640.0012937317 Forage maize 5.37101E-060.0000130687.69699E-0641.7415681596 Fodder beet 7.60451E-061.85328E-051.09283E-0541.8111057536 Rye grass 4.64491E-060.0000126728.02709E-0646.3516171646 Sweet sorghum 6.13662E-060.000015849.70338E-0644.1512937317 Wheat Feed 3.66221E-061.07712E-057.10899E-0649.2519025466 Technology cost estimation (AD) (Data for MARKAL modelling)

27 Biomass Crop yield tdm (ha −1 ) Carbohydrate for H 2 production as % of dm Holo-cellulose for CH 4 production as % of dm H 2 yield mol mol −1 hexose converted Barley4.555.1 starch131.9 Flax5.5Not found81— Fodder beet1463.9 WSC21.75 a 1.7 Forage maize1931 starch361.9 Hemp75.5 soluble sugars82.31.7 Miscanthus13.5Not found710.7 Oats4.753.5 starch6.11.9 Perennial rye grass1425.3 soluble sugars57.50.7 Potato3.486 starchNot found1.9 Reed canary grass7.5Not found50— Sugar beet1367.35 soluble sugars21.751.7 Sweet sorghum24.543 soluble sugars47.441.7 Switch grass9.2 11.2 (starch and soluble sugars) 67.61.9 Wheat (whole plant)1410.5 starch471.9 Data used for the calculation of hydrogen and methane production Evaluation of energy crops for fermentative H 2 /CH 4 production in UK

28 Evaluation of energy crops for fermentative H 2 /CH 4 production in UK Biomass Energy output from H 2 (MJ ha −1 ) Energy output from CH 4 (MJ ha −1 ) Total gross energy output (MJ ha −1 ) Net energy output (MJ ha −1 ) Barley565329,52235,17515,613 Flax045,441 36,785 Fodder beet19,263116,046135,309117,063 Forage maize13,429125,723139,152121,522 Hemp82962,41963,24845,618 Miscanthus097,767 91,533 Oats573326,81232,54517,451 Perennial rye grass3140115,759118,899114,189 Potato725927,73735,037−13,163 Reed canary grass038,250 34,168 Sugar beet18,853112,017130,871112,624 Sweet sorghum22,685219,642242,327223,928 Switch grass233873,18075,51969,190 Wheat (whole crop)335181,08184,43262,538 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.

29 General view of the pilot plant installed at IBERS

30 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

31 Thank you for your attention! www.tsec-biosys.ac.uk


Download ppt "TSEC-BIOSYS: The potential for hydrogen-enriched biogas production from crops: Scenarios in the UK Bharat K.V. Penumathsa, Manuel Vargas, Sandra Esteves,"

Similar presentations


Ads by Google