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Bharat K.V. Penumathsa, Manuel Vargas, Sandra Esteves,

T SEC-BIOSYS: T he 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.

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Bharat K.V. Penumathsa, Manuel Vargas, Sandra Esteves,

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  1. 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

  2. Wastewater Treatment Research Centre WWTRU Anaerobic Digestion Biohydrogen Microbial Electrolysis Biological Fuel Cells Bioenergy Hydrogen Research Centre Environmental Monitoring Hydrogen Energy Systems Waste Treatment Environmental Analysis Hydrogen Storage

  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 (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.

  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, H2 and NH4 are incorporated • First order kinetics to represent disintegration, hydrolysis and decay processes, while Monod-type expressions for uptake, growth, and inhibition

  5. CO2 H2 H2O CH4 composites gas NH4 + NH3 Ac -, Pr -, Bu -, Val -, HCO3 -, NH4 +,LCFA- HAc, HPr, HBu, HVal, CO2, NH3,LCFA HCO3 - growth gas H2O microorganisms ADM1 conversion processes gas death/decay inerts lipids proteins liquid carbohydrates aminoacids mono saccharides Biochemical H2 HAc CO2 CH4 from A. Puñal with permission from A. Puñal Physicochemical/Transfer

  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. (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.

  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. 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.

  11. 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.

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

  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 = 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

  14. Dead biomass Wheat Feed Disintegration r = k0 * e-(d/d0) * Xbs r = kdis * XC (first order kinetics) New model framework for H2-CH4 reactor system Particulate fast degradable matter (starch; hemicellulose; lipids; proteins) Particulate slow degradable matter (cellulose) Inerts Hydrolysis r = khyd,ce * Xce (first order kinetics) r = khyd,ch,pr,li * Xch,pr,li (first order kinetics) New implementation Old implementation Modelling anaerobic hydrolysis

  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 (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.

  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 H2. • CH4 production reaches peak concentration (at pH-7) as the H2 production ceases.

  18. (a) (b) Simulation studies (a) Single stage (b) Two-stage continuous • Model simulation results indicating the particle diameter. • 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.

  19. 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.

  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 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.

  21. 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.

  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) Biofuels, Production Methods, and Source Crops Considered Net Energy Associated with Biofuels from Energy Crops

  24. Transport biofuels using energy crops (UK context) Potential Contribution of Biomethane to Total U.K. Transport Fuel Demand and Biofuels Directive Target Theoretical Energy Output from Biohydrogen and Methane Production

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

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

  27. Evaluation of energy crops for fermentative H2/CH4production in UK Data used for the calculation of hydrogen and methane production

  28. Evaluation of energy crops for fermentative H2/CH4production in UK 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

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