Application of a simplified ADM1 for full-scale anaerobic co-digestion of cattle slurry and grass silage: assessment of input variability

Abstract

● Simplified ADM1 can predict biogas production from a full-scale biogas plant.

● Default parameters allowed for an accurate process simulation.

● Measurement variability did not affect simulated biogas and methane flow.

● Degradability of carbohydrates had a remarkable effect on gas yields.

Mathematical modeling of anaerobic digestion is a powerful tool to predict gas yields and optimize the process. The Anaerobic Digestion Model No. 1 (ADM1) is a widely implemented model for this purpose. However, modeling full-scale biogas plants is challenging due to the extensive substrate and parameter characterization required. This study describes the modification of the ADM1 through a simplification of individual process phases, characteristic components and required parameters. Consequently, the ability of the simplified model to simulate the co-digestion of grass silage and cattle slurry was evaluated using data from a full-scale biogas plant. The impacts of substrate composition (crude carbohydrate, protein and lipid concentration) and variability of carbohydrate degradability on simulation results were assessed to identify the most influential parameters. Results indicated that the simplified version was able to depict biogas and biomethane production with average model efficiencies, according to the Nash-Sutcliffe efficiency (NSE) coefficient, of 0.70 and 0.67, respectively, and was comparable to the original ADM1 (average model efficiencies of 0.71 and 0.63, respectively). The variability of crude carbohydrate, protein and lipid concentration did not significantly impact biogas and biomethane output for the data sets explored. In contrast, carbohydrate degradability seemed to explain much more of the variability in the biogas and methane production. Thus, the application of simplified models provides a reliable basis for the process simulation and optimization of full-scale agricultural biogas plants.

Graphical abstract

Keywords

ADM1 / Agricultural feedstocks / Biogas technology / Input variability / Parameter estimation

References

[1] Amon T, Amon B, Kryvoruchko V, Machmüller A, Hopfner-Sixt K, Bodiroza V, Hrbek R, Friedel J, Pötsch E, Wagentristl H, Schreiner M, Zollitsch W. (2007a). Methane production through anaerobic digestion of various energy crops grown in sustainable crop rotations. Bioresource Technology, 98(17): 3204–3212

[2] Amon T, Amon B, Kryvoruchko V, Zollitsch W, Mayer K, Gruber L. (2007b). Biogas production from maize and dairy cattle manure-Influence of biomass composition on the methane yield. Agriculture, Ecosystems & Environment, 118(1–4): 173–182

[3] Batstone D J, Keller J, Angelidaki I, Kalyuzhnyi S V, Pavlostathis S G, Rozzi A, Sanders W T M, Siegrist H, Vavilin V A. (2002). The IWA anaerobic digestion model No 1 (ADM1). Water Science and Technology, 45(10): 65–73

[4] Batstone D J, Puyol D, Flores-Alsina X, Rodríguez J. (2015). Mathematical modelling of anaerobic digestion processes: applications and future needs. Reviews in Environmental Science and Biotechnology, 14(4): 595–613

[5] Beltramo T, Hitzmann B. (2019). Evaluation of the linear and non-linear prediction models optimized with metaheuristics: Application to anaerobic digestion processes. Engineering in Agriculture, Environment and Food, 12(4): 397–403

[6] Dandikas V, Heuwinkel H, Lichti F, Drewes J E, Koch K. (2014). Correlation between biogas yield and chemical composition of energy crops. Bioresource Technology, 174: 316–320

[7] Galí A, Benabdallah T, Astals S, Mata-Alvarez J. (2009). Modified version of ADM1 model for agro-waste application. Bioresource Technology, 100(11): 2783–2790

[8] GehringT, Lübken M, KochK, WichernM (2013). ADM1 simulation of the thermophilic mono-fermentation of maize silage–Use of an uncertainty analysis for substrate characterization. In: 13th World Congress on Anaerobic Digestion: Recovering (bio) Resources for the World (28). Santiago de Compostela: RedBiogas

[9] György A, Kocsis L. (2011). Efficient multi-start strategies for local search algorithms. Journal of Artificial Intelligence Research, 41: 407–444

[10] Iman (2022). lhsgeneral(pd,correlation,n). MATLAB Central File Exchange. UK: MathWorks

[11] KaiserF L (2007). Einfluss der stofflichen Zusammensetzung auf die Verdaulichkeit nachwachsender Rohstoffe beim anaeroben Abbau in Biogasreaktoren. Dissertation for the Doctoral Degree. München: Technische Universität München

[12] Kalamaras S D, Vasileiadis S, Karas P, Angelidaki I, Kotsopoulos T A. (2020). Microbial adaptation to high ammonia concentrations during anaerobic digestion of manure-based feedstock: biomethanation and 16S rRNA gene sequencing. Journal of Chemical Technology and Biotechnology, 95(7): 1970–1979

[13] Keating T, O’Kiely P. (2000). Comparison of old permanent grassland, Lolium perenne and Lolium multiflorum swards grown for silage: 2. Effects on conservation characteristics in laboratory silos. Irish Journal of Agricultural and Food Research, 39: 25–33

[14] Koch K, Lübken M, Gehring T, Wichern M, Horn H. (2010). Biogas from grass silage: measurements and modeling with ADM1. Bioresource Technology, 101(21): 8158–8165

[15] Li Y, Jin Y, Borrion A, Li H, Li J. (2017). Effects of organic composition on mesophilic anaerobic digestion of food waste. Bioresource Technology, 244: 213–224

[16] LiebetrauJ, Pfeiffer D (2020). Methods for the determination of fundamental parameters. In: Liebetrau J, Pfeiffer D, eds. Collection of Methods for Biogas - Methods to determine parameters for analysis purposes and parameters that describe processes in the biogas sector. 2nd ed. Leipzig: Biomass Energy Use

[17] Lübken M, Gehring T, Wichern M. (2010). Microbiological fermentation of lignocellulosic biomass: current state and prospects of mathematical modeling. Applied Microbiology and Biotechnology, 85(6): 1643–1652

[18] Lübken M, Wichern M, Schlattmann M, Gronauer A, Horn H. (2007). Modelling the energy balance of an anaerobic digester fed with cattle manure and renewable energy crops. Water Research, 41(18): 4085–4096

[19] Mason K, Duggan J, Howley E. (2018). Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks. Energy, 155: 705–720

[20] McEniryJ, Crosson P, FinneranE, McGeeM, KeadyT W J, O’KielyP (2013). How much grassland biomass is available in Ireland in excess of livestock requirements? Irish Journal of Agricultural and Food Research, 52(1): 67–80

[21] McKay M D, Beckman R J, Conover W J. (1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21: 239–245

[22] Nash J E, Sutcliffe J V. (1970). River flow forecasting through conceptual models. Part I: A discussion of principles. Journal of Hydrology, 10(3): 282–290

[23] Page D I, Hickey K L, Narula R, Main A L, Grimberg S J. (2008). Modeling anaerobic digestion of dairy manure using the IWA Anaerobic Digestion Model No. 1 (ADM1). Water Science and Technology, 58(3): 689–695

[24] Solon K, Flores-Alsina X, Mbamba C K, Volcke E I, Tait S, Batstone D, Gernaey K V, Jeppsson U. (2015). Effects of ionic strength and ion pairing on (plant-wide) modelling of anaerobic digestion. Water Research, 70: 235–245

[25] ThamsirirojT, NizamiA S, MurphyJ D (2012). Why does mono-digestion of grass silage fail in long term operation? Applied Energy, 95: 64–76 10.1016/j.apenergy.2012.02.008

[26] Wall D M, O’Kiely P, Murphy J D. (2013). The potential for biomethane from grass and slurry to satisfy renewable energy targets. Bioresource Technology, 149: 425–431

[27] Wang Z, Wang S, Hu Y, Du B, Meng J, Wu G, Liu H, Zhan X. (2022). Distinguishing responses of acetoclastic and hydrogenotrophic methanogens to ammonia stress in mesophilic mixed cultures. Water Research, 224: 119029

[28] Weinrich S, Mauky E, Schmidt T, Krebs C, Liebetrau J, Nelles M. (2021). Systematic simplification of the Anaerobic Digestion Model No. 1 (ADM1) – Laboratory experiments and model application. Bioresource Technology, 333: 125104

[29] Weinrich S, Nelles M. (2021a). Basics of anaerobic digestion: biochemical conversion and process modelling. DBFZ-Report, 40: 9–76

[30] Weinrich S, Nelles M. (2021b). Systematic simplification of the Anaerobic Digestion Model No. 1 (ADM1) – Model development and stoichiometric analysis. Bioresource Technology, 333: 125124

[31] Weissbach F. (2008). On assessing the gas formation potential of renewable primary products. Landtechnik, 63(6): 356–358

[32] Wichern M, Gehring T, Fischer K, Andrade D, Lübken M, Koch K, Gronauer A, Horn H. (2009). Monofermentation of grass silage under mesophilic conditions: Measurements and mathematical modeling with ADM 1. Bioresource Technology, 100(4): 1675–1681

[33] Wichern M, Lübken M, Schlattmann M, Gronauer A, Horn H. (2008). Investigations and mathematical simulation on decentralized anaerobic treatment of agricultural substrate from livestock farming. Water Science and Technology, 58(1): 67–72

[34] Xie S, Hai F I, Zhan X, Guo W, Ngo H H, Price W E, Nghiem L D. (2016). Anaerobic co-digestion: a critical review of mathematical modelling for performance optimization. Bioresource Technology, 222: 498–512

[35] Yang G, Zhang P, Zhang G, Wang Y, Yang A. (2015). Degradation properties of protein and carbohydrate during sludge anaerobic digestion. Bioresource Technology, 192: 126–130

[36] Zhang Y, Jiang Y, Wang S, Wang Z, Liu Y, Hu Z, Zhan X. (2021). Environmental sustainability assessment of pig manure mono- and co-digestion and dynamic land application of the digestate. Renewable & Sustainable Energy Reviews, 137: 110476

RIGHTS & PERMISSIONS

The Author(s) 2024. This article is published with open access at link.springer.com and journal.hep.com.cn