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Frontiers of Environmental Science & Engineering

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Front. Environ. Sci. Eng.    2024, Vol. 18 Issue (4) : 50    https://doi.org/10.1007/s11783-024-1810-9
Application of a simplified ADM1 for full-scale anaerobic co-digestion of cattle slurry and grass silage: assessment of input variability
Sofia Tisocco1,2, Sören Weinrich3,4, Gary Lyons5, Michael Wills5, Xinmin Zhan1,6,7(), Paul Crosson2
1. Civil Engineering, College of Science and Engineering, University of Galway, Galway, H91 TK33, Ireland
2. Teagasc Animal and Bioscience Research Department, Animal and Grassland Research and Innovation Centre, Dunsany, C15 PW93, Ireland
3. Biochemical Conversion Department, Deutsches Biomasseforschungszentrum gemeinnützige GmbH, Leipzig 04347, Germany
4. Faculty of Energy · Building Services · Environmental Engineering, Münster University of Applied Sciences, Steinfurt 48565, Germany
5. Agri-Food and Biosciences Institute, Hillsborough, BT26 6DR, UK
6. Ryan Institute, University of Galway, Galway, H91 TK33, Ireland
7. MaREI Research Centre for Energy, Climate and Marine, Ryan Institute, University of Galway, Galway, H91 TK33, Ireland
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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.
Keywords ADM1      Agricultural feedstocks      Biogas technology      Input variability      Parameter estimation     
Corresponding Author(s): Xinmin Zhan   
Issue Date: 02 January 2024
 Cite this article:   
Sofia Tisocco,Sören Weinrich,Gary Lyons, et al. Application of a simplified ADM1 for full-scale anaerobic co-digestion of cattle slurry and grass silage: assessment of input variability[J]. Front. Environ. Sci. Eng., 2024, 18(4): 50.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-024-1810-9
https://academic.hep.com.cn/fese/EN/Y2024/V18/I4/50
Parameter Unit Data set A Data set B
Grass silage Cattle slurry Grass silage Cattle slurry
TS b) [% FM] 20.1 6.8 25.2 7.5
XA b) [g/kg TS] 185 241 185 241
XC b, c, d) [g/kg TS] 624 (41) 609 (29) 653 (27) 595 (24)
XP b, d) [g/kg TS] 160 (36) 101 (29) 135 (21) 115 (24)
XL b, d) [g/kg TS] 31 (7) 48.5 (4.8) 27 (7) 48.5 (4.8)
NH4-N b) [g/kg TS] 25.6 65.3 21.8 57.8
Acetic acid [g/kg TS] 20 e) 4.9 b) 31 e) 4.6 b)
Butyric acid [g/kg TS] 1.8 e) 0.6 b) 1 e) 0.6 b)
Propionic acid [g/kg TS] 0.6 e) 1.3 b) 1 e) 1.3 b)
Lactic acid [g/kg TS] 97 e) – 116 e) –
Valeric acid [g/kg TS] – 0.2 b) – 0.2 b)
DQXC f) [% XC] 85 69 80 69
Tab.1  Characteristics of the substrates used in this study a)
Fig.1  Schematic procedure of data collection from plant measurements to implementation in the ADM1_R3. Grass silage was taken as an example. Green boxes indicate plant measurements, orange boxes mark parameters taken from literature and purple boxes refer to indirect calculation by other parameters. Red outlines indicate unknown variability analyzed during this study.
Parameter Unit Grass silage Cattle slurry
ADL (Low DQXC) [g/kg TS] 147 b) 175 c)
XF [g/kg TS] 290 b) 213 c)
nXC (High DQXC) [g/kg TS] 212 d) 283 d)
Tab.2  Average values from literature implemented for degradability scenarios a)
Fig.2  Simulation results of biogas production, methane flow, ammonium nitrogen and pH from ADM1 and ADM1-R3 of each data set. Green dots in biogas and methane production indicate weekends (incl. Mondays) when no silage was fed.
Model output Data set A Mean difference to ADM1-R3 (Data set A) Data set B Mean difference to ADM1-R3 (Data set B)
ADM1
Biogas production 0.86 0.0 0.55 0.02
Methane flow 0.78 −0.04 0.48 −0.05
Ammonium nitrogen 0.18 0.0 0.08 −0.06
pH value 0.22 −0.02 −3.91 −0.05
ADM1-R3
Biogas production 0.86 0.53
Methane flow 0.82 0.53
Ammonium nitrogen 0.18 0.14
pH value 0.24 −3.86
Substrate composition scenario a)
Biogas production 0.86–0.9 (0.89) 0.03 0.47 – 0.57 (0.54) 0.01
Methane flow 0.7–0.87 (0.82) 0.0 0.33 −0.55 (0.50) −0.03
Ammonium nitrogen −1.03 – 0.21 (0.04) −0.14 −2.79 – 0.52 (−0.06) −0.2
pH value −0.47 – 0.31 (0.26) 0.02 −6.5 – −1.6 (−3.63) 0.23
Degradability quotients scenario
Biogas production High, 0.88 0.02 High, 0.55 0.02
Low, 0.38 −0.48 Low, −0.76 −1.29
Methane flow High, 0.72 −0.1 High, 0.55 0.02
Low, 0.86 0.03 Low, 0.23 −0.3
Ammonium nitrogen High, 0.18 0.0 High, 0.14 0.0
Low, 0.18 0.0 Low, 0.42 0.28
pH value High, 0.25 0.01 High, −3.93 −0.07
Low, 0.08 −0.16 Low, −2.19 1.67
Tab.3  Weekly Nash-Sutcliffe efficiency (NSE) of each model output for all the scenarios analyzed
Parameters Data set A Data set B
Baseline scenario
　kch 0.3 0.22
　kpr 0.24 0.16
Substrate composition scenario a)
　kch 2.54 (3.4) 0.6 (0.6)
　kpr 2.76 (4.0) 1.5 (3.1)
Degradability scenario
High DQ
　kch 0.25 0.17
　kpr 0.2 0.13
Low DQ
　kch 0.53 0.29
　kpr 0.46 0.23
Tab.4  Parameter estimation of hydrolysis rates of carbohydrates (kch) and proteins (kpr) obtained from numerical assessment
Fig.3  Measurements and simulation results of biogas production, methane flow, ammonium nitrogen and pH from varying 100 times the crude carbohydrates, proteins and lipids content of each data set.
Parameter Unit Grass silage Cattle slurry
Mean a) Min Max Mean a) Min Max
Crude carbohydrates [g/kg TS] 638 549 701 582 497 656
Crude proteins [g/kg TS] 146 88 227 130 54 214
Crude lipids b) [g/kg TS] 30 18 43 48 43 53
Tab.5  Concentrations of crude carbohydrates, proteins and lipids of grass silage and cattle slurry obtained from Latin Hypercube Sampling (LHS) and implemented in the substrate composition scenario
Fig.4  Measurements and simulation results of biogas production, methane flow, ammonium nitrogen and pH by implementing a high and low degradability quotient (DQ) of carbohydrates for each data set. ADM1-R3 indicates the baseline scenario. Green dots in biogas and methane production indicate weekends (incl. Mondays) when no silage was fed.
Fig.5  Measurements and simulation results of biogas production and methane flow after modifying the degradability quotient of carbohydrates (DQXC) on days 68–90 and 68–99 of Data sets A and B, respectively, assuming (a) high hydrolysis rate of carbohydrates (kch = 0.8) and (b) low hydrolysis rate of carbohydrates (kch = 0.1). Squares in biogas production indicate days when DQXC was lowered to 0.5 while triangles mark days in which DQXC was increased to 1. Green dots in biogas and methane production indicate weekends (incl. Mondays) when no silage was fed.
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