Fig 1.
Example of short- and long-term responses of methane production to temperature change.
Differences between short- and long-term response to temperature change measured by Elsgaard et al. [22]. Labels identify source: C = cattle manure (from barn), D = fresh digestate (directly from anaerobic digester), S = stored (> 1 month) digestate. Red arrows show short-term effects (differences between samples from the same source when incubated for 17 hours), and blue arrows apparent long-term effects (differences for samples stored for short and long time (weeks or months) at the same temperature)).
Fig 2.
Flow diagram of model concept.
Black lines indicate flows of organic matter, black dashed lines indicate flows of decayed microbial biomass. Red dashed lines indicate factors that inhibit microbial conversion processes (green boxes). Temperature affects all conversion processes, as well as chemical speciation.
Table 1.
Petersen matrix of model state variables (following [61], based on [62]).
See S3 Appendix for parameter descriptions and default values.
Table 2.
Equations used for inhibition factors.
Fig 3.
(a) Predicted CH4 production of single (dashed colored lines) and multiple (solid lines) methanogen groups as compared to observed CH4 production in cattle slurry by Elsgaard et al 2016 (circles). (b) Predicted short- and long-term responses to temperature change for different sets of methanogen groups. Note: Lines are shifted up or down by 0.01 for clarity.
Fig 4.
Default temperature responses.
(a) Temperature dependence of maximum substrate utilization rates (qmax) of default methanogen groups in the model (m1 to m5). (b) Steady-state microbial biomass as a function of temperature for default parameter values. The residual fraction of slurry (fresid) was set to 0.95 in this simulation.
Fig 5.
Predicted effects of residual slurry fraction on methanogens and methane production.
(a) Total methanogen biomass and (b) Cumulative CH4 emission as affected by the residual fraction (fresid) of slurry after slurry removal assuming a high degree of microbial enrichment (aenrich), or no enrichment.
Fig 6.
Predicted temperature effects on methanogens and methane production.
(a) Methanogen biomass and (c) CH4 emission during gradual slurry temperature changes as predicted with a high and low residual slurry fraction in the storage (fresid). (b) Methanogen biomass and (d) CH4 emission during short- and long-term temperature changes using a large residual fraction of slurry (fresid) of 0.95.
Fig 7.
Predicted pH effects on methanogens and methane production.
(a) Methanogen biomass and (b) CH4 emission responses to pH changes. The residual fraction of slurry (fresid) was set to 0.95.
Fig 8.
Model sensitivity to parameters and input variables.
Model output sensitivities to (a) parameters and (b) input variables. Initial microbial biomass refers to changes in both the initial concentration of methanogens in the slurry inoculum and the fresh influent slurry. For parameter values, see S3 Appendix.
Fig 9.
Model application to case study.
Model results versus measured [6] CH4 emission from a full scale slurry tank with periodic slurry introduction applying different (a) hydrolysis rates (αopt) and (b) maximum substrate utilization rates at optimum temperature (qmax,opt).