Martins, Antonio

Born and raised in a city lacking wastewater treatment and facing multiple water challenges, I am eager to develop holistic, innovative solutions for drinking water and wastewater treatment. My PhD research focuses on using microbial community structure to predict anaerobic digestion function and Im also involved in a startup project to improve energy conservation in municipal wastewater treatment using anaerobic biotechnology. I hold a Masters in Environmental Engineering (UFRGS/Brazil) and a Bachelors in Civil and Environmental Engineering (UNIVALE/Brazil).

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Description: Monod Kinetic Parameters for Different Anaerobic Digesters Vary over a Wide Range:...

Monod Kinetic Parameters for Different Anaerobic Digesters Vary over a Wide Range: Implication for Modelling and Correlation with Microbial Community Data

Abstract

Introduction Anaerobic digestion (AD) is employed for municipal wastewater solids stabilization and industrial and agricultural wastewater treatment generating methane-rich biogas that can be used for renewable energy (Speece, 2008). Specific methanogenic activity (SMA) values (mL CH4/gVSS-d) are often measured to assess the health of methanogenic biomass and one way to increase biogas production is to increase the utilization rates of key substrates and intermediates. Rates are often modelled using the Monod kinetic expressions (Metcalf & Eddy, 2014), and the constants of that expression, the maximum specific substrate utilization rate (km) (mg COD/mg CODx-d), half saturation coefficient (Ks) (mg COD/L), and active biomass fraction (X) (mg CODx/L) are required for each substrate and intermediate in models such as AD Model Number 1 (ADM1) (Batstone et al., 2002). Currently, no practical methods are described in the literature and none are consistently used in the field to measure km, Ks and X (Benn et al., 2024); therefore, default values have been suggested for each substrate or intermediate in ADM1 (Batstone et al., 2006). Alternatively, the values that give the best fit between observed and predicted model output are determined during model calibration and employed (Batstone et al., 2006). However, determining the constant values de novo for a specific anaerobic biomass or determining the range of typical values for a given anaerobic process configuration would be beneficial when attempting to improve process performance or ADM1 modeling (Benn et al., 2023). Furthermore, some constant values vary greatly from one specific biomass source to another (Benn et al., 2024). Therefore, it is hypothesized that kinetic constant values may be correlated with microbial community composition (Venkiteshwaran et al., 2016). The exact identity and number of taxa in an anaerobic digester may dictate, in part, the value of kinetic constants. In this study, values of acetate SMA (SMAac), km (km,ac), Ks (Ks,ac) and X (Xac) were determined for a suite of different methanogenic, anaerobic biomass samples having significantly different microbial communities from different reactor configurations. One goal was to determine appropriate ranges of the constant values to use in modelling. A second goal was to correlate the km,ac, Ks,ac and Xac values with the identity and abundance of acetoclastic methanogens present in the samples and identify potential biomass characteristics (taxa identity, taxa abundance values, biomass source, biomass type, digester type) that associate with high values of km,ac and Xac. Material and Methods Anaerobic biomass samples were collected from seven different full-scale anaerobic digesters including different biomass sources (digesters fed with municipal sludge, beverage wastewater, dairy wastewater, and paper mill wastewater), digester types (complete mix stirred tank reactors, upflow anaerobic sludge blanket, and anaerobic lagoons), and biomass types (granular and flocculent). To measure km,ac, Ks,ac and Xac values, activity tests were performed at 35 °C in triplicate using three subsequent doses of acetate substrate (2 g COD/L each dose) and cumulative methane production over time was measured using an automated respirometer system (Benn et al., 2023). Samples were collected before substrate addition and after each substrate dose for microbial community analysis using partial 16S rRNA gene amplicon sequencing. The km,ac, Ks,ac and Xac values were calculated using the approach described elsewhere (Benn et al., 2024) and SMAac values were calculated using km,ac and Xac values. The Spearman correlation coefficient was computed between km,ac, Ks,ac and Xac values and the relative abundance of acetoclastic methanogens. Results and Discussion The results for km,ac and Ks,ac are within the literature range recommended by ADM1 (Batstone et al., 2002) but the parameters high coefficient of variation values (CV) (km,ac CV = 78.1%; Ks,ac CV = 66.2%; Xac CV = 49.5%; SMAac CV = 90.6%) suggest that specific kinetic parameters vary greatly from one biomass to another and, therefore, it would be helpful to determine them for different anaerobic biomasses (Table 1). Granular biomass km,ac values were significantly higher than those of flocculant samples. Therefore, there are ostensibly fundamental differences between flocculant and granular biomass processes, such as direct interspecies electron transfer, that warrant further investigation. The average granular biomass km,ac value was 29.3 ± 13.6 mg COD/mg CODx-d, 3.4 times higher than the average flocculent biomass km,ac value (p = 7.72E-16). The Ks,ac and Xac values were also higher for granular versus flocculent biomass (p- Ks,ac = 0.003; p- Xac = 0.035). Granular biomass also demonstrated significantly higher SMAac values compared to flocculent biomass (p = 6.14E-12). The average SMAac values for granular and flocculent biomass samples were, respectively, 180 ± 95.8 mL CH4/g VSS-d and 45.6 ± 31.2 mL CH4/g VSS-d. These values are within the SMA range reported in the literature for granular and flocculent biomass samples (Hussain & Dubey, 2017; van Haandel & Lettinga, 1994). A positive relationship was observed between km,ac values and the relative abundance of acetoclastic methanogens in the samples (r = 0.70; p = 1.43E-10) (Figure 1). Weaker relationships were observed for Xac (r = 0.35; p = 4.91E-03) and Ks,ac (r = 0.40; p = 1.05E-03) values and acetoclastic methanogen relative abundance. This may be because relative abundance may be a poor indicator of total methanogen mass and correlation with absolute abundance determined via qPCR or other methods may be more appropriate. The biomass type (granular vs flocculent) was strongly associated with the abundance of acetoclastic methanogens (p = 2.05E-07); for this, granular biomass had significantly higher relative abundance of acetoclastic methanogens (2.42 ± 0.96 %; range of 0.47 -- 4.62 %) than flocculent biomass (1.18 ± 0.52 %; range of 0.36 -- 2.39 %). Strong relationships were observed between SMAac and km,ac values under different Xac conditions (Figure 2), showing that SMAac and km,ac values increase in granular biomass regardless of the active biomass fraction, whereas all the flocculant biomass km,ac values were approximately 8 mg COD/mgCODx-d. Therefore, the identity and acetate utilization rates of different acetoclastic methanogens present (Figure 1) may be more important than Xac to achieve high methane production rates. Conclusion The SMAac and constants (km,ac, Ks,ac and Xac) values using anaerobic biomass samples from seven full-scale digesters determined in this study can inform AD modelling, design, operation, and control. The km,ac, Ks,ac and Xac and SMAac values were significantly higher in granular compared to flocculent biomass. The fundamental kinetic and microbial community differences between flocculant and granular biomass processes warrant further investigation. A positive relationship was observed between km,ac values and the relative abundance of acetoclastic methanogens. This correlation can be used to assess AD function and increase substrate utilization rate and methane production in AD systems. The SMA values reported herein can be used as benchmark values to assess the performance of other flocculant and granular AD systems. Additionally, the range of Monod kinetic constant values reported herein can inform value selection when calibrating ADM1. Acknowledgment The authors thank Mr. Ethan Yen of Black & Veatch for reviewing the draft abstract and for providing comments that helped the authors improve the abstract.

This paper was presented at the WEF Residuals & Biosolids and Innovations in Treatment Technology Joint Conference, May 6-9, 2025.

SpeakerMartins, Antonio

Presentation time

16:05:00

16:25:00

Session time

15:45:00

16:45:00

SessionAdvances in Process Modeling: Aeration, Scaling, and Anaerobic Digestion Dynamics

Session number20

Session locationBaltimore Convention Center, Baltimore, Maryland, USA

TopicChemical Treatment, Modeling, Phosphorus Removal and Recovery, Specific Methanogenic Activity Test