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Quantitative relationships between methanogen concentration, temperature and methane production in a pilot-scale anaerobic baffled reactor
Douglas, Jahhe Mingo
Douglas, Jahhe Mingo
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2020
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A demonstration-scale anaerobic baffled reactor (ABR) treats domestic wastewater from a 250-unit student housing complex (Mines Park) at the Colorado School of Mines. The anaerobic approach provides economic incentives by reducing costly aeration and producing methane, which can be used as a fuel source. In the ABR, wastewater moves sequentially through three reactor compartments that each contain distinct microbial communities. Limited historic sequencing data from the reactor indicate the proportion of methanogens (methane-producing archaea) relative to other microorganisms is lower at a higher temperature. Conversely, methane production is higher at higher temperatures. Based on the mesophilic growth of methanogens detailed in other studies, optimal growth is likely at higher temperatures and the absolute number of methanogens should increase with increasing temperature. I sought to determine (1) if the absolute concentration of methanogens is higher at higher temperatures and (2) if a higher methane flow rate correlates with a higher methanogen concentration or if the higher methane production is solely due to faster kinetics at higher temperatures. My initial hypotheses were that (1) the absolute concentration of methanogens is higher at higher temperatures and (2) methane flow increases as the methanogen absolute concentration increases. The absolute number of methanogens is not known for the Mines Park ABR. Quantitative polymerase chain reaction (qPCR) targeting the gene encoding the alpha subunit of the methyl coenzyme M reductase (mcrA) protein was conducted on ABR samples from each compartment representing a wide range of operating temperatures and methane flow rates to determine if the methanogen absolute concentration within the reactor system was higher at higher temperatures. mcrA catalyzes the final step in the formation of methane, is highly conserved and has been used in a number of studies to quantify methanogens. Acetogens (acetate-producing microorganisms), total bacteria, and total archaea were also enumerated using qPCR. Results from qPCR assays targeting functional or taxonomic groups were compared to reactor operating conditions and performance. Statistically significant positive relationships were identified between (1) mcrA concentration and monthly average temperature, (2) gaseous methane flow by compartment and mcrA concentration, and (3) gaseous methane flow by compartment and monthly average temperature. Multiple regression analysis demonstrated that mcrA concentration did not explain more of the variability within the methane flow data beyond that explained by temperature alone. From five potential explanatory variables, regression tree analysis identified average monthly temperature as the most predictive of methane flow; separation based on temperature resulted in two nodes and two groups of distinct predicted methane flows. The groups were as follows: Group 1: monthly average temperature < 18.7 ˚C and Group 2: monthly average temperature ≥ 18.7 ˚C. Group 2 had higher methane flow predicted values of 22.9 L/d compared to Group 1 at 13.5 L/d. Through linear and multiple regression analysis, regression tree analysis, and estimation statistics, the absolute methanogen concentration was shown to exhibit a positive relationship with monthly average temperature and methane flow per compartment. Temperature is the main driver of methane flow within the Mines Park ABR system. The findings of this study indicate that methane production will be highest at reactor compartment temperatures ≥ 18.7 ˚C. This temperature is below the range of temperatures reported by most high-producing ABRs, extending the temperature range of highly productive ABRs into temperate zones. Furthermore, the reactor concentrations found within this study can be used as inputs in reactor microbial models. Process models can thus be tailored to be system-specific. With the microbial concentrations found within this study, a realistic data-driven process model can be constructed instead of a model based on empirical values.
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