Modeling and optimization of an upflow anaerobic sludge blanket (UASB) system treating blackwaters

In the present work, a novel mathematical dynamic model describing the upflow anaerobic sludge blanket (UASB) has been developed. The model incorporates both the biological processes and the transport of sludge particles specifically related to the UASB treatment of blackwater. The processes occurring in the reactor are modeled as a sequence of two completely-mixed reactive tanks, one representing the sludge bed where most solids concentrate and the other representing a less solid-concentrated liquid volume. Solids from the sludge bed are assumed to be dragged to the upper zone of the reactor by the methane gas produced. The time variation of the sludge bed volume is included by assuming a constant sludge density. The model is calibrated against published data obtained from a stable operation of a lab-scale UASB system treating at different organic loading rates blackwater collected from real vacuum toilets. Good model prediction compatibility with the measurements of emitted biogas and methane gas flow rates, effluent COD, volatile fatty acids, pH and sludge bed volume was revealed. Furthermore, the model was successfully validated against another independent set of data from a UASB reactor treating a more diluted type of blackwater collected from conventional toilets. A scenario analysis performed using such a validated model reveals higher methane production yield but also higher effluent COD concentration at increasing organic loading rates and sludge bed volumes. It is found that longer HRTs can improve COD removal efficiency and methane production in blackwater treatment processes.

Automated summary

In this study, a new mathematical dynamic model describing the upflow anaerobic sludge blanket (UASB) has been developed, which incorporates both the biological processes and the transport of sludge particles related to the UASB treatment of blackwater. The model shows good compatibility with experimental measurements, predicting the biogas and methane gas flow rates, effluent COD, volatile fatty acids, pH, and sludge bed volume accurately.