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Examining the Phenomenon of Self Regulation of Biofilm Density Under Dynamic Conditions Using a Biofilm Model

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The moving bed biofilm reactor (MBBR) is a well accepted technology in wastewater treatment. The process modeling and simulation is often used to design and optimize the full scale MBBR facilities. In this study, a process model calibrated to experimental data from a post–denitrification MBBR pilot plant is used to elucidate the effect of temperature and NOx–N loading on the biomass areal density. During the 122 day long experimental period, the biofilm density was observed to change under variable NOx–N loading and temperature condition. The visual inspections of the trends in temperature and measured biofilm density implied that the temperature may affect the biofilm density significantly. The simulation results using the calibrated model however suggest that the observed variation in the biofilm density is mainly due to the variation in NOx–N loading to the plant. The model also indicates that the temperature variation did not affect the biofilm density suggesting that the temperature dependent growth and decay processes did not affect total solid production at different temperatures in the methanol fed denitrification system. This observation seems to be the artifact of the model structure in which the methylotroph decay byproduct Xs accumulates in the system due to limited hydrolysis. Further simulations with modified model with hypothetical temperature dependent detachment rate indicated that the temperature dependent biofilm density change could improve the fit between simulated and measured density. The detachment rate depends on the physical and biological characteristics of the system and is influenced by hydrodynamic shear and more importantly the self immobilization strength of bacteria. It is likely that these physical and biological factors are influenced by temperature, but more research is needed to quantify these effects.
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Keywords: Denitrification; MBBR; external Carbon; nutrient removal; process model; simulation

Document Type: Research Article

Publication date: 01 January 2011

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