Kinetic Modeling of the Sulfide Pollutant Removal in the Activated Sludge Bioreactor in the Presence of Silica Nanoparticles and Graphene Oxide

Nowadays, the presence of sulfurous compounds in aqueous media has become one of the most serious risks due to their high toxicity. Therefore, selecting and assessing a suitable and efficient method for decreasing or eliminating these pollutants is a vital issue. In this study, a comprehensive comparative analysis among various specific growth kinetic models of biomass were carried out to investigate the biological removal of sulfide pollutant in different bioreactors. For this aim, three types of bioreactor systems, including activated sludge without nanoparticles, in the presence of silica nanoparticles, and graphene oxide nanosheets were employed. Batch process experiments were performed to investigate the effect of different concentration of sulfide pollutant on the specific growth rates of biomass and also to study the growth of biomass during 14 days for all three bioreactor systems. Moreover, different biokinetic models, such as Monod, Haldane, Andrews and Noack, Yano and Koga and Webb were compared using obtained experimental data in a batch activated sludge process. According to the results, the maximum specific growth rates of EM, SS and GO bioreactor systems were mmax= 0.1871, 0.667, and 0.1277/ day-1, respectively. The Yano and Koga kinetic model had better fit with the experimental specific growth rate data than the other models thorough sulfide concentrations ranges with the correlation coefficients (R2) of 0.954 0.965 and 0.941 for EM, SS and GO bioreactor systems, respectively. In addition, the analysis of different error functions including, SSE, ARE, HYBRID and MPSD, showed that the bioreactor containing SiO2 nanoparticles has the best performance towards the other systems. Development and using these kinetic models and predictive methods can be considerably useful and efficient due to their analyzability and investigation of different operational parameters in the biological process of pollutants removal.