Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/691
Title: Predictive capability of response surface methodology and cybernetic models for cyanogenic simultaneous nitrification and aerobic denitrification facilitated by cyanide-resistant bacteria
Authors: Mpongwana, N.,
Ntwampe, S.K.,
Razanamahandry, L.C.
Chidi, B.S.
Omodanisi, E.I.
Keywords: Aerobic denitrification, Cybernetic model, Free cyanide, Nitrification, Response surface methodology (RSM), Simultaneous nitrification and aerobic denitrification (SNaD)
Issue Date: 2021
Publisher: Environ. Eng. Res.
Citation: Mpongwana, N., Ntwampe, S.K., Razanamahandry, L.C., Chidi, B.S. & Omodanisi, E.I.(2021). Predictive capability of response surface methodology and cybernetic models for cyanogenic simultaneous nitrification and aerobic denitrification facilitated by cyanide-resistant bacteria. Environ. Eng. Res.
Abstract: Free cyanide (CN-) is a threat to metabolic functions of the microbial population used for the treatment of wastewater, particularly, total nitrogenremoval (TN) consortia which gets inhibited by CN- in wastewater treatment plants (WWTPs). Many other methods are used to treat CN-prior to the TN removal stages; however, these methods increase the operational cost of the WWTPs. The capability of a microbial population to use multiple substrates is critical in WWTP and in eliminating inhibition associated with CN-. Previously, cyanide resistant bacteria were used to eliminate the inhibitory effect of CN-towards simultaneous nitrification and aerobic denitrification (SNaD). However, a study to predict the degradation efficiency of the microorganism was required. In this study, response surface methodology (RSM) and cybernetic models were used to predict and optimize SNaD performance for TN removal under CN- conditions. Physiological parameters influencing the SNaD were pH6.5 and 36.5oC, with TN and CN- degradation efficiency of 78.6 and 80.2%, respectively. These results show a complete elimination of the CNinhibitory effect towards SNaD and show the prediction ability of both RSM and the cybernetic models used. These results exhibited a promising solution in the control, management, and optimization of SNaD.
URI: http://localhost:8080/xmlui/handle/123456789/691
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