Sulfur-oxidizing bacteria （SOB） are the main microorganisms that participate in the natural sulfur cycle. To obtain SOBwith high sulfur-oxidizing ability under aerobic or anaerobic conditions, aerobic and anaerobic enrichmentswere carried out. Denaturing gradient gel electrophoresis （DGGE） profiles showed that the microbial community changed according to the thiosulfate utilizationduring enrichments, and Rhodopseudomonas and Halothiobacilluswere the predominant bacteria in anaerobic enrichment and aerobic enrichment, respectively,which mainly contributed to the thiosulfate oxidization in the enrichments. Based on the enriched cultures, six isolateswere isolated from the aerobic enrichment and four isolateswere obtained from the anaerobic enrichment. Phylogenetic analysis suggested the 16S rRNA gene of isolates belonged to the genus Acinetobacter, Rhodopseudomonas, Pseudomonas, Halothiobacillus,0chrobactrum, Paracoccus, Thiobacillus, and Alcaligenes, respectively. The tests suggested isolates related to Halothiobacillus and Rhodopseudomonas had the highest thiosulfate oxidizing ability under aerobic or anaerobic conditions, respectively; Paracoccus and Alcaligenes could aerobically and anaerobically oxidize thiosulfate. Based on the DGGE and thiosulfate oxidizing ability analysis, Rhodopseudomonas and Halothiobacilluswere found to be the main SOB in the sulfide-removing reactor, andwere responsible for the sulfur-oxidizing in the treatment system.
Two artificial intelligence techniques, artificial neural network and genetic algorithm, were applied to optimize the fermentation medium for improving the nitrite oxidization rate of nitrite oxidizing bacteria. Experiments were conducted with the composition of medium components obtained by genetic algorithm, and the experimental data were used to build a BP (back propagation) neural network model. The concentrations of six medium components were used as input vectors, and the nitrite oxidization rate was used as output vector of the model. The BP neural network model was used as the objective function of genetic algorithm to find the optimum medium composition for the maximum nitrite oxidization rate. The maximum nitrite oxidization rate was 0.952 g 2 NO-2-N·(g MLSS)-1·d-1 , obtained at the genetic algorithm optimized concentration of medium components (g·L-1 ): NaCl 0.58, MgSO 4 ·7H 2 O 0.14, FeSO 4 ·7H 2 O 0.141, KH 2 PO 4 0.8485, NaNO 2 2.52, and NaHCO 3 3.613. Validation experiments suggest that the experimental results are consistent with the best result predicted by the model. A scale-up experiment shows that the nitrite degraded completely after 34 h when cultured in the optimum medium, which is 10 h less than that cultured in the initial medium.
