Effect of N/S ratio on anoxic thiosulfate oxidation in a fluidized bed reactor: Experimental and artificial neural network model analysis

被引:29
作者
Khanongnuch, Ramita [1 ,2 ]
Di Capua, Francesco [3 ,4 ]
Lakaniemi, Aino-Maija [1 ]
Rene, Eldon R. [2 ]
Lens, Piet N. L. [1 ,2 ]
机构
[1] Tampere Univ Technol, Lab Chem & Bioengn, Fac Nat Sci, POB 541, FIN-33101 Tampere, Finland
[2] UNESCO IHE Inst Water Educ, NL-2611 AX Delft, Netherlands
[3] Univ Cassino & Southern Lazio, Dept Civil & Mech Engn, I-03043 Cassino, Italy
[4] Univ Napoli Federico II, Dept Civil Architectural & Environm Engn, I-80125 Naples, Italy
关键词
Anoxic thiosulfate oxidation; Kinetic constants; Nitrate reducing-sulfur oxidizing bacteria; Thiobacilus denitrificans; Artificial neutral network; AUTOTROPHIC DENITRIFICATION PROCESS; SULFUR-OXIDIZING BACTERIA; HYDROGEN-SULFIDE REMOVAL; WASTE-WATER; CHEMOLITHOTROPHIC DENITRIFICATION; DRIVEN DENITRIFICATION; ELECTRON-ACCEPTOR; BIOFILM REACTORS; ELEMENTAL SULFUR; S/N RATIO;
D O I
10.1016/j.procbio.2018.02.018
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Anoxic thiosulfate (S2O32-) oxidation using autotrophic denitrification by a mixed culture of nitrate reducing, sulfur oxidizing bacteria (NR-SOB) was studied in a fluidized bed reactor (FBR). The long-term performance of the FBR was evaluated for 306 days at three nitrogen-to-sulfur (N/S) molar ratios (0.5, 0.3 and 0.1) and a hydraulic retention time (HRT) of 5 h. S2O32- removal efficiencies > 99% were obtained at a N/S ratio of 0.5 and a S2O32- and nitrate (NO3-) loading rate of 820 (+/- 84) mg S-S2O32- L-1 d(-1) and 173 (+/- 10) mg N-NO3- L-1 d(-1), respectively. The S2O32- removal efficiency decreased to 76% and 26% at N/S ratios of 0.3 and 0.1, respectively, and recovered to 80% within 3 days after increasing the N/S ratio from 0.1 back to 0.5. The highest observed half-saturation (K-s) and inhibition (K-I) constants of the biofilm-grown NR-SOB obtained from batch cultivations were 172 and 800 mg S-S2O32- L-1, respectively. Thiobacilus denitrificans was the dominant microorganism in the FBR Artificial neural network modeling successfully predicted S2O32- and NO3- removal efficiencies and S2O32- production in the FBR. Additionally, results from the sensitivity analysis showed that the effluent pH was the most influential parameter affecting the S2O32- removal efficiency.
引用
收藏
页码:171 / 181
页数:11
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