Machine learning-based model construction and identification of dominant factor for simultaneous sulfide and nitrate removal process.

Accurate water quality prediction models are essential for the successful implementation of the simultaneous sulfide and nitrate removal process (SSNR). Traditional models, such as regression and analysis of variance, do not provide accurate predictions due to the complexity of microbial metabolic pathways. In contrast, Back Propagation Neural Networks (BPNN) has emerged as superior tool for simulating wastewater treatment processes. In this study, a generalized BPNN model was developed to simulate and predict sulfide removal, nitrate removal, element sulfur production, and nitrogen gas production in SSNR. Remarkable results were obtained, indicating the strong predictive performance of the model and its superiority over traditional mathematical models for accurately predicting the effluent quality. Furthermore, this study also identified the crucial influencing factors for the process optimization and control. By incorporating artificial intelligence into wastewater treatment modeling, the study highlights the potential to significantly enhance the efficiency and effectiveness of meeting water quality standards.

Response of simultaneous sulfide and nitrate removal process on acute toxicity of substrate concentration and salinity: Single toxicity and combined toxicity.

Simultaneous sulfide and nitrate removal process has performed excellent to treat nitrogen and sulfur pollutants in wastewater treatment. A high salinity stress poses a great challenge to the treatment of highly saline wastewater containing nitrate and sulfide. In addition, sulfide and nitrates are also toxic for the process, and their high concentration would inhibit the process. Therefore, the current work explores the single acute toxic effect and combined toxic effect of salinity and substrate concentration on the performance of the process from the perspective of toxicology. Considering sulfide and nitrate removal performance as an indicator, the IC50 values of sulfide were 293.20 mg S/L and 572.30 mg S/L, respectively; while those of salinity were 6.14% wt (91.78 mS/cm) and 6.63% wt (98.73 mS/cm), respectively. High substrate concentration or salinity resulted in elemental sulfur generation. The molar ratio of generated elemental sulfur to consumed sulfide(R-Sulfate) was close to 1. The response of nitrate reduction product to the elevating substrate concentration was not obvious, while its response to increasing salinity was on the contrary. With the increasing salinity (1.2% wt to 9.6% wt), molar ratio of generated nitrogen gas to consumed nitrate (R-Nitrogen gas) increased from 0.58 to 1, while molar ratio of generated nitrite to consumed nitrate (R-Nitrite) decreased from 0.43 to 0. Factorial analysis test revealed that the combined acute toxicity of substrate and salinity on sulfide oxidization and nitrate reduction were both antagonistic effects.

Metagenomic analysis of microbial community and metabolic pathway of simultaneous sulfide and nitrite removal process exposed to divergent hydraulic retention times.

The role of hydraulic retention time (HRT) on S0 production was assessed through metagenomics analyses. Considering comprehensive performance for the tested HRTs (0.25-13.33 h), the optimal HRT was 1 h, while respective sulfide and nitrite loading rate could reach 6.84 kg S/(m3·d) and 1.95 kg N/(m3·d), and total S0 yield was 0.36 kg S/(kg (VSS)·d). Bacterial community richness decreased along the shortening of HRT. Microbacterium, Sulfurimonas, Sulfurovum, Paracoccus and Thauera were highly abundant bacteria. During sulfur metabolism, high expression of sqr gene was the main reason of maintaining high desulfurization load, while lacking soxB caused the continuous increase of S0. Regarding nitrogen metabolism, the rapid decrease of nitrite transporter prevented nitrite to enter in cells, which caused a rapid decrease of nitrite removal under extreme HRT. Adjusting HRT is an effective way to enhance S0 production for the application of the simultaneous sulfide and nitrite removal process.

Response difference of simultaneous sulfide and nitrite removal process to different cooling modes.

The effects of various cooling modes (sudden cooling (25℃→10℃) and step cooling (25℃→20℃→15℃→10℃)) on the performance of simultaneous sulfide and nitrite removal process were reported. Regardless of cooling mode adopted, the process maintained good sulfide removal performance, and removal percentage was 100.00%. Considering nitrite removal percentage, the process was more sensitive to step cooling mode (k = 0.06707) in comparison to sudden cooling mode (k = 0.02760). Lowering temperature promoted the transformation from sulfate to elemental sulfur, and it was easier to increase the proportion of elemental sulfur (79.90%) by means of step cooling. The sulfide oxidation rate and nitrite reduction rate were 0.01540 mg /(L∙min) and 0.00354 mg /(L∙min), respectively, in the sudden cooling mode, and 0.01168 mg /(L∙min) and 0.00138 mg /(L∙min), respectively, in the step cooling mode. Low temperature reduced the diversity of microbial community, and Sulfurovum was still a dominant bacterial member in both cooling modes.