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.

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.

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 S production was assessed through metagenomics analyses. Considering comprehensive performance for the tested HRTs (0.25-13.

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%.