Xiaoyan Dang , Xu Li , Chaoyue Zhao , Yanping Zhang , Xiaolong Gao , Shumin He , Jie Han , Yaonan Zhu , Youzhao Wang , Tong Zhu
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引用次数: 0
Abstract
Seasonal temperature alternation severely affects the biological denitrification of high nitrogen concentrations in industrial wastewater. This study used sulfur-based filled particles for the sulfur autotrophic denitrification technique, and evaluated the performance of the sulfur autotrophic denitrification (SAD) reactor for the removal of high concentrations of NO3--N by simulating seasonal shifts and switching between 10℃ and 30℃ for 275 days. By precisely adjusting influent nitrate-nitrogen and Hydraulic Retention Times (HRTs), studies revealed enhanced nitrogen removal loading (NRL) at 30℃. Growth of sulfur autotrophs and electron transfer were inhibited at 10℃, and metabolites and biological residues supplied organic matter to heterotrophic denitrifying bacteria, resulting in lower sulfate production and significantly lower S/N ratios than at 30℃. Response surface optimization indicated optimal conditions: 83 % denitrification rate at 27.94℃, 18.7 h HRT, and 135.79 mg/L influent concentration, aligning closely with experimental findings. Thiobacillus and Thermomonas were more abundant at 30℃. And higher correlation of microorganisms between the two samples at high and low temperatures under high-concentration influent conditions. Additionally, the application of the SAD system is economically feasible because its raw materials are low-cost sulfur-based materials. This study aims to demonstrate the advantages of temperature in sulfur autotrophic denitrification, enhance the understanding of its mechanism, and provide a basis for wastewater treatment applications.
期刊介绍:
The Biochemical Engineering Journal aims to promote progress in the crucial chemical engineering aspects of the development of biological processes associated with everything from raw materials preparation to product recovery relevant to industries as diverse as medical/healthcare, industrial biotechnology, and environmental biotechnology.
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Metabolic Engineering, Systems and Synthetic Biology including OMICS, bioinformatics, in silico biology, and metabolic flux analysis
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