Technologies for in-situ H2S control in wastewater treatment plants: A review

被引:9
作者
Shi, Lei [1 ]
Yang, Qing [1 ]
Xu, ZongZe [1 ]
Yang, Chen [1 ]
机构
[1] Beijing Univ Technol, Natl Engn Lab Adv Municipal Wastewater Treatment &, Beijing 100124, Peoples R China
基金
中国国家自然科学基金;
关键词
Hydrogen sulfide; In -situ removal; Odor control; Wastewater treatment plants; Integration; SULFATE-REDUCING BACTERIA; FLUIDIZED-BED REACTOR; ACTIVATED-SLUDGE; SULFIDE REMOVAL; ODOR PRODUCTION; MIXED CULTURES; SULFUR; NITRATE; OXIDATION; EMISSION;
D O I
10.1016/j.jwpe.2024.105716
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The odor emission from wastewater treatment plants (WWTPs) has sparked widespread concern, with H2S emerging as one of the most worrisome odors. End-of-pipe treatment has been the predominant method for addressing H2S in recent decades. It is plagued by complexities in operation and high costs. Technologies for insitu H2S control have garnered increasing attention due to their high economic efficiency and low energy consumption. However, technologies for in-situ H2S control in WWTPs still need to be further systematically summarized and categorized while technologies for in-situ H2S control for overall WWTPs are still lacking. This paper provides a retrospective analysis of the principles, drawbacks, and limitations of traditional technologies for insitu H2S control while highlighting several promising and advanced technologies. Characteristic analyses of these technologies are conducted. The shortcomings they must overcome and the primary challenges they face in the implementation of WWTPs are identified. Finally, the paper forecasts the future research directions and proposes strategies to tackle the associated challenges of technologies for in-situ H2S control. We emphasize the necessity of prioritizing the overall management of WWTPs, with a primary focus on process reduction-oriented operational optimization measures, advocating for the practical adoption of technologies for in-situ H2S control, synergistic integration of multiple treatment modalities, and utilizing mathematical methods such as machine learning to optimize design parameters and solutions.
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页数:13
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