WeChat Mini Program
Old Version Features

How Rhodococcus Ruber Accelerated Denitrification with Soybean-Processing Wastewater As the Electron Donor.

Haiyun Zhang, Yue Ma,Fei Liu, Songyun Chen, Xu Peng,Fu Chen,Yongming Zhang,Bruce E Rittmann

Journal of environmental management(2025)

Cited 0|Views3
Abstract
Total nitrogen removal is a bottleneck for achieving acceptable effluent quality for many municipal wastewater treatment plants (WWTPs). Inadequate denitrification is often the cause of insufficient total-nitrogen removal, and a deficiency of electron donor is a frequent cause. Soybean-processing wastewater (SPW) is potential electron donor. SWP contains electron donors to drive denitrification, but they are polymers that need to be hydrolyzed first. This work evaluated how bioaugmentation with a small amount of Rhodococcus ruber accelerated denitrification with soluble SWP. Compared with normal acclimated denitrifying biomass alone, addition of 0.45% (w/w) R. ruber accelerated the NO3--N removal rate by 22%, and it also led to greater release of NH4+-N from the breakdown of amino acids in SWP. The acceleration was attributed to the proteins and polysaccharides being more rapidly hydrolyzed, since R. ruber contained the genes for a variety of protein and polysaccharide hydrolases. Hydrolysis released simple amino acids and carbohydrates that could be oxidized by the denitrifying biomass, as well as by R. ruber. While R. ruber also could oxidize amino acids and carbohydrate via oxygen and nitrate respirations, its main role in bioaugmentation was to accelerate the first step, hydrolysis. This work introduces the practice novelty that bioaugmentation with R. ruber could accelerate denitrification and the fundamentals novelty that R. ruber accelerated denitrification by catalyzing hydrolysis of proteins and polysaccharides in SPW.
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined