A/O-MBR工艺调试强化炼化废水脱氮除碳及模拟研究
doaj（2025）
Abstract
以陕西省某石化公司污水处理厂为研究对象,通过调试A/O-MBR中试系统的运行参数优化脱氮除碳处理效果,并基于活性污泥模型(ASMs)建立污水生物处理系统数学模型,对污水处理过程进行模拟研究.运行调试结果表明,优化运行参数后AO5工艺仍不能确保出水达标排放,而将原AO5工艺改为A2O4工艺使缺氧区停留时间由 4 h延长至 8 h后,在好氧池填料投加比 20%,缺氧池 100%污泥内回流和 200%硝态液回流条件下,出水COD、NH4+-N、TN分别为28.53、0.24、12.22 mg/L,满足《陕西省黄河流域污水综合排放标准》(DB 61/224-2018)中表2其他单位水污染物排放质量浓度限值标准.此外,基于ASMs模型构建的Ind.ASM-Petrochem模型可较为准确地模拟系统对COD和NH4+-N的去除,对TN的模拟则需要进一步提升.
MoreTranslated text
Key words
A/O-MBR,nitrogen and carbon removal,retention time,filler,Ind.ASM-Petrochem model
上传PDF
View via Publisher
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