Simulating a combined lysis-cryptic and biological nitrogen removal system treating domestic wastewater at low C/N ratios using artificial neural network
文献类型：期刊论文
| 作者 | Yang, Shan-Shan; Yu, Xin-Lei; Ding, Meng-Qi; He, Lei; Cao, Guang-Li; Zhao, Lei; Tao, Yu; Pang, Ji-Wei; Bai, Shun-Wen; Ding, Jie |
| 刊名 | WATER RESEARCH
 |
| 出版日期 | 2021-02-01 |
| 卷号 | 189页码:- |
| 关键词 | Backpropagation artificial neural network Real-time control Low C/N ratio wastewater Biological nitrogen removal (BNR) Lysis-cryptic plus BNR system |
| ISSN号 | 0043-1354 |
| 英文摘要 | In this study, a combined alkaline (ALK) and ultrasonication (ULS) sludge lysis-cryptic pretreatment and anoxic/oxic (AO) system (AO ALK/ULS) was developed to enhance biological nitrogen removal (BNR) in domestic wastewater with a low carbon/nitrogen (C/N) ratio. A real-time control strategy for the AO ALK/ULS system was designed to optimize the sludge lysate return ratio (R-SLR) under variable sludge concentrations and variations in the influent C/N (<= 5). A multi-layered backpropagation artificial neural network (BPANN) model with network topology of 1 input layer, 3 hidden layers, and 1 output layer, using the Levenberg-Marquardt algorithm, was developed and validated. Experimental and predicted data showed significant concurrence, verified with a high regression coefficient (R-2 = 0.9513) and accuracy of the BPANN. The BPANN model effectively captured the complex nonlinear relationships between the related input variables and effluent output in the combined lysis-cryptic BNR system. The model could be used to support the real-time dynamic response and process optimization control to treat low C/N domestic wastewater. (C) 2020 Elsevier Ltd. All rights reserved. |
| WOS研究方向 | Engineering, Environmental ; Environmental Sciences ; Water Resources |
| 源URL | [http://ir.rcees.ac.cn/handle/311016/46153]  |
| 专题 | 生态环境研究中心_中国科学院环境生物技术重点实验室 |
| 作者单位 | 1.China Energy Conservat & Environm Protect Grp, Beijing 100089, Peoples R China 2.Harbin Inst Technol, State Key Lab Urban Water Resource & Environm, Harbin 150000, Peoples R China 3.Harbin Inst Technol Shenzhen, Sch Civil & Environm Engn, Shenzhen 518055, Peoples R China 4.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Key Lab Environm Biotechnol, Beijing 100085, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Shan-Shan,Yu, Xin-Lei,Ding, Meng-Qi,et al. Simulating a combined lysis-cryptic and biological nitrogen removal system treating domestic wastewater at low C/N ratios using artificial neural network[J]. WATER RESEARCH,2021,189:-. |
| APA | Yang, Shan-Shan.,Yu, Xin-Lei.,Ding, Meng-Qi.,He, Lei.,Cao, Guang-Li.,...&Ren, Nan-Qi.(2021).Simulating a combined lysis-cryptic and biological nitrogen removal system treating domestic wastewater at low C/N ratios using artificial neural network.WATER RESEARCH,189,-. |
| MLA | Yang, Shan-Shan,et al."Simulating a combined lysis-cryptic and biological nitrogen removal system treating domestic wastewater at low C/N ratios using artificial neural network".WATER RESEARCH 189(2021):-. |
入库方式： OAI收割
来源：生态环境研究中心
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