Prediction of a full scale WWTP activated sludge SVI test using an LSTM neural network-Reference-Cited by-同舟云学术

Prediction of a full scale WWTP activated sludge SVI test using an LSTM neural network

Published:2022 Issue:11 Volume:8 Page:2786-2795
ISSN:2053-1400
Container-title:Environmental Science: Water Research & Technology
language:en
Short-container-title:Environ. Sci.: Water Res. Technol.

Author:

Kohen Efrat¹^ORCID,Farhi Nitzan²^ORCID,Shavitt Yuval²,Mamane Hadas¹

Affiliation:

1. School of Mechanical Engineering, Tel-Aviv University, Israel

2. School of Electrical Engineering, Tel-Aviv University, Israel

Abstract

The two key parameters to control and prevent sludge separation problems in activated sludge are SVI test and microscopic observations. To predict SVI we propose a bidirectional LSTM model which shows an accuracy rate of 89% and an F1-score of 82%.

Funder

Microsoft

Publisher

Royal Society of Chemistry (RSC)

Subject

Water Science and Technology,Environmental Engineering

Link

http://pubs.rsc.org/en/content/articlepdf/2022/EW/D2EW00025C

Reference26 articles.

1. D.Jenkins , M. G.Richard and G. T.Daigger , Manual on the Causes and Control of Activated Sludge Bulking, Foaming, and Other Solids Separation Problems , CRC Press , 2003

2. Prediction of sludge volume index bulking using image analysis and neural network at a full-scale activated sludge plant

3. Relationships between biological solids retention time and settling characteristics of activated sludge

4. Microscopic Image Analysis versus Sludge Volume Index to Monitor Activated Sludge Bioflocculation: A Case Study

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-Scale Temporal Convolutional Networks for Effluent COD Prediction in Industrial Wastewater;Applied Sciences;2024-07-03

2. Effects of contact/stabilization time ratio on organic carbon-methane conversion in high-rate contact stabilization system;Journal of Water Process Engineering;2024-04

3. Prediction of Activated Sludge Sedimentation Performance Using Deep Transfer Learning;ACS ES&T Engineering;2024-03-11

4. Efficient fault monitoring in wastewater treatment processes with time stacked broad learning network;Expert Systems with Applications;2023-12