Monitoring and detecting faults in wastewater treatment plants using deep learning.

Clicks: 331
ID: 88068
2020
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #51 of 221 articles by views in Environmental monitoring and assessment

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 221 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Wastewater treatment plants use many sensors to control energy consumption and discharge quality. These sensors produce a vast amount of data which can be efficiently monitored by automatic systems. Consequently, several different statistical and learning methods are proposed in the literature which can automatically detect faults. While these methods have shown promising results, the nonlinear dynamics and complex interactions of the variables in wastewater data necessitate more powerful methods with higher learning capacities. In response, this study focusses on modelling faults in the oxidation and nitrification process. Specifically, this study investigates a method based on deep neural networks (specifically, long short-term memory) compared with statistical and traditional machine-learning methods. The network is specifically designed to capture temporal behaviour of sensor data. The proposed method is evaluated on a real-life dataset containing over 5.1 million sensor data points. The method achieved a fault detection rate (recall) of over 92%, thus outperforming traditional methods and enabling timely detection of collective faults.
Reference Key
mamandipoor2020monitoringenvironmental Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mamandipoor, Behrooz;Majd, Mahshid;Sheikhalishahi, Seyedmostafa;Modena, Claudio;Osmani, Venet;
Journal Environmental monitoring and assessment
Year 2020
DOI
10.1007/s10661-020-8064-1
URL
Keywords
Deep learning lstm fault detection ammonia feedback wastewater plant treatment

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.