Evidence map›Paper›PMID 39224261›Full record

ArticleHeliyon2024

Real-time prediction of the chemical oxygen demand component parameters in activated sludge model using backpropagation neural network.

Ping Wang, Yanqiong Chen, Chen Zhang, Yuzhen Shi, Bin Wang, Chaochao Lai, Huan He, Bin Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ping WangFaculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming, 650500, China.
Yanqiong ChenFaculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming, 650500, China.
Chen ZhangFaculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming, 650500, China.
Yuzhen ShiFaculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming, 650500, China.
Bin WangSchool of Environment and Resource, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, China.
Chaochao LaiFaculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming, 650500, China.
Huan HeFaculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming, 650500, China.
Bin HuangFaculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming, 650500, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Activated sludge models are increasingly being adopted to guide the operation of wastewater treatment plants. Chemical oxygen demand (COD) is an indispensable input for such models. To ensure that the activated sludge mathematical model can adapt to various water quality conditions and minimize prediction errors, it is essential to predict the parameters of the COD components in real-time based on the actual influent COD concentrations. However, conventional methods of determining the components' contributions are too intricate and time-consuming to be really useful. In this study, the chemical oxygen demand in the actual waste water treatment plant was disassembled and analyzed. The research involved determining the proportions of each COD component, assessing the reliability of the measurement parameters, and examining potential factors affecting measurement accuracy, including weather conditions, pipeline conditions, and residents' habits. Then, a backpropagation neural network was developed which can deliver real-time predictions for five important contributors to COD in real time. In addition, using the receiver operating characteristics curve and prediction accuracy to evaluate the performance of the prediction model. For all five components, which S

Indexed as

Activated sludge modelsBackpropagation neural networksChemical oxygen demand componentsDynamic model

Identifiers

PMID39224261
PMCPMC11367277

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.