Evidence map›Paper›PMID 39989620›Full record

ArticleWater research X2025

Machine learning-driven benchmarking of China's wastewater treatment plant electricity consumption.

Minjian Li, Chongqiao Tang, Junhan Gu, Nianchu Li, Ahemaide Zhou, Kunlin Wu, Zhibo Zhang, Hui Huang, Hongqiang Ren

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Water research X, 2025. 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

9 authors.

Minjian LiState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR China.
Chongqiao TangState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR China.
Junhan GuState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR China.
Nianchu LiState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR China.
Ahemaide ZhouState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR China.
Kunlin WuState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR China.
Zhibo ZhangState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR China.
Hui HuangState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR China.
Hongqiang RenState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Benchmarking electricity consumption of wastewater treatment plants (WWTPs) is fundamental for sustainable wastewater management, as these facilities have a concomitant electricity-intensive nature along with their pollutant removal and resource recovery functions. Due to the challenge of characterizing influent water quality using traditional methods, satisfactory benchmarks have long been elusive. To overcome the complexity of wastewater compositions, an unsupervised machine learning algorithm, spectral clustering, is introduced to analyze 2,576 WWTPs across China, effectively characterizing influent quality as a single variable and contributing to robust benchmarks with 75 % of the fittings achieving coefficients of determination (R

Indexed as

BenchmarkingElectricity consumptionInfluent indicatorMachine learningRegional analysisWastewater treatment plant

Identifiers

PMID39989620
PMCPMC11847466

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.