Prediction, Uncertainty Quantification, and ANN-Assisted Operation of Anaerobic Digestion Guided by Entropy Using Machine Learning.

Zhipeng Zhuang, Xiaoshan Liu, Jing Jin, Ziwen Li, Yanheng Liu, Adriano Tavares, Dalin Li

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 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

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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

7 authors.

Zhipeng ZhuangSchool of Life Sciences, Zhuhai College of Science and Technology, Zhuhai 519041, China.ORCID 0009-0004-4574-9709

Xiaoshan LiuSchool of Computer Science, Zhuhai College of Science and Technology, Zhuhai 519041, China.ORCID 0009-0000-3694-5928

Jing JinSchool of Electronic Information Engineering, Zhuhai College of Science and Technology, Zhuhai 519041, China.

Ziwen LiSchool of Electronic Information Engineering, Zhuhai College of Science and Technology, Zhuhai 519041, China.

Yanheng LiuSchool of Computer Science, Zhuhai College of Science and Technology, Zhuhai 519041, China.

Adriano TavaresIndustrial Electronics Department, University of Minho, 4800-058 Guimaraes, Portugal.

Dalin LiSchool of Computer Science, Zhuhai College of Science and Technology, Zhuhai 519041, China.

Funding

National Natural Science Foundation of China 62372494

6 · The paper itself

Abstract

Anaerobic digestion (AD) is a nonlinear and disturbance-sensitive process in which instability is often induced by feedstock variability and biological fluctuations. To address this challenge, this study develops an entropy-guided machine learning framework that integrates parameter prediction, uncertainty quantification, and entropy-based evaluation of AD operation. Using six months of industrial data (~10,000 samples), three models-support vector machine (SVM), random forest (RF), and artificial neural network (ANN)-were compared for predicting biogas yield, fermentation temperature, and volatile fatty acid (VFA) concentration. The ANN achieved the highest performance (accuracy = 96%, F1 = 0.95, root mean square error (RMSE) = 1.2 m

Indexed as

anaerobic digestionANN-assisted operationerror entropymachine learninguncertainty quantification

Identifiers

PMID41440436

DOI10.3390/e27121233

PMCPMC12732000

What OpenQuestion holds

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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.