Evidence map›Paper›PMID 38649611›Full record

ArticleEnvironmental science and pollution research international2025

Evaluation of traditional and machine learning approaches for modeling volatile fatty acid concentrations in anaerobic digestion of sludge: potential and challenges.

Umar Alfa Abubakar, Gul Sanga Lemar, Al-Amin Danladi Bello, Aliyu Ishaq, Aliyu Adamu Dandajeh, Zainab Toyin Jagun, Mohamad Rajab Houmsi

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Article in Environmental science and pollution research international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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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

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Umar Alfa AbubakarDepartment of Water Resources and Environmental Engineering, Ahmadu Bello University, Zaria, 1045, Kaduna, Nigeria.
Gul Sanga LemarDepartment of Botany, Faculty of Biology, Kabul University, Kart-E-Char, Kabul, Afghanistan.
Al-Amin Danladi BelloDepartment of Water Resources and Environmental Engineering, Ahmadu Bello University, Zaria, 1045, Kaduna, Nigeria.
Aliyu IshaqDepartment of Water Resources and Environmental Engineering, Ahmadu Bello University, Zaria, 1045, Kaduna, Nigeria.
Aliyu Adamu DandajehDepartment of Water Resources and Environmental Engineering, Ahmadu Bello University, Zaria, 1045, Kaduna, Nigeria.
Zainab Toyin JagunSchool of Built Environment Engineering and Computing, Leeds Beckett University City Campus, Leeds, UK. z.t.jagun@leedsbeckett.ac.uk.ORCID http://orcid.org/0000-0002-7441-7138
Mohamad Rajab HoumsiNew Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluates models for predicting volatile fatty acid (VFA) concentrations in sludge processing, ranging from classical statistical methods (Gaussian and Surge) to diverse machine learning algorithms (MLAs) such as Decision Tree, XGBoost, CatBoost, LightGBM, Multiple linear regression (MLR), Support vector regression (SVR), AdaBoost, and GradientBoosting. Anaerobic bio-methane potential tests were carried out using domestic wastewater treatment primary and secondary sludge. The tests were monitored over 40 days for variations in pH and VFA concentrations under different experimental conditions. The data observed was compared to predictions from the Gaussian and Surge models, and the MLAs. Based on correlation analysis using basic statistics and regression, the Gaussian model appears to be a consistent performer, with high R

Indexed as

Fatty Acids, VolatileMachine LearningSewageAnaerobiosisWaste Disposal, FluidFatty Acids, VolatileSewageAnaerobic digestionGaussian functionMachine learning algorithms (MLA)Surge functionsVolatile fatty acid

Identifiers

PMID38649611
PMCPMC12711933

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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.