ReviewBioengineering (Basel, Switzerland)2023
A Systematic Review of Machine-Learning Solutions in Anaerobic Digestion.
Review in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed.
- Reactor-Aware Machine Learning Coupled with Differential Evolution for Predicting and Optimizing Cumulative Methane Production from Agro-Industrial Waste Co-Digestion.Foods (Basel, Switzerland) · 2026Article
- Artificial intelligence in bread making: Applications in quality control, formulation and sensory prediction.Food chemistry: X · 2026Review
- Prediction, Uncertainty Quantification, and ANN-Assisted Operation of Anaerobic Digestion Guided by Entropy Using Machine Learning.Entropy (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Anaerobic Digestion: A Review of Sensors, Modeling Approaches, and Optimization Strategies.Sensors (Basel, Switzerland) · 2025Review
- Model predictive control of anaerobic digestion processes using a long short-term memory network predictor.Water science and technology : a journal of the International Association on Water Pollution Research · 2025Article
- Machine learning-based optimization of biogas and methane yields in UASB reactors for treating domestic wastewater.Biodegradation · 2025Article
- Application of Machine Learning for FOS/TAC Soft Sensing in Bio-Electrochemical Anaerobic Digestion.Molecules (Basel, Switzerland) · 2025Article
- Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses.Bioengineering (Basel, Switzerland) · 2024Review
- Justifying the prediction of major soil nutrients levels (N, P, and K) in cabbage cultivation.MethodsX · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The use of machine learning (ML) in anaerobic digestion (AD) is growing in popularity and improves the interpretation of complex system parameters for better operation and optimisation. This systematic literature review aims to explore how ML is currently employed in AD, with particular attention to the challenges of implementation and the benefits of integrating ML techniques. While both lab and industry-scale datasets have been used for model training, challenges arise from varied system designs and the different monitoring equipment used. Traditional machine-learning techniques, predominantly artificial neural networks (ANN), are the most commonly used but face difficulties in scalability and interpretability. Specifically, models trained on lab-scale data often struggle to generalize to full-scale, real-world operations due to the complexity and variability in bacterial communities and system operations. In practical scenarios, machine learning can be employed in real-time operations for predictive modelling, ensuring system stability is maintained, resulting in improved efficiency of both biogas production and waste treatment processes. Through reviewing the ML techniques employed in wider applied domains, potential future research opportunities in addressing these challenges have been identified.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.