ReviewHeliyon2025
CFD modelling and simulation of anaerobic digestion reactors for energy generation from organic wastes: A comprehensive review.
Review in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
No grant is acknowledged in the PubMed record.
Abstract
Anaerobic digestion (AD) has been recognized as one of the most viable options for the treatment of a wide range of waste materials. Complex structure of wastes is safely broken down to destroy pollutants and pathogens. Biogas is produced as a by-product of this process which is considered as a clean energy resource. However, provision of controlled environment for microbial activities is critical to ensure the required process efficiency. This can only be achieved with the efficient design of controlled vessels used for anaerobic digestion, termed as AD reactors. AD functions such as mixing, hydrodynamics, multiphase interaction, heat transfer, temperature distribution and bio kinetics are significantly affected by the reactor shape, design and configurations, hence making it essential to optimize the reactor design before installation at large scale. Mostly, such optimization is carried out with the help of lab scale experimentations and testing protocols which result in high costs for repeating several design experiments. Computational fluid dynamics (CFD) is an applied mathematical tool which helps to understand and predict the fluid dynamics, heat flow as well as species transport in different domains. This approach contributes to minimize the experimental costs while optimizing the reactor configurations in less time. The current review is presented to summarize and discuss the core characteristics of AD process followed by concerned CFD attributes. Research gaps and critical challenges are identified in different aspects such as reactor design, and configuration, mixing, multiphase flow, heat transfer, biokinetics as well as machine learning approaches.
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.