ReviewHeliyon2023
A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique.
Review in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 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
8 citing papers in PubMed.
- Critical review of adsorption technologies for heavy metal removal: mechanistic, computational, and policy perspectives.RSC advances · 2026Review
- Artificial Intelligence-Guided Discovery of Covalent Organic Frameworks for Next-Generation Polyfluoroalkyl Substances Removal.ACS applied materials & interfaces · 2026Review
- Advanced nanocomposites for microplastic remediation: a critical review of materials, mechanisms, and scalability challenges.RSC advances · 2026Review
- Evaluating and Selecting Kinetic and Isotherm Models for Copper and Nickel Removal Using Cow Bone Char as an Adsorbent via Excel Solver Functions.International journal of molecular sciences · 2025Article
- Review
- Recent Progress on the Adsorption of Heavy Metal Ions Pb(II) and Cu(II) from Wastewater.Nanomaterials (Basel, Switzerland) · 2024Review
- Adsorption of lead ions from wastewater using electrospun zeolite/MWCNT nanofibers: kinetics, thermodynamics and modeling study.RSC advances · 2024Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them.
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.