ArticleScientific reports2017
Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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.
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Who cites it
15 citing papers in PubMed.
- Head and neck squamous cell carcinoma: current and emerging therapeutic strategies.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- A unified framework of response surface methodology and coalescing of Firefly with random forest algorithm for enhancing nano-phytoremediation efficiency of chromium via in vitro regenerated aquatic macrophyte coontail (Ceratophyllum demersum L.).Environmental science and pollution research international · 2024Article
- Application of Machine Learning Algorithms to Classify Peruvian Pisco Varieties Using an Electronic Nose.Sensors (Basel, Switzerland) · 2023Article
- Article Application of neural network in metal adsorption using biomaterials (BMs): a review.Environmental science. Advances · 2023Article
- Article
- Graphene-based photocatalytic nanocomposites used to treat pharmaceutical and personal care product wastewater: A review.Environmental science and pollution research international · 2022Review
- Artificial Intelligence Assisted Ultrasonic Extraction of Total Flavonoids fromMolecules (Basel, Switzerland) · 2021Article
- Carbon-Based Nanocomposites as Fenton-Like Catalysts in Wastewater Treatment Applications: A Review.Materials (Basel, Switzerland) · 2021Review
- Efficient removal of antibiotic oxytetracycline from water using optimized montmorillonite-supported zero-valent iron nanocomposites.Environmental science and pollution research international · 2020Article
- Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel.Environmental monitoring and assessment · 2020Article
- Influence of humic acid and its different molecular weight fractions on sedimentation of nanoscale zero-valent iron.Environmental science and pollution research international · 2020Article
- Techno-economic estimation of electroplating wastewater treatment using zero-valent iron nanoparticles: batch optimization, continuous feed, and scaling up studies.Environmental science and pollution research international · 2019Article
- BP-ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System.International journal of environmental research and public health · 2019Article
- Optimizing the removal of nitrate from aqueous solutions via reduced graphite oxide-supported nZVI: synthesis, characterization, kinetics, and reduction mechanism.Environmental science and pollution research international · 2019Article
- Removal of Crystal Violet by Using Reduced-Graphene-Oxide-Supported Bimetallic Fe/Ni Nanoparticles (rGO/Fe/Ni): Application of Artificial Intelligence Modeling for the Optimization Process.Materials (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) magnetic nanocomposites were prepared and then applied in the Cu(II) removal from aqueous solutions. Scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy and superconduction quantum interference device magnetometer were performed to characterize the nZVI/rGO nanocomposites. In order to reduce the number of experiments and the economic cost, response surface methodology (RSM) combined with artificial intelligence (AI) techniques, such as artificial neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), has been utilized as a major tool that can model and optimize the removal processes, because a tremendous advance has recently been made on AI that may result in extensive applications. Based on RSM, ANN-GA and ANN-PSO were employed to model the Cu(II) removal process and optimize the operating parameters, e.g., operating temperature, initial pH, initial concentration and contact time. The ANN-PSO model was proven to be an effective tool for modeling and optimizing the Cu(II) removal with a low absolute error and a high removal efficiency. Furthermore, the isotherm, kinetic, thermodynamic studies and the XPS analysis were performed to explore the mechanisms of Cu(II) removal process.
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Registered trials
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