Growth of MWCNTs from Azadirachta indica oil for optimization of chromium(VI) removal efficiency using machine learning approach

The main objective of the present study is to develop artificial neural networks (ANN) to predict the adsorption efficiency of multi-walled carbon nanotubes (MWCNTs) on Cr(VI) removal. Polydisperse MWCNTs were synthesized at 750 °C on alumina supported Fe-Co-Mo catalyst using CVD (chemical vapor dep...

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Veröffentlicht in:	Environmental science and pollution research international 2022-05, Vol.29 (23), p.34841-34860
Hauptverfasser:	Uthayakumar, Haripriyan, Radhakrishnan, Pravina, Shanmugam, Kalaiselvan, Kushwaha, Omkar Singh
Format:	Artikel
Sprache:	eng
Schlagworte:	Adsorbents Adsorption Algorithms Aluminum oxide Aquatic Pollution Argon Artificial neural networks Atmospheric Protection/Air Quality Control/Air Pollution Azadirachta indica Catalysts Chemical vapor deposition Chromium Correlation coefficient Correlation coefficients Earth and Environmental Science Ecotoxicology Efficiency Endothermic reactions Environment Environmental Chemistry Environmental Health Environmental science High resolution electron microscopy Isotherms Kinetics Learning theory Machine learning Metal ions Microscopy Multi wall carbon nanotubes Nanotechnology Nanotubes Neem Neural networks Neurons Optimization Parameters Pyrolysis Research Article Scanning electron microscopy Spray pyrolysis Thermodynamics Transfer functions Transmission electron microscopy Waste Water Technology Water Management Water Pollution Control X-ray diffraction
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