AIMC Topic: Iron Compounds

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Simulation, prediction and optimization for synthesis and heavy metals adsorption of schwertmannite by machine learning.

Environmental research Nov 26, 2024
Due to its sea urchin-like structure, Schwertmannite is commonly applied for heavy metals (HMs) pollutant adsorption. The adsorption influence parameters of Schwertmannite are numerous, the traditional experimental enumeration is powerless. In recent...
Machine Learning Water Pollutants, Chemical Metals, Heavy Iron Compounds Adsorption Models, Chemical
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Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid surface complexation-machine learning model.

Water research Apr 6, 2024
This study aimed to develop surface complexation modeling-machine learning (SCM-ML) hybrid model for chromate and arsenate adsorption on goethite. The feasibility of two SCM-ML hybrid modeling approaches was investigated. Firstly, we attempted to uti...
Iron Compounds Chromium Arsenates Arsenic Kinetics Minerals Machine Learning Water Pollutants, Chemical Adsorption
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On-line prediction of ferrous ion concentration in goethite process based on self-adjusting structure RBF neural network.

Neural networks : the official journal of the International Neural Network Society Mar 29, 2019
Outlet ferrous ion concentration is an essential indicator to manipulate the goethite process in the zinc hydrometallurgy plant. However, it cannot be measured on-line, which leads to the delay of this feedback information. In this study, a self-adju...
Feedback Minerals Algorithms Forecasting Iron Compounds Metallurgy Neurons Ferrous Compounds Neural Networks, Computer
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Sorptive equilibrium profile of fluoride onto aluminum olivine [(FeMg)SiO] composite (AOC): Physicochemical insights and isotherm modeling by non-linear least squares regression and a novel neural-network-based method.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering Jun 5, 2018
A novel aluminum/olivine composite (AOC) was prepared by wet impregnation followed by calcination and was introduced as an efficient adsorbent for defluoridation. The adsorption of fluoride was modeled with one-, two- and three-parameter isotherm equ...
Water Purification Temperature Magnesium Compounds Neural Networks, Computer Aluminum Oxide Least-Squares Analysis Fluorides Absorption, Physicochemical Iron Compounds Kinetics Aluminum Silicates Chemical Phenomena
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Adsorptive removal of arsenic by novel iron/olivine composite: Insights into preparation and adsorption process by response surface methodology and artificial neural network.

Journal of environmental management Jan 4, 2018
Olivine, a low-cost natural material, impregnated with iron is introduced in the adsorptive removal of arsenic. A wet impregnation method and subsequent calcination were employed for the preparation of iron/olivine composite. The major preparation pr...
Silicates Iron Compounds Magnesium Compounds Kinetics Hydrogen-Ion Concentration Arsenic Water Purification Water Pollutants, Chemical Adsorption Iron Neural Networks, Computer
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Degradation and mineralization of phenol compounds with goethite catalyst and mineralization prediction using artificial intelligence.

PloS one Apr 7, 2015
The efficiency of phenol degradation via Fenton reaction using mixture of heterogeneous goethite catalyst with homogeneous ferrous ion was analyzed as a function of three independent variables, initial concentration of phenol (60 to 100 mg /L), weigh...
Ferric Compounds Hydrogen Peroxide Water Pollutants Phenols Oxidants Ferrous Compounds Artificial Intelligence Neural Networks, Computer Catalysis Iron Compounds Software Minerals
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