S. Mirshrkari; V. Shojaei; H. Khoshdast
Abstract
A coal waste sample loaded with Fe3O4 nanoparticles is employed as an efficient adsorbent to remove Cd from synthetic wastewater. The synthesized nanocomposite is characterized using the Fourier transform-infrared (FT-IR), X-ray diffraction (XRD), and transmission electron microscopy (TEM) techniques. ...
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A coal waste sample loaded with Fe3O4 nanoparticles is employed as an efficient adsorbent to remove Cd from synthetic wastewater. The synthesized nanocomposite is characterized using the Fourier transform-infrared (FT-IR), X-ray diffraction (XRD), and transmission electron microscopy (TEM) techniques. The visual analysis of the microscopic image shows that the mean size of the magnetite nanoparticles is about 10 nm. The effects of the operating variables of the initial solution pH (3-11) and nanocomposite to pollutant ratio (7-233) are evaluated using the response surface methodology on cadmium adsorption. The process is also optimized using the quadratic prediction model based on the central composite design. The statistical analysis reveals that both factors play a significant role in Cd adsorption. The maximum Cd removal of 99.24% is obtained under optimal operating conditions at pH 11 and nanocomposite/cadmium ratio of 90 after 2 h of equilibrium contact time. A study of the adsorption kinetics indicates that the maximum removal could be attained in a short time of about 2 min following a first-order model. The isotherm investigations present that the Cd adsorption on the Fe3O4/coal waste nanocomposite has a linearly descending heat mechanism based on the Temkin isotherm model with the minor applicability parameters than the other isotherm models. The overall removal behaviour is attributed to a two-step mechanism including a rapid adsorption of cadmium ion onto the active sites at the surface of nanocomposite followed by a slow cadmium hydroxide precipitation within the pores over the nanocomposite surface.
F. Hadadi; B. Jodeiri Shokri; M. Zare Naghadehi; F. Doulati Ardejani
Abstract
In this paper, we investigate a probabilistic approach in order to predict how acid mine drainage is generated within coal waste particles in NE Iran. For this, a database is built based on the previous studies that have investigated the pyrite oxidation process within the oldest abandoned pile during ...
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In this paper, we investigate a probabilistic approach in order to predict how acid mine drainage is generated within coal waste particles in NE Iran. For this, a database is built based on the previous studies that have investigated the pyrite oxidation process within the oldest abandoned pile during the last decade. According to the available data, the remaining pyrite fraction is considered as the output data, while the depth of the waste, concentration of bicarbonate, and oxygen fraction are the input parameters. Then the best probability distribution functions are determined on each one of the input parameters based on a Monte Carlo simulation. Also the best relationships between the input data and the output data are presented regarding the statistical regression analyses. Afterward, the best probability distribution functions of the input parameters are inserted into the linear statistical relationships to find the probability distribution function of the output data. The results obtained reveal that the values of the remaining pyrite fraction are between 0.764% and 1.811% at a probability level of 90%. Moreover, the sensitivity analysis carried out by applying the tornado diagram shows that the pile depth has, by far, the most critical factors affecting the pyrite remaining