Mineral Processing
Mojtaba Saeidi; Vahideh Shojaei; Hamid Khoshdast; Alireza Gholami
Abstract
Effective blast design is crucial for downstream comminution efficiency. However, modeling its impact on size-based production remains challenging due to complex, non-linear rock–fragmentation interactions. This study developed an integrated mine-to-mill framework using optimized Artificial Neural ...
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Effective blast design is crucial for downstream comminution efficiency. However, modeling its impact on size-based production remains challenging due to complex, non-linear rock–fragmentation interactions. This study developed an integrated mine-to-mill framework using optimized Artificial Neural Networks (ANNs) to predict total and size-fractioned production (0–40 mm, 40–80 mm) from blasting parameters at the Sar-Asiab Limestone Complex, Iran. The dataset comprises 55 real industrial observations after preprocessing. Two optimization algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), were employed to enhance ANN performance. The PSO-optimized ANN demonstrated significantly superior generalization, achieving a testing RMSE of 1341.8 (vs. 1549.5 for GA), an MAE of 1108.9 (vs. 1260.4), and an R² of 0.9487 (vs. 0.8937). Sensitivity-based feature importance analysis quantified the dominant predictors: explosive quantity (Emulite) contributed 18.4%, the number of boosters 14.9%, and key geometric parameters (mean hole depth, diameter, and spacing) collectively accounted for over 41%. In contrast, initiation system variables such as cortex length showed high linear correlation (r = –0.91) but minimal predictive importance (1.5%). These results underscore that explosive energy and its distribution, not just initiation configuration, are the primary drivers of size-based output. This data-driven framework provides a practical tool for aligning blast design with processing needs, directly supporting energy reduction and production stability in open-pit mining.
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.
