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Effect of an acetonized pyrolysis oil recycled from spent-car tires on coal flotation performance

Hasanizadeh Iman, Khoshdast Hamid, Rahmanian Ahmad, Asgari Kaveh, Hassanzadeh Ahmad

Physicochemical Problems of Mineral Processing

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2023

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Vol. 59, iss. 2

art. no. 163109

EN

In this paper, an extended Historical Data (HD) design was applied for evaluating the effect of an acetonized pyrolysis oil (PO) produced by pyrolysis of spent-car tires in coal. Experimental and statistical analyses were applied for examining the influence of some operating variables such as concentration of diesel oil (0, 10, and 20 L/t), pine oil (0.55, 0.1, and 1 L/t), and the pyrolysis oil (0, 10, and 20 L/t) as well as solid content of pulp (5, 10, and 15% (w/w)) on the yield and ash content of final concentrate. Fourier Transform Infrared Spectroscopy (FTIR) measurements showed that PO contained hydroxyl, aldehyde, aliphatic, and aromatic compounds. Based on the results of Analysis of Variance (ANOVA), the main effect of all variables, except concentration of pine oil, on the flotation responses were found significant. Batch flotation experimental results indicated that using pyrolysis oil resulted in a 2% increase in ash content and a 35% decrease of the yield, through a nonlinear trend. The curved behavior of flotation measures was due to the possible competitive adsorption between PO and diesel oil and nonselective interaction between pyrolysis oil and other reagents. The negative effect of PO on coal flotation efficiency was also ascribed to the interaction between hydrophilic groups in PO structure and the oxide nature of non-combustible materials of coal particles.

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A hybrid geometallurgical study using coupled Historical Data (HD) and Deep Learning (DL) techniques on a copper ore mine

Gholami Alireza, Asgari Kaveh, Khoshdast Hamid, Hassanzadeh Ahmad

Physicochemical Problems of Mineral Processing

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2022

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Vol. 58, iss. 3

art. no. 147841

EN

This research work introduces a novel hybrid geometallurgical approach to develop a deep and comprehensive relationship between geological and mining characteristics with metallurgical parameters in a mineral processing plant. This technique involves statistically screening mineralogical and operational parameters using the Historical Data (HD) method. Further, it creates an intelligent bridge between effective parameters and metallurgical responses by the Deep Learning (DL) simulation method. In the HD method, the time and cost of common approaches in geometallurgical studies were minimized through the use of available archived data. Then, the generated DL-based predictive model was enabled to accurately forecast the process behavior in the mineral processing units. The efficiency of the proposed method for a copper ore sample was practically evaluated. For this purpose, six representative samples from different active mining zone were collected and used for flotation tests organized using a randomizing code. The experimental results were then statistically analyzed using HD method to assess the significance of mineralogical and operational parameters, including the proportions of effective minerals, particle size, collector and frother concentration, solid content and pH. Based on the HD analysis, the metallurgical responses including the copper grade and recovery, copper kinetics constant and iron grade in concentrate were modeled with an accuracy of about 90%. Next, the geometallurgical model of the process was developed using the long short-term memory neural network (LSTM) algorithm. The results showed that the studied metallurgical responses could be predicted with more than 95% accuracy. The results of this study showed that the hybrid geometallurgy approach can be used as a promising tool to achieve a reliable relationship between the mining and mineral processing sectors, and sustainable and predictable production.

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Effect of externally adding pyrite and electrical current on galvanic leaching of chalcopyrite concentrate

Asgari Kaveh, Hassanzadeh Ahmad, Nazari Sabereh, Vakylabad Ali Behrad, Hosseinzadeh Mostafa

Physicochemical Problems of Mineral Processing

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2021

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Vol. 57, iss. 2

105--119

EN

Although the operating properties of GalvanoxTM leaching have been widely studied in the literature, several factors concerning chalcopyrite passivation during the process remain unknown so far. The present work hence aims at investigating the significant effect of externally added pyrite features with a particular focus on its particle size (d80 of 0.52, 20, 45 and 2000 µm) through a series of experiments performed in a 2-L stirred-tank electro-reactor. To this end, the role of pyrite: chalcopyrite ratio (0.49:1, 2:1 and 4:1) and presence of electrical current were examined while the rest of the parameters kept constant (80 °C temperature, 400–500 mV (Ag/AgCl) redox potential, pulp density of 10% (w/v), and stirring rate of 1200 rpm). Plus, kinetic models of the leaching tests were studied based on the diffusion and chemical controlling concepts. It was found that the coarser the pyrite particles, the more favorable the copper extraction from the concentrate due to acceleration of reactions in the cathodic electrode and high mass transfers. However, this was in contradiction with the existing reports in the literature. Moreover, galvanic interactions became intensive in the presence of pyrite meaning extensive chalcopyrite dissolution with significantly reduced passivation. Ultimate copper extraction values of 24.17±1.25%, 55.79±0.91% and 57.26±1.59% were resulted at Py:Cp ratios of 0.49:1 (natural), 2:1 and 4:1, respectively. The results showed that maximum copper recovery of 67.32±2.34% was obtained at an optimum condition of pyrite grain size=2000 µm, Py:Cp=4:1, current application=500 mA, 8 h and 80 °C. Finally, detailed kinetic modeling indicated that the chemical control mechanism was dominant in the early reaction stages (t<3.5 h) concerning the availability of fresh surface for chemical agents; however, the second half of the process (8.0 h>t>3.5 h) was controlled by the diffusion control.