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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Even though vibrating screens have been used in mining industries for over a century, their use has often been cited as challenging in terms of understanding its dynamic responses to different operating factors, particularly those that affect the structural aspects. Among the various aspects of screen design, the control of vibrational energies imposed on various parts of the screen is of particular importance because these vibrations directly affect the separation efficiency and useful life of the screen. This study proposes the use of vibration absorbers to control the adverse effects of severe screen vibrations. The dynamic behavior of a medium-sized vibrating screen utilized in the aggregate industry was investigated using the finite element method for both spring/dashpots and conventional solely spring systems. The modeling process was performed in loaded and unloaded conditions and in three frequencies of 15, 23, and 27 rad/s. Numerical simulation results showed that the use of dashpots can significantly reduce the maximum stress in the screen, such that the maximum stress in the center of gravity of the screen at the optimal frequency of 23 decreased from 237 to about 97 MPa. Also, sieve modal analysis showed that the stress in the sieve equipped with the spring/dashpots system had a more uniform distribution. The results revealed that the use of vibration absorbers can be a promising solution to prevent damage caused by high vibrational energies in screens. [ABSTRACT FROM AUTHOR]

Design of experiments (DOE) is an effective method providing useful information about the interaction of operating variables and the way the total system works by using statistical analyses. However, its industrial application is limited because it is almost difficult to maintain variables in DOE matrix at desired constant levels in industrial environment. Thus, this paper aims to present a new mixed modeling method which is a combination of fuzzy logic and design of experiments methods to overcome such practical limitations. The method first uses a fuzzy model which is trained by practical data gathered from industry to predict DOE response corresponding to each run in DOE matrix. Then, a statistical parametric model is constructed for the prediction of process response to any change of operating parameters under real industrial conditions. The proposed mixed method was successfully validated by using data obtained from a coal hydraulic classifier at Zarand Coal Washing Plant (Kerman, Iran). The method also seems to be a promising tool for modeling all devices and processes in real industrial environment and allows researchers to benefit from all the advantages of experimental design and fuzzy logic methods simultaneously. [ABSTRACT FROM AUTHOR]

In the present research study, the efficient removal of hexavalent chromium from aqueous solutions by precipitate flotation method was investigated. The experiments were carried out with the use of ferrous sulfate as a precipitating agent for chromium and rhamnolipid bio surfactant (RL) as a precipitate collector. The effects of rhamnolipid and co-precipitate concentrations, aeration rate, solution pH, and salt addition on the chromium removal were studied using a full factorial design. The chromium removal and water recovery to foam products were analyzed as process responses. Statistical analyses showed that the effects of all factors on the chromium removal followed a non-linear trend with a peak at the middle level. After the process optimization, the maximum chromium removal of 96.75±0.3% was obtained at pH value of 8, RL/Cr ratio of 0.01, Fe/Cr ratio of 3, and aeration rate of 50 cm³/min. Addition of salt with different cationic and anionic groups negatively influenced the removal efficiency. Kinetic studies suggested that the process of chromium removal by the precipitate flotation followed the first-order process with a rate constant of about 0.018 sec-1. Given the good removal capacity and kinetics, rhamnolipid biosurfactants can be a promising environmental-friendly bio collector for the removal of chromium ions from aqueous solutions. [ABSTRACT FROM AUTHOR]