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This investigation assessed the influence of a cationized pyrolysis oil (PO) produced by the pyrolysis of used car tires on coal flotation using an extended Historical Data (HD) design. The effect of operating factors, including pulp solid content, diesel oil, pine oil, and pyrolysis oil dosages, was examined using experimental and statistical analyses. Fourier Transform Infrared Spectroscopy (FTIR) measurement showed that PO contains hydrocarbons and their oxides (alkane, ester, or carboxylic acid). Statistical analysis using analysis of variance (ANOVA) revealed that concentrate ash content is influenced by solid content and pyrolysis oil concentration through a non-linear trend such that maximum ash removal may be obtained at the middle level of solid content and in the absence of PO. The solid content and diesel oil had a significant impact on process yield. Maximum yield was achieved at the low and middle levels of solid content and collector dosage, respectively. A comparison of individual and interaction effects of significant operating variables showed their complicated effects on process responses. Overall, the PO affected the coal flotation efficiency adversely, which can be due to the interaction between hydrophilic groups in the PO structure and the oxide nature of non-combustible materials in coal particles. [ABSTRACT FROM AUTHOR]

This research assesses the effect of carbonated pyrolysis oil (CPO) derived from scrap car tires on the metallurgical efficiency of coal flotation as a flotation additive. Using a statistical experimental design, the influence of various operational variables, including solid percent of feed pulp and dosages of reagents, i.e., CPO as an additive, diesel oil as a collector, and pine oil as a frother, on the ash content and yield of the final concentrate were investigated. Experimental data vary significantly based on operational conditions, ranging from 6.6% ash content with a 15% yield to 19.1% ash content with a 76.8% yield. The composition of the pyrolysis oil was identified by using Fourier transform infrared spectroscopy (FTIR). The analysis of variance (ANOVA) of experimental results demonstrated that almost all variables had a substantial effect on the flotation responses, positive or negative, depending on the variable or variable interaction. It was discovered that the usage of CPO intensified the total yield and ash content of concentrate in a nonlinear fashion in a range of 15% and 4%, respectively. The results revealed a non-selective interaction effect between CPO and pine oil, as well as competitive adsorption between diesel oil and CPO, which contributed to the curved behavior of flotation measurements. The detrimental effect of CPO on the flotation response of the studied coal sample was also related to the interaction of the hydrophilic groups in the CPO structure and the oxide groups of ash material in coal particles. This work shows the potential of carbonated pyrolysis oil to enhance coal flotation performance and sheds light on the underlying mechanisms. [ABSTRACT FROM AUTHOR]

Today, with the ever-increasing concerns about the widespread environmental impact of the mining industry, the movement of the mineral processing sectors, which heavily rely on various chemical and petroleum-origin reagents, to the green industry is imperative. Due to the advantages biotechnological approaches provide, bioprocessing is considered a benign alternative to conventional mineral processing techniques. Recent fundamental research works have directed the attention to the development of biotechnology and using microorganisms in the new field of biobeneficiation, especially bioflotation. The current review categorizes bioflotation methods into direct and indirect processes giving an in-depth insight into their concepts. Furthermore, this study provides a comprehensive review of research on different aspects of bioflotation, such as the effects of operating (mainly solid content and pH) and biotechnological parameters, potential mechanisms, and unexplored points, but from different, and practical viewpoints. Recent studies were summarized by categorizing microorganisms based on their wall structures, and growth temperature. It was disclosed based on the literature data that the nutritional requirement of variant microorganisms was only chemolithotroph. Further, the conducted researches in investigating optimum conditions for maximization of contact angle and adhesion forces to reach the maximum recovery were reviewed. In addition, existing problems and challenges of this field were investigated and it was found that economic barriers and obscurities were the main obstacles of industrial application in mining and mineral processing industries. Finally, new perspectives and suggestions were presented to shed light on future research and help to solve the challenges currently facing its application. [ABSTRACT FROM AUTHOR]

Flotation process is the most common beneficiation method of almost all types of minerals. The desirable result of the process is dependent on various factors. One of the most effective parameters in this regard is the particle size. Generally, acceptable results can be obtained by adjusting particle size in an optimum and specific range, and flotation of coarser or finer than this range is always intricate and problematic. Specifically, in the case of fine and ultra-fine particles, this complexity becomes more aggravated most importantly because of the massive specific area and the reduction of particle-bubble collision and attachment probabilities. There is a vast amount of literature on fine particles flotation using micro- and nanobubbles, intensified flotation machines, and different chemical reagents, however, the most promising procedure in this regard can be considered as the increment of particle sizes via agglomeration and flocculation techniques combined with flotation. This technique provides great opportunities for recovering fine and ultra-fine particles economically and increment of sustainable processing. However, there are some gaps and obscure points such as the exact mechanism of bubble-floc adsorption, interaction between flocs and reagents, pulp properties variations due to flocculation, and its effects on flotation behavior. This paper categorizes and describes diverse aspects of this combined novel technique in detail and provides a comprehensive source for readers to have a deeper insight into this intricate process. First of all, the physicochemical characteristics of the process have been described thoroughly from perspectives including the type and structure of applied chemicals, influential parameters, and involved adsorption mechanisms. It is followed by a detailed interpretation of different floc-flotation techniques as well as their cons and pros, effective parameters, and possible improvement and optimization approaches. Further, some similar techniques such as oil agglomeration, hydrophobic-hydrophilic Separation, and carrier flotation were described and compared with present technique of floc-flotation. Technological aspects of the process as well as an industrial case study of this technique were other areas which have been investigated in this paper and finally, scientific and technical gaps in this branch of flotation technology were extracted to open-up new horizon ahead of researchers active and/or interested in this field. [ABSTRACT FROM AUTHOR]

Many reagents are used as frother in flotation process, however, a general reference for their identification and characterization is not available. The present work categorizes the commonly used frothers and evaluates the available methodologies to determine their properties. In this paper, frothers properties such as molecular weight, viscosity, boiling and freezing point, hydrophile-lipophile balance number, solubility, surface activity and critical micelle concentration were discussed. The commonly used measurement methods for froth structure and dynamic and static frothability were also presented along with water recovery index and critical coalescence concentration (CCC). Results showed that the practical identification of frothers properties has not been thoroughly evaluated and therefore, there is a need for developing novel and easy-to-apply experimental methods in this area. For example, it was found that there is no promising single process to measure the CCC value. This review revealed that the 2D froth structure measurement methods are associated with a considerable bias, while 3D techniques can effectively represent the actual froth properties. It was also found that the potential interactions between various frothers properties and how they can influence the flotation performance in an industrial environment are not yet understood and needs further investigation. It was also identified that there is no standard instruction to provide a reliable connection between both manufactures and frothers consumers. In our opinion, the current review paves the path for the elaboration of knowledge and technology to provide promising methods for determining conventionally used and complex frothers properties. [ABSTRACT FROM AUTHOR]

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]

Coal beneficiation processes are often based on gravitational enrichment techniques. Therefore, various types of jigs are often used in this area. A number of factors determine the quality of the final product. Therefore, proper and systematic monitoring of such a process is necessary. The present work examined the effect of the hutch water amount and the amount of processed coal (system capacity) on the yield and ash grade in individual density–size fractions. A statistical method in the form of the Kruskal–Wallis test and the Friedman test was used to evaluate the process. These tests were proposed as universal and reliable alternatives to classical analysis of variance (ANOVA) analysis. The performed analysis allowed for the determination of what process conditions should be selected in order to obtain certain effects. Therefore, it allowed us to apply certain combinations of hutch water amount, system capacity and particle characteristics to maximize the expected effects. The application of statistical analysis methods in the form of Kruskal–Wallis and Friedman tests to evaluate process efficiency creates new possibilities to monitor and optimize the jigging process in both context of amount and quality of final product and is very important not only for mining enterprise but also from an environmental point of view. [ABSTRACT FROM AUTHOR]

In this research work, the role of a pyrolitic oil (PO) produced by pyrolysis of used car tires in coal flotation was evaluated using an extended full factorial design. Effects of operating parameters including pulp solid content and the concentration of pine oil, diesel oil, and pyrolitic oil on ash content and yield of final concentrate as process responses were assessed using experimental and statistical analyses. Fourier Transform Infrared Spectroscopy (FTIR) technique revealed that PO consists of hydrocarbons and their hydrophilic oxides. Analysis of variance (ANOVA) results indicated that the main effect of all parameters on flotation responses was significant. Experimental results showed that pyrolitic oil increased both the ash content and total yield through a nonlinear trend in ranges of 5% and 40%, respectively. Nonselective interaction between pyrolitic oil and pine oil molecules, and competitive adsorption of PO and diesel oil on the surface of coal particles were found to be the chief reasons behind the observed curved behavior of flotation measures. The negative effect of PO on ash content of final concentrate was also ascribed to the interaction between hydrophilic groups in PO structure and the oxide nature of noncombustible materials in coal particles. [ABSTRACT FROM AUTHOR]

The present study investigates the optimization and advanced simulation of the flotation process of coarse particles (–425 + 106) using micro-nanobubbles (MNBs). For this purpose, flotation experiments in the presence and absence of MNBs were performed on coarse quartz particles, and the results were statistically analyzed. Methyl isobutyl carbinol (MIBC) was employed as a frother for generating MNBs through hydrodynamic cavitation. The significance of the operating variables, including impeller speed, air flow rate, together with the bubble size, and particle size on the flotation recovery was assessed using historical data (HD) design and analysis of variance (ANOVA). The correlation between the flotation parameters and process response in the presence and absence of MNBs was modeled using hybrid convolutional neural networks (CNNs) and recurrent neural networks (RNNs) as the deep learning (DL) frameworks to automatically extract features from input data using a CNN as the base layer. The ANOVA results indicated that all variables affect process responses statistically and meaningfully. Significant interactions were found between air flow rate and particle size as well as impeller speed and MNB size. It was found that a CNN-RNN model could finally be used to model the process based on the intelligent simulation results. Based on Pearson correlation coefficients (PCCs), it was evident that particle size had a strong linear relationship with recovery. However, Shapley additive explanations (SHAP) was considerably more accurate in predicting relationships than Pearson correlations, even though the model outputs agreed well. [ABSTRACT FROM AUTHOR]

Recent attempts to produce effective adsorbents for wastewater treatment purposes have led to development of multiphase nanocomposite consisting of a bed, nanoparticle enhancer(s) and one or more polymeric modifiers. However, availability and final price have been always prohibitive factors challenging the wide application of many of multiphase adsorbents. In this research work, a novel bipolymeric/Fe3O4 nanocomposite loaded on coal tailings was synthesized using a two-step procedure and evaluated as a potential low-cost adsorbent for treating synthetic wastewater polluted by heavy metals. Starting material and synthesized quadriphase nanocomposite were characterized using different instrumental analysis techniques. The adsorptive capacity of the nanocomposite was assessed using response surface methodology (RSM) by considering the nanocomposite-to-cadmium ratio (7–233) and initial pH of solution (3–11) as operating factors and cadmium removal as process response. The significance of the effect of operating variables on removal efficiency was assessed and confirmed using analysis of variance (ANOVA) method. The maximum cadmium removal of 99.30% was obtained under optimum conditions including pH of 10 and quadriphase nanocomposite/cadmium ratio of 40 after 2 h of adsorption. Kinetics and multistep adsorption studies showed that an optimum adsorption of 97% could be obtained only after 1.2 min following a first-order single-step process. Isotherm plots revealed that cadmium adsorb as monolayer on the surface of synthesized quadriphase nanocomposite based on Temkin model with the highest correlation coefficient of 97.67% and the least relative error of 16.15%. [ABSTRACT FROM AUTHOR]

Prediction of metallurgical responses during the flotation process is extremely vital to increase the process efficiency using a proper modeling approach. In this study, two new variants of the recurrent neural network (RNN) method were used to predict the copper ore flotation indices, i.e., grade and recovery within different operating conditions. The model input parameters including pulp pH and solid content as well as frother and collector dosages were first analysed and then optimized using a two-step factorial approach. The statistical analysis showed a reliable correlation between operating parameters and copper grade and recovery with coefficients of 99.86% and 94.50%, respectively. The main effect plots indicated that pulp pH and solid content positively affect copper grade while increasing the frother and collector dosages negatively influenced the quality of the final concentrate. Despite the same effect from pulp pH, reverse effects from other variables were observed for copper recovery. Process optimization revealed that maximum copper recovery of 44.39% with a grade of 11.48% could be achieved under the optimal condition as pulp pH of 10, solid content of 20%, and frother and collector concentrations of 25 g/t and 9.9 g/t, respectively. Then, the predictive efficiency of long short-term memory (LSTM) and gated recurrent unit (GRU) networks with proper structure were evaluated using mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R2). The simulation results showed that the LSTM network with higher R2 of 0.963 and 0.934 for copper grade and recovery, respectively, was more effective than the GRU algorithm with the corresponding values of 0.956 and 0.919, respectively. The results show that the LSTM model could be useful in predicting the copper flotation behaviour in response to changes in the operating parameters. [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]

This paper reviews recent developments in the fundamental understating of ultrafine (nano) bubbles (NBs) and presents technological advances and reagent types used for their generation in flotation. The generation of NBs using various approaches including ultrasonication, solvent exchange, temperature change, hydrodynamic cavitation, and electrolysis was assessed. Most importantly, restrictions and opportunities with respect to the detection of NBs were comprehensively reviewed, focusing on various characterization techniques such as the laser particle size analyzer (LPSA), nanoparticle tracking (NTA), dynamic light scattering (DLS), zeta-phase light scattering (ZPALS), and zeta sizer. As a key feature, types and possible mechanisms of surfactants applied to stabilize NBs were also explored. Furthermore, flotation-assisted nano-bubbles was reported as an efficient method for recovering minerals, with a special focus on flotation kinetics. It was found that most researchers reported the existence and formation of NBs by different techniques, but there is not enough information on an accurate measurement of their size distribution and their commonly used reagents. It was also recognized that a suitable method for generating NBs, at a high rate and with a low cost, remains a technical challenge in flotation. The application of hydrodynamic cavitation based on a venturi tube and using the LPSA and NTA in laboratory scales were identified as the most predominant approaches for the generation and detection of NBs, respectively. In this regard, neither pilot- nor industrial-scale case studies were found in the literature; they were only highlighted as future works. Although the NB-stabilizing effects of electrolytes have been well-explored, the mechanisms related to surfactants remain the issue of further investigation. The effectiveness of the NB-assisted flotation processes has been mostly addressed for single minerals, and only a few works have been reported for bulk materials. Finally, we believe that the current review paves the way for an appropriate selection of generating and detecting ultrafine bubbles and shines the light on a profound understanding of its effectiveness. [ABSTRACT FROM AUTHOR]

Although column flotation cells have been extensively studied in the literature, the hydrodynamic impact of orifice properties has not been adequately addressed yet. This paper initially investigates the effect of sparger orifice diameter on hydrodynamic behavior of coal particles in a pilot-scale column flotation. The metallurgical responses (i.e., ash, yield and recovery contents) were evaluated for four different sparger orifice sizes including 1, 3, 5, and 7 mm under constant operating conditions by experimental measurements associated with computational fluid dynamics (CFD) simulations. A numerical model of a two-phase air-water flow in the column (3 m × 0.5 m) was chosen and the turbulence patterns inside the collection zone and at the pulp-froth interface were simulated by employing the Volume-Of-Fluid (VOF) model. The experimental results indicated that desiring concentrated product i.e., ash content of 8.8% and yield of 57.2% could be obtained using the 3 mm orifice size. Simulation results revealed that as the sparger orifice size decreased to 1 mm, the reduced carrying capacity of the bubbly regime led to the poor flotation performance. In contrast, the turbulence condition intensified as the sparger orifice size was increased to 7 mm. Another negative consequence of the 7 mm orifice was attributed to the exceeded entrainment of high ash fines due to a bubble swarm mechanism. The 7 mm sparger orifice diameter showed a concentrate of 17.1% ash and 12.5% yield. Finally, according to the obtained results in this work and literature studies, several necessary highlights were suggested for future works in this scope. [ABSTRACT FROM AUTHOR]

Comminution, particularly post-crushing, is a critical phase in mineral processing that reduces run-of-mine ore to an acceptable particle size before undergoing a downstream beneficiation process. Despite its importance, this stage has been under-investigated probably due to challenges of sampling and data collection and inadequate technical and financial support. This research presents a multi-method machine learning framework designed to simulate the power dynamics within an industrial-scale iron ore cone crusher circuit. The framework integrates advanced artificial intelligence techniques to evaluate energy consumption and estimate cone crushers output K80 accurately. The developed framework spans from different algorithms by data preprocessing techniques such as normalization and Principal Component Analysis (PCA) to standardize and reduce the dataset’s dimensionality, coupled with multiple machine learning algorithms, including Gradient Boosting Machines (CatBoost, LightGBM, XGBoost), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks. The study utilized real-time data collected over six months by considering the key operational parameters such as total tonnage, hopper filling levels, power draw, cone position, and crusher discharge settings. The modeling approach was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Asymmetric Loss (AL), and the coefficient of determination (R2) as performance metrics. Results indicated that the LSTM model outperformed other methods by achieving the lowest MAE and RMSE and the highest R2 score of 0.92, thereby demonstrating superior precision in estimation. However, XGBoost showed optimal performance in managing asymmetric loss, highlighting its efficiency in error management across different scenarios. Model residual analysis, performance metrics box plots, and scatter plots comparing measured and estimated data across the models are also provided for further analysis. This study demonstrates the transformative potential of machine learning models in refining industrial process simulations, ultimately driving enhanced productivity and sustainability in the mining industry. [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]

The present study investigated the effects of rhamnolipid (RL) biosurfactant on the flotation performance of a coal sample. The significance of the effect of RL on the system responses, including ash content, coal recovery, yield and separation efficiency, and process kinetics and selectivity, was statistically assessed using One-Way Analysis of Variance (ANOVA) methodology. The ANOVA results revealed that adding RL to the system significantly challenged the studied metallurgical aspects. Briefly, RL biosurfactant depressed coal flotation through physical interaction with the surface of coal particles through chemical bonding between the carboxyl group in the RL structure with those on the surface of coal particles. The potential mechanisms involved were schematically proposed. The correlation between the condition of RL addition and process responses was modeled using the Artificial Neural Network (ANN) approach. Rested in the mean squared error (MSE), root mean squared error (RMSE), and percentage error as the measures of model accuracy, the Levenberg-Marquardt algorithm (LMA) with [7–16-1] structure was found to be the most reliable algorithm to predict the process response. As evidenced, the correlation coefficient values of test data were 98.09%, 96.93%, 98.37%, 98.46%, 99.50%, and 97.37% for ash content, coal recovery, yield, separation efficiency, the rate constant, and selectivity index, respectively. These values confirmed that the process could be simulated using the ANN method with an appropriate structure. [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]

Inthis research, the potential of cadmium removal from aqueous solutionby foam flotation with a rhamnolipid biosurfactant sample was studied.The effects of different major operating conditions, such as rhamnolipidand cadmium concentrations, solution pH, aeration rate, and frothertype and concentration, on the cadmium removal were investigated.The selectivity coefficients in the presence of rhamnolipid maintaineddefinite orders as follows: cadmium > zinc, cadmium > copper,and zinc > copper. The selectivity coefficient of cadmium overcopper was the highest one. The maximum removals of Cd from cleanand Zn- and Cu-contaminated solutions were about 57%, 36%, and 48%,respectively. Kinetic studies indicated that ion flotation of cadmiumfollows a first-order equation with a kinetic rate of 0.0071 min–1. Although the removals are rather low, it seems thatthe use of rhamnolipid biosurfactant can be promising in heavy-metalremoval from wastewaters by foam flotation with some modifications. [ABSTRACT FROM AUTHOR]

Abstract: The effect of a concentrated rhamnolipid biosurfactant (purity > 97%) produced by Pseudomonas aeruginosa MA01 on flotation performance of coal and minerals was studied and compared with those from synthetic flotation reagents in both single and mixed systems. Results of phosphate flotation tests showed that phosphate content in the floated product decreased by rhamnolipid concentration. However, final recovery of phosphate at appropriate dosage of reagent was insignificant. This could be ascribed to the increase of iron recovery to final concentrate likely due to collecting action of rhamnolipid for iron minerals. In coal flotation, the increase of rhamnolipid concentration in mixture with pine oil reduced both ash content and coal yield of concentrates. In desulfurization of a sample iron concentrate using reverse flotation, rhamnolipid addition successfully decreased the sulfur content of the iron concentrate. The flotation responses of the studied sample ores showed that rhamnolipid can be used as a promising frother or co-frother in mineral processing practices. [Copyright &y& Elsevier]

This work aims at presenting an advanced simulation approach for a novel rhamnolipidic-based bioflotation process to remove chromium from wastewater. For this purpose, the significance of key influential operating variables including initial solution pH (2, 4, 6, 8, 10 and 12), rhamnolipid to chromium ratio (RL:Cr = 0.010, 0.025, 0.050, 0.075 and 0.100), reductant (Fe) to chromium ratio (Fe:Cr of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0), and air flowrate (50, 100, 150, 200 and 250 mL/min) were investigated and evaluated using Analysis of Variance (ANOVA) method. The RL as both collector and frother was produced using a pure strain of Pseudomonas aeruginosa MA01 under specific conditions. The bioflotation tests were carried out within a bubbly regimed column cell with the dimensions of 60 × 5.70 × 0.1 cm. Four optimization techniques based on Artificial Neural Network (ANN) including Cuckoo, genetic, firefly and biogeography-based optimization algorithms were applied to 113 experiments to identify the optimum values of studied factors. The ANOVA results revealed that all four variables influence the bioflotation performance through a non-linear trend. Their influences, except for aeration rate, were found statistically significant (p-value < 0.05), and all parameters followed the normal distribution according to Anderson-Darlin (AD) criterion. Maximum chromium removal of about 98% was achieved at pH of 6, rhamnolipid to chromium ratio of 0.05, air flowrate of 150 mL/min, and Fe to Cr ratio of 1.0. Flotation kinetics study indicated that chromium bioflotation follows the first-order kinetic model with a rate of 0.023 sec−1. According to the statistical assessment of the model accuracy, the firefly algorithm (FFA) with a structure of 4-9-1 yielded the highest level of reliability with the mean squared, root mean squared, percentage errors and correlation coefficient values of test-data of 0.0038, 0.0617, 3.08% and 96.92%, respectively. These values were evidences of the consistency of the well-structured ANN method to simulate the process. [ABSTRACT FROM AUTHOR]

According to the wide application of muscovite in various industries, many studies have focused on its fabrication. However, the process of its synthesis faces long-standing challenges mainly related to the elevated temperature and pressure ambient, together with time and cost-consuming processes. This research work aims at synthesizing muscovite through a straightforward and direct wet thermal oxidation of an ash sample produced from biotite-rich coal tailings. For this purpose, the lab ash powder was mixed with 35% H2O2 at the room temperature of 25 °C while stirring at 480 rpm. Then, the temperature was gradually raised to 80 °C, and the process ran for 180 min. The dried product and the raw lab ash were characterized by the X-ray diffraction (XRD), scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR) methods. The XRD results indicated that the biotite was efficiently converted to the muscovite as the number of relevant peaks was significantly increased in the synthesized product's pattern. The SEM and FTIR results showed some structural changes, from pseudo-hexagonal in the starting material to amorphous pseudo-crystals in the synthetic product, as well as the growth of the product's crystals. The crystallographic study and lattice parameter calculations revealed that the starting material and product peaks matched to International Center for Diffraction Data (ICDD reference patterns of 01-080-1110 and 01-082-2450 for the biotite and the muscovite, respectively. Moreover, the calculation of the mean crystallite size of the starting material and treated samples were obtained as 55 nm and 87 nm, respectively. Finally, according to the characterization properties of synthesized muscovite, the presented method was introduced as an effective technique. Therefore, we highly suggest it for further consideration and its development in future investigations. [ABSTRACT FROM AUTHOR]

[Display omitted] • Froth flotation of fine and coarse particles is studied, focusing on cell designs. • Generating micro-bubbles, intensive turbulence, and high gas hold-up are advantages of intensified cells. • Scaling up/down procedures for the intensified cells are found unclear. • Positioning the optimum location of discussed cells in flotation circuits is found challenging. After more than a century applying flotation to the mining industry, two completely different strategies have been introduced for processing purposes. One is the classical approach viz. grinding ores to a certain extent (fine particles) and floating them via conventional mechanical and pneumatic cells i.e., Jameson, Imhoflot™ and Reflux™. This strategy continues because mines face declining cut-off grades, complex and poly-mineralized ores, and they are required to achieve an acceptable degree of mineral liberation. The other school of thought deals with coarse particle processes mainly owing to the low energy requirements, that includes SkimAir® flash, fluidized bed and HydroFloat™ cells. There is no study in the literature to comparatively present the recent developments of flotation apparatuses versus the conventional mechanical cells. To cover this knowledge gap in the literature, the present paper endeavors to critically evaluate these concepts from several points of view, including existing technological advancements, water and energy usage, kinetics, and circuit design. A brief introduction of advanced technologies, along with their applications is presented. The data from literature and case studies showed that the Jameson, Imhoflot™ and recently developed Reflux™ flotation cells can be very effective for recovering fine particles owing to their specific hydrodynamic designs, intensive energy dissipation rate and generation of micron-sized bubbles (100–700 µm). Very low (less than a few minutes) mean particle residence time, high gas-hold up (ca. 50–70 %), no agitation and high efficiency of particle-bubble collision were identified as their main advantages compared to traditional mechanical flotation cells. In addition to their common applications in cleaner stage, these cells were used in pre-flotation and scalping (producing final concentrate from the rougher feed) duties. Their main challenges were recognized as relatively unclear procedure on their scale up/down, optimization and simulation. The HydroFloat™ cell was indicated as a promising technology for recovering coarse particle fraction sizes by taking advantage of the fluidized-bed concept with plug-flow dispersion regime, high particle residence time, and limited cell turbulence. We finally concluded that fine particle flotation may remain as the main focus of re-processing tailings dams, while coarse particle treatment should be the focus of this century to reduce total energy consumptions. [ABSTRACT FROM AUTHOR]