RESUMEN

Enrichment of ultrafine liberated valuable minerals from their associated gangue phases is one of the emerging investigation topics within mineral processing and recycling. Using green flotation reagents and turning processes into eco-friendly systems is also one of the challenges in the green transition of ore beneficiation plants. Starch and Tanin as biodegradable depressants for hematite depression have been commercially used in various iron ore processing plants. However, their depression effects on ultrafine particles were not systemically assessed and compared. To fill this gap, this investigation examined the effects of starch, tannin, their mixtures (different ratios), and their different conditioning sequence on the floatability of ultrafine quartz and hematite (- 15 µm). Since the macromolecular polymer of these biodegradable depressants can bind particles together and flocculate them, turbidity analyses were used to assess their optimum ratio for hematite depression without affecting quartz floatability. Turbidity analyses provided a mixture of tannin and starch might enhance the flotation separation of quartz from hematite. Starch could flocculate ultrafine hematite particles, while tannin could disperse ultrafine quartz particles. Floatability experiments indicated that starch had the highest performance in hematite depression (lowest effect on quartz particles) compared to other conditions. Surface analyses (zeta potential and FTIR) proved floatability outcomes and highlighted starch had stronger adsorption on the hematite surface than tannin.

RESUMEN

A considerable amount of ultrafine magnetite as the iron source will end up in the tailing dams since the magnetic separation process markedly drops as the particle size. Cationic reverse flotation could be one of the main alternatives for recovering ultrafine magnetite. As a systematic approach, this study explored the flotation efficiency and interaction mechanisms of two biodegradable ether amines (diamine and monoamine) to separate ultrafine quartz from magnetite (- 20 µm). Several assessments (single and mixed mineral flotation, zeta potential, contact angle, surface tension measurement, turbidity, and Fourier transform infrared) were conducted to explore the efficiency of the process and the interaction mechanisms. Results indicated that ether diamine and monoamine could highly float ultrafine quartz particles (95.9 and 97.7%, respectively) and efficiently separate them from ultrafine magnetite particles. Turbidity assessments highlighted that these cationic collectors could aggregate magnetite particles (potentially hydrophobic coagulation) and enhance their depression. Surface analyses revealed that the collector mainly adsorbed on the quartz particles, while it was essentially a weak interaction on magnetite.

RESUMEN

Exponential increasing demands for base metals have made meaningful processing of their quite low-grade (>1%) resources. Froth flotation is the most important physicochemical pretreatment technique for processing low-grade sulfide ores. In other words, flotation separation can effectively upgrade finely liberated base metal sulfides based on their surface properties. Various sulfide surface characters can be modified by flotation surfactants (collectors, activators, depressants, pH regulators, frothers, etc.). However, these reagents are mostly toxic. Therefore, using biodegradable flotation reagents would be essential for a green transition of ore treatment plants, while flotation circuits deal with massive volumes of water and materials. Pyrite, the most abundant sulfide mineral, is frequently associated with valuable minerals as a troublesome gangue. It causes severe technical and environmental difficulties. Thus, pyrite should be removed early in the beneficiation process to minimize its problematic issues. Recently, conventional inorganic pyrite depressants (such as cyanide, lime, and sulfur-oxy compounds) have been successfully assisted or even replaced with eco-friendly and green reagents (including polysaccharide-based substances and biodegradable acids). Yet, no comprehensive review is specified on the biodegradable acid depression reagents (such as tannic, lactic, humic acids, etc.) for pyrite removal through flotation separation. This study has comprehensively reviewed the previously conducted investigations in this area and provides suggestions for future assessments and developments. This robust review has systematically explored depression performance, various adsorption mechanisms, and aspects of these reagents on pyrite surfaces. Furthermore, factors affecting their efficiency were analyzed, and gaps within each area were highlighted.

RESUMEN

Reverse cationic flotation is currently the main processing technique for upgrading fine hematite from silicates. Flotation is known as an efficient method of mineral enrichment that deals with possibly hazardous chemicals. Thus, using eco-friendly flotation reagents for such a process is an emerging need for sustainable development and green transition. As an innovative approach, this investigation explored the potential of locust bean gum (LBG) as a biodegradable depressant for the selective separation of fine hematite from quartz through reverse cationic flotation. Various flotation conditions (micro and batch flotation) were conducted, and the mechanisms of LBG adsorption have been examined by different analyses (contact angle measurement, surface adsorption, zeta potential measurements, and FT-IR analysis). The micro flotation outcome indicated that the LBG could selectively depress hematite particles with negligible effect on quartz floatability. Flotation of mixed minerals (hematite and quartz mixture in various ratios) indicated that LGB could enhance separation efficiency (hematite recovery > 88%). Outcomes of the surface wettability indicated that even in the presence of the collector (dodecylamine), LBG decreased the hematite work of adhesion and had a slight effect on quartz. The LBG adsorbed selectively by hydrogen bonding on the surface of hematite based on various surface analyses.

RESUMEN

Cement production is one of the most energy-intensive manufacturing industries, and the milling circuit of cement plants consumes around 4% of a year's global electrical energy production. It is well understood that modeling and digitalizing industrial-scale processes would help control production circuits better, improve efficiency, enhance personal training systems, and decrease plants' energy consumption. This tactical approach could be integrated using conscious lab (CL) as an innovative concept in the internet age. Surprisingly, no CL has been reported for the milling circuit of a cement plant. A robust CL interconnect datasets originated from monitoring operational variables in the plants and translating them to human basis information using explainable artificial intelligence (EAI) models. By initiating a CL for an industrial cement vertical roller mill (VRM), this study conducted a novel strategy to explore relationships between VRM monitored operational variables and their representative energy consumption factors (output temperature and motor power). Using SHapley Additive exPlanations (SHAP) as one of the most recent EAI models accurately helped fill the lack of information about correlations within VRM variables. SHAP analyses highlighted that working pressure and input gas rate with positive relationships are the key factors influencing energy consumption. eXtreme Gradient Boosting (XGBoost) as a powerful predictive tool could accurately model energy representative factors by R-square ever 0.80 in the testing phase. Comparison assessments indicated that SHAP-XGBoost could provide higher accuracy for VRM-CL structure than conventional modeling tools (Pearson correlation, Random Forest, and Support vector regression.

Asunto(s)

Inteligencia Artificial , Fenómenos Fisiológicos , Humanos , Aprendizaje Automático , Fenómenos Físicos

RESUMEN

In cement mills, ventilation is a critical key for maintaining temperature and material transportation. However, relationships between operational variables and ventilation factors for an industrial cement ball mill were not addressed until today. This investigation is going to fill this gap based on a newly developed concept named "conscious laboratory (CL)". For constructing the CL, a boosted neural network (BNN), as a recently developed comprehensive artificial intelligence model, was applied through over 35 different variables, with more than 2000 records monitored for an industrial cement ball mill. BNN could assess multivariable nonlinear relationships among this vast dataset, and indicated mill outlet pressure and the ampere of the separator fan had the highest rank for the ventilation prediction. BNN could accurately model ventilation factors based on the operational variables with a root mean square error (RMSE) of 0.6. BNN showed a lower error than other traditional machine learning models (RMSE: random forest 0.71, support vector regression: 0.76). Since improving the milling efficiency has an essential role in machine development and energy utilization, these results can open a new window to the optimal designing of comminution units for the material technologies.

RESUMEN

Industrial wastes may contain high concentrations of valuable metals. Extraction and recovery of these metals have several economic and environmental advantages. Various studies showed positive effects of microwaves as a pretreatment method before the leaching of minerals. However, there are empty rooms for exploring simultaneous microwave and leaching (microwave-leaching) of industrial waste material for the production of valuable metals. This investigation examined the microwave-leaching method to extract copper and zinc from a copper-smelter dust (CSD). The results of microwave-leaching mechanism were compared with conventional heating leaching based on kinetics modelling. The final Cu recovery in the conventional heating and microwave irradiation was 80.88% and 69.83%, respectively. Kinetic studies indicated that the leaching reactions follow diffusion across the product layer. Based on X-ray powder diffraction (XRD) analyses, during conventional experiments sulfate; components formed with high intensity as an ash layer which prevents reagent access to the solid surface and decreases the Cu dissolution. While the sulfate components did not detect in the microwave-leaching residuals which means that microwave irradiation helped to decrease the ash layer formation. Taking all mentioned results into consider it can be concluded that microwave-leaching can be considered as an efficient method for extraction of valuable metals from waste materials.