Parametric Optimization in Rougher Flotation Performance of a Sulfidized Mixed Copper Ore

Hybrid Serving of DOE and RNN-Based Methods to Optimize and Simulate a Copper Flotation Circuit

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.

Drilling Process of GFRP Composites: Modeling and Optimization Using Hybrid ANN

This paper aims to optimize the machining parameters of the drilling process of woven-glass-fiber reinforced epoxy (WGFRE) composites. It will focus on modeling and optimizing drill spindle speed and feed with different laminate thicknesses, with respect to torque and delamination factor. The response surface analysis and artificial neural networks are utilized to model and evaluate the effect of control parameters and their interaction on the drilling process outcomes. The particle swarm optimization algorithm is used to improve the ANN training, to increase its performance in prediction. The optimization method of desirability, based on RSM, is applied to validate the optimal combination of control factors, in the space of the study. The influences of the control parameters on the drilling process outcomes are discussed in detail. The optimal machining parameters were 0.025 mm/r and 1600 rpm for feed and spindle speed, respectively, with a GFRE laminate of 5.4 mm thickness. The RSM and ANN–PSO models applied to predict the drilling-process parameters showed a very high agreement with the experimental data.

Life Cycle Modelling of Extraction and Processing of Battery Minerals—A Parametric Approach

Sustainable battery production with low environmental footprints requires a systematic assessment of the entire value chain, from raw material extraction and processing to battery production and recycling. In order to explore and understand the variations observed in the reported footprints of raw battery materials, it is vital to re-assess the footprints of these material value chains. Identifying the causes of these variations by combining engineering and environmental system analysis expands our knowledge of the footprints of these battery materials. This article disaggregates the value chains of six raw battery materials (aluminum, copper, graphite, lithium carbonate, manganese, and nickel) and identifies the sources of variabilities (levers) for each process along each value chain. We developed a parametric attributional process-based life cycle model to explore the effect of these levers on the greenhouse gas (GHG) emissions of the value chains, expressed in kg of CO2e. The parametric life cycle inventory model is used to conduct distinct life cycle assessments (LCA) for each material value chain by varying the identified levers within defined engineering ranges. 570 distinct LCAs are conducted for the aluminum value chain, 450 for copper, 170 for graphite, 39 for lithium carbonate via spodumene, 20 for lithium carbonate via brine, 260 for manganese, and 440 for nickel. Three-dimensional representations of these results for each value chain in kg of CO2e are presented as contour plots with gradient lines illustrating the intensity of lever combinations on the GHG emissions. The results of this study convey multidimensional insights into how changes in the lever settings of value chains yield variations in the overall GHG emissions of the raw materials. Parameterization of these value chains forms a flexible and high-resolution backbone, leading towards a more reliable life cycle assessment of lithium-ion batteries (LIB).

Recycling lead from a zinc plant residue (ZPR) using brine leaching and cementation with aluminum powder

This research investigated the treatment process of an Iranian zinc plant residue for recycling lead utilizing brine leaching and cementation with aluminum powder. Response surface modeling was employed for this purpose and accordingly, two quadratic mathematical models with R² of 0.9058 and 0.9463 were identified for relationship between process parameters. The ANOVA and 3D response surface graphs exhibited that the leaching and cementation processes were significantly depended on the interactive effects between influential parameters. The interaction effects of liquid/solid ratio with NaCl concentration, temperature and stirring rate, and quadratic effect of NaCl concentration had the largest impact on the recovery. It was also distinguished that the most impressive parameters on the cementation performance were the linear effect of Al:Pb molar ratio, cementation time and temperature, and the quadratic impact of agitation rate. Additionally, numerical optimization was carried out by desirability function approach and the maximum leaching recovery of lead (77.14%) was achieved at 400 g/L NaCl concentration, 10 mL/g liquid/solid ratio, 300 rpm stirring speed, 50 °C temperature, and 60-min leaching time. Also, the highest cementation efficiency (74.97%) was determined after 75 min at 1.5 Al:Pb molar (stoichiometry) ratio, ~ 420 rpm agitation rate, and 50 °C temperature. Furthermore, thermodynamic conclusions implied an endothermic nature and good affinity of lead toward each two processes.

Advanced Simulation of Removing Chromium from a Synthetic Wastewater by Rhamnolipidic Bioflotation Using Hybrid Neural Networks with Metaheuristic Algorithms

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.

Effect of Power Ultrasound on Wettability and Collector-Less Floatability of Chalcopyrite, Pyrite and Quartz

Numerous studies have addressed the role of ultrasonication on floatability of minerals macroscopically. However, the impact of acoustic waves on the mineral hydrophobicity and its physicochemical aspects were entirely overlooked in the literature. This paper mainly investigates the impact of ultrasonic power and its time on the wettability and floatability of chalcopyrite, pyrite and quartz. For this purpose, contact angle and collectorless microflotation tests were implemented on the ultrasonic-pretreated and non-treated chalcopyrite, pyrite and quartz minerals. The ultrasonic process was carried out by a probe-type ultrasound (Sonopuls, 20 kHz and 60 W) at various ultrasonication time (0.5–30 min) and power (0–180 W) while the dissolved oxygen (DO), liquid temperature, conductivity (CD) and pH were continuously monitored. Comparative assessment of wettabilities in the presence of a constant low-powered (60 W) acoustic pre-treatment uncovered that surface of all three minerals became relatively hydrophilic. Meanwhile, increasing sonication intensity enhanced their hydrophilicities to some extent except for quartz at the highest power-level. This was mainly related to generation of hydroxyl radicals, iron-deficient chalcopyrite and elemental sulfur (for chalcopyrite), formation of OH and H radicals together with H2O2 (for pyrite) and creation of SiOH (silanol) groups and hydrogen bond with water dipoles (for quartz). Finally, it was also found that increasing sonication time led to enhancement of liquid temperature and conductivity but diminished pH and degree of dissolved oxygen, which indirectly influenced the mineral wettabilities and floatabilities. Although quartz and pyrite ultrasound-treated micro-flotation recoveries were lower than that of conventional ones, an optimum power-level of 60–90 W was identified for maximizing chalcopyrite recovery.