The lead recovery prediction from lead concentrate by an artificial neural network and particle swarm optimization

Sobouti, Arash; Hoseinian, Fatemeh Sadat; Rezai, Bahram; Jalili, Sara

doi:10.1080/12269328.2019.1644205

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…Nnanwube and Onukwuli [15], however, reported on the modeling and optimization of zinc recovery from Enyigba sphalerite in a binary solution of hydrochloric acid and hydrogen peroxide. Sobouti et al [16], applied PSO in optimizing lead recovery from its concentrate using fluoroboric acid. Hence, in this work, the optimization of sphalerite leaching in nitric acid solution is reported.…”

Section: Znsmentioning

confidence: 99%

“…As the acid concentration was increased beyond 3 M, and the leaching temperature also increased beyond 68 °C, the percentage zinc recovery decreased. In the first instance, as the acid concentration increases and the rate of formation of the product increases, the amount of product gets to a saturation value near the solid particle and forms a solid product (elemental sulfur) layer around the particle, resulting in a decrease in dissolution rate [16]. This phenomenon may also indicate a change in the rate-determining step as the nitric acid concentration was increased beyong 3 M [23].…”

Section: Response Surface Plots and Contour Plotsmentioning

confidence: 99%

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Optimization of Zinc Recovery from Sphalerite Using Response Surface Methodology and Particle Swarm Optimization

Onukwuli

Nnanwube

2021

Period. Polytech. Chem. Eng.

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Hydrometallurgical leaching process has been identified as a viable procedure for recovering metals of value from their matrices. The optimization of zinc recovery from sphalerite in nitric acid solution was carried out in this study. The Central Composite Rotatable Design (CCRD) of Response Surface Methodology (RSM) and Particle Swarm Optimization (PSO) tool in matlab were deployed for the optimization studies. RSM modeling gave optimum conditions of 73.0 °C leaching temperature, 3.48 M acid concentration, 0.027 g/mL solid/liquid ratio, 411.02 rpm stirring rate, and 82.82 minutes leaching time; with a zinc yield of 87.67 %. With PSO, about 86.9 % zinc was recovered at a leaching temperature of 69.1 °C, acid concentration of 1.8 M, solid/liquid ratio of 0.031 g/mL, stirring rate of 270 rpm and leaching time of 85 minutes. Thus, PSO and RSM proved to be good optimization tools.

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Section: Znsmentioning

confidence: 99%

Section: Response Surface Plots and Contour Plotsmentioning

confidence: 99%

Optimization of Zinc Recovery from Sphalerite Using Response Surface Methodology and Particle Swarm Optimization

Onukwuli

Nnanwube

2021

Period. Polytech. Chem. Eng.

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“…In another research, Hoseinian et al [15] used a hybrid neural-genetic algorithm to simulate copper recovery during a column leaching process of a copper ore sample on a pilot scale, and found that using an appropriate algorithm could yield reliable prediction results. In a recent study, using hybrid artificial neural networks and particle swarm optimization (PSO), Sobouti et al [16] simulated lead recovery during the leaching of lead concentrate. The noteworthy aspect of the study was the wide range of operating parameters used in the simulation, such as temperature, liquid/solid ratio, stirring speed, fluoroboric acid concentration, and leaching time.…”

Section: Introductionmentioning

confidence: 99%

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

Gholami

Movahedifar

Khoshdast³

et al. 2022

Minerals

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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.

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“…One of the main applications of neural networks is forecasting based on a set of input data that has also yielded excellent results. Thanks to their good performance, ANNs have been frequently used in various scientific fields, including mining and mineral processing [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

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

Khoshdast¹,

Gholami

Hassanzadeh

et al. 2021

Materials

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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.

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The lead recovery prediction from lead concentrate by an artificial neural network and particle swarm optimization

Cited by 9 publications

References 24 publications

Optimization of Zinc Recovery from Sphalerite Using Response Surface Methodology and Particle Swarm Optimization

Optimization of Zinc Recovery from Sphalerite Using Response Surface Methodology and Particle Swarm Optimization

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

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

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