A hybrid geometallurgical study using coupled Historical Data (HD) and Deep Learning (DL) techniques on a copper ore mine

Gholami, Alireza; Asgari, Kaveh; Khoshdast, Hamid; Hassanzadeh, Ahmad

doi:10.37190/ppmp/147841

Cited by 11 publications

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“…Generally, the copper grade decreases significantly with the increasing solid content mainly due to transporting gangue minerals (i.e., pyrite, silicates and clay minerals) into the concentrate through the false flotation process [35]. This result is in line with the outcomes presented by previous studies [20,36]. Copper grade is also significantly affected by pH and A65 concentration.…”

Section: Statistical Analysis Of Screening Doesupporting

confidence: 88%

“…In addition, according to the type of effect, the amount of levels considered in the optimization plan was also modified. For example, according to Figure 7, the effect of solid content on both responses is negative; therefore, the levels of this parameter in the screening design (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) were reduced in the optimization design i.e., 10-20, and similarly, the amount of levels was increased for the frother concentration and decreased for the collector concentration. The pH levels were not changed due to the operational limitations.…”

Section: Statistical Analysis Of Screening Doementioning

confidence: 99%

“…Their investigation into different algorithms for developing the ANN model demonstrated that the simulation accuracy is greatly influenced by the choice of the network algorithm. Recently, Gholami et al [20] coupled historical data and deep learning techniques to predict the flotation behaviour of a copper mine in response to mineralogical and operational variables. They showed that mixed statistical/intelligent methods can be a promising approach to accurately simulate the flotation process with an accuracy of more than 95%.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Statistical Analysis Of Screening Doesupporting

confidence: 88%

Section: Statistical Analysis Of Screening Doementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Gholami

Movahedifar

Khoshdast³

et al. 2022

Minerals

Self Cite

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“…7. Main plots show the effect of operating variables on the yield Precious information regarding the individual effect of operating factors and their interaction can be obtained from the 3D plots of the response of a process against other independent variables (Gholami et al, 2022). Hence, experimental results were used for developing the 3D interaction plots for the flotation responses.…”

Section: Interpretation Of Main and Interaction Effectsmentioning

confidence: 99%

Effect of an acetonized pyrolysis oil recycled from spent-car tires on coal flotation performance

Hassanzadeh¹

2023

Physicochem. Probl. Miner. Process.

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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.

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“…Al-Thyabat [41] studied the effect of various operating factors on flotation grade and recovery and simulated the flotation process using a multilayered, feed-forward artificial neural network (ANN). In a study by Gholami et al [42], copper flotation behavior was predicted from historical data combined with deep learning techniques. It was demonstrated that mixed statistical/intelligent methods were effective in accurately simulating the flotation process with an accuracy greater than 95%.…”

Section: Introductionmentioning

confidence: 99%

Advanced Simulation of Quartz Flotation Using Micro-Nanobubbles by Hybrid Serving of Historical Data (HD) and Deep Learning (DL) Methods

Nazari

Gholami

Khoshdast³

et al. 2023

Minerals

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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.

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A hybrid geometallurgical study using coupled Historical Data (HD) and Deep Learning (DL) techniques on a copper ore mine

Cited by 11 publications

References 0 publications

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

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

Effect of an acetonized pyrolysis oil recycled from spent-car tires on coal flotation performance

Advanced Simulation of Quartz Flotation Using Micro-Nanobubbles by Hybrid Serving of Historical Data (HD) and Deep Learning (DL) Methods

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