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

Abstract

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.

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

“…Figure 7 shows the main effect plots for operating factors considered in the experimental design. Detailed explanations regarding the construction of main effect plots are addressed elsewhere [34,[65][66][67]. According to Figure 7, quartz recovery decreases as the air flow rate increases.…”

“…Coal flotation optimization using modified flotation parameters and combustible recovery in a Jameson cell was investigated by Vapur et al Combustible recovery (%) and ash content (%) were used for the optimization of the Jameson flotation variables, and it was found that d 80 = 0.250 mm particle size, 1/1 vegetable oil acids/kerosene ratio, 20% pulp density, 0.600 L/min wash water rate, and 40 cm downcomer immersion depth could be used to separate coal from ash efficiently [33]. A recent approach to simulate complicated separation techniques uses expert systems such as deep learning [27,[34][35][36][37]. Convolutional neural networks (CNNs) stand out among all simulation techniques [38] and now play an increasingly important role in big data predictive analytics [39].…”

“…According to their research, the choice of the network algorithm greatly impacts the simulation accuracy of the ANN model. In a recent study, Nakhaei et al [34] conducted an investigation on multivariate nonlinear regression (MNLR), radial basis function (RBF), and recurrent neural networks to predict flotation column performance. As compared to RBF and MNLR models, the RNN provided superior predictions of the metallurgical performance of flotation columns at the training and testing stages.…”

Editorial for Special Issue “Design, Modeling, Optimization and Control of Flotation Process”

“…These studies explored the stability of the froth zone under varying flotation conditions and highlighted the significance of the relationship between particle and bubble sizes as critical factors impacting successful collection, froth transport processes, and the flotation rate and efficiency. The third group of papers in this Special Issue delved into modeling and optimizing the flotation process performance utilizing advanced computational tools and algorithms such as the response surface methodology (RSM), GA, ANN, deep learning, and fuzzy systems [10][11][12][13][14]. Some of the studies published in this Special Issue aimed to address the challenges of recovering target elements more effectively using these methods.…”