Comparison of Fuzzy and Neural Network Computing Techniques for Performance Prediction of an Industrial Copper Flotation Circuit

Abstract

This paper presents the development and validation of five different soft computing methods for flotation performance prediction: (1) two models based on fuzzy logic (Mamdani and Takagi-Sugeno fuzzy inference system) and (2) three models based on artificial neural networks. Copper content in the ore feed, collector dosage in the rougher and the scavenger flotation circuits, slurry pH in the rougher flotation circuit and frother consumption were selected as input parameters to estimate the copper grade and recovery of final concentrate, as well as the copper content in the final tailings of the flotation plant. The training and evaluation of the proposed models were performed on the basis of real process data collected by the multiannual monitoring of industrial flotation plant operation in “Veliki Krivelj Mine”. The results showed that the proposed soft computing-based models well describe the behavior of the industrial flotation plant in a wide range of circumstances. Among the proposed algorithms, artificial neural networks gave the most accurate predictions for the final copper concentrate grade and recovery (R2 = 0.98 and R2 = 0.99, respectively) and copper content in final tailings (R2 = 0.87). At some points, fuzzy logic models are almost equally efficient, but artificial neural networks had lower values for all error functions.

Developments and applications of 3D imaging systems in mineral processing

“…Technological advances like GPUs (Graphics Processing Units), high speed cameras and the rapid development of artificial intelligence have resulted in a revolution in the development of machines or tools that may be used in mining [1][2][3][4]. With the development of technologies, the capability of computer vision technology has been significantly developed, so that the automation technology based on computer vision systems is becoming a vital part of mining industries to improve productivity and efficiency.…”

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.…”