A hybrid approach to modeling the flotation process from the 'Veliki Krivelj' plant

Jovanović, Ivana; Nešković, Jasmina; Petrović, Sanja; Milanović, Dragan

doi:10.5937/mmeb1802001j

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Intelligent control is a discipline where the control methods are developed so that they mimic important characteristics of hu-man intelligenceadaptation and lear-ning, planning under high uncertainty, and computing immense quantities of data [4,5].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of ai models in the modeling of flotation process

Jovanović¹,

Magdalinović²,

Kržanović³

et al. 2018

Min Metal Eng Bor

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This paper presents a comparative overview the modeling results of flotation process from the Veliki Krivelj plant. Within the research, a total of ten models were formed using the various methods of soft computing-fuzzy logic, artificial neural networks and hybrid system ANFIS. It was found that the flotation concentration process is best modeled using artificial neural networks, where the best correlation coefficients between the actual and predicted values of copper content in concentrate and tailings, as well as the copper recovery in concentrate are achieved. They also gave the minimal RMSE in all cases.

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Section: Introductionmentioning

confidence: 99%

Comparative analysis of ai models in the modeling of flotation process

Jovanović¹,

Magdalinović²,

Kržanović³

et al. 2018

Min Metal Eng Bor

Self Cite

View full text Add to dashboard Cite

show abstract

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

et al. 2022

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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.

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A hybrid approach to modeling the flotation process from the 'Veliki Krivelj' plant

Cited by 2 publications

References 4 publications

Comparative analysis of ai models in the modeling of flotation process

Comparative analysis of ai models in the modeling of flotation process

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

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