Statistical modeling of copper losses in the silicate slag of the sulfide concentrate smelting process

Abstract

This article presents the results of the statistical modeling of copper losses in the silicate slag of the sulfi de concentrates smelting process. The aim of this study was to defi ne the correlation dependence of the degree of copper losses in the silicate slag on the following parameters of technological processes: SiO 2 , FeO, Fe 3 O 4 , CaO and Al 2 O 3 content in the slag and copper content in the matte. Multiple linear regression analysis (MLRA), artifi cial neural networks (ANNs) and adaptive network based fuzzy inference system (ANFIS) were used as tools for mathematical analysis of the indicated problem. The best correlation coeffi cient (R 2 = 0.719) of the fi nal model was obtained using the ANFIS modeling approach.

Development of resource-saving technology for copper production at “Almalyk MMC” JSC

“…After the concentration of magnetite is reduced to a residual 3-5% in the slag, the process of recovery moves into the diffusion region; i.e., restoring the excess content of ferric iron in iron silicate melts takes a short period of timeup to 10 minutes-during which the process can be carried out without significantly delaying the operation of the main industrial converter. Kinetic parameters such as the reaction order, activation energy, rate constant and transition diagram from the kinetic to the diffuse region of the process of reducing ferric iron from an iron silicate melt with clinker, which are necessary to improve the technology, have been established [4][5][6].…”

Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal

“…Gui et al [60] Pyrometallurgy • Deng et al [61] • D. Liu et al [28] • J. Liu et al [62] • Savic et al [63] • Ghea Puspita et al [64] • Cardoso et al [65] • Qian et al [66] • Cardoso et al [67] • Wang et al [68] • Yang et al [69] • Zhao et al [70] • RF: Random forest, EXS: expert system, FL: fuzzy logic, ANN: artificial neural network, CNN: convolutional neural network, MPC: model predictive control. • Olivier et al [39] • Estrada et al [29] •…”

“…The results were validated through the chemical compositions (mass%) of Fe, Al, and Si. Savic et al [63] used statistical modelling approaches such as multiple linear regression analysis, artificial neural networks, and an adaptive network-based fuzzy inference system (ANFIS), where this approach was found to be the most accurate in predicting copper losses in the silicate slag of the sulphur concentrate smelting process, with a coefficient of determination of 0.989 in the training stage and 0.719 in the testing stage. J. Liu et al [62], Cardoso et al [67], and D. Liu et al [28] developed an artificial neural network model to predict the production and quality control of hot metal in a blast furnace.…”

New Approach to Multi-Criteria Ranking of the Copper Concentrate Smelting Processes based on the PROMETHEE/GAIA Methodology

“…Also, Cu content in slag should be at the lowest possible level to obtain the best possible copper recovery Priority is given to smelters with possibility to melt poor concentrates, hence parameter of minimal Cu content in concentrate has been defined as Min. [5,78,79]. Multicriteria comparative analyses of copper obtaining technologies has been performed by using the software package Decision Lab 2000 [5,34,79].…”

“…[5,78,79]. Multicriteria comparative analyses of copper obtaining technologies has been performed by using the software package Decision Lab 2000 [5,34,79]. Main screen of the software package Decision Lab 2000 is presented in Figure 1.…”