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The relationships between the kinetic grinding parameters with interstitial fillings and the liberation degree of a chromite ore

Umucu Yakup, Deniz Vedat, Gursoy Hakan Y., Tokcan Isıl, Arsoy Zeyni

Physicochemical Problems of Mineral Processing

2023

Vol. 59, iss. 5

art. no. 168254

In this study, the relationships between the liberation degree and the kinetic breakage parameters of a chromite ore obtained from the Burdur (Türkiye) were examined under the laboratory conditions. Firstly, liberation degrees for chromite particles were determined by the particle counting method in a metal microscope for 4 different size groups. Secondly, the breakage parameter of chromite ore which was dry ground with a ball mill was obtained using standard test methods. Additionally, the model parameters were also determined for the fractional interstitial fillings (U) of the chromite sample. Thirdly, the kinetic grinding parameters were compared with the liberation degree of the chromite sample. As a result of the tests, a very good correlation was obtained with R2=0.998, and regression analysis in the grinding processing of the chromite ore was used to verify the validity of the relationship parameter of Si that was produced. To these, it was found that interstitial fillings (U=0.6, 0.8, 1.0, and 1.2) have an effect on the grinding.

Estimation of grade and recovery in the concentration of barite tailings by the flotation using the MLR and ANN analyses

Deniz Vedat, Umucu Yakup, Deniz Orcun T.

Physicochemical Problems of Mineral Processing

2022

Vol. 58, iss. 5

art. no. 150646

This study aimed to find optimal models in a comparative framework to estimate the recovery and grade of barite concentrate obtained from the rougher flotation of the barite tailings. Therefore, firstly, the effect of four operating parameters (flotation time, pH, collector dosage, and depressant dosage) on the rougher flotation of the barite tailings containing 37.23% BaSO4 was experimentally investigated. Secondly, two models called the multivariable linear regression (MLR) and the artificial neural network (ANN) were used for the estimation of the recovery and grade of the barite concentrate for the rougher flotation optimization. The R2 values found from the MLR and ANN models were 0.828 and 0.995 for the concentrate recovery, and 0.977 and 0.960 for the barite concentrate grade, respectively. In the comparison of the models determined, it was found that the ANN model expressed quite well than the MLR models, especially for the recovery of the rougher concentrate.