Six different particle size distribution (Gates–Gaudin–Schuhmann (GGS), Rosin–Rammler (RR), Lognormal, Normal, Gamma, and Swebrec) models were compared under different metallurgical coke grinding conditions (ball size and grinding time). Adjusted R2, Akaike information criterion (AIC), and the root mean of square error (RMSE) were employed as comparison criteria. Swebrec and RR presented superior comparison criteria with the higher goodness-of-fit and the lower AIC and RMSE, containing the minimum variance values among data. The worst model fitting was GGS, with the poorest comparison criteria and a wider results variation. The undulation Swebrec parameter was ball size and grinding time-dependent, considering greater b values (b > 3) at longer grinding times. The RR α parameter does not exhibit a defined tendency related to grinding conditions, while the k parameter presents smaller values at longer grinding times. Both models depend on metallurgical coke grinding conditions and are hence an indication of the grinding behaviour. Finally, oversize and ultrafine particles are found with ball sizes of 4.0 cm according to grinding time. The ball size of 2.54 cm shows slight changes in particle median diameter over time, while 3.0 cm ball size requires more grinding time to reduce metallurgical coke particles.
Specific rate of breakage (Sj) is an important parameter for grinding kinetics behavior due to it isreverserelated with the process energy consumption. Size grinding media, viscosity medium, and fine particle formation are some ofmodifiablevariableforto reduce the energy in the grinding process.Nowadays, there is no model that explains the relationship among Sj and parameters described above.Aclassification model based on linear discriminant analysisfor quartz wet grinding wasproposedto identify conditions with the high Sj.Three grinding kinetic behavior groups have been found through cluster analysis and two discriminant functions that explicate difference among groups. The first function was themost powerful differentiating dimension with 89.01% of prediction percentage,and the second onerepresented an additional significant dimension with 10.99%of prediction.
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