Comparison of Semirigorous and Empirical Models Derived Using Data Quality Assessment Methods

Abstract

With the increase in available data and the stricter control requirements for mineral processes, the development of automated methods for data processing and model creation are becoming increasingly important. In this paper, the application of data quality assessment methods for the development of semirigorous and empirical models of a primary milling circuit in a platinum concentrator plant is investigated to determine their validity and how best to handle multivariate input data. The data set used consists of both routine operating data and planned step tests. Applying the data quality assessment method to this data set, it was seen that selecting the appropriate subset of variables for multivariate assessment was difficult. However, it was shown that it was possible to identify regions of sufficient value for modeling. Using the identified data, it was possible to fit empirical linear models and a semirigorous nonlinear model. As expected, models obtained from the routine operating data were, in general, worse than those obtained from the planned step tests. However, using the models obtained from routine operating data as the initial seed models for the automated advanced process control methods would be extremely helpful. Therefore, it can be concluded that the data quality assessment method was able to extract and identify regions sufficient and acceptable for modeling.

Parameters evaluation in the process of solid phase pulp sedimentation in technological units of beneficiation plants

“…On figure 1 it is shown a typical technological scheme of the process of grinding ore during its enrichment, which is widely used in processing plants [22]. Initial ore and water are supplied to the mill, which operates in a closed cycle with hydrocyclones.…”

“…On figure 2 it is shown the main notation and variables used in the mathematical model of the sump in the technological chain of the closed grinding cycle [22,30,31]. Q(t) denotes the flows at the corresponding points of the technological process, W is the content of the solid phase of the pulp, M (t) is the mass of material in the technological units at time t. In addition, when modeling the grinding cycle, the following designations were adopted: P (x, t) is the cumulative yield of solid particles whose size is less than x; Xi = ∂P (x,t) ∂x i ∆xfraction of the crushed material, the particle size of which belongs to the size interval i.…”

“…In work [22] the importance of data quality assessment for the development of semi-rigorous and empirical models of the primary grinding scheme at the concentrator is emphasized.…”

Reliability of the Non-linear Modeling in Predicting the Size Distribution of the Grinding Products Under Different Operating Conditions

“…As mentioned earlier in this paper, the n values can be determined using either linear or non-linear regression analysis, and the method used defines the accuracy of the RR distribution model. The accuracy of this model was determined using the correlation coefficient (R 2 ) [35,46] which showed that general higher R 2 values are obtained when non-linear regression analysis was used. In addition, it was revealed that the estimated n values were significantly different under the same operating conditions after the two methods were applied.…”