Validation of a dynamic non-linear grinding circuit model for process control

Roux, J.D. le; Steyn, Christiaan Weyers

doi:10.1016/j.mineng.2022.107780

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…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.…”

Section: Use Of the Non-linear Modelmentioning

confidence: 99%

“…For this, several grinding tests were carried out using marble as test material, and the effect of the media (balls) size on the grinding efficiency was investigated through the use of the non-linear model. In each series, the accuracy of the RR distribution model was determined by employing well-known metrics, such as the correlation coefficient R 2 values [34,35], the root-mean-square error (RMSE), and the modified index of agreement (IoA′) [36] using different regression methods.…”

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confidence: 99%

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

Petrakis

Varouchakis

Komnitsas

2023

Mining, Metallurgy & Exploration

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During the modeling of grinding systems, population balance modeling (PBM) which considers a constant breakage rate has been widely used over the past years. However, in some cases, PBM exhibited some limitations, and time-dependent approaches have been developed. Recently, a non-linear framework which considers the traditional linear theory of the PBM as a partial case was introduced, thus allowing the estimation of product particle size distribution in relation to grinding time or the specific energy input to the mill. In the proposed model the simplified form of the fundamental batch grinding equation was transformed into the well-known Rosin–Rammler (RR) distribution. Besides, the adaptability and reliability of the prediction model are among others dependent upon the operating conditions of the mill and the adjustment of the RR distribution to the experimental data. In this study, a series of grinding tests were performed using marble as test material, and the adaptability of the non-linear model was investigated using three loads of single size media, i.e., 40, 25.4, and 12.7 mm. The results indicate that the proposed model enables a more accurate analysis of grinding, compared to PBM, for different operating conditions.

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Section: Use Of the Non-linear Modelmentioning

confidence: 99%

mentioning

confidence: 99%

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

Petrakis

Varouchakis

Komnitsas

2023

Mining, Metallurgy & Exploration

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“…The mathematical models generated based on population balance modeling provided an accurate forecast of the particle-size distribution. A step-wise algebraic routine in [13] is used to fit a dynamic non-linear model, specifically developed for process control, to steady-state process data of an industrial single-stage grinding mill circuit. The results indicate that the model provides a qualitatively accurate response of the main process variables.…”

Section: Introductionmentioning

confidence: 99%

Statistical distributions for modeling mineral liberation

et al. 2023

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Using the algorithm and the results of laboratory studies on grinding of loparite ores from the Lovozero deposit in a ball and rod mill, simulation grinding models were designed based on the transformation of a discrete function of the density distribution of outputs of mineral particle size classes. The model’s adequacy was quantitatively analysed carried out by calculating the determination coefficient without taking into account the constant component of the distribution; for a qualitative assessment of the result the authors used the Cheddock scale. The authors studied the minerals disintegration at ball and rod grinding with the following verification of the proposed algorithm. To identify the distribution of loparite by size classes in the initial ore samples and in the samples obtained in the disintegration taking place at different productivity, the X-ray diffraction analysis and the combined (weight, optical and geometric) methods were used.

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