Integration of Lineal Geostatistical Analysis and Computational Intelligence to Evaluate the Batch Grinding Kinetics
The kinetic characterization of the grinding process has always faced a special challenge due to the constant fluctuations of its parameters. The weight percentage of each size (WPES) should be mentioned. There are particular considerations for WPESs, because their tendencies are not monotonic. The...
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MDPI AG
2022-06-01
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Series: | Minerals |
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Online Access: | https://www.mdpi.com/2075-163X/12/7/823 |
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author | Freddy A. Lucay José Delgado Felipe D. Sepúlveda |
author_facet | Freddy A. Lucay José Delgado Felipe D. Sepúlveda |
author_sort | Freddy A. Lucay |
collection | DOAJ |
description | The kinetic characterization of the grinding process has always faced a special challenge due to the constant fluctuations of its parameters. The weight percentage of each size (WPES) should be mentioned. There are particular considerations for WPESs, because their tendencies are not monotonic. The objective of this work is to provide a methodology and model that will allow us to better understand the kinetics of grinding through the analysis of the Response Surface (RS), using geostatistical (data reconstruction) and computational intelligence (meta-model) techniques. Six experimental cases were studied and trends were evaluated/adjusted with multiple parameters, including an identity plot adjusted to 0.75–0.90, a standardized error histogram with a mean of −0.01 to −0.05 and a standard deviation of 0.63–1.2, a standardized error based on an estimated value of −0.09 to −0.02, a meta-model adjusted to between 92 and 99%, and finally, using the coefficient of variation, which classifies the information (stable/unstable). In conclusion, it was feasible to obtain the results of the WPES from RS, and it was possible to visualize the areas of greatest fluctuation, trend changes, error adjustments, and data scarcity without the need for specific experimental techniques, a coefficient analysis of the fracturing or the use of differential equation systems. |
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issn | 2075-163X |
language | English |
last_indexed | 2024-03-09T13:19:30Z |
publishDate | 2022-06-01 |
publisher | MDPI AG |
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series | Minerals |
spelling | doaj.art-7b329391c8074fc889433d1d4d61f1132023-11-30T21:31:41ZengMDPI AGMinerals2075-163X2022-06-0112782310.3390/min12070823Integration of Lineal Geostatistical Analysis and Computational Intelligence to Evaluate the Batch Grinding KineticsFreddy A. Lucay0José Delgado1Felipe D. Sepúlveda2Escuela de Ingeniería Química, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340000, ChileDepartamento de Ingeniería en Minas, Universidad de Antofagasta, Antofagasta 1240000, ChileDepartamento de Ingeniería en Minas, Universidad de Antofagasta, Antofagasta 1240000, ChileThe kinetic characterization of the grinding process has always faced a special challenge due to the constant fluctuations of its parameters. The weight percentage of each size (WPES) should be mentioned. There are particular considerations for WPESs, because their tendencies are not monotonic. The objective of this work is to provide a methodology and model that will allow us to better understand the kinetics of grinding through the analysis of the Response Surface (RS), using geostatistical (data reconstruction) and computational intelligence (meta-model) techniques. Six experimental cases were studied and trends were evaluated/adjusted with multiple parameters, including an identity plot adjusted to 0.75–0.90, a standardized error histogram with a mean of −0.01 to −0.05 and a standard deviation of 0.63–1.2, a standardized error based on an estimated value of −0.09 to −0.02, a meta-model adjusted to between 92 and 99%, and finally, using the coefficient of variation, which classifies the information (stable/unstable). In conclusion, it was feasible to obtain the results of the WPES from RS, and it was possible to visualize the areas of greatest fluctuation, trend changes, error adjustments, and data scarcity without the need for specific experimental techniques, a coefficient analysis of the fracturing or the use of differential equation systems.https://www.mdpi.com/2075-163X/12/7/823kinetic grinding metamodelgeostatistics analysiscomputational intelligence techniques |
spellingShingle | Freddy A. Lucay José Delgado Felipe D. Sepúlveda Integration of Lineal Geostatistical Analysis and Computational Intelligence to Evaluate the Batch Grinding Kinetics Minerals kinetic grinding metamodel geostatistics analysis computational intelligence techniques |
title | Integration of Lineal Geostatistical Analysis and Computational Intelligence to Evaluate the Batch Grinding Kinetics |
title_full | Integration of Lineal Geostatistical Analysis and Computational Intelligence to Evaluate the Batch Grinding Kinetics |
title_fullStr | Integration of Lineal Geostatistical Analysis and Computational Intelligence to Evaluate the Batch Grinding Kinetics |
title_full_unstemmed | Integration of Lineal Geostatistical Analysis and Computational Intelligence to Evaluate the Batch Grinding Kinetics |
title_short | Integration of Lineal Geostatistical Analysis and Computational Intelligence to Evaluate the Batch Grinding Kinetics |
title_sort | integration of lineal geostatistical analysis and computational intelligence to evaluate the batch grinding kinetics |
topic | kinetic grinding metamodel geostatistics analysis computational intelligence techniques |
url | https://www.mdpi.com/2075-163X/12/7/823 |
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