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Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

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Departamento de Ingeniería Química y Procesos de Minerales, Universidad de Antofagasta, Antofagasta 1240000, Chile
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Escuela de Ingeniería Química, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340000, Chile
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Author to whom correspondence should be addressed.
Minerals 2020, 10(1), 22; https://doi.org/10.3390/min10010022
Received: 5 November 2019 / Revised: 18 December 2019 / Accepted: 21 December 2019 / Published: 25 December 2019
Multiphase systems are important in minerals processing, and usually include solid–solid and solid–fluid systems, such as in wet grinding, flotation, dewatering, and magnetic separation, among several other unit operations. In this paper, the current trends in the process system engineering tasks of modeling, design, and optimization in multiphase systems, are analyzed. Different scales of size and time are included, and therefore, the analysis includes modeling at the molecular level (molecular dynamic modeling) and unit operation level (e.g., computational fluid dynamic, CFD), and the application of optimization for the design of a plant. New strategies for the modeling, design, and optimization of multiphase systems are also included, with a strong focus on the application of artificial intelligence (AI) and the combination of experimentation and modeling with response surface methodology (RSM). The integration of different modeling techniques such as CFD with discrete element simulation (DEM) and response surface methodology (RSM) with artificial neural networks (ANN) is included. The paper finishes with tools to study the uncertainty, both epistemic and stochastic, based on uncertainty and global sensitivity analyses, which is present in all mineral processing operations. It is shown that all of these areas are very active and can help in the understanding, operation, design, and optimization of mineral processing that involves multiphase systems. Future needs, such as meso-scale modeling, are highlighted. View Full-Text
Keywords: computational fluid dynamic; molecular dynamics; density functional theory; discrete element simulation; smoothed particle hydrodynamics; flotation; grinding; response surface methodology; machine learning; artificial neural networks; support vector machine; hydrocyclone; global sensitivity analysis; uncertainty analysis computational fluid dynamic; molecular dynamics; density functional theory; discrete element simulation; smoothed particle hydrodynamics; flotation; grinding; response surface methodology; machine learning; artificial neural networks; support vector machine; hydrocyclone; global sensitivity analysis; uncertainty analysis
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MDPI and ACS Style

Cisternas, L.A.; Lucay, F.A.; Botero, Y.L. Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing. Minerals 2020, 10, 22.

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