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

Abstract

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.

Use of Radial Basis Function Network to Predict Optimum Calcium and Magnesium Levels in Seawater and Application of Pretreated Seawater by Biomineralization as Crucial Tools to Improve Copper Tailings Flocculation

“…The immediate consequence is incorrect optimization. In these cases, the most popular alternative is to use ANNs [28]. For this reason, the RBFN method was chosen, and it was advantageous to show more comprehensively the results (see Section 3.2).…”

Radial Basis Function Neural Network based on Growing Neural Gas Network applied for evaluation of oil agglomeration process efficiency

“…Nowadays, one of the areas of research neural modeling has been often applied to is minerals processing. Mineral systems and processes are hard to measure (Cisternas et al, 2020), therefore, neural models can be used for their better representation and analysis. Numerous examples of the usage of neural modeling have been presented in the literature.…”

“…Thus, neural models can be created to perform tasks of predicting the behavior of a selected chemical process under certain conditions. Compared to conveying traditional experiments, research involving neural modeling gives results faster, is more cost-efficient, and allows optimization of the conditions of future experiments (Cisternas et al, 2020). However, the studies regarding the application of neural networks for oil agglomeration prediction is limited.…”

Editorial for Special Issue “Modeling, Design and Optimization of Multiphase Systems in Minerals Processing”

“…Multiphase systems are analyzed at different time and size scales in the review article [4] because the modeling and post-modeling activities depend on those scales (see Figure 1a). For example, molecular modeling is necessary to understand the phenomena that occur at the atomic or molecular level, such as the adsorption of chemical agents on the surface of minerals, while computational fluid dynamics is a suitable tool at the fluid level.…”

“…To solve this problem, new modeling strategies must be proposed, possibly based on artificial intelligence. Precisely, several applications of artificial intelligence in the design, optimization, and modeling of multiphase systems were analyzed in the review [4], including artificial neural networks and support vector machines. In fact, there has been an exponential growth in research associated with artificial intelligence; in 1990, there were 29 publications that included artificial neural networks in their title, while last year this figure was 1430 in Web of Science.…”