A methodology for the conceptual design of flotation circuits by combining group contribution, local/global sensitivity analysis, and reverse simulation

Sepúlveda, Felipe D.; Lucay, Freddy A.; González, J. F.; Cisternas, Luís A.; Gálvez, Edelmira D.

doi:10.1016/j.minpro.2017.05.008

Cited by 13 publications

(10 citation statements)

References 31 publications

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“…In other words, the control of the uncertainty of the significant input variables allows for the control of uncertainty in the mill system. GSA has also been applied in the design or optimization of flotation circuits under uncertainty [169][170][171][172]. Sepúlveda et al [169] proposed a methodology for the conceptual design of flotation circuits.…”

Section: Uncertainty and Sensitivity Analysesmentioning

confidence: 99%

“…GSA has also been applied in the design or optimization of flotation circuits under uncertainty [169][170][171][172]. Sepúlveda et al [169] proposed a methodology for the conceptual design of flotation circuits. The methodology involved three decision levels: level I-the definition of the analysis of the problem, level II-the synthesis and screening of alternatives, and level III-the final design.…”

Section: Uncertainty and Sensitivity Analysesmentioning

confidence: 99%

“…(a) Designed circuit using the methodology. (b) Sobol total index for each stage and for chalcopyrite (Cp), chalcopyrite-pyrite (CpPy), pyrite-arsenopyrite, and silica (Sc)[169].…”

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confidence: 99%

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

2019

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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.

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Section: Uncertainty and Sensitivity Analysesmentioning

confidence: 99%

Section: Uncertainty and Sensitivity Analysesmentioning

confidence: 99%

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

2019

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“…The superstructures are important because they define the alternatives and the size of the problem [2]. According to Sepúlveda et al [40], the circuits generally use between three and five flotation stages. Then, the equipment superstructure ( Figure 1) used in this work considers the following stages: rougher stage (R), cleaner stage (C1), re-cleaner stage (C2), scavenger stage (S1), and re-scavenger stage (S2).…”

Section: Superstructurementioning

confidence: 99%

Design of Flotation Circuits Using Tabu-Search Algorithms: Multispecies, Equipment Design, and Profitability Parameters

2019

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The design of a flotation circuit based on optimization techniques requires a superstructure for representing a set of alternatives, a mathematical model for modeling the alternatives, and an optimization technique for solving the problem. The optimization techniques are classified into exact and approximate methods. The first has been widely used. However, the probability of finding an optimal solution decreases when the problem size increases. Genetic algorithms have been the approximate method used for designing flotation circuits when the studied problems were small. The Tabu-search algorithm (TSA) is an approximate method used for solving combinatorial optimization problems. This algorithm is an adaptive procedure that has the ability to employ many other methods. The TSA uses short-term memory to prevent the algorithm from being trapped in cycles. The TSA has many practical advantages but has not been used for designing flotation circuits. We propose using the TSA for solving the flotation circuit design problem. The TSA implemented in this work applies diversification and intensification strategies: diversification is used for exploring new regions, and intensification for exploring regions close to a good solution. Four cases were analyzed to demonstrate the applicability of the algorithm: different objective function, different mathematical models, and a benchmarking between TSA and Baron solver. The results indicate that the developed algorithm presents the ability to converge to a solution optimal or near optimal for a complex combination of requirements and constraints, whereas other methods do not. TSA and the Baron solver provide similar designs, but TSA is faster. We conclude that the developed TSA could be useful in the design of full-scale concentration circuits.

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“…Reverse simulation is another way. It uses vehicle motion parameters (such as velocity and accelerated velocity) as input and simulates power required for vehicles to move forward via the efficiency diagram and power model, and hence is suitable for the micro-analysis of energy flow process [25]. In this paper, we mainly use reverse simulation to calculation FCVs and BEVs.…”

Section: Introductionmentioning

confidence: 99%

Real-World Driving Cycles Adaptability of Electric Vehicles

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Zhao

et al. 2020

WEVJ

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Electric vehicles (EVs) include battery electric vehicles (BEVs), fuel-cell vehicles (FCVs) and fuel-cell hybrid electric vehicles (FCHEVs). The performance of vehicles is usually evaluated using standardized driving cycle tests; however, the results from standardized driving cycle tests deviate from the real-world driving cycle. In order to test the adaptability of EVs to real-world driving cycles, conditions of three typical routes in Tianjin are collected and their characteristics analyzed; then BEV and FCV models are created based on a type of FCHEV to simulate 0–100 km/h acceleration and cruising performance under a real-world driving cycle; finally, a motor bench is used to test the performance of FCHEV under the NEDC (New European Driving Cycle). After the adaptability of the three models to real-world driving cycle is compared based on the simulation and test results, it is found that FCHEV can recycle braking energy and has quick dynamic response, which can be well adapted to the real-world driving cycle.

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A methodology for the conceptual design of flotation circuits by combining group contribution, local/global sensitivity analysis, and reverse simulation

Cited by 13 publications

References 31 publications

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

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

Design of Flotation Circuits Using Tabu-Search Algorithms: Multispecies, Equipment Design, and Profitability Parameters

Real-World Driving Cycles Adaptability of Electric Vehicles

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