Emerging Trends of Computational Chemistry and Molecular Modeling in Froth Flotation: A Review

IF 4.3 Q2 ENGINEERING, CHEMICAL
Abolfazl Alizadeh Sahraei, Dariush Azizi, Abdol Hadi Mokarizadeh, Daria Camilla Boffito* and Faïçal Larachi*, 
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引用次数: 1

Abstract

Froth flotation is the most versatile process in mineral beneficiation, extensively used to concentrate a wide range of minerals. This process comprises mixtures of more or less liberated minerals, water, air, and various chemical reagents, involving a series of intermingled multiphase physical and chemical phenomena in the aqueous environment. Today’s main challenge facing the froth flotation process is to gain atomic-level insights into the properties of its inherent phenomena governing the process performance. While it is often challenging to determine these phenomena via trial-and-error experimentations, molecular modeling approaches not only elicit a deeper understanding of froth flotation but can also assist experimental studies in saving time and budget. Thanks to the rapid development of computer science and advances in high-performance computing (HPC) infrastructures, theoretical/computational chemistry has now matured enough to successfully and gainfully apply to tackle the challenges of complex systems. In mineral processing, however, advanced applications of computational chemistry are increasingly gaining ground and demonstrating merit in addressing these challenges. Accordingly, this contribution aims to encourage mineral scientists, especially those interested in rational reagent design, to become familiarized with the necessary concepts of molecular modeling and to apply similar strategies when studying and tailoring properties at the molecular level. This review also strives to deliver the state-of-the-art integration and application of molecular modeling in froth flotation studies to assist either active researchers in this field to disclose new directions for future research or newcomers to the field to initiate innovative works.

Abstract Image

泡沫浮选中计算化学和分子模拟的新趋势
泡沫浮选是矿物选矿中最通用的工艺,广泛用于浓缩各种矿物。该过程包括或多或少释放的矿物、水、空气和各种化学试剂的混合物,涉及水环境中一系列混合的多相物理和化学现象。当今泡沫浮选工艺面临的主要挑战是从原子水平深入了解其控制工艺性能的固有现象的性质。虽然通过试错实验来确定这些现象通常很有挑战性,但分子建模方法不仅可以加深对泡沫浮选的理解,还可以帮助实验研究节省时间和预算。由于计算机科学的快速发展和高性能计算(HPC)基础设施的进步,理论/计算化学现在已经足够成熟,可以成功且有收益地应用于应对复杂系统的挑战。然而,在矿物加工中,计算化学的高级应用越来越受到重视,并在应对这些挑战方面表现出了优势。因此,这一贡献旨在鼓励矿物科学家,特别是那些对合理试剂设计感兴趣的科学家,熟悉分子建模的必要概念,并在分子水平上研究和调整性质时应用类似的策略。这篇综述还致力于在泡沫浮选研究中提供最先进的分子建模集成和应用,以帮助该领域的活跃研究人员披露未来研究的新方向,或帮助该领域新来者启动创新工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Engineering Au
ACS Engineering Au 化学工程技术-
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期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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