Autonomous Processes in Particle Technology

Nirschl, Hermann; Winkler, Moritz; Sinn, Tabea; Menesklou, Philipp

doi:10.1002/cite.202100059

Cited by 3 publications

(2 citation statements)

References 47 publications

(46 reference statements)

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“…Furthermore, general reviews exist, such as of mechanical engineering (Patel et al, 2021), electrical engineering (Sakunthala et al, 2017), material science (Pilania, 2021), and chemical engineering (Schweidtmann et al, 2021;Ashraf et al, 2021;Venkatasubramanian, 2019). On the other hand, focused review articles for specific subfields within particle technology exist, such as for related autonomous processes (Nirschl et al, 2022) or nanoparticle drug formulations (Uhlemann et al, 2021), although, to date, no conclusive overview of the field of particle technology has been provided, which is the focus of this review article.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Evolutionary Approaches in Particle Technology

Thon,

Röhl,

Hosseinhashemi

et al. 2024

KONA

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Since the early 2010s, after decades of premature excitement and disillusionment, the field of artificial intelligence (AI) is experiencing exponential growth, with massive real-world applications and high adoption rates both in daily life and in industry. In particle technology, there are already many examples of successful AI applications, for predictive modeling, process control and optimization, fault recognition, even for mechanistic modeling. However, in comparison to its still untapped potential and to other industries, further expansion in adoption rates and, consequently, gains in productivity, efficiency, and cost reduction are still possible. This review article is intended to introduce AI and its application scenarios and provide an overview and examples of current use cases of different aspects and unit operations in particle technology, such as grinding, extrusion, synthesis, characterization, or scale up. In addition, hybrid modeling approaches are presented with examples of the intelligent combination of different methods to reduce data requirements and achieve beneficial synergies. Finally, an outlook for future opportunities is given, depicting promising approaches, currently being in the conception or implementation phase.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Evolutionary Approaches in Particle Technology

Thon,

Röhl,

Hosseinhashemi

et al. 2024

KONA

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“…Lastly, compared to simulations based on Computational Fluid Dynamics (CFD) and the Discrete Element Method (DEM), the dynamic process models demand a low investment in computational power. Consequently, this leads to a possible application in model predictive control strategies that demand real-time process simulation speeds [40].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Modeling of Volume and Form Dependent Nanoparticle Fractionation in Tubular Centrifuges

et al. 2022

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A dynamic process model for the simulation of nanoparticle fractionation in tubular centrifuges is presented. Established state-of-the-art methods are further developed to incorporate multi-dimensional particle properties (traits). The separation outcome is quantified based on a discrete distribution of particle volume, elongation and flatness. The simulation algorithm solves a mass balance between interconnected compartments which represent the separation zone. Grade efficiencies are calculated by a short-cut model involving material functions and higher dimensional particle trait distributions. For the one dimensional classification of fumed silica nanoparticles, the numerical solution is validated experimentally. A creation and characterization of a virtual particle system provides an additional three dimensional input dataset. Following a three dimensional fractionation case study, the tubular centrifuge model underlines the fact that a precise fractionation according to particle form is extremely difficult. In light of this, the paper discusses particle elongation and flatness as impacting traits during fractionation in tubular centrifuges. Furthermore, communications on separation performance and outcome are possible and facilitated by the three dimensional visualization of grade efficiency data. Future research in nanoparticle characterization will further enhance the models use in real-time separation process simulation.

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