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Processes
Special Issues
State-of-the-Art Research and Development in Particle Technology
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State-of-the-Art Research and Development in Particle Technology

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Particle Processes".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 1267

Special Issue Editors

Dr. Ali Hassanpour

E-Mail Website
Guest Editor
School of Chemical and Process Engineering, University of Leeds, West Yorkshire, Leeds LS2 9JT, UK
Interests: particle characterization; powder flow; particulate solid processing and manufacturing; distinct element method (DEM) coupled with computational fluid dynamics (CFD)
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaodong Jia

E-Mail Website
Guest Editor
School of Chemical and Process Engineering, University of Leeds, West Yorkshire, Leeds LS2 9JT, UK
Interests: particle-level simulation models; particle packing and packing optimisation; porous structures; property–structure relationships; DEM (discrete element method); LBM (lattice Boltzmann method); X-ray CT; use of AI in particle technology

Special Issue Information

Dear Colleagues,

Particulate solids make up more than half of the globally manufactured products in various sectors such as food, healthcare, homecare, personal care, minerals, additive manufacturing, energy and fine chemicals. The science and knowledge of manufacturing, processing and handling of particulate solids, commonly known as particle technology, has significant industrial importance. Understanding and predicting the collective behaviour of particles in different conditions, for example, in packing, mixing and flow, is vitally important for the design, optimization and control of many processes; at the same time, however, it is very challenging due to the complexity and heterogeneity of particulate systems. To address the aforementioned challenge, a multidisciplinary approach based on a thorough characterization of particulate solids, advanced process measurements and modelling across different length and time scales is required. Based upon recent scientific and technological advances, this Special Issue aims to present state-of-the-art research and developments in the field of particle technology in the following key areas:

  • Cutting-edge particle characterization;
  • Advanced process measurements and optimization;
  • Multiscale and multiphase modelling of particulate systems.

Dr. Ali Hassanpour
Dr. Xiaodong Jia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • particle characterization
  • bulk solid handling
  • particulate solid processing
  • milling and griding
  • granulation
  • powder flow
  • powder mixing
  • formulated powders
  • particulate process system modelling

Published Papers (2 papers)

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Research

14 pages, 9599 KiB  
Article
Predicting Bulk Density for Agglomerated Raspberry Ketone via Integrating Morphological and Size Metrics Using Artificial Neural Networks
by Xiaomeng Zhou, Shutian Xuanyuan, Yang Ye, Ying Sun, Haowen Du, Luguang Qi, Chang Li and Chuang Xie
Processes 2024, 12(5), 902; https://doi.org/10.3390/pr12050902 - 29 Apr 2024
Viewed by 462
Abstract
The bulk density of the particles, which is directly related to transportation and storage costs, is an important basic characteristic of products as well as an important parameter in many processing systems. This work quantified the relationship between the tapped bulk density of [...] Read more.
The bulk density of the particles, which is directly related to transportation and storage costs, is an important basic characteristic of products as well as an important parameter in many processing systems. This work quantified the relationship between the tapped bulk density of raspberry ketone with different degrees of agglomeration and morphological metrics (particle shape descriptors and roughness descriptors) and size metrics (size descriptors) and developed an artificial neural network (ANN) prediction model for the tapped bulk density of raspberry ketone. Samples prepared under different conditions were sieved and remixed, the tapped bulk density of the particles was then measured, and the descriptor features of the particles were obtained by combining them with image processing. The dimensions of the variables were decreased by principal component analysis and variance processing. To overcome the hyperparameter estimation of the heuristic-based artificial neural networks, the network model architectures were optimized by a neural architecture search strategy combining two-objective optimization. The results demonstrated that the tapped bulk density of raspberry ketone products is not only related to the descriptors of particle size and shape but also has a non-negligible relationship with particle roughness descriptors. The performance of the optimal ANN model demonstrated that the model can well predict the tapped bulk density of raspberry ketone with different degrees of agglomeration. The ANN model obtained by extracting morphology and size metrics through online image analysis can be used to measure the tapped bulk density in real-time and has the potential to be used for developing model-based online process monitoring. Full article
(This article belongs to the Special Issue State-of-the-Art Research and Development in Particle Technology)
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14 pages, 2226 KiB  
Article
Modelling and Prediction of Fe/MWCNT Nanocomposites for Hexavalent Chromium Reduction
by Zeyu Kang, Xiaodong Jia, Xiaolong Ma and Dongsheng Wen
Processes 2023, 11(12), 3271; https://doi.org/10.3390/pr11123271 - 22 Nov 2023
Viewed by 551
Abstract
Chromium (Cr) is a heavy metal pollutant prevalent in freshwater resources. Current investigations into Cr(VI) removal materials primarily involve multi-component materials. Among them, iron nanoparticles and multi-walled carbon nanotubes (MWCNTs) have exhibited great promise of removal capabilities. However, determining the optimal component ratio(s) [...] Read more.
Chromium (Cr) is a heavy metal pollutant prevalent in freshwater resources. Current investigations into Cr(VI) removal materials primarily involve multi-component materials. Among them, iron nanoparticles and multi-walled carbon nanotubes (MWCNTs) have exhibited great promise of removal capabilities. However, determining the optimal component ratio(s) experimentally still requires a substantial amount of effort. This paper presents a novel, model-based approach which can lessen the burden by predicting the performance of new materials. The model is based on reaction kinetics equations and derives its input parameters from the size and surface area characterisations of the components, individual components removal performance, and their mixture performance at one specific component ratio. The model is validated against experimental results for Fe/MWCNT mixtures at six ratios. The root mean square error of our model is 3.95 mg/g, which is less than 3% of the total adsorption capacity, indicating that the model is reliable. The model can be used to identify the optimal component ratios of the Fe-MWCNT composite and to reveal the relationship between performance and time. To the best of our knowledge, this is the first semi-empirical model that can predict the adsorption capacity of a composite material for heavy metals. The model is founded on the generic reduction theory of adsorption, and model parameters are not tied specifically to Fe/MWCNT. Thus, it can be used for predicting the adsorption reduction properties of other multiphase materials to speed up the new material design process. Full article
(This article belongs to the Special Issue State-of-the-Art Research and Development in Particle Technology)
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