Exclusive Collection: Papers from the Editorial Board Members (EBMs) of ChemEngineering

A special issue of ChemEngineering (ISSN 2305-7084).

Deadline for manuscript submissions: closed (25 December 2023) | Viewed by 6086

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Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro, 1, 1959-007 Lisboa, Portugal
Interests: chemical engineering; supercritical fluids; antioxidants; thermodynamics; modelling; food technology
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This is a Special Issue comprising high-quality papers in open-access format authored by the Editorial Board Members of ChemEngineering, or those recommended and invited by the Editorial Board Members and the Editor-in-Chief.

Papers on any area covered by the scope of the journal are welcome.

Dr. George Z. Papageorgiou
Dr. José P. Coelho
Guest Editors

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Keywords

  • process engineering
  • transport phenomena 
  • artificial intelligence solutions
  • product engineering
  • materials engineering
  • molecular engineering
  • energy and environmental engineering
  • chemical and catalytic reaction engineering
  • advanced process control
  • process intensification

Published Papers (3 papers)

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Research

30 pages, 12189 KiB  
Article
Recent Progress in the Viscosity Modeling of Concentrated Suspensions of Unimodal Hard Spheres
by Rajinder Pal
ChemEngineering 2023, 7(4), 70; https://doi.org/10.3390/chemengineering7040070 - 27 Jul 2023
Cited by 2 | Viewed by 1755
Abstract
The viscosity models for concentrated suspensions of unimodal hard spheres published in the twenty-first century are reviewed, compared, and evaluated using a large pool of available experimental data. The Pal viscosity model for unimodal suspensions is the best available model in that the [...] Read more.
The viscosity models for concentrated suspensions of unimodal hard spheres published in the twenty-first century are reviewed, compared, and evaluated using a large pool of available experimental data. The Pal viscosity model for unimodal suspensions is the best available model in that the predictions of this model agree very well with the low (zero)-shear experimental relative viscosity data for coarse suspensions, nanosuspensions, and coarse suspensions thickened by starch nanoparticles. The average percentage error in model predictions is less than 6.5%. Finally, the viscous behavior of concentrated multimodal suspensions is simulated using the Pal model for unimodal suspensions. Full article
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10 pages, 3321 KiB  
Article
Prediction of Particle Size Distribution of Mill Products Using Artificial Neural Networks
by Akira Otsuki and Hyongdoo Jang
ChemEngineering 2022, 6(6), 92; https://doi.org/10.3390/chemengineering6060092 - 25 Nov 2022
Cited by 1 | Viewed by 1524
Abstract
High energy consumption in size reduction operations is one of the most significant issues concerning the sustainability of raw material beneficiation. Thus, process optimization should be done to reduce energy consumption. This study aimed to investigate the applicability of artificial neural networks (ANNs) [...] Read more.
High energy consumption in size reduction operations is one of the most significant issues concerning the sustainability of raw material beneficiation. Thus, process optimization should be done to reduce energy consumption. This study aimed to investigate the applicability of artificial neural networks (ANNs) to predict the particle size distributions (PSDs) of mill products. PSD is one of the key sources of information after milling since it significantly affects the subsequent beneficiation processes. Thus, precise PSD prediction can contribute to process optimization and energy consumption reduction by avoiding over-grinding. In this study, coal particles (−2 mm) were ground with a rod mill under different conditions, and their PSDs were measured. The variables studied included volume% (vol.%) of feed (coal particle), vol.% rod load, and grinding time. Our supervised ANN models were developed to predict PSDs and trained by experimental data sets. The trained models were verified with the other experimental data sets. The results showed that the PSDs predicted by ANN fitted very well with the experimental data after the training. Root mean squared error (RMSE) was calculated for each milling condition, with results between 0.165 and 0.965. Also, the developed ANN models can predict the PSDs of ground products under different milling conditions (i.e., vol.% feed, vol.% rod load, and grinding time). The results confirmed the applicability of ANNs to predict PSD and, thus the potential contribution to reducing energy consumption by optimizing the grinding conditions. Full article
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16 pages, 2087 KiB  
Article
Optimization of Oil Recovery from Japonica Luna Rice Bran by Supercritical Carbon Dioxide Applying Design of Experiments: Characterization of the Oil and Mass Transfer Modeling
by José P. Coelho, Maria Paula Robalo, Inês S. Fernandes and Roumiana P. Stateva
ChemEngineering 2022, 6(4), 63; https://doi.org/10.3390/chemengineering6040063 - 10 Aug 2022
Cited by 1 | Viewed by 1834
Abstract
This study presents an optimization strategy for recovery of oil from Japonica Luna rice bran using supercritical carbon dioxide (scCO2), based on design of experiments (DoE). Initially, a 24−1 two level fractional factorial design (FFD) was used, and pressure, temperature, [...] Read more.
This study presents an optimization strategy for recovery of oil from Japonica Luna rice bran using supercritical carbon dioxide (scCO2), based on design of experiments (DoE). Initially, a 24−1 two level fractional factorial design (FFD) was used, and pressure, temperature, and scCO2 flow rate were determined as the significant variables; while the yield, total flavonoids content (TFC), and total polyphenols content (TPC) were the response functions used to analyze the quality of the extracts recovered. Subsequently, central composite design (CCD) was applied to examine the effects of the significant variables on the responses and create quadratic surfaces that optimize the latter. The following values of pressure = 34.35 MPa, temperature = 339.5 K, and scCO2 flow rate = 1.8 × 10−3 kg/min were found to simultaneously optimize the yield (6.83%), TPC (61.28 μmol GAE/g ext), and TFC (1696.8 μmol EC/g ext). The fatty acid profile of the oils was characterized by GC-FID. It was demonstrated that the acids in largest quantities are C16:0 (15–16%), C18:1 (41%), and C18:2 (38–39%). Finally, three mass transfer models were applied to determine the mass transfer coefficients and assess the cumulative extraction curves, with an AAD% of 4.16, for the best model. Full article
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