154 research outputs found
Replication Data for: Boosting Process Efficiency Through Concentrate Recycling in Suspension Melt Crystallization
No full text
Project and Dataset Description
PROJECT DESCRIPTION
Suspension melt crystallization is a low-energy purification technology with growing potential in fields such as water and wastewater treatment. To increase melt (water) yield without compromising purity, we studied the impact of internal concentrate recycling on the performance of an integrated suspension melt crystallization pilot plant combining a scraped cooling crystallizer and a mechanical piston-type wash column.
Systematic recycling experiments were conducted for three aqueous model systems NaCl–H₂O, MgSO₄–H₂O, and a mixed NaCl/MgSO₄–H₂O solution under controlled suspension densities and stepwise decreasing operating temperatures. A key observation is that the process concentrate concentration deviates from solid–liquid equilibrium (SLE), primarily due to heat-transfer limitations that limit crystal growth despite near-equilibrium operating temperatures.
Additionally, the study identifies challenges related to ice entrainment in the recycle and demonstrates partial mitigation strategies to improve process stability. Overall, concentrate recycling shifts the process trajectory within the SLE diagram, enabling higher concentrate levels and maximizing water yield while maintaining high purification efficiency (>99%) and stable operation. The results further indicate that the developed operating strategies are transferable across systems with both known and unknown phase equilibria. Overall, the study highlights that controlled concentrate recycling can enhance production capacity while preserving purification efficiency.
Overall, the study highlights that controlled concentrate recycling can enhance production capacity while preserving purification efficiency.
DATASET DESCRIPTION
This dataset provides a comprehensive collection of raw experimental data and visual representations from crystallization experiments. The focus is on investigating the influence of an internal concentrate recycle in a suspension melt crystallization plant on process performance, specifically water yield and purification efficiency.
1. General Structure
The dataset is organized into two main categories:
Figures
.opju files (OriginLab project files) containing experimental data and corresponding plots.
Raw_data
Excel files with raw experimental data for Test Series 1 and 2 (RAW_DATA_TS1_TS2), including two subfolders:
RAW_DATA_TS1 (Test Series 1)
RAW_DATA_TS2 (Test Series 2)
Particle videos with analyzed particle size distributions for Test Series 1 (RAW_DATA_PSD_TS1_NaCl_1_3.5), including:
Subfolders containing videos for each individual experiment
One subfolder containing PSD results for all experiments
2. Substance Systems
The experiments were conducted using different substance systems in Test Series 1 and 2:
Sodium chloride (NaCl) as impurity in aqueous solution
Feed concentrations: 1 wt% and 3.5 wt%
Magnesium sulfate (MgSO4) as impurity in aqueous solution
Feed concentration: 3.5 wt%
NaCl and MgSO4 in equal proportion as impurities in aqueous solution
Feed concentration: 1 wt%
Due to internal recycling, the operating temperature was reduced on each experimental day in order to decrease the solution concentration.
3. File Contents and Data Description
3.1 opju Files (OriginLab Project Files)
These files contain processed experimental data and visualizations:
Concentration trajectories and suspension densities
Three subfolders for each substance system
Each contains two worksheets and corresponding graphs:
Concentration trajectories in the solid–liquid equilibrium (SLE) diagram
Suspension densities
Data provided for both test series
Particle Size Distribution (PSD)
One worksheet and graph containing cumulative volume distributions
Includes all NaCl experiments from Test Series 1 and both feed concentrations
Purification efficiency
One graph showing purification efficiency
Covers all three substance systems for experiments conducted in Test Series 1
Yield
One graph showing water yield
Covers all three substance systems for experiments conducted in Test Series 1
3.2 RAW_DATA_TS1_TS2 Excel Files
File naming convention:
Test Series 1 (TS1):
Impurity_Date_Feed concentration_Set point temperature for recycling
Test Series 2 (TS2):
TS2_Impurity_Date_Start concentration of experiment day
Test Series 1 contains:
Raw data from the human–machine interface (HMI):
Temperature data (process medium, melt loop, cooling medium)
Light transmission value
Scraper blade energy input
Raw data from LabVIEW:
Inline conductivity data
Pressure data from the wash column melt loop
Offline conductivity measurements:
Conductivity data for each sample taken during the process used to calculate salt concentration
Test Series 2 contains:
Concentrations:
Conductivity data for each sample taken during the process used to calculate salt concentration
Results:
Raw data from the human–machine interface (HMI):
Temperature data (process medium, melt loop, cooling medium)
Light transmission value
Scraper blade energy input
Raw data from LabVIEW:
Inline conductivity data
Pressure data from the wash column melt loop
3.3 RAW_DATA_PSD_TS1_NaCl_1_3.5
Particle Size Distribution (PSD) results for the NaCl–water system at both feed concentrations.
PSD Results folder
Excel files named:
PSDResults_Impurity_Date_Feed concentration_Set point temperature recycling
Cumulative distribution function (Qi)
Characteristic variables and summation function
Characteristic parameters:
X10, X50 (median), X90 (percentile values)
Agglomeration degree (Ag)
Probability density function (qi):
Size distribution data for single crystals and agglomerates
Other folder
Folder name format:
Impurity_Date_Feed concentration_Set point temperature recycling_QICPIC
Particle videos for each individual experiment
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Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features
No full textIn recent years, deep learning has been widely studied for remote sensing image analysis. In this paper, we propose a method for remotely-sensed image classification by using sparse representation of deep learning features. Specifically, we use convolutional neural networks (CNN) to extract deep features from high levels of the image data. Deep features provide high level spatial information created by hierarchical structures. Although the deep features may have high dimensionality, they lie in class-dependent sub-spaces or sub-manifolds. We investigate the characteristics of deep features by using a sparse representation classification framework. The experimental results reveal that the proposed method exploits the inherent low-dimensional structure of the deep features to provide better classification results as compared to the results obtained by widely-used feature exploration algorithms, such as the extended morphological attribute profiles (EMAPs) and sparse coding (SC)
Kwaliteit van arbeid, geautomatiseerd... Een studie naar kwaliteit van arbeid en de relatie tussen automatisering, arbeid en organisatie
No full textTechnology, Policy and Managemen
Temporomandibular Joint Pain is Negatively Correlated to TNF Alpha and Osteoprotegrin Content in Synovial Fluid in Patients with Juvenile Idiopathic Arthritis.
No full textObjective: Temporomandibular joint (TMJ) involvement occurs in up to 80% of children with juvenile idiopathic arthritis (JIA). Little is known with regard to the complexity of the protein profile in synovial fluid (SF) from JIA arthritis during growth as compared to both JIA and rheumatoid arthritis (RA) of adults.
Design: Synovial fluid was collected from 54 joints/30 patients with TMJ arthritis (JIA 35 joints/20 patients, JIA adults 9 joints/5 patients, RA 10 joints/5 patients). Three cytokines and seven bone markers were quantified using Luminex multiplex assays and compared to demographic and clinical data of function and pain.
Results: Pain (spontaneous and upon palpation) and duration of pain were all negatively correlated with the TMJ SF content of tumor necrosis factor (TNF)-α. The level of Adrenocorticotropic hormone (ACTH) was negatively correlated to TMJ pain upon palpation and post-treatment pain and function. The concentration of ACTH was significantly lower in SF in JIA (1.4 ± 2.8 pg/ml) compared to adults with JIA (4.7 ± 12.2 pg/ml) and significantly higher compared to adults with RA (0.8 ± 1.5 pg/ml). Osteoprotegerin (OPG) was negatively correlated to spontaneous pain.
Conclusions: Our results indicate that the local concentrations of TNF-α, ACTH and OPG in TMJ fluid may not contribute to TMJ pain and tissue destruction in JIA/RA patients.
© 2014 Olsen-Bergem H, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Damage detection in semiconductor devices by non-linear elastic wave spectroscopy
No full textSemiconductor devices can be found everywhere in our daily lives, for example in self-driving cars, bank cards and personal devices such as smart phones or notebooks. Once part of these personal devices, one does not want them to show failure. Although reliability of such devices is taken into account in the design, the fabrication process might lead to the emergence of small damages in the product. Since these damages or their propagation might cause failure of the device, a series of visual inspections and functional tests is part of the fabrication process. The obtained resolution by optical microscopy, currently the state of the art inspection method, is not sufficient for the detection of small damages such as microcracks or damages located inside a sample. To keep improving the reliability of semiconductor devices, these damages need to be detected in another high speed, low cost way.As semiconductor devices shrink in size, their natural vibration frequencies increase and approach theMHz-range. Vibration based damage detection methods might therefore offer an alternative high speed in-situ inspection method. The main goal of this thesis is to identify and experimentally verify vibration characteristics that indicate the presence of damage, with a focus on microcracks, in semiconductor devices. While linear vibration based damage methods have proven to be insensitive to small damages such as microcracks, non-linear vibration based damage detection methods show much higher sensitivities to this type of damage. The non-linear elastic wave spectroscopy (NEWS) of several damaged materials have shown two characteristic phenomena: amplitude dependent natural frequency shifts and the generation of higher harmonics. Both are explained by a phenomenological non-linear hysteric elastic model. While the applicability of NEWS is proven for several materials, its performance for silicon, in particular at microscale, is still unknown.Mechanical Engineering | Micro and Nano Engineerin
Acute psychological stress promotes general alertness and attentional control processes: An ERP study
No full textThe drama of the forests, romance and adventure, by Arthur Heming; illustrated by the author with reproductions from a series of his paintings owned by the Royal Ontario museum.
No full textxiii, 324 p
Frontal Control Process in Intentional Forgetting: Electrophysiological Evidence
No full textIn this study, we aimed to seek for the neural evidence of the inhibition control process in directed forgetting (DF). We adopted a modified item-method DF paradigm, in which four kinds of cues were involved. In some trials, the words were followed by only a forgetting (F) cue. In the other trials, after a word was presented, a maintenance (M) cue was presented, followed by an explicit remembering (M-R) cue or an forgetting (M-F) cue. Data from 19 healthy adult participants showed that, (1) compared with the remembering cue (i.e., M-R cue), forgetting cues (i.e., M-F cue and F cue) evoked enhanced frontal N2 and reduced parietal P3 and late positive complex (LPC) components, indicating that the forgetting cues might trigger a more intensive cognitive control process and that fewer amounts of cognitive resources were recruited for the further rehearsal process. (2) Both the M cue and the F cue evoked enhanced N2 and decreased P3 and LPC components than the M-R or M-F cue. These results might indicate that compared with the M-R and M-F cues, both the M and F cues evoked a more intensive cognitive control process and decreased attentional resource allocation process. (3) The F cue evoked a decreased P2 component and an enhanced N2 component relative to the other cues (i.e., M-R, M-F, M), indicating that the F cue received fewer amounts of attentional resources and evoked a more intensive cognitive control process. Taken together, forgetting cues were associated with enhanced N2 activity relative to the maintenance rehearsal process or the remembering process, suggesting an enhanced cognitive control process under DF. This cognitive control process might reflect the role of inhibition in DF as attempting to suppress the ongoing encoding
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