1,720,956 research outputs found
Replication Data for: A Mechanistic Process Model for Suspension Melt Crystallization
No full textPROJECT DESCRIPTION:
The increasing global demand for freshwater, coupled with the challenges of climate change and declining groundwater resources, underscores the importance of effective water treatment methods.
Suspension melt crystallization, also known as freeze concentration, is a promising technique due to its ability to produce high-purity water from aqueous solutions while maintaining lower energy requirements than traditional evaporation processes.
This work focuses on developing a mechanistic model that incorporates hydrodynamics and energy balances to elucidate crystallization phenomena, such as nucleation and crystal growth rates.
The model is validated against experimental data derived from sodium chloride and magnesium sulfate water systems.
With the model, key nucleation and growth kinetics parameters can be successfully predicted to simulate and optimize temperature trajectories and particle size distributions.
This repository contains a structured research dataset developed using a modular workflow inspired by the gPROMS Process Builder Version 2.1.1.
The project includes two main models, multiple process scenarios, and several optimization routines.
Model Structure
Mechanistic_Model model: Contains all equations and the defined parameters and variables.
Parameter_set model: References the Mechanistic_model to set global parameters. Used as the foundation for all process simulations and optimizations.
Process Definitions
For each experimental operating point (Impuritiy_scraper rotational rate_volume flow rate_cooling rate, example: NaCl_210_8_1), a dedicated process file (example: NaCl_210_8_1) is provided. These include:
Input specifications (e.g. feed concentrations, flow rates, temperature)
Experimental results used for comparison and fitting
For NaCl_210_8_3 additional implementation of the task for conti simulation
Optimization Files
Several optimization scripts are included to fit model parameters based on specific operating conditions (Name: fit_parameter_impurity_scraper rotational rate_volume flow rate_cooling rate, example: fit_fhet_NaCl_210_8_1):
NaCl-tagged operating points:
fhet fitting for all NaCl cases
ksec fitting for all NaCl cases
ksec and glim joint optimization, limited to the special case: NaCl_210_8_3
All model, process, and optimization components are included as plain-text .txt files for transparency, reproducibility, and ease of reuse.
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The impact of process parameters on the performance of a wash column in an integrated suspension melt crystallization pilot plant
No full textProject:
The development of efficient and environmentally friendly industrial purification technologies is becoming a key trend in the chemical industry. Suspension melt crystallization, also called freeze concentration, is gaining attention due to its high purification efficiency and low energy consumption, mainly when using renewable energy sources, as it only requires electricity. This technology could offer an alternative for dewatering processes such as desalination and wastewater treatment. Effective solid-liquid separation and crystal purification are essential to fully harness the benefits of suspension melt crystallization, typically achieved through continuously operated wash columns in an integrated plant. However, the complex operation of these columns and their integration with the suspension crystallization unit requires a deep understanding of how various process parameters influence the operating range and the purification efficiency. By a systematic experimental approach using a binary aqueous model substance system with sodium chloride, the performance of a mechanical piston-type wash column with a melt loop is determined regarding three process parameters: the wash front height, the melt loop temperature, and the compression time of the piston. Within stable operating conditions, the effects of the process parameters on the melt production rate and the purification efficiency are quantified and optimized for a high production rate with a high purification efficiency. The wash front height and the melt loop temperature significantly affect the purification efficiency, resulting in a value of about 99.9 % for both parameters on low values.
Dataset
This dataset contains raw and processed data used in the analysis of wash column crystallization processes. It includes measurements from center point experiments (CP), operating point experiments (OP), and calibration data, with a focus on purification efficiency, particle size distribution (PSD), melt and suspension temperatures, and other process parameters. Data is provided as both raw values and as aggregated statistics with standard deviations. Graphs referenced in associated Excel Origin files are also included, with corresponding PNG images for each figure presented in the related publication.
The experiments were conducted with different combinations of the following parameters and four center point experiments:
A: Wash front height → 1/3 to 2/3 of 24 cm total height
B: Melt temperature → 1 °C to 3 °C
C: Compression time → 1 to 5 s
Contents Overview:
Raw Data:
Center Point (CP) and Operating Point (OP) Experiments: Includes experimental values for conductivity, wash front position, melt temperature, suspension temperature, particle sizes (d50,3, d90,3–d10), pressures, and purification efficiency.
Production rates: Contains raw measurement values of the production rates and effect calculation.
Calibration Data: Contains raw measurement values with associated linear regression models for sensor calibration.
Wash front position.m: Matlab code to evaluate the wash front position from mp4 files.
Figures Data (Processed):
Aggregated averages and standard deviations from raw data for graphical representation.
Figures are grouped by topic (e.g., center point experiments, PSD comparisons, purification efficiency effects, calibration).
Each graph is traceable to an Origin file and specific graph number, with matching PNG exports for publication use.
Topics Covered:
Wash Front Tracking
Conductivity Profiles
Thermal Measurements (Melt/Suspension Temperature)
Particle Size Distribution (PSD)
Purification Efficiency and Operating Parameter Effects
Pump Frequency Influence on Operating Point B
Calibration Curves
File Types:
.txt, .xlsx, .opju (Origin project files), .png (graphs)
Use and Purpose:
This dataset supports the reproducibility of results from a study on optimization and performance analysis of wash column-based crystallization processes. It allows for further investigation of process parameter impacts on purification and crystal quality.
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Understanding the impact of process parameters on the crystallization process within an integrated suspension melt crystallization pilot plant
No full textProject:
Developing environmentally-friendly and efficient purification technologies is an inevitable trend in the chemical industry.
Freeze concentration is gaining attention due to its high separation efficiency and low energy demands, relying only on electricity, which allows for integration with renewable energy sources and could be an alternative for dewatering processes like desalination and wastewater treatment.
To utilize the full potential of freeze concentration, the solid-liquid separation and purification of ice crystals are essential, and they are efficiently combined in continuously operated wash columns.
However, the complex operation of these columns and their connection to the suspension crystallization unit requires a fundamental understanding of the effects of different process parameters on the operating window and the quality attributes of the suspension, especially the particle size distribution.
Using a simple binary aqueous substance system with sodium chloride, the operating window of the scraped cooling crystallizer with a forced circulation loop is determined regarding the three main process parameters: the scraper rotational rate, the volume flow rate in the circulation loop, and the cooling rate.
A vibration measurement is implemented and validated as a suitable tool to detect unstable process conditions, such as a crystal layer formation on equipment walls. The effects of the process parameters on the particle size distribution are quantified and optimized to achieve a desired large median particle diameter and a narrow size distribution.
Here, the volume flow rate highly significantly affects the particle size distribution and the interaction with the scraper rotational and cooling rate shows a significant effect. Prioritizing the median particle diameter results in a diameter of 553 µm with a low volume flow, low scraper rotational, and low cooling rate. However, the stirred tank behavior results in a relatively broad distribution of 510 µm.
Data set:
The dataset provides a comprehensive collection of raw experimental data, processed analyses, and visual representations of crystallization experiments. The focus is on understanding the influence of scraper rotational rate, volume flow rate, and cooling rate on layer formation, particle size distribution, and vibration behavior at various operating points.
General Structure:
The dataset consists of three main categories:
Figures: opju files and PNG files (OriginLab project files containing experimental data, plots, and regressions and the corresponding pictures)
Raw_data: Excel files with raw data and analyses
Processed: data: Excel files with processed data
Operating Parameters (A, B, C):
The experiments were conducted with different combinations of the following parameters and four center point experiments:
A: Scraper rotational rate → 140 rpm or 70 rpm
B: Volume flow rate → 35 Hz (20 m³/h) or 15 Hz (8 m³/h)
C: Cooling rate → 3 K/10 min or 1 K/10 min
File Contents and Data Description:
Figures: opju Files (OriginLab Project Files)
Figure 1 (Sheet: SLE NaCl)
Experimental data on the solubility of sodium chloride in water
Quadratic regression with equation in comments
Figure 5 (Sheet: CP1-4)
Raw data from center point experiments CP 1-4
Mean values and standard deviation
Figure 6 (Sheet: Data points for plotting)
Graph with red squares (layer formation) and green spheres (no layer formation)
Figure 7 (Four different sheets: OPx and OPy)
Operating parameters A (scraper rotational rate), B (volume flow rate), and C (cooling rate)
Time data in seconds/minutes, energy input, temperature measurements
Figure 8 (Sheets: Effects & Regression)
Effects of operating parameters on particle size distribution
Regression values for d50 and d90-d10, standardized between 0 and 1
Image Files (PNG)
Figure 1: Experimental data on NaCl solubility with quadratic regression
Figure 5: Temperature trajectories, scraper blade drive current, and standard deviation of crystallizer acceleration
Figure 6a: Effects on layer formation with confidence intervals
Figure 6b: Operating window (stable/unstable conditions)
Figure 7a-d: Temperature and scraper current at different operating points
Figure 8 & 9: Influence of process parameters on particle size distribution
Processed data: Excel Files
Layer_new_X_Y and Layer_new_CP
Excel tables with raw vibration data of the operating points (X, Y, or CP = center points) and acceleration evaluation
Two sheets:
High scraper rotational rate (excluding center point experiments)
Low scraper rotational rate (excluding center point experiments)
Key columns:
Time data (seconds): Start time when process medium reaches 1°C
Acceleration values (xyz-direction): Raw data and derived metrics
Standard deviation & mean values over different time spans
SI: Evaluation DoE (Design of Experiments)
Summary of conducted experiments with respective operating points and parameters
Abbreviation: SD = Standard Deviation
Raw Data: Excel Files and videos
File name format: OperatingPoint_ScraperRotationalRate_VolumeFlow_CoolingRate_date
Contains:
Concentrations:
Measured sample weights in feed and concentrate containers
Calculation of NaCl weight fraction
Results:
Raw data from the human-machine interface (HMI)
Temperature data (process medium, melt loop, cooling medium)
Scraper blade energy input
For experiments without layer formation:
Additional vibration data (acceleration in x, y, z directions)
Particle Size Distribution (PSD) Results
File name format: PSDResults_OperatingPoint_ScraperRotationalRate_VolumeFlow_CoolingRate_date
Contains:
Cumulative distribution function (Q_i) → Characteristic variables and summation function
Characteristic parameters:
X_10, X_50 (median), X_90 (percentile values)
Agglomeration degree (Ag)
Probability density function (q_i):
Size distribution data for single crystals and agglomerates
Video Folder: QICPICVideos_OperatingPoint_ScraperRotationalRate_VolumeFlow_CoolingRate_date
Contains particle videos (only for experiments without layer formation)
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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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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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