DOI: 10.17877/tudodata-2025-m7akj6m5
title: Understanding the impact of process parameters on the crystallization process within an integrated suspension melt crystallization pilot plant

description: &lt;b&gt;Project:&lt;/b&gt;
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.

&lt;br&gt;&lt;br&gt;
&lt;b&gt;Data set:&lt;/b&gt;
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:
&lt;ul&gt;
 &lt;li&gt;Figures: opju files and PNG files (OriginLab project files containing experimental data, plots, and regressions and the corresponding pictures)
 &lt;/li&gt;

 &lt;li&gt;Raw_data: Excel files with raw data and analyses&lt;/li&gt;

 &lt;li&gt;Processed: data: Excel files with processed data&lt;/li&gt;
&lt;/ul&gt;

Operating Parameters (A, B, C):

The experiments were conducted with different combinations of the following parameters and four center point experiments:
&lt;ul&gt;
 &lt;li&gt;A: Scraper rotational rate → 140 rpm or 70 rpm&lt;/li&gt;

 &lt;li&gt;B: Volume flow rate → 35 Hz (20 m³/h) or 15 Hz (8 m³/h)&lt;/li&gt;

 &lt;li&gt;C: Cooling rate → 3 K/10 min or 1 K/10 min&lt;/li&gt;
&lt;/ul&gt;


File Contents and Data Description:
Figures: opju Files (OriginLab Project Files)
&lt;ul&gt;
 &lt;li&gt;Figure 1 (Sheet: SLE NaCl)
 Experimental data on the solubility of sodium chloride in water
 Quadratic regression with equation in comments&lt;/li&gt;

 &lt;li&gt;Figure 5 (Sheet: CP1-4)
 Raw data from center point experiments CP 1-4
 Mean values and standard deviation&lt;/li&gt;

 &lt;li&gt;Figure 6 (Sheet: Data points for plotting)
 Graph with red squares (layer formation) and green spheres (no layer formation)&lt;/li&gt;

 &lt;li&gt;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&lt;/li&gt;

 &lt;li&gt;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&lt;/li&gt;
&lt;/ul&gt;
Image Files (PNG)
&lt;ul&gt;
 &lt;li&gt;Figure 1: Experimental data on NaCl solubility with quadratic regression&lt;/li&gt;

 &lt;li&gt;Figure 5: Temperature trajectories, scraper blade drive current, and standard deviation of crystallizer acceleration&lt;/li&gt;

 &lt;li&gt;Figure 6a: Effects on layer formation with confidence intervals&lt;/li&gt;

 &lt;li&gt;Figure 6b: Operating window (stable/unstable conditions)&lt;/li&gt;

 &lt;li&gt;Figure 7a-d: Temperature and scraper current at different operating points&lt;/li&gt;

 &lt;li&gt;Figure 8 & 9: Influence of process parameters on particle size distribution&lt;/li&gt;
&lt;/ul&gt;
Processed data: Excel Files 
Layer_new_X_Y and Layer_new_CP
&lt;ul&gt;
 &lt;li&gt;Excel tables with raw vibration data of the operating points (X, Y, or CP = center points) and acceleration evaluation&lt;/li&gt;

 &lt;li&gt;Two sheets:
 High scraper rotational rate (excluding center point experiments)
 Low scraper rotational rate (excluding center point experiments)&lt;/li&gt;

 &lt;li&gt;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&lt;/li&gt;
&lt;/ul&gt;
SI: Evaluation DoE (Design of Experiments)
&lt;ul&gt;
 &lt;li&gt;Summary of conducted experiments with respective operating points and parameters&lt;/li&gt;
 &lt;li&gt;Abbreviation: SD = Standard Deviation&lt;/li&gt;
&lt;/ul&gt;
Raw Data: Excel Files and videos
&lt;ul&gt;
 &lt;li&gt;File name format: OperatingPoint_ScraperRotationalRate_VolumeFlow_CoolingRate_date&lt;/li&gt;

 &lt;ul&gt;
 &lt;li&gt;Contains:
 Concentrations:
 Measured sample weights in feed and concentrate containers
 Calculation of NaCl weight fraction&lt;/li&gt;
 &lt;li&gt; Results:
 Raw data from the human-machine interface (HMI)
 Temperature data (process medium, melt loop, cooling medium)
 Scraper blade energy input&lt;/li&gt;
 &lt;/ul&gt;

 &lt;li&gt;For experiments without layer formation:
 Additional vibration data (acceleration in x, y, z directions)&lt;/li&gt;
&lt;/ul&gt;
Particle Size Distribution (PSD) Results
&lt;ul&gt;
 &lt;li&gt;File name format: PSDResults_OperatingPoint_ScraperRotationalRate_VolumeFlow_CoolingRate_date&lt;/li&gt;
 &lt;li&gt;Contains:
 Cumulative distribution function (Q_i) → Characteristic variables and summation function
 &lt;ul&gt;
 &lt;li&gt;Characteristic parameters:
 X_10, X_50 (median), X_90 (percentile values)
 Agglomeration degree (Ag)&lt;/li&gt;
 &lt;li&gt;Probability density function (q_i):
 Size distribution data for single crystals and agglomerates&lt;/li&gt;
 &lt;/ul&gt;

 &lt;li&gt;Video Folder: QICPICVideos_OperatingPoint_ScraperRotationalRate_VolumeFlow_CoolingRate_date
 Contains particle videos (only for experiments without layer formation)&lt;/li&gt;
&lt;/ul&gt;
description: added related publication