3 research outputs found

    Performance prediction and optimization for industrial sieves by simulation : a two-tier approach

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    We present a numerical study of sieving behavior on industrial sieves, composed of several vibrating screens, a process widely used in diverse industries. The modeling approach is twofold: on the one hand, particle flow is modeled in some detail by means of a discrete element model (DEM). This allows studying the influence of various parameters on the behavior of individual particles, particularly transport velocities and collision rates. Computational complexity however forbids the simulation of an entire sieve as a DEM. Instead, the overall sieving behavior is modeled separately by means of a more phenomenological model, the so called thick layer model (TLM), which is based on mass-balance equations, that translate into an ordinary differential equation. The TLM obtains its most crucial input parameters as results of the DEM. Comparison of simulation results with measurements shows, that this combined approach is capable of accurately describing the sieving process at a reasonable computational cost

    Discharge of springs monitored during the WABEsense project

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    <p>Water discharge and temperature of the springs monitored during the <a href="https://www.aramis.admin.ch/Grunddaten/?ProjectID=47484">WABEsense project (UTF 642.21.20)</a>.</p><p>The data corresponding to the all field measurements performed between Feb. 2021 and Dec 2023. Data for each spring might not cover the whole period.</p><p>For each spring there are two files: *.csv and *.meta. The .csv file contains the recorded data. The .meta fiel contains further information about the spring (e.g. location) and the data.</p><p>## List of springs:</p><p>- Bonaduz BS Paliu Fravi<br>- Bonaduz BS Salums Friedrich<br>- Bonaduz BS Salums Leo<br>- Bonaduz SS Leo Friedrich<br>- Bregaglia BS Acqua d'Balz 1<br>- Bregaglia BS Acqua d'Balz 2<br>- Hergiswil BS Rossmoos<br>- Hergiswil SS Muesli<br>- Oberriet SS Ulrika<br>- Schiers BS Chalta Wasser<br>- Schiers BS Grapp rechts<br>- Susch BS Prada bella suot<br>- Susch BS Prada bella sura<br>- Zernez BS Sarsura<br>- Zug SS Nidfuren</p><p>More details about each spring is given in the corresponding .meta file.</p><p>We are very grateful for the work of the operators responsible for collecting the data.</p><p>## Files formats</p><p>All files are plain text. The .csv files are UTF-8 encoded and separated by ";" (semi-colon) . The .meta files follow the YAML format.</p><p>An example of loading the data in python using pandas is given below:</p><p>import pandas as pd data = pd.read_csv("Bonaduz.BS.Paliu_Fravi_discharge.csv", encoding="utf-8", sep=";")</p><p> </p&gt

    Discharge of springs monitored during the WABEsense project

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    <p>Water discharge and temperature of the springs monitored during the <a href="https://www.aramis.admin.ch/Grunddaten/?ProjectID=47484">WABEsense project (UTF 642.21.20)</a>.</p><p>The data corresponding to the all field measurements performed between Feb. 2021 and Dec 2023. Data for each spring might not cover the whole period.</p><p>For each spring there are two files: *.csv and *.meta. The .csv file contains the recorded data. The .meta fiel contains further information about the spring (e.g. location) and the data.</p><p>## List of springs:</p><p>- Bonaduz BS Paliu Fravi<br>- Bonaduz BS Salums Friedrich<br>- Bonaduz BS Salums Leo<br>- Bonaduz SS Leo Friedrich<br>- Bregaglia BS Acqua d'Balz 1<br>- Bregaglia BS Acqua d'Balz 2<br>- Hergiswil BS Rossmoos<br>- Hergiswil SS Muesli<br>- Oberriet SS Ulrika<br>- Schiers BS Chalta Wasser<br>- Schiers BS Grapp rechts<br>- Susch BS Prada bella suot<br>- Susch BS Prada bella sura<br>- Zernez BS Sarsura<br>- Zug SS Nidfuren</p><p>More details about each spring is given in the corresponding .meta file.</p><p>## Files formats</p><p>All files are plain text. The .csv files are UTF-8 encoded and separated by ";" (semi-colon) . The .meta files follow the YAML format.</p><p>An example of loading the data in python using pandas is given below:</p><p>import pandas as pd data = pd.read_csv("Bonaduz.BS.Paliu_Fravi_discharge.csv", encoding="utf-8", sep=";")</p><p> </p&gt
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