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Subtitle: Jupyter Notebooks for Data Regression and Graph Generation

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Additional titleSubtitle: Jupyter Notebooks for Data Regression and Graph Generation
Person(s) who is (are) responsible for the content of the research dataBuchwald, Thomas - TU BAF (ORCID: 0000-0002-2953-0510)
Description of further data processingThe experimental data (cake filtration experiments) has been analysed by different methods, most importantly nonlinear parameter estimation. Several different model equations have been used to derive additional theoretical insight.
AbstractThis collection belongs to the doctoral thesis "Nonlinear Parameter Estimation of Experimental Cake Filtration Data". Most of the content are Jupyter notebooks which contain the Python code which reproduces graphs found in the thesis from the original experimental data. The lab practice dataset that contains 500 filtration experiments is contained in the folder for section 3.5. The notebooks are not strictly sorted by section. At any rate, the Readme will guide you to the notebook which produces a certain graph, if it is not part of the main notebook of that specific section. The original Python environment was set up with Anaconda. Please use the provided .yml file to create a Python environment which contains all the necessary packages. Some notebooks may not work with the most current versions of the packages, so updating is not necessarily a good idea.
Applied methods and techniquesNonlinear Parameter Estimation
Additional descriptive information to understand the dataThe experimental data was generated between 2010 and 2021 on two different pressure filtration apparatuses.
Counties, the data is referencingGERMANYde
Additional keywordsJupyter notebook
Additional keywordsPython
Additional keywordsDataset
Additional keywordsCake Filtration
Languageeng
Year or period of data production2010-2021
Publication year2021
PublisherTechnische Universität Bergakademie Freiberg
References on related materialsIsPartOf: 123456789/1955 (Handle)
Content of the research dataText, Image, Dataset, Model: Experimental data and Jupyter notebooks which contain Python code that analyses the dataset and produces all the graphs contained in the PhD thesis.
Holder of usage rightsTechnische Universität Bergakademie Freiberg
Usage rights of the dataCC-BY-4.0
SoftwareResource Processing: Python 3.7
Additional precise description of disciplineMechanical Solid/Liquid Separation, Cake Filtration
Discipline(s)Engineeringde
Title of the datasetExperimental Cake Filtration Data and Code for Analysis, Graph Generation


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  • Experimental Data and Diagrams [1]Open Access Icon
    This collection belongs to the doctoral thesis "Nonlinear Parameter Estimation of Experimental Cake Filtration Data". Most of the content are Jupyter notebooks which contain the Python code which reproduces graphs found in the thesis from the original experimental data. The lab practice dataset that contains 500 filtration experiments is contained in the folder for section 3.5. The notebooks are not strictly sorted by section. At any rate, the Readme will guide you to the notebook which produces a certain graph, if it is not part of the main notebook of that specific section. The original Python environment was set up with Anaconda. Please use the provided .yml file to create a Python environment which contains all the necessary packages. Some notebooks may not work with the most current versions of the packages, so updating is not necessarily a good idea.

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