# ReadMe: Research Data

## 1. Title of the Dataset
Development of an Aerated High-Pressure Reactor for Biotechnological Applications

## 2. DOI of the Dataset
https://doi.org/10.15480/882.14608

## 3. Authors
  **Name:** Daniel Niehaus  
  **Affiliation:** Hamburg University of Technology, Institute of Multiphase Flows  
  **E-Mail:** daniel.niehaus@tuhh.de  
  **ORCiD:** https://orcid.org/0000-0001-7217-7583  

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## 4. Research Context and Hypotheses
This research is part of the industrial research alliance Prot P.S.I. The goal of the innovation alliance “Prot P.S.I.” is to utilize the process parameter pressure for
process engineering applications, primarily in fine chemistry, to achieve efficiency gains both in product development and in terms of the economic viability of production processes.
This is based on the study and identification of critical protein stability limits and activating and deactivating pressure ranges.

Within this research framework, the dataset is linked to subproject B1, which focuses on the development of an aerated enzymatic high-pressure reactor and the investigation of suitable
measurement techniques for monitoring oxygen concentration under pressure.

Thereforethis  study presents an aerated high-pressure batch reactor designed and characterized for investigating enzymatic systems on a laboratory scale up to a pressure of 15.0 MPa.
The characterisation comprises three key aspects:

1. The application of optical sensors for online measurement of dissolved oxygen concentration under pressure. It is shown that the optical sensors used can reliably detect oxygen
 concentrations of up to 227 mg l⁻¹ even under strong pressure fluctuations.

2. Characterization of mass transfer in the high-pressure bubble column used. Optical access to the bubble column enables measurement of the bubble size distribution and determination of
key mass transfer factors such as gas holdup and interfacial area. The influence of pressure on bubble diameter is particularly highlighted. The volumetric mass transfer coefficient is
also determined using optical sensors.
  
3. Validation experiments integrate the results of the previous sections, demonstrating the functionality of the setup with immobilized glucose oxidase. It is shown that the reaction can
be monitored using optical sensors and that process intensification can be achieved due to increased oxygen availability. At the same time, it is evident that the mass transfer performance
of the bubble column used is insufficient to overcome mass transfer limitations.

## 5. Creation Date and Version
- **Creation Date:** 14.03.2025
- **Version:** 1.0

## 6. Data Collection and Methodology
The data was collected experimentally using a high-pressure aeration unit equipped with various sensors and actuators. These include pressure and temperature sensors, as well as flow
meters and regulators for gas and liquid streams. Optical oxygen sensors from PreSens were used to measure oxygen concentration.

Additionally, the setup includes an optical observation cell that allows analysis of bubble size and gas holdup. A detailed description of the measurement techniques and data evaluation
is provided in the corresponding dissertation.

## 7. Software Used
Microsoft Excel 2013, PreSens Measurement Studio 2 (v3.0.0.1353), MATLAB R2020B, SOPAT (v2.1.17.1623), Microsoft PowerPoint 2013, GIMP 2.10.12

## 8. Data Structure

This repository only contains versions that were published in the corresponding dissertation. The data is divided into four major datasets:

1. Images:
   - Organized by chapter and sequential numbering.
   - Each numbered entry contains a folder with:
     - A print-ready version of the figure.
     - An editable version (created using PowerPoint or GIMP).

The structure of the following sections (Raw Data, Processed Data, and Analysis) is designed to be analogous, ensuring consistency and ease of navigation. Each of these categories contains
four subfolders, corresponding to the different aspects investigated:

   - Oxygen Sensors under Pressure
   - Hydrodynamic Characterization
   - Glucose Oxidase under Pressure

2. Raw Data:
   - Further subdivided by the examined parameters.
   - Includes folders for each individual measurement, containing the generated data.
   - Folder names reflect the varied parameters to ensure clear identification and traceability.

3. Processed Data:
   - Based on the raw data compiled from the process control system and PreSens Measurement Studio, using timestamps to synchronize data from different sources.
   - Data was trimmed to match the relevant experimental time period.
   - For repeated measurements, mean values were calculated, and the standard deviation was determined.

4. Analysis:
   - Includes MATLAB scripts for further data processing and visualization.
   - The scripts always require an input dataset, which is provided by the Processed Data folder.
   - The datasets associated with the scripts are named analogously to the corresponding scripts, ensuring easy identification and reproducibility.

## 9. Software Code
- **Files:** MATLAB script files are located within the respective analysis folders.
- **Programming Language:** MATLAB
- **Description:** The MATLAB code is used for data processing, analysis, and visualization. All scripts are fully commented and enable reproducible evaluation of experimental results.

## 10. Additional Documentation Files
No additional documentation files, such as codebooks, lab notebooks, or questionnaires, are included.

## 11. Access and Licensing
- **License:** CC BY 4.0 (Attribution)