## 2 Methodology
### Data Logging and Database Development  
The experimental filter press setup incorporates essential hardware components powered by a 24 V DC input, including a suspension pressure sensor, a flow sensor, and a delphin data logger. The data logger, which operates at a sampling rate of 10 Hz per channel with 24-bit resolution, is suitable for capturing high-resolution data necessary for the analysis of the filtration process. It features an integrated web server and supports the open platform communications unified architecture (OPC UA) protocol, facilitating seamless data transmission and remote access (Figure 4). While the logger enables local data retrieval via USB using proprietary software, it does not inherently support the direct transmission of data to the model environment or provide control functionality for the active components of the filter press. Therefore, an independent control computer was introduced, equipped with a development board containing a quad-core 64-bit ARM Cortex A72 processor. This control system also includes an output module that interfaces with and manages the operation of the active elements in the filter press.  
The control computer runs on a 64-bit Ubuntu Linux-based operating system and utilizes an InfluxDB time-series database for local data storage. Node-RED programming facilitates the management of local input and output (I/O) control and orchestrates the data exchange between the control computer and the data logger. Communication between the development board and the data logger is established over an Ethernet connection via the OPC UA protocol, where the data logger functions as the OPC UA server. Once data is transferred to the control system, it undergoes preprocessing, including filtering and analysis, to optimize the data storage load in the database.  
The database architecture comprises two interconnected tables: one dedicated to experimental metadata and the other to measured data. The experiment table records user-defined parameters, including the experiment number, number of filter cycles, number of filter chambers, maximum operating pressure, and suspension concentration. These parameters are entered by the user via a graphical interface before initiating each experimental run. The measured data table contains time-stamped data points, such as pressure and flow rate readings, which are linked to the corresponding experiment via the experiment number as a foreign key. This relational structure allows for efficient organization and retrieval of experimental data.  
Since InfluxDB utilizes time-based indexing, accurate synchronization between the control computer and the data logger is critical to maintaining data integrity. Initially, the network time protocol (NTP) is employed to synchronize the system clocks. However, given that NTP cannot achieve millisecond-level precision on the data logger, a precision time protocol (PTP) server is subsequently initiated on the control computer. This ensures precise time synchronization between the two systems, which is essential for accurate data logging and analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Concentration [g/L]** & **Filter Plate Number** & **End Pressure [bar]** & **Cycles** & **Frequency** \\ \hline
25.00 & 2 & 6.0 & 19 & 1 \\ \hline
25.00 & 2 & 7.0 & 20 & 1 \\ \hline
25.00 & 2 & 8.0 & 21 & 1 \\ \hline
25.00 & 2 & 9.0 & 22 & 1 \\ \hline
25.00 & 2 & 10.0 & 23 & 1 \\ \hline
25.00 & 3 & 10.0 & 25 & 1 \\ \hline
25.00 & 4 & 10.0 & 26 & 1 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._The system architecture is designed to accommodate various data transmission strategies, depending on the operational context of the filter press. For the prototype system described, the direct transmission of measurement data from the control system to the model cloud server via LTE was selected as the preferred method. This approach facilitates real-time monitoring and data exchange with external databases for further analysis and archiving.