## 2 Methodology
### Digital Twin Architecture  
The concept of the DT for the chamber filter press revolves around a continuously improving NN model that is updated with new training data as the filter press operates. This dynamic updating process ensures that the model predictions become increasingly reliable and accurate over time. As illustrated in Figure 2, the operator initiates the process by setting up a new experiment with the static parameters identified, including the number of filter chambers, the filter cloth cycle, the maximum operating pressure, and the suspension concentration. These parameters are sent simultaneously to the database and the NN model. The model uses this input to predict the expected course of pressure  
Figure 1: **(a) Full view of the chamber filter press, showing the filter press unit with multiple filter chambers. The number of chambers directly correlates with the volume of suspension that can be filtered in a single cycle. (b) Close-up view of an individual filter chamber, highlighting the filter cloth (black) inside. Each chamber is where the filtration process occurs, with the slurry being pressurized, allowing the liquid to pass through the cloth while retaining the solids as filter cakes.**and flow rate over time, providing the operator with an estimated filtration time and an efficiency forecast for the process. This predictive insight helps in planning and optimizing the filtration cycle. During the filtration process, sensors installed on the filter press continuously monitor and record real-time data, specifically flow rate and pressure. This data is transmitted to the database and linked to the corresponding experiment entry, ensuring traceability and coherence between static parameters and dynamic measurements. Once the filtration cycle is complete, the recorded data can be fed back into the NN as new training data. This iterative feedback loop enhances the model's accuracy by refining its ability to predict future filtration performance based on historical patterns and real-time observations. This continuous learning approach not only optimizes the performance of the filter press, but also supports proactive decision-making, reducing downtime and improving process outcomes through more accurate predictions and insights.  
This DT concept can also be expanded by integrating AR technology. AR could enable operators and maintenance personnel to visualize the state of the filter press directly on its physical counterpart, providing a real-time overlay of critical data such as performance metrics, sensor readings, or potential error states. For example, AR could highlight issues such as wear or misalignment of the filter cloth or deviations in pressure distribution, allowing immediate troubleshooting. Furthermore, AR could dynamically visualize the operational status of the filter press, including the progress of filtration cycles and system diagnostics, making complex data more intuitive and actionable. Although this combination of DT and AR has significant potential to improve system understanding, troubleshooting, and maintenance, these aspects will not be explored further in this work. However, Figure 3 illustrates a preliminary demonstration, from previous work, where a three-dimensional model of the chamber filter press is overlaid on its real geometry, showcasing the potential for future developments in this direction [31].  
Figure 2: Architecture of the DT framework for a chamber filter press, illustrating the communication flow between the filter press, real-time measuring techniques, the central database, the predictive model, and the operator.