## 4 Conclusions  
This study introduces a novel ML-based DT framework to sustainably predict key operational parameters of chamber filter presses, specifically pressure and flow rates. The framework was evaluated using two datasets: one for training and validation, and another with unknown experimental configurations. **Summary of evaluation:**  
* **Pressure prediction (training and validation):** Overall, MSE was 0.048, RMSE was 0.185 and RL2N was 5.0%. Deviation from the CI90% bounds was 1.8%. * **Flow rate prediction (training and validation):** Overall, MSE was 2.579, RMSE was 1.545 and RL2N was 9.3%. Deviation from the CI90% bounds was 3.7%. * **Pressure prediction (unknown data):** Overall MSE was 0.403, RMSE was 0.605 and RL2N was 18.4%. Deviation from the CI90% bounds was 8.2%. * **Flow rate prediction (unknown data):** Overall MSE was 8.229, RMSE was 2.772, and RL2N was 15.4%. Deviation outside CI90% bounds was 4.8%. The comparison of RMSE and MSE values suggests fewer significant errors and more frequent small deviations in flow rate predictions. Furthermore, the model's ability to interpolate between known configurations (e.g., 12.5 g/L and 25 g/L) to approximate unknown configurations (e.g., 15 g/L) highlights its practical utility, even in cases with limited training data. However, errors for specific configurations, such as 6.25 g/L, indicate that broader training datasets should be considered in future studies to further enhance performance in edge cases. This DT framework, which integrates real-time data and predictive analytics, represents a significant step forward in optimizing chamber filter press operations. By accurately forecasting pressure and flow rates and estimating the lifespan of the filter medium, the system enables more efficient process control, reduces downtime, enhances resource utilization, and supports sustainable practices. The model can be adopted by operators in industrial filtration settings such as mining, wastewater treatment, and pharmaceuticals, where data-driven optimization is essential. It is particularly suited for applications in volving repetitive, measurable processes that benefit from predictive maintenance and efficiency improvements. Future work will focus on expanding the diversity of operational scenarios, improving prediction accuracy for sparsely trained configurations, and further refining the model's adaptability to evolving industrial conditions. Conceptualization, D.T. ; methodology, D.T. ; software, D.T. ; validation, D.T. and S.S.; formal analysis, D.T. and S.S.; investigation, D.T. ; resources, M.J.K. and T.W.-C.; data curation, D.T. ; writing--original draft preparation, D.T. ; writing--review and editing, S.S. and M.J.K. ; supervision, M.J.K. ; project administration, M.J.K. ; funding acquisition, M.J.K. and T.W.-C. All authors have read and agreed to the published version of the manuscript. The current research is part of the Invest BW project BW1_0027/01 \"Filt-AR: Entwicklung einer neuen Filt-AR-Filterpresse mit wissensbasierter Regelung anhand eines digitalen Zwillings und Augmented Reality-Visualisierung\". Not applicable. Data Availability Statement: Data is contained within the article. Author [PERSON] was employed by the company Simex Filterpressen und Wassertechnik GmbH & Co. KG. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The following abbreviations are used in this manuscript: