## 1 Introduction  
Filter presses play a critical role in numerous industries, including wastewater treatment, pharmaceuticals, and mining, by efficiently separating solids from liquids. A chamber filter press is a specific type of filter press that operates by pumping slurry into a series of recessed chambers lined with filter cloth. As pressure builds, liquid passes through the cloth and exits the system, while solids are retained to form filter cakes. This design enableshigh solid-liquid separation efficiency and is widely used in industrial applications due to its robustness and reliability. In the mining sector, where vast quantities of ore are processed daily and water consumption is significant [1], filter presses are indispensable for reducing fluid content in the separated material [2]. This reduction not only minimizes water usage but also mitigates the risks associated with sludge storage, such as dam failures [3]. As mining operations can have a big impact on the environment [4], it faces increasing environmental regulations and demands for sustainability. The optimization of filtration processes through innovations such as augmented reality (AR) and machine learning (ML) presents a significant opportunity to enhance automation, monitoring, and operational efficiency. Machine learning has been widely recognized for its role in predictive analytics and adaptive control in filtration systems [5; 6]. In parallel, recent work has demonstrated the potential of AR to support real-time process monitoring, operator training, and decision-making in industrial environments [7; 8; 9], suggesting further possibilities for its integration into filtration processes. By embedding predictive capabilities within filter press systems, operators can achieve higher precision in process control, leading to greater efficiency and better quality outputs. This idea has been researched through different approaches. For example, [PERSON] et al. [10] used the theory of compressive rheology to predict the filtration time and maximize suspension throughput. Other approaches utilize neural networks (NNs) to predict metrics such as filtered volume [11], flow rate, and turbidity [12], or to predict and optimize particle counts [13]. NNs are particularly suited for modelling complex tasks due to their capability as universal function approximators, allowing them to capture intricate, non-linear relationships within diverse datasets [14]. Their adaptability and effectiveness have been demonstrated across various domains, including engineering, medical, and industrial applications, where they are used for tasks such as pattern recognition, classification, and prediction. Furthermore, recent works highlight their effectiveness in time-series forecasting and industrial prediction problems, showcasing their flexibility in real-world scenarios [15]. Another possibility is the application of computational fluid dynamics (CFD). For instance, [PERSON] et al. [16] employed a numerical model based on the Brinkman equation to predict pressure drops and filtration efficiency. Highly performant simulation software based on the lattice Boltzmann method (LBM) could also be utilized for this purpose [17]. However, the setup required for such simulations can be very challenging, covering aspects such as sedimentation [18; 19; 20], behaviour during the filling phase, and actual filtering processes aided by porous areas [21; 22]. Additionally, while LBM are significantly faster than other simulation approaches [23; 24], the results are still not available in real time. Traditional filter press operations are highly dependent on manual monitoring and adjustments, which often result in inefficiencies, human errors, and increased downtime. Predicting performance parameters such as pressure and flow rates in real time is challenging, as these variables are influenced by fluctuating operating conditions and the state of the filter medium.