## 4 Related Work
This approach aligns with ML principles, where predictive models trained on historical data can forecast potential overload scenarios, enabling proactive operational adjustments. [PERSON] et al. [23] conducted a comparative analysis of neural networks (NN) and support vector machines (SVM) for state estimation in semi-autogenous mills, focusing on the mill's filling level, a critical performance factor. The study found that, while both models were effective, SVMs demonstrated superior predictive accuracy, particularly in model predictive output tasks, as reflected in their lower mean squared error(MSE) values. These results highlight the potential of SVMs to improve scalability and reliability in state estimation, offering significant benefits for optimizing the efficiency of ore-grinding processes. [PERSON] et al. [24] proposed a novel approach for understanding and predicting the operational states of grinding circuits in mineral processing using recurrence texture analysis (RTA) on time series data from operational parameters. RTA identified distinct patterns and dynamic behaviors indicative of various operational states by analyzing the texture of recurrence matrices. This method highlighted the potential of advanced data analysis, closely tied to machine learning, to enhance predictive accuracy and operational efficiency in mineral processing. The study demonstrated how adapting machine learning techniques can address process engineering and optimization challenges, driving significant advancements in mineral processing operations. [PERSON] et al. [16] significantly improved overload detection in semi-autogenous grinding (SAG) mills through advanced machine learning techniques. The study employed kernel principal component analysis (KPCA), local linear embedding (LLE), and linear discriminant analysis (LDA) to address the challenges posed by high-dimensional and nonlinear operational data. KPCA extended traditional PCA by capturing nonlinear relationships, enabling a nuanced analysis of complex interactions in mill parameters by projecting data into a high-dimensional space with enhanced linear separability. LLE effectively preserved local geometrical features while reducing dimensionality, which is crucial for modeling the dynamic behavior of SAG mills, as it highlighted subtle changes indicative of overload onset. LDA further supported the classification of operational states by maximizing the between-class and within-class variance ratio, facilitating the distinction between normal and overload conditions. This study's integration of KPCA, LLE, and LDA provided a robust framework for early overload detection, enabling timely interventions and improving operational efficiency. This work underscores the potential of machine learning in addressing complex challenges in SAG mill operations. Forecasting overload conditions in semi-autogenous grinding (SAG) mills is crucial for optimizing operations and avoiding costly downtime. In a previous study [10], [PERSON] et al. proposed a method combining convolutional neural networks (CNNs) with Gram matrix-based encoding techniques. This approach encoded multivariate time-series data into Gram matrices, capturing angular differences between feature pairs to better represent temporal dependencies and interactions. A key component of this methodology was the use of conditional mutual information (CMI) to identify feature pairs most relevant to the target variable (overload condition). The study selected the most informative pairs by calculating and ranking the CMI values, transforming them into Gram matrices as input for the CNN model. This innovative approach enhanced the predictive accuracy of overload conditions by effectively leveraging critical variable relationships. In a subsequent study [10], we proposed a method that penalizes older data to emphasize recent information, which is crucial for accurate overload prediction. This penalization is applied to the encoded matrices, differentiating the temporal data in each time window.