dditionally, negative and inconsistent values were removed. In parallel, operational variables were integrated from the PI System by matching timestamps. Only representative operating conditions were considered, specifically: TPH F1 > 3500 and Warman pump current > 200 A. 3.2.1. Description of Variables Considered in the ODJIT System To capture the short-term temporal dynamics of the SAG milling process, the original 15minute resolution data were transformed into sliding windows of 1.5 hours (equivalent to six consecutive time steps), the windows were constructed, ensuring strict temporal continuity between observations. Only sequences with uninterrupted timestamps were considered, preventing artificial transitions caused by missing records or operational gaps. This approach guarantees that each input sample represents a physically consistent and continuous segment of the process, preserving the temporal structure required for convolutional feature extraction within the CVAE framework. 3.2.2. Data Normalization Prior to model training, all variables were scaled using the Min-Max normalization method implemented in the scikit-learn library. The transformation rescales each feature to the range [0,1] according to: = − − (1) This normalization ensures numerical stability during neural network training, prevents dominance of variables with larger magnitudes, and facilitates balanced learning across geological, geochemical, and geomechanical features. The scaler parameters were computed exclusively from the training data and subsequently applied to the complete dataset to avoid data leaking. 3.3. Deep Clustering Framework 3.3.1. Variational Autoencoders (VAE) A Variational Autoencoder (VAE) is a generative probabilistic model that extends the traditional autoencoder architecture by introducing a structured and regularized latent space. Unlike deterministic autoencoders, which learn a fixed latent representation, a VAE assumes that the latent variables follow a probability distribution. Given a input , the encoder does not map it to a single talent vector , but instead learns the parameters of a conditional probability distribution. ( | ) = Ν( ( ), 2( )) (2)
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