Advancing Mining Operations with Predictive Insights and Quality through Deep Learning

Mining operations often face challenges like fluctuating process efficiency, unpredictable equipment performance, and suboptimal resource utilization, leading to increased operational costs and reduced productivity. Traditional methods struggle to handle complex time-series data and fail to provide accurate predictive insights for decisionmaking. Leveraging deep learning (DL) techniques are address these limitations, enabling smarter, more efficient mining operations with enhanced quality control. This paper proposes an Advancing Mining Operations with Predictive Insights and Quality through Deep Learning. It focus on the use of Hyper Temporal Recurrent Neural Networks (RNNs) to predict key operational metrics, particularly in flotation processes. In this method, the preprocessing is applied on the time series data to detect the outliers and handle the missing values for feature extraction. For training and testing, the flotation data undergoes feature and scaling transformations. The proposed work, implemented in Python, is evaluated using MAE, RMSE, MSE, and MAPE to assess accuracy and reliability. Results show that this predictive method enhances mining operation efficiency with higher accuracy. This approach provides actionable insights for decision-making, enabling mining operations to achieve higher productivity, reduce waste, and improve overall operational performance, thereby contributing to the advancement of smart and efficient mining practices.