Building and Testing a Digital Twin Using Machine Learning Within a Mining Environment in South Africa

Abstract

This study presents the development and testing of a Digital Twin (DT) for the crushing circuit in a South African mining environment, using advanced machine learning (ML) methods. The key objective was to select the best ML algorithm for the crushing circuit DT development. Data was collected over a 12-month period from sensors and Supervisory Control and Data Acquisition systems across key components of the crushing circuit, then pre-processed and integrated for analysis. The methodology involved defining the requirements and establishing data exchange protocols to select, train and validate ML models. Multiple approaches were evaluated, including traditional statistical methods (Autoregressive Integrated Moving Average) and advanced neural networks, including the Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM), and Transformer models. Hyperparameter optimisation using frameworks like Optuna was employed to fine-tune model performance, with predictive accuracy assessed via the coefficient of determination, root mean square deviation, and mean absolute error metrics. The results highlighted that the DT reliably captures equipment behaviour, with Recurrent Neural Network-based models (LSTM and GRU) achieving high predictive accuracy. The study concluded that the GRU model was the best ML approach and provides the mining industry with an efficient DT to predict the percentage product material under 80% of the target particle size distribution, which is crucial to maximise throughput and downstream milling efficiency. This paves the way for future advancement of DT in the mining industry. The complexity of this work lies in its comprehensive integration and comparative analysis of diverse ML models to develop a high-fidelity DT tailored for a mining application, an area with limited prior research. By addressing challenges unique to the South African mining context, this study offers a robust DT framework that can be used for DT development in the crushing circuit. Future work is recommended to expand DT applications to other mining processes and further validate the framework under real-time operational conditions. These advancements will contribute to sustainable mining practices and accelerate the adoption of digital transformation initiatives within the industry.