Document Type : Original Research Paper
Authors
Mining and Materials Engineering Department, Tarbiat Modares University,
Abstract
The Carbon-in-Leach (CIL) process involves complex mass transfer among solid, liquid, and carbon phases, making predictive modeling and soft-sensor development inherently challenging. Reliable time-resolved data remain one of the main obstacles to digital transformation in mineral processing, as measurement noise and phase inconsistencies hinder reproducibility, mass balance closure, and model calibration. Despite extensive kinetic and simulation studies on CIL circuits, validated dynamic data reconciliation frameworks that explicitly account for carbon transfer and transient multiphase behavior remain limited, leaving an important gap between theoretical modeling and industrial-scale implementation. This study introduces a Dynamic Data Reconciliation approach based on the Extended Kalman Filter (EKF), designed to enhance the accuracy and internal consistency of key process variables. In contrast to steady-state reconciliation, the EKF-DDR explicitly incorporates process dynamics, system uncertainties, and periodic carbon transfer, allowing robust reconciliation of dynamic plant data. The framework was tested in two stages: first on a simulated dataset with known ground truth and added noise, and then on a 163-day industrial dataset. In simulation, the EKF-DDR reduced RMSE by up to 76%, validating its noise-rejection and state-recovery capability. When applied to plant data, it achieved variance reduction indices above 0.49 and improved adherence to mass balance constraints, lowering gold accounting imbalances from 12.63% to 5.13%. These results demonstrate that EKF-DDR not only suppresses random disturbances but also restores physical consistency across process phases, providing high-quality reconciled data for modeling, soft sensing, and advanced process control in CIL operations.
Keywords
- Extended Kalman Filter
- Process State Estimation
- Mass Balance
- Measurement Uncertainty
- Mineral Processing
Main Subjects
)