A data-driven approach for modeling and optimization gold recovery efficiency from the froth flotation process at wagagai mining (u) ltd

Abstract

Froth flotation is a cornerstone process in the mining industry for separating valuable minerals like gold from gangue, yet its efficiency is often compromised by the complex, non-linear interactions among various operational parameters (e.g., reagent dosage, particle size, pH). Traditional optimization methods heavily rely on subjective operator experience, leading to suboptimal and inconsistent gold recovery rates. This research addressed this challenge by developing and validating data-driven predictive models and an optimization framework to enhance gold recovery from the froth flotation process at Wagagai Mining (U) Ltd. Using 1,000 historical operational records, predictive models based on Multiple Linear Regression (MLR), Random Forest (RF), and Artificial Neural Networks (ANN) were developed after relevant feature selection (Modifier, Activator, Collector 1, Collector 2, Frother, and Residence Time). Model performance was evaluated using R-squared (R²) and Root Mean Squared Error (RMSE). The ANN model demonstrated superior predictive performance (Testing R² = 0.879) compared to RF (Testing R² = 0.819) and MLR (Testing R² = 0.556), effectively capturing the complex relationships within the data. The best-performing ANN model was then coupled with a Genetic Algorithm (GA) to identify optimal process parameter combinations for maximizing gold recovery, constrained by operational limits. The GA optimization identified an optimal reagent dosage configuration that the ANN predicted could yield a maximum gold recovery of 99.06%. Sensitivity analysis on the optimized model revealed that Modifier (Na₂CO₃, 38%), Activator (CuSO₄, 25%), and Collector 1 (HB-33A, 20%) were the most influential reagents affecting recovery. A comparison with Wagagai Mining's existing practices showed historical monthly gold recovery fluctuating between 87.00% and 94.01%, significantly lower than the predicted optimum. The company's current approach involved inconsistent reagent dosing, often leading to over- or underdosing relative to the identified optimal levels. Implementing the ANN-GA optimized strategy is estimated to yield an annual reagent cost saving of $657.01 while achieving substantially higher recovery. This study underscores the significant potential of integrating machine learning and genetic algorithms for optimizing mineral processing operations, offering a data-driven approach to improve gold recovery efficiency, reduce operational costs, and support more sustainable mining practices at Wagagai Mining and potentially similar operations.