Computational optimization of flotation time in Cu–Pb sulphide processing: A multi‐algorithm analysis of kinetic models

This study presents a computational framework for optimizing flotation time in bulk Cu–Pb sulphide processing by integrating kinetic modelling with advanced optimization algorithms. Three flotation kinetic models—first‐order, second‐order, and Agar's—were parameterized using both traditional nonlinear estimation and three metaheuristic algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimization (GWO). Theoretical optimum flotation times derived from these models were validated against experimental incremental grade (IG) data. The first‐order and Agar's models exhibited superior convergence stability, yielding nearly identical parameters across all optimization methods. Agar's kinetic model demonstrated the lowest SSE and RMSE values, providing the best fit and most accurate prediction of optimum flotation time (7.32–7.45 min), closely matching the IG‐derived value of 7.35 min. In contrast, the second‐order model displayed higher sensitivity to algorithmic conditions and occasional local convergence by GA. The integration of global optimization algorithms with kinetic modelling enhances the precision and reproducibility of flotation time estimation, establishing a robust methodological framework for improving flotation process efficiency in complex sulphide systems.