Abstract To address the impact of arsenic accumulation on production stability during copper electrolyte purification and achieve precise optimization and dynamic regulation of process parameters, this paper develops a dynamic modelling and parameter estimation framework integrating mechanism and data. Based on the continuous stirred tank reactor (CSTR), a coupled dynamic model incorporating mass, energy, and electrochemical principles is established for the arsenic electrowinning removal process. Six key kinetic parameters in the model are efficiently inverted using the sequential quadratic programming (SQP) algorithm. Dynamic response tests based on the optimized model further reveal the influence mechanism of operating variables on arsenic removal efficiency. The model is comprehensively validated using industrial data from three typical periods, demonstrating favourable operational adaptability and predictive robustness. Parameter sensitivity analysis clarifies the influence degree of each kinetic parameter and identifies the relative contributions of key parameters. The model and optimization method developed in this study provide reliable theoretical and tool support for real‐time optimization and precise control of copper electrolyte purification.
Ding et al. (Sun,) studied this question.