Browsing by Author "Grobbelaar, Suné"
Now showing 1 - 1 of 1
Results Per Page
Sort Options
- ItemDevelopment of a dynamic model for direct copper electrowinning operations(Stellenbosch : Stellenbosch University, 2023-12) Grobbelaar, Suné; Tadie, Margreth; Dorfling, Christie; Stellenbosch University. Faculty of Engineering. Dept. of Chemical Engineering. Process Engineering.ENGLISH ABSTRACT: Innovation is essential for fostering sustainable and environmentally conscious growth in copper production, particularly for operations employing resource-intensive direct copper electrowinning. A dynamic model can be coupled with advanced control strategies in an innovative approach to addressing the control and optimisation challenges associated with copper electrowinning. Previous studies have primarily focused on steady-state models, and limited research has been conducted on dynamic models for copper electrowinning. Consequently, this project aimed to develop a dynamic model for copper electrowinning, with a specific focus on the direct electrowinning process. The main original contribution of this project is the validated semi-empirical dynamic copper electrowinning model. The model can be calibrated for a specific tankhouse, including direct electrowinning operations. An offline parameter-fitting approach was developed for fitting initial model parameters, and for use when limited data are available. The project also introduced an accompanying online parameter-fitting approach that uses moving horizon estimation to continuously adjust the model parameters based on evolving input data. The approach ensures the parameters remain up to date as process conditions change. The least-squares error objective function was selected for use in the online approach, with two types of system models investigated: fundamental and surrogate. The surrogate models were investigated mainly as a future-orientated strategy for online parameter-fitting using computationally intensive datasets. The model incorporated a conceptual resistance network, mass conservation equations, and reactionrate and mass-transfer kinetics. Key performance indicators (copper yield, current efficiency, and specific energy consumption) were used to quantify electrowinning performance. The model included input variables such as current, and the concentrations of copper, iron, nickel, cobalt, and sulfuric acid. The effect of nickel and cobalt were accounted for through existing empirical density and conductivity correlations, and a newly regressed limiting-current density correlation. Validation using dynamic industrial tankhouse data showed the credibility of the model for representing real-life systems. The average normalised residual mean square errors over the five 14-day validation cycles investigated (with the online approach activated) were 10.0%, 29.3%, 79.2%, and 3.9%, for the current efficiency, copper plating rate, specific energy consumption, and potential, respectively. The quantifier values for the specific energy consumption were consistently above the threshold for acceptable model fit. Caution was, therefore, advised in interpreting the model-predicted specific energy consumption values. Overall, the model's performance, particularly with inclusion of the online parameter-fitting approach, however, exhibited satisfactory agreement with the industrial data. The developed model has the potential to make a meaningful contribution to the field. The model's versatility and accuracy make it a valuable tool for use in operator training, process monitoring, and early-fault detection. It also opens avenues for exploration of advanced control strategies. By everaging these potential benefits, operations can enhance productivity, reduce costs, and minimise environmental impact. It is recommended that future work should focus on developing online data validation strategies to further enhance model fidelity, as well as exploring advanced surrogate model structures.