Detection of current inefficiencies in copper electrowinning with multivariate data analysis

To further advance existing laboratory studies, the influence of different process parameters onto current efficiency was evaluated based on real industrial process history data obtained from conventional electrowinning circuit. Multivariate calibration model under partial least squares algorithm was applied to predict current efficiency in the process. The basic model was developed using values of electrolyte cupric and ferric concentrations, and total current applied. Pairwise interaction of parameters and moving average technique were applied to improve the prediction ability of the calibration. However, model construction based on the entire data set appeared to be unreliable due to high unexplained variance in the target variable, as sensor data were daily averaged. According to cluster analysis and further Monte-Carlo simulation, the phenomena of current inefficiency causing variation in the prediction of current efficiency appeared to be of random nature, i.e. daily averaging brought random variation to the multivariate model. For this reason, the data set was analyzed with multivariate process control charts to reveal the most important samples for predictive control. Multivariate calibration model was obtained using 58 samples, while the original data set contained 214 observations. Using the model, current efficiency values can be predicted on-line based on process sensor data. Multivariate process control tool was proposed in order to effectively monitor electrowinning process and detect current inefficiencies based on direct comparison of predicted and measured values of current efficiency.