Predicting cobalt ion concentration in hydrometallurgy zinc process using data decomposition and machine learning.

Solid waste is one of the primary contributors to environmental pollution currently, it is crucial to enhance the prevention and control of solid waste pollution in environmental management. The effectiveness of the second stage of purification in the industrial zinc hydrometallurgy is determined by the concentration of cobalt ion. Manual testing and monitoring of cobalt ion concentration are time consuming and costly, and prone to delays, which can result in discharge of cobalt ion concentration that does not meet the standards, leading to water pollution. Additionally, over-addition of zinc powder leads to a waste of resources, increasing the production cost of the company. Here, this work proposes a hybrid prediction model that combines the advantages of data decomposition and machine learning algorithms to predict the metal cobalt ion concentration in the effluent solution of a section of zinc hydrometallurgy refining purification in factory A. According to the different types of experiments, ablation experiments and contrast experiments are designed in this work under the same training and test data were used in the modeling process. Analytic and experimental results show that the proposed hybrid prediction model has the smallest error and the best fit between the actual and predicted values of cobalt ion concentration, and the appropriate graphs were finally selected for quantitative metrics analysis. The root mean square error was reduced by 4.2 %-73.9 %, the mean absolute error by 7.1 %-93.4 %, the mean percentage error by 7.7 %-86.7 % and the coefficient of determination by 1.3 %-134.6 %. The hybrid prediction model not only avoided the pollution of water resources by the cobalt ion concentration discharged in the purification, which is also of practical significance for the technicians to control the input quantity of zinc powder according to the prediction data in time and reduce the waste of resources.