Interpretable Dynamic Modelling and Prediction of Free Acid in Zinc Leaching Process

Abstract

In the metallurgical processing industry, the leaching process converts a concentrated slurry of zinc sulphide to zinc sulphate solution. The leaching process occurs within a multi-compartment autoclave in the presence of sulphuric acid and oxygen at high temperatures and pressure. The amount of unreacted acid (free acid) within each autoclave compartment is crucial for achieving high zinc recovery but is not directly measured, necessitating an efficient model. This work involves developing a dynamic model utilizing both the first principles and machine learning techniques to predict the free acid, making the model physically interpretable. Due to the dependency of free acid on upstream process variables, several sub-models were built for each preceding unit. The main challenge was the unavailability of several measurements required for the mass balance model, while some available measurements were sampled at a slower rate. Moreover, bias correction was performed, considering delays in receiving laboratory analysis results and the lack of exact timestamps for samples provided by the field operator. The proposed model is validated with integrated zinc and lead smelter process data. The model successfully predicts free acid at a fast rate despite several practical constraints. It performs well under various process conditions, detects abnormalities, and enhances stability in the leaching process.