Soft sensor for cobalt oxalate synthesis process in cobalt hydrometallurgy based on hybrid model

摘要

In the cobalt oxalate synthesis process in cobalt hydrometallurgy, the key end-product quality index, average particle size of cobalt oxalate, needs to be monitored and controlled. It is difficult to measure such particle size online by existing hardware sensors. Soft sensor technique has been widely used for estimating product quality or other important variables when online instruments and sensors are not available. In this paper, a hybrid modeling approach for cobalt oxalate synthesis process in cobalt hydrometallurgy is proposed by combining simplified first principle model with stacked LSSVR model. The former based on population balance equations and mass conservation equation with some assumptions is used for description and analysis of synthesis process in general; and the latter is developed to compensate the unmodeled characteristic and to enhance model generalization capability. Furthermore, a model output offset compensation strategy is also employed to increase the model prediction accuracy. Applications to a cobalt hydrometallurgy pilot plant demonstrate that the proposed approach is more precise and effective than the other conventional models.