Adaptive multi-LSSVR based soft sensing for cobalt oxalate synthesis process

An adaptive multiple least squares support vector regression (multi-LSSVR) method to enhance the model prediction accuracy and generalization capability is presented. In the proposed approach, data for building single LSSVR models is re-sampled based on bootstrap techniques to form a number of sets of training and test data. For each data set, a LSSVR model is developed which are then aggregated through partial least squares (PLS). In order to identify the changes of process, an efficiently adaptive strategy based on batch-to-batch information is used. It efficiently updates a trained multi-LSSVR model by means of incremental updating and decremental pruning algorithms whenever a new batch sample is added to, or removed from the training set. The proposed method is demonstrated on a simulated batch process and utilized to develop a soft sensor model for cobalt oxalate synthesis process of hydrometallurgy. It is shown that the method can not only enhance model prediction accuracy, but also track the system drift by compared against single LSSVR method.