Hierarchical batch-to-batch optimization of cobalt oxalate synthesis process based on data-driven model

The synthesis process has been widely used in cobalt hydrometallurgical industry. To better operate the cobalt oxalate synthesis process, a data-driven model based hierarchical batch to-batch optimization method is presented in this work. In the upper level of hierarchy, the proposed response surface model based modifier-adaptation (MA) strategy is used to calculate the nominal control profile for the next level, and the design of dynamic experiment (DODE) method is also employed to symmetrically generate the dataset for response surface model building. In the lower level of hierarchy, the batch-wise unfolded PLS (BW-PLS) model based self-tuning batch-to-batch optimization method is utilized to further refine the control profile on the basis of the result of the upper level. The main advantages of the proposed method are: (i) the size of the dataset for data-driven model building are rather modest, (ii) the control profile can be discretized into a large number of intervals to further improve the optimization performances, and (iii) the unqualified batches is efficiently avoided during the evolution of batch-to-batch optimization. The superior performances for the cobalt oxalate synthesis process are verified through simulation study. (C) 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.