A batch-to-batch adaptive optimization for the cobalt oxalate synthesis process
HUANG Bi-xuan
MAO Zhi-zhong
JIA Run-da
Abstract：This paper takes the background of cobalt oxalate synthesis in cobalt hydrometallurgy process, and an adap-tation optimization strategy for mean particle size of cobalt oxalate based on multi-way partial least squares (MPLS) model is studied. Firstly, the MPLS algorithm is used to build the data model of mean particle size of cobalt oxalate. In or-der to overcome the problem that it is difficult to obtain the optimal manipulated variables under model uncertainty, a modifier-adaptation strategy based batch-to-batch optimization method is proposed to make the iteration results converge to the practical optimal operating point. Additionally, T 2 statistic soft constraint is used to confine the optimal solution in the valid region of the data-driven model. The simulation results show that the proposed method can efficiently solve the batch-to-batch adaptation optimization problem for cobalt oxalate synthesis process, and better optimization results can be achieved compared with traditional two-step approach and iterative learning control (ILC).
Keywords：cobalt oxalate synthesis processdata modelsmodifier-adaptationbatch-to-batch optimizationmodel uncertainty
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 189-195 )