草酸钴合成过程批次间自适应优化
A batch-to-batch adaptive optimization for the cobalt oxalate synthesis process

本文以钴湿法冶金过程草酸钴合成为背景, 研究基于多向偏最小二乘回归(MPLS)模型的草酸钴平均粒度 批次间自适应优化策略. 本文首先利用MPLS方法建立草酸钴平均粒度的数据模型; 针对模型不确定性情况下难以 获得最优操作变量的问题, 提出利用批次间修正项自适应优化方法, 使迭代优化结果逐渐趋向于实际最优值; 本文 还通过引入T2统计量软约束将优化结果限制在数据模型的有效区间之内. 数值仿真表明该方法可以有效解决草酸 钴合成过程的批次间自适应优化问题, 且与传统两步方法和迭代学习控制相比具有更好的优化效果.
This paper takes the background of cobalt oxalate synthesis in cobalt hydrometallurgy process, and an adaptation 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 order 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, T2 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).