基于扩展得分矩阵的多阶段间歇过程质量预测

Quality prediction of multistage batch processes based on extended score matrices

为考虑质量变量对阶段划分结果的影响,提高建模精度,提出了一种基于扩展得分矩阵的多阶段间歇过程质量预测方法。首先将三维过程数据沿批次方向展开为二维数据矩阵,对每个时间片矩阵进行偏最小二乘(partial leastsquares,PLS)分析得到可以表征过程变量的得分矩阵和可以表征质量变量的得分矩阵;然后构建每个时间片的扩展得分矩阵,利用扩展得分矩阵捕捉质量变量信息对划分阶段的影响,采用 CS (Cauchy-Schwarz)统计量计算相邻两个扩展得分矩阵的相似度,依据相似度将操作过程划分为不同的操作阶段,对划分后的各个阶段分别建立 MPLS 质量预测模型;最后将该算法在青霉素发酵仿真实验平台和大肠杆菌生产数据上进行了实验验证,实验结果表明了本文所提方法的可行性和有效性。

In order to highlight the impact of quality variables on stage division and improve the quality prediction accuracy, a quality prediction method for multi-stage batch processes based on extended score matrix was proposed. Original three-dimensional data were first unfolded along the batch direction, and score matrices representing process variables and quality variables were obtained by PLS (partial least squares) analysis of each slice matrix. The extended scoring matrix was obtained by combining the two scoring matrices, and the similarity of the two adjacent extended scoring matrices was calculated by CS (Cauchy-Schwarz) statistics to divide the stages. MPLS (multiway PLS) quality prediction models were then established in the transition stage and the stable stage. Finally, the effectiveness and utility of the proposed method were validated through a fed-batch penicillin fermentation simulation platform and E. coli production of interleukin-2. The results demonstrate the feasibility and effectiveness of the proposed method.