In this work, a domain-adaption joint-Y partial least squares (JYPLS) is proposed to solve the problem of transfer learning for end-product quality prediction of batch processes. The difference from the variances in source and target domains is included as a regular term in the objective function of the traditional JYPLS model to realize the trade-off between minimizing the difference of empirical distribution in source and target domains and maximizing the covariance between latent variables and output variables. Since the issue of domain-adaption is considered in the proposed method, the prediction performances can be further improved. And the merits of JYPLS method can also be retained at the same time. The proposed method is tested on simulated data sets to verify the efficiency of the additional index in the objective function. It is also applied to predict the final mean particle size of a new cobalt oxalate synthesis process, and the data sets used to build the data-driven model is obtained from two synthesis processes with different model parameters and control policies. Compared with traditional JYPLS method, the prediction accuracy of the proposed method in the target domain has been greatly improved.

在这项工作中，提出了一种域自适应联合Y偏最小二乘（JYPLS），以解决批生产过程最终产品质量预测中的转移学习问题。传统JYPLS模型的目标函数将源域和目标域的方差之差作为常规项包括在内，以实现在最小化源域和目标域中的经验分布之差与最大化潜在变量之间的协方差之间的权衡和输出变量。由于该方法考虑了域自适应的问题，因此可以进一步提高预测性能。JYPLS方法的优点也可以同时保留。在模拟数据集上测试了该方法，以验证目标函数中附加索引的效率。它也可用于预测新的草酸钴合成工艺的最终平均粒径，并且用于构建数据驱动模型的数据集是从具有不同模型参数和控制策略的两个合成工艺中获得的。与传统的JYPLS方法相比，该方法在目标域的预测精度大大提高。