The advantages of maximally transferring similar process data for modeling make the process transfer model attract increasing attention in quality prediction and optimal control. Unfortunately, due to the difference between similar processes and the uncertainty of data-driven model, there are usually a more serious mismatch between the process transfer model and the actual process, which may result in the deterioration of process transfer model-based control strategies. In this research, a process transfer model based optimal compensation control strategy using just-in-time learning and trust region method is proposed to cope with this problem for batch processes. First, a novel JITL-JYKPLS (Just-in-time learning Joint-Y kernel partial least squares) model combining the JYKPLS (Joint-Y kernel partial least squares) process transfer model and just-in-time learning is proposed and employed to obtain the satisfactory approximation in a local region with the assistance of sufficient similar process data. Then, this paper integrates JITL-JYKPLS model with the trust region method to further compensate for the NCO (necessary condition of optimality) mismatch in the batch-to-batch optimization problem, and the problem of estimating experimental gradients is also avoided. Meanwhile, a more elaborate model update scheme is designed to supplement the lack of new data and gradually eliminate the adverse effects of partial differences between similar process production processes. Finally, the feasibility of the proposed optimal compensation control strategy is demonstrated through a simulated cobalt oxalate synthesis process.

最大程度地传输用于建模的相似过程数据的优势使得过程传递模型在质量预测和最优控制方面引起了越来越多的关注。不幸的是，由于相似过程之间的差异以及数据驱动模型的不确定性，过程转移模型与实际过程之间通常存在更严重的不匹配，这可能导致基于过程转移模型的控制策略恶化。在这项研究中，提出了一种使用实时学习和信任区域方法的基于过程转移模型的最优补偿控制策略，以解决批处理过程中的这一问题。第一，结合JYKPLS（Joint-Y核偏最小二乘）过程转移模型和实时学习的新的JITL-JYKPLS（实时学习Joint-Y核偏最小二乘）模型，提出并采用在足够的相似过程数据的帮助下，在局部区域获得令人满意的近似值。然后，本文将JITL-JYKPLS模型与信任域方法集成在一起，以进一步补偿批次间优化问题中的NCO（最优性的必要条件）不匹配，并且避免了估计实验梯度的问题。同时，设计了一种更加详尽的模型更新方案，以补充新数据的缺乏，并逐步消除相似过程生产过程之间局部差异的不利影响。最后，