步进MPCA及其在间歇过程监控中的应用

针对多向主元分析法(MPCA)在间歇过程监控过程中需要预测过程未来输出的困难，提出了一种新的步进多向主元分析方法。该方法通过建立一系列的PCA模型，避免了对预估过程变量未来输出的需要，通过引入遗忘因子能够自然地处理多阶段间歇过程的情况。对于多阶段链霉素发酵过程的监控表明，相对于普通MPCA，步进MPCA能够更精确地对过程故障行为进行描述。

Step-by-Step Adaptive MPCA Applied to an Industrial Batch Process

Multi-way principal component analysis (MPCA) has been successfully applied to the monitoring of batch and semi-batch process in most chemical industry. A new approach using the process variable trajectories to monitoring batch process was proposed. It overcomes the need of estimating or filling in the unknown part of the process variable trajectory deviations from the current time until the end. The approach is based on a multi-way PCA method that processes the data in a sequential and adaptive manner. The adaptive rate is easily controlled through a parameter that controls the weight of past data in a summation manner. This algorithm was evaluated with industrial fermentation process data and was compared with the traditional MPCA. The method has significant benefit especially in monitoring multi-stage batch process where the latent vector structure can change at several points during the batch.