In the procedure to monitor a new batch using the method proposed by Nomikos and MacGregor [AIChE J. 1994, 40 (8), 1361−1375], an assumption about the unknown future samples in the batch has to be taken. This work demonstrates that using the missing data option and solving the score estimation problem with an appropriate method are equivalent to the use of an accurate adaptive forecast model for the future samples over the shrinking horizon of the remainder of the batch. The dynamic properties of the principal component analysis (PCA) model are illustrated by re-expressing the projection model as a time-varying multivariate prediction model. The benefits of using the missing data estimation option are analyzed by contrasting it with other options on the basis of (i) the accuracy of the forecast done for the unknown samples, (ii) the quality of the score estimates, and (iii) the detection performance during monitoring. Because of the tremendous structural information built into these multivariate PCA models for batch processes, the missing data option is shown to yield the best performance by all measures in predicting the future unknown part of the trajectory, even from the beginning of the batch. However, for the purpose of online detection of process faults (in process monitoring), the differences among the trajectory estimation methods appear to be much less critical because the control charts used in each case are tailored to the filling-in mechanism employed. All of the approaches appear to provide powerful charting methods for monitoring the progress of batch processes.

中文翻译：

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批处理的模型预测监视

在该过程中监视使用由Nomikos和麦格雷戈[提出的方法的新的一批AIChE的Ĵ。1994年，40（8），1361年至1375年，约在批次中的未知的未来样本的假设，必须考虑。这项工作表明，使用丢失的数据选项和用适当的方法解决分数估算问题，就等于在剩余批次的缩减范围内，对将来的样本使用精确的自适应预测模型。通过将投影模型重新表达为随时间变化的多元预测模型，可以说明主成分分析（PCA）模型的动态特性。通过与（i）对未知样本进行的预测的准确性，（ii）得分估计的质量和（iii）进行比较，分析了使用缺失数据估计选项的好处，并将其与其他选项进行了对比。监视期间的检测性能。由于这些多元PCA模型中内置了用于批处理的大量结构信息，在预测轨迹的未来未知部分时，即使从批处理开始，通过所有度量表明，缺失数据选项都能产生最佳性能。但是，出于在线检测过程故障（在过程监视中）的目的，轨迹估计方法之间的差异似乎不那么关键，因为在每种情况下使用的控制图都针对所采用的填充机制进行了调整。所有这些方法似乎都提供了用于监视批处理过程进度的强大图表方法。轨迹估计方法之间的差异似乎并不那么重要，因为在每种情况下使用的控制图都是针对所采用的填充机制量身定制的。所有这些方法似乎都提供了用于监视批处理过程进度的强大图表方法。轨迹估计方法之间的差异似乎并不那么重要，因为在每种情况下使用的控制图都是针对所采用的填充机制量身定制的。所有这些方法似乎都提供了用于监视批处理过程进度的强大图表方法。