Abstract This article presents a new integrated multiresolution framework for quality prediction and process monitoring in complex batch operations: MR-QP&PM. For quality prediction, the proposed framework uses a multiresolution soft sensor that identifies the Critical to Quality (CTQ) variables and optimizes their resolution. This later tuning dimension has been overlooked in previous works and has a significant impact on the predictive accuracy of soft sensors, especially for batch processes. To complement the soft sensor and effectively assess the validity of its predictions and whether the process is operating under normal operating conditions (NOC), a monitoring scheme with multiresolution adaptability is applied. In case a fault is detected, MR-QP&PM is capable to discern whether the fault is CTQ and possibly estimate its impact. MR-QP&PM was tested and compared with current benchmark methods using several batch testing systems, including the well-known PENSIM simulator, where its superior predictive performance and detection ability was demonstrated. A critical analysis of when the quality predictions of MR-QP&PM can and cannot be trusted is also made, in order to set the boundaries where the proposed methodology can be safely applied.

中文翻译：

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用于批处理过程中质量预测和过程监控的集成多分辨率框架

摘要 本文提出了一种新的集成多分辨率框架，用于复杂批量操作中的质量预测和过程监控：MR-QP&PM。对于质量预测，所提出的框架使用多分辨率软传感器来识别质量关键（CTQ）变量并优化其分辨率。这个后来的调整维度在之前的工作中被忽略了，并且对软传感器的预测精度有重大影响，特别是对于批处理过程。为了补充软传感器并有效评估其预测的有效性以及过程是否在正常运行条件 (NOC) 下运行，应用了具有多分辨率适应性的监测方案。如果检测到故障，MR-QP&PM 能够辨别故障是否为 CTQ 并可能估计其影响。MR-QP&PM 使用多个批量测试系统（包括著名的 PENSIM 模拟器）进行了测试并与当前的基准方法进行了比较，展示了其卓越的预测性能和检测能力。还对 MR-QP&PM 的质量预测何时可以和不可信进行了批判性分析，以便确定可以安全应用所提出的方法的边界。