Process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs. Graphical abstract.

中文翻译：

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数据融合策略将传感器和多变量模型输出组合在一起，以进行多变量统计过程控制。

应用于过程监控的过程分析技术（PAT）通常提供多个输出，这些输出可能来自不同的传感器，也可能来自单个多元传感器生成的不同模型的输出。本文为多元统计过程控制（MSPC）模型的开发中传感器和/或模型输出的组合为当前的数据融合策略做出了贡献。通过三个真实的过程示例探索数据融合，这些示例将来自同一传感器的多元模型的输出独特地组合在一起（在两阶段聚酯生产过程中基于近红外（NIR）的终点检测中），或者将这些输出与其他过程变量传感器（在流化床干燥过程的终点检测和蒸馏过程的在线控制中使用基于NIR的模型输出和温度值）。所研究的三个示例清楚地表明了选择模型输出的灵活性（例如，通过多元校准预测关键属性，从多变量解析方法发出的过程配置文件）以及使用基于融合信息（包括模型输出）的MSPC模型相对于基于原始单个传感器输出的模型输出进行过程控制以及异常过程情况的诊断和解释的优势。提出的数据融合策略对于产生多个传感器和/或模型输出的任何分析或生物分析过程具有普遍适用性。图形概要。