The statistical framework to systematically detect mean stationarity in the context of continuous manufacturing is described in this article. The methods presented in this article use econometric and financial time‐series analysis concepts in the form of unit‐root and stationarity hypothesis tests. The tests under discussion are the augmented Dickey‐Fuller, Philips‐Perron, Leybourne‐McCabe, and Kwiatkowski‐Phillips‐Schmidt‐Shin. These hypothesis tests are evaluated on data generated by a focused‐beam reflectance measurement sensor implemented on‐line in a continuous plug‐flow crystallizer. This contribution has shown that the hypothesis tests can be used to detect steady‐state conditions on‐line in a plug‐flow crystallizer. Furthermore, this econometric framework can be used as a mean stationarity “certificate” of collected samples to document that the process was mean stationary during the sampling. The statistical framework described in this article can be applied to any continuously operated unit operation or sensor measurement. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2426–2437, 2018

中文翻译：

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连续制造：该过程是否意味着平稳？

本文介绍了用于在连续制造的情况下系统地检测平均平稳性的统计框架。本文介绍的方法以单位根和平稳假设检验的形式使用计量经济和金融时间序列分析概念。讨论中的测试是增强的Dickey-Fuller，Philips-Perron，Leybourne-McCabe和Kwiatkowski-Phillips-Schmidt-Shin。这些假设检验是根据在连续推流结晶器中在线实施的聚焦光束反射率测量传感器生成的数据进行评估的。这一贡献表明，假设检验可用于在活塞流结晶器中在线检测稳态条件。此外，该计量经济学框架可以用作所收集样本的平均平稳性“证明”，以证明该过程在抽样过程中是平稳的。本文中描述的统计框架可以应用于任何连续操作的单元操作或传感器测量。©2018美国化学工程师学会AIChE J，64：2426-2437，2018