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Premium Continuous manufacturing: Is the process mean stationary?
Author(s)
Simon Levente L.
Publication year2018
Publication title
aiche journal
Resource typeJournals
PublisherWiley-Blackwell
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
Subject(s)algorithm , archaeology , certificate , context (archaeology) , econometrics , economics , electrical engineering , engineering , filter (signal processing) , geography , mathematics , sampling (signal processing) , statistical hypothesis testing , statistics , unit root
Language(s)English
SCImago Journal Rank0.958
H-Index167
eISSN1547-5905
pISSN0001-1541
DOI10.1002/aic.16125

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