Abstract
Data collected by process information and control systems are almost always correlated due to process dynamics combined with short sampling times. The traditional time series approach for dealing with autocorrelated data has been to model the autocorrelation. This modeling effort is substantially more difficult than simply treating the time series as a sequence of independent data. In this article, we develop and demonstrate a methodology that yields insight into the error introduced by not modeling the autocorrelation of the process data when performing material balances around process equipment. This work shows that in many cases data analysis (e.g., gross-error detection) can be done using the simplified models.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
