The φrelation and a simple method to predict how many data points are needed for relevant steadystate detection
Steady–state detection is of vital importance for experiments and simulations in chemical engineering, as well as also other fields of science, engineering, and finance—particularly when the full timescale of interest cannot be measured or simulated. We present a breakthrough for estimating the number of data points required before successful steady–state detection is feasible. Using an initial window of data, the method enables predicting the prerequisites for steady state detection (ppSSD), given as a number of data points. The method is shown to be accurate for data with realistic distributions (uniform, normal, and sine–wave), and data from actual kinetic Monte Carlo simulations. In conclusion, users need only to use the algebraic equations derived and provided in this work to estimate the required number of data points for relevant steady–state detection.
 Authors:

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 Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Publication Date:
 Grant/Contract Number:
 AC0500OR22725; LOIS 7427
 Type:
 Accepted Manuscript
 Journal Name:
 AIChE Journal
 Additional Journal Information:
 Journal Volume: 64; Journal Issue: 9; Journal ID: ISSN 00011541
 Publisher:
 American Institute of Chemical Engineers
 Research Org:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org:
 USDOE
 Country of Publication:
 United States
 Language:
 English
 Subject:
 42 ENGINEERING; steady state; steady state detection; ppSSD; projected; slope; SSD
 OSTI Identifier:
 1464027
 Alternate Identifier(s):
 OSTI ID: 1438963
Nellis, Christopher E., Hin, Celine N., and Savara, Aditya Ashi. The φrelation and a simple method to predict how many data points are needed for relevant steadystate detection. United States: N. p.,
Web. doi:10.1002/aic.16199.
Nellis, Christopher E., Hin, Celine N., & Savara, Aditya Ashi. The φrelation and a simple method to predict how many data points are needed for relevant steadystate detection. United States. doi:10.1002/aic.16199.
Nellis, Christopher E., Hin, Celine N., and Savara, Aditya Ashi. 2018.
"The φrelation and a simple method to predict how many data points are needed for relevant steadystate detection". United States.
doi:10.1002/aic.16199.
@article{osti_1464027,
title = {The φrelation and a simple method to predict how many data points are needed for relevant steadystate detection},
author = {Nellis, Christopher E. and Hin, Celine N. and Savara, Aditya Ashi},
abstractNote = {Steady–state detection is of vital importance for experiments and simulations in chemical engineering, as well as also other fields of science, engineering, and finance—particularly when the full timescale of interest cannot be measured or simulated. We present a breakthrough for estimating the number of data points required before successful steady–state detection is feasible. Using an initial window of data, the method enables predicting the prerequisites for steady state detection (ppSSD), given as a number of data points. The method is shown to be accurate for data with realistic distributions (uniform, normal, and sine–wave), and data from actual kinetic Monte Carlo simulations. In conclusion, users need only to use the algebraic equations derived and provided in this work to estimate the required number of data points for relevant steady–state detection.},
doi = {10.1002/aic.16199},
journal = {AIChE Journal},
number = 9,
volume = 64,
place = {United States},
year = {2018},
month = {5}
}