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The φ ‐relation and a simple method to predict how many data points are needed for relevant steady‐state detection
Author(s) -
Nellis Chris,
Hin Céline,
Savara Aditya
Publication year - 2018
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16199
Subject(s) - steady state (chemistry) , computer science , monte carlo method , experimental data , data point , statistical physics , algorithm , mathematics , statistics , physics , chemistry
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. 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. © 2018 American Institute of Chemical Engineers AIChE J , 64: 3354–3359, 2018