Evolutionary Operation: A Statistical Method for Process Improvement
This book is about the philosophy and practice of Evolutionary Operation (called EVOP for short), a simple but powerful statistical tool with wide application in industry. Experience has long shown that statistical methods, sometimes quite sophisticated in character, can be of great value in improving the efficiency of laboratory and pilot-plant investigations made by specially trained chemists and engineers. What originally motivated the introduction of EVOP, however, was the idea that the widespread and daily use of simple statistical design and analysis during routine production by process operatives themselves could reap enormous additional rewards.
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SIMPLE STATISTICAL PRINCIPLES ON WHICH EVOP IS BASED
THE 22 AND 23 rxcroRnu DESIGNS
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23 factorial design additional analysis averages for block batch batch process Calculation of Standard catalyst change in mean chemical concentration conﬁdence contours current best-known deﬁned discussed eﬁ'ect eﬂect empirical feedback Evolutionary Operation EVOP committee EVOP program EVOP scheme example experimental ﬁgure ﬁnd ﬁve ﬁxed ﬂuidity function illustrated increase information board interaction investigation large number linear programming main effect measure method modiﬁcations normal distribution null hypothesis number of observations obtained occur optimization over-all particular phase mean plant population possible pressure Previous sum Prior estimate procedure process conditions process superintendent proﬁt quantities random random variables range reference conditions response surface S.E. limits scientiﬁc feedback serial correlation sets of conditions shown in Figure signiﬁcance test situation speciﬁcations standard deviation standard error statistical statistically independent steepest ascent sums for block Suppose tear resistance temperature three variables three-variable tion usually variance variation viscosity Yates yield