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Long‐term statistical stability of industrial plants: Performance indicators and monitoring of an industrial pet plant
Author(s) -
Filgueiras Viviane,
Lima Enrique Luis,
Pinto José Carlos
Publication year - 2013
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.21819
Subject(s) - statistical process control , statistical analysis , process engineering , work (physics) , process (computing) , crystallinity , computer science , control (management) , quality (philosophy) , reliability engineering , environmental science , materials science , engineering , mechanical engineering , mathematics , physics , statistics , composite material , quantum mechanics , artificial intelligence , operating system
In the present work, usual statistical process control (SPC) definitions are reformulated to allow for long‐term analyses, giving rise to extended statistical process control (ESPC) procedures. In order to allow for implementation of the ESPC approach, t ‐ and F ‐control charts and monitoring indexes (NEPM, EPY and OEP) are designed in this work and are used to monitor the performance of a real industrial poly(ethylene terephthalate) (PET) site based on six process outputs (intrinsic viscosity, crystallinity, acetaldehyde concentration and colours a, b and L). As shown in this manuscript, ESPC tools allow for proper and systematic analysis of huge amounts of industrial data, using simple, fast and efficient statistical techniques, which can be used to improve the quality of the process operation in most industrial polymerisation plants.