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Fluid catalytic cracking optimisation using factorial design and genetic algorithm techniques
Author(s) -
Cuadros José F.,
Melo Delba C.,
Filho Rubens Maciel,
Maciel Maria R. Wolf
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.21700
Subject(s) - fluid catalytic cracking , factorial experiment , process (computing) , cracking , genetic algorithm , factorial , computer science , design of experiments , nonlinear system , function (biology) , fractional factorial design , mathematical optimization , optimal design , algorithm , process engineering , engineering , materials science , mathematics , machine learning , statistics , physics , composite material , mathematical analysis , quantum mechanics , evolutionary biology , biology , operating system
Statistical techniques coupled with genetic algorithm (GA) were used to identify optimal values of key operational variables in fluid catalytic cracking (FCC) process. A Kellog Orthoflow F fluid catalytic cracking process model was considered. It is known as a highly nonlinear process with a large number of variables with strong interactions among them. A reduced process model was obtained through factorial design technique to be used as a process function in the optimisation work giving as result the operational conditions that maximise conversion without infringing operational restrictions with savings in computational burden and time. An increase of 8.71% in process conversion was achieved applying GA as optimisation technique. © 2012 Canadian Society for Chemical Engineering
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