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Identification of kinetic models of methanol oxidation on silver in the presence of uncertain catalyst behavior
Author(s) -
Quaglio Marco,
Bezzo Fabrizio,
Gavriilidis Asterios,
Cao Enhong,
AlRifai Noor,
Galvanin Federico
Publication year - 2019
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.16707
Subject(s) - catalysis , formaldehyde , reactivity (psychology) , methanol , kinetic energy , chemistry , microreactor , kinetics , work (physics) , thermodynamics , process (computing) , chemical engineering , organic chemistry , computer science , physics , quantum mechanics , engineering , medicine , alternative medicine , pathology , operating system
Catalytic oxidation of methanol to formaldehyde is an important industrial process due to the value of formaldehyde either as a final product or as a precursor of numerous chemicals. The study of kinetics in this system is hindered by sources of uncertainty that are inherently associated to the nature and state of the catalyst (e.g., uncertain reactivity level, deactivation phenomena), the measurement system and the structure of the kinetic model equations. In this work, a simplified kinetic model is identified from data collected from continuous flow microreactor systems where catalysts with assorted levels of reactivity are employed. Tailored model‐based data mining methods are proposed and applied for the effective estimation of the kinetic parameters and for identifying robust experimental conditions to be exploited for the kinetic characterization of catalysts with different reactivity, whose kinetic behavior is yet to be investigated.