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An approach to mechanistic event recognition applied on monitoring organic matter depletion in SBRs
Author(s) -
Cruz Bournazou Mariano N.,
Junne Stefan,
Neubauer Peter,
Barz Tilman,
ArellanoGarcia Harvey,
Kravaris Costas
Publication year - 2014
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.14536
Subject(s) - observability , event (particle physics) , process (computing) , process engineering , organic matter , computer science , biochemical engineering , artificial intelligence , chemistry , engineering , mathematics , physics , organic chemistry , quantum mechanics , operating system
A fundamental practice in process engineering is monitoring the state dynamics of a system. Unfortunately, observability of some states is related to high costs, time, and efforts. The mechanistic event recognition (MER) aims to detect an event (defined as a change of the system with specific significance to the operation of the process) that cannot be directly observed but has some predictable effect on the dynamics of the systems. MER attempts to apply fault diagnosis techniques using mechanistic “recognition” models to describe the process. A systematic method for building recognition models using optimal experimental design tools is presented. As proof of concept, the MER approach to detect organic matter depletion in sequencing batch reactors, measuring only ammonia, dissolved oxygen, and nitroxides is applied. The event, that is, consumption of organic matter to a level below 50 gCOD/m 3 , was successfully detected even though microbial activity is known to continue after organic matter depletion. © 2014 American Institute of Chemical Engineers AIChE J , 60: 3460–3472, 2014