Calibration and validation of a model describing complete autotrophic nitrogen removal in a granular SBR system

• Published in: Journal of chemical technology and biotechnology (1986) Vol. 88; no. 11; pp. 2007 - 2015
• Main Authors: Vangsgaard, Anna Katrine, Mutlu, A. Gizem, Gernaey, Krist V., Smets, Barth F., Sin, Gürkan
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.11.2013; Wiley; Wiley Subscription Services, Inc
• Subjects: anammox; Applied sciences; autotrophic nitrogen removal; Biological and medical sciences; Biotechnology; Calibration; calibration and validation; Chemical engineering; Customizing; Design engineering; Dynamical systems; Dynamics; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; Mathematical models; Methods. Procedures. Technologies; model fitting; Monte Carlo; Monte Carlo methods; Others; Reactors; sequencing batch reactors; Various methods and equipments; Validation; Monte Carlo method; calibration and validation; anammox; Autotrophy; anaerobic ammonium oxidation; Calibration; Nitrogen; sequencing batch reactors; Modeling; model fitting; Sequencing batch reactor; Monte Carlo; Models; autotrophic nitrogen removal
• ISSN: 0268-2575; 1097-4660
• DOI: 10.1002/jctb.4060

BACKGROUND A validated model describing the nitritation‐anammox process in a granular sequencing batch reactor (SBR) system is an important tool for: (a) design of future experiments; and (b) prediction of process performance during optimization, while applying process control, or during system scale‐up. RESULTS A model was calibrated using a step‐wise procedure customized for the specific needs of the system. The important steps in the procedure were initialization, steady‐state and dynamic calibration, and validation. A fast and effective initialization approach was developed to approximate pseudo steady‐state in the biofilm system. For oxygen mass transfer coefficient (kLa) estimation, long‐term data, removal efficiencies, and the stoichiometry of the reactions were used. For the dynamic calibration a pragmatic model fitting approach was used – in this case an iterative Monte Carlo based screening of the parameter space – to find the best fit of the model to dynamic data. Finally, the calibrated model was validated with an independent data set. CONCLUSION The calibration procedure presented is the first customized procedure for this type of system and is expected to contribute to achieving a fast and effective model calibration, an important enabling tool for various biochemical engineering design, control and operation problems. © 2013 Society of Chemical Industry