Application of a data assimilation method via an ensemble Kalman filter to reactive urea hydrolysis transport modeling View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-08-25

AUTHORS

Juxiu Tong, Bill X. Hu, Hai Huang, Luanjin Guo, Jinzhong Yang

ABSTRACT

With growing importance of water resources in the world, remediations of anthropogenic contaminations due to reactive solute transport become even more important. A good understanding of reactive rate parameters such as kinetic parameters is the key to accurately predicting reactive solute transport processes and designing corresponding remediation schemes. For modeling reactive solute transport, it is very difficult to estimate chemical reaction rate parameters due to complex processes of chemical reactions and limited available data. To find a method to get the reactive rate parameters for the reactive urea hydrolysis transport modeling and obtain more accurate prediction for the chemical concentrations, we developed a data assimilation method based on an ensemble Kalman filter (EnKF) method to calibrate reactive rate parameters for modeling urea hydrolysis transport in a synthetic one-dimensional column at laboratory scale and to update modeling prediction. We applied a constrained EnKF method to pose constraints to the updated reactive rate parameters and the predicted solute concentrations based on their physical meanings after the data assimilation calibration. From the study results we concluded that we could efficiently improve the chemical reactive rate parameters with the data assimilation method via the EnKF, and at the same time we could improve solute concentration prediction. The more data we assimilated, the more accurate the reactive rate parameters and concentration prediction. The filter divergence problem was also solved in this study. More... »

PAGES

729-741

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00477-013-0786-y

DOI

http://dx.doi.org/10.1007/s00477-013-0786-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1024929901


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