Mathematical Modeling of Wastewater Oxidation under Microgravity Conditions
Boyun Guo, Donald W. Holder & David S. Schechter
American Journal of Applied Sciences, Volume 2, Issue 2, pp. 473–482 · doi.org/10.3844/ajassp.2005.473.482
Abstract
Volatile removal assembly (VRA) is a module installed in the International Space Station for removing contaminants (volatile organics) in the wastewater produced by the crew. The VRA contains a slim pack bed reactor to perform catalyst oxidation of the wastewater at elevated pressure and temperature under microgravity conditions. Optimal design of the reactor requires a thorough understanding about how the reactor performs under microgravity conditions. The objective of this study was to theoretically investigate factors affecting the performance of the VRA reactor under microgravity conditions. The efficiency of catalyst oxidation is controlled by catalyst oxidation kinetics and oxygen gas distribution in the reactor. The process involves bubbly flow in porous media with chemical reactions in a microgravity environment, which has not been previously studied. We have developed and used a mathematical model in this study to simulate the organics oxidation process in the VRA reactor. On the basis of theoretical studies with the model, we conclude that 1) the remaining oxygen gas in the VRA reactor should decline exponentially with reactor length; 2) the decline rate is directly proportional to the rate of oxidation per unit area and bubble sphericity and inversely proportional to bubble size, oxygen density and bubble velocity; and 3) gravity affects oxygen utilization through changing several parameters including oxygen bubble size.
Licence and reuse
Free to read. This article carries the publisher’s copyright, as printed on it: © 2005 Science Publications. It is free to read; ask us before reusing it.
How to reuse this articleResponses (0)
About this article
Guo, B., Holder, D. W. & Schechter, D. S. (2005). Mathematical Modeling of Wastewater Oxidation under Microgravity Conditions. American Journal of Applied Sciences, 2(2), 473–482. https://doi.org/10.3844/ajassp.2005.473.482
- Licence
- © Science Publications
- Issue
- Volume 2, Issue 2
- Policies
- Editorial independenceUse of generative AIData availabilityAppeals and complaints
- Version
- Version of record
