Paragon Space Development Corp. is developing the Carbon Dioxide Control Assembly (CCA) Orbital Replacement Unit (ORU) to interface with the Air Revitalization System (ARS) for a human rated spaceflight program with NASA requirements. The CCA is a redundant method of atmospheric carbon dioxide (CO2) removal via irreversible chemical reaction with reactive plastic (RP) lithium hydroxide (LiOH) to maintain atmospheric concentrations below required limits. Nominal control of crew metabolic CO2 output is assumed from other systems, whereas the CCA provides CO2 removal capability for a 9 mission-day contingency, for a 95th percentile crew of four. The LiOH consumable has been tested under representative parameters with relevant operational boundary conditions to characterize the performance and expected life of a CCA ORU. These results were used to identify optimal operating conditions, calibrate the computational transient performance models, and guide consumable mass estimates for the given contingency period. The unit was parametrically tested under several relative humidity (RH) conditions since it is known that hydration of the LiOH is a precursor to its CO2 reaction. It was observed that a decrease in RH led to a nonlinear decrease in LiOH efficiency, with a significant performance drop-off under a certain RH. This has several implications on system tradeoffs. An equivalent CCA performance could alternately require increased consumable mass given practical humidity restrictions for limitation of condensation in tandem with a non-condensing heat exchanger, or, reduced consumable mass with constrained environment adjustability for best maintenance of an orbital module and prevention of mold growth. Furthermore, the relationship of the ratio of relative humidity, hydrated LiOH, and released water is complex and not easily defined. This has interesting implications for future testing and consumable performance optimization under constrained environmental conditions. This paper describes the CCA, its testing, and the resulting implications for performance optimization.
Jillian Weber (Sun,) studied this question.