feasibility assessment of liquid amine carbon dioxide

48th International Conference on Environmental Systems ICES-2018-0039 8-12 July 2018, Albuquerque, New Mexico

Feasibility Assessment of Liquid Amine Carbon Dioxide

Removal System for Microgravity and Terrestrial

Applications

Tanya Rogers 1

Jacobs Technology, Inc., Houston, Texas, 77058

Michael J. Swickrath 2

HX5, LLC, Houston, Texas, 77058

Rafael Verduzco 3 and Saurabh Sharma 4

Rice University, Houston, Texas, 77005

and

John Graf 5

NASA Johnson Space Center, Houston, Texas, 77058

Carbon dioxide (CO2) is a metabolic byproduct of respiration and can rapidly accumulate

in closed volumes unless actively controlled. Traditionally, solid media, such as zeolites or

lithium hydroxide (LiOH) have been used for CO2 capture for space exploration. Such

materials are either non-regenerable, or regenerable but at high power requirements. As an

alternative, liquid amines, such as monoethanolamine (MEA), have a history for CO2 capture

in naval applications. Herein, diglycolamine (DGA) is considered as a sorbent due to its similar

capacity of MEA but substantially lower vapor pressure. A process flow diagram is developed

along with the chemical and thermodynamic principles governing the system. Steady-state

process modeling shows the efficacy of DGA to maintain a safe breathing environment with

in a closed volume. Dynamic process modeling indicates the system will be capable of rapidly

responding to transient environmental conditions. Finally, applications and considerations are

presented for space exploration. In aggregate, these data indicate liquid amine CO2 capture

processes demonstrate merit for microgravity application.

Nomenclature

ACM = Aspen Custom Modeler software package

CAD = Computer Aided Drafting

CO2 = Carbon Dioxide

CSTR = Continuous Stirred Tank Reactor

DGA = Diglycolamine

JSC = Johnson Space Center

LiOH = Lithium Hydroxide

MDEA= Monodiethanolamine

MEA = Monoethanolamine

1 Technology Development Engineer, Crew and Thermal Systems Division, 2101 NASA Parkway/EC3 2 Chemical Process Simulation Engineer, Thermal and Environmental Analysis Section, 2224 Bay Area Blvd 3 Professor of Chemical Engineering, Rice University, Houston, Texas 4 Graduate Student, Rice University, Houston, Texas 5 Principal Investigator, Life Support Branch, Crew and Thermal Systems Division, 2101 NASA Parkway/EC3

International Conference on Environmental Systems

2

NASA = National Aeronautics and Space Administration

SMAC = Spacecraft Minimum Allowable Concentration

I. Introduction

As an outcome of the metabolism of food and oxygen, humans produce water vapor and carbon dioxide. With

respect to space exploration, both materials should be controlled. Water vapor is generally a nuisance as it can affect

electronics, fog transport materials and gauges, and create slippery surfaces. Carbon dioxide poses a much greater

risk. Time and concentration levels both influence symptoms which range from mild headaches and dyspnea (i.e.,

shortness of breath) to severe decrements in visual acuity, hearing, and neurocognitive faculties1. As a result, NASA

has spacecraft maximum allowable concentration (SMAC) limits. While the research is ongoing, NASA has set a 180-

day limit of 5.2 mm Hg and a 1000-day limit of 3.6 mm Hg (see Table 4.2 of reference2). Recent research indicates

exposure to levels as low as 0.8 mm Hg can influence cognitive abilities3 which has increased the scrutiny of these

limits4,5. The toxicology research is ongoing; however, the outcome with respect to designing life support equipment

is that there is a great impetus for hardware and processes that are capable of controlling CO2 limits across a wide

variety of conditions. Moreover, technologies are needed that remain adaptable for rescaling to a variety of vehicles

and mission scenarios. To that end, NASA at Johnson Space Center (JSC) is developing a liquid amine carbon capture

technology for microgravity application.

The development efforts and testing stages of this study are in its early phases. To augment progress, modeling

and simulation are being performed in parallel. The parallel efforts inform the overall system design and allow for

testing of scenarios that are cost prohibitive or unsafe to test. The experimental efforts serve to verify and validate the

modeling work. All in all, the aim of the coordinated effort is to reduce the overall design cycle of the liquid amine

CO2 capture technique.

As presented herein, a multiscale modeling approach has been developed to evaluate a process involving a liquid

sorbent-based gas-liquid contactor for CO2 removal, a degasser/evaporator, and various other thermal management

components for maintaining a cabin CO2 concentration at or less than dictated by current requirements. The contactor

has been evaluated at the gas/liquid interface scale (on the scale of millimeters and seconds) using multiphysics

modeling with the finite element method. The outcome of the contactor model was then used to setup performance

relationships for a chemical process simulation model (on the scale of meters and hours). The process model was then

used to (1) assess contactor thermal requirements, (2) consider trades in size/weight/power as a function of mission

requirements, (3) explore the dynamics required to achieve steady-state for cabins of a variety of sizes and (4)

investigate the ability of the process to respond to transient challenges associated with crew sleep, wake, and exercise

schedules. In summary, the results herein indicate the liquid amine CO2 capture process demonstrates promise for

achieving the requisite removal rates under a variety of conditions and that the technology can be tailored to various

mission requirements.

II. Gas-Liquid Contactor Design

Due to the nature of utilizing liquid sorbents for carbon dioxide (CO2) removal, a gas/liquid contactor must control

the flow of two fluids, allow for efficient contact between the two fluid phases, allow for easy management of the

fluids once in contact, and be designed for optimal residence time to support absorption and reaction processes. In

addition, an optimized contactor must have a high interfacial surface area to facilitate the necessary transport

phenomena. Liquid sorbents for microgravity applications have been considered in the form of membrane absorbers

or spray systems. Membrane absorbers, whether a flat-plate or hollow-fiber configuration, suffer from slow mass

transfer rates through the porous medium and membrane fouling, which drastically reduces the performance and

available active surface area. Spray systems place the CO2 rich air and liquid sorbent in efficient contact, but also

suffer from slow kinetics and prove to be difficult to phase separate the liquid droplets from the scrubbed air stream.

Because of the inefficiencies of the traditional contactor design approaches, the JSC air team has 3-D printed a new

design for a direct air/liquid contact methodology.

In the absence of gravity, free floating liquids form a sphere in order to minimize surface energy in a favorable

surface area to volume ratio. When in contact with a solid surface with an indented geometry, assuming solid/liquid

interactions, the liquids adhere to the solid surface via surface tension and form a concave meniscus at the air/liquid

interface to maintain surface energy minimization. This knowledge is exploited in our design via the use of corrugated

walls for the indented geometry. At the liquid/solid interface, capillary forces promote liquid flow in a thin film

configuration. Surface tension forces dominate at the boundary layer and prevents the liquid from shearing out of the


3

capillary channels. By utilizing capillary channels, the liquid stream can be effectively controlled. In the design shown

in Figure 1, thin films of liquid sorbent flow along both sides of the corrugated capillary channels. To maximize the

amount of surface area exposure, the corrugated channels are double-sided. Simultaneously, CO2 laden air is directed

from the cabin and cross-flows between each plate. CO2 is absorbed from the air into the liquid stream through direct

contact. Without the restriction of a porous medium between the air and liquid streams, the kinetics proceed favorably

and are not hindered. The CO2 rich liquid stream is processed and desorbed for continuous cycling and reuse.

Figure 1. Three-dimensional model of the direct air/liquid absorber

III. Contactor Performance Modeling

A model for the absorber geometry was developed in the COMSOL Multiphysics (Burlington, MA) finite element

method modeling software. Prior to analyzing the more complex absorber design, the COMSOL simulations were

validated by analyzing a 1-D falling film and comparing with the analysis. The model was validated by reproducing

the study and report by Chermiti et al.6 for CO2 absorption in falling film MEA solution. Our COMSOL model also

reproduces the analytical solution to the generic 1-D problem described in transport phenomena.

The specific geometry involved triangular grooves for liquid flow along with a uniform gas flow perpendicular to

the primary direction of liquid flow. The absorber consists of 192 channels for liquid flow. A single channel was

modeled and analyzed quantitatively. For a description of the fluid flow profile, a no-slip condition was assumed at

the solid surface and the film was assumed to be of constant thickness across the groove and a decrease in film

thickness is expected due to capillary-driven flow. An inlet boundary condition of 0.4 mm/s was used for the liquid

and the flow profile was calculated. As expected, the liquid velocity is greatest for distances further from the surfaces.

The velocity profile is assumed to be fully developed within the grooves and independent of the composition during

absorber operation. To calculate CO2 uptake by the liquid, a model was developed in COMSOL using the equilibrium condition such

that concentration of component 𝑖 in the liquid phase is related to concentration in gas phase, 𝐶𝑖,𝑜, at the interface

(denoted with subscript 𝑜) and Henry’s coefficient, 𝐻𝑖 , 𝐶𝑖,𝑜 = 𝑝𝑖,𝑜/𝐻𝑖 . The absorption temperature was taken as 298 K.

The second order rate constant of 17.3 m3/mol-s for the reaction was taken from Al-Juaied et al.7. Other parameters

including diffusivity were calculated using Stokes Einstein relation relative to liquid water8,9. A liquid film thickness

of 6 mm was used in order to maximize the available surface area for reaction with the gas. A second order reaction

between CO2 and DGA was implemented in order to predict both CO2 uptake and reaction and DGA depletion. Three snapshots of concentration distributions for an inlet gas-phase CO2 concentration of 0.26 % by volume,

corresponding to 2 mm Hg CO2 are shown in Figure 2: the DGA concentration in the liquid phase, the CO2

concentration in the liquid phase, and the CO2 concentration in the gas phase. A depletion of CO2 is evident in the


4

gas phase (right), and an increase in CO2 concentration is observed only near the gas-liquid interface (middle). These

results are consistent with the falling film analysis presented above.

Figure 2. COMSOL simulation predictions for the DGA concentration in the liquid phase (left), the CO2

concentration in the liquid phase (middle), and the CO2 concentration in the gas phase (right).

An interesting prediction of the COMSOL model is that the gas-phase resistance is less significant than interfacial

or liquid-phase resistance. This can be seen clearly in Figure 3 (left frame) where a significant depletion of CO2 in the

gas phase near the gas-liquid interface is evident. By contrast, the liquid phase has an appreciable accumulation of

CO2 only near the edge where the gas enters the channel. The depletion of DGA in the liquid phase is similarly only

present at one edge of the channel, near the gas entrance. Damko ̈hler number was computed for falling film case and

it had very high value, which implies that reaction between CO2 and DGA is much faster than time it takes to penetrate

CO2 in the liquid. A 2-D plot of the CO2 concentration in the gas phase is presented in Figure 3. These results

demonstrate that the gas flow rate and mixing in the gas phase will strongly impact the rate of CO2 uptake. A faster

gas flow rate and/or significant mixing will enhance CO2 flux across the interface.

Figure 3. COMSOL simulation predictions for the CO2 concentration in the gas phase along the channel. In

the plot shown, the gas-liquid interface is on the left-side.

The predicted data using COMSOL simulations for CO2 uptake is shown in Figure 4 as a function of air velocity.

These data are calculated for an inlet CO2 gas concentration of 0.26 % by volume (0.11 mol/m3), inlet gas velocity of


5

1.01 m/s, a liquid film height of 6.0 mm, and a liquid velocity of 4 mm/s. As we can see from Figure 4, CO2 uptake

increases with increase in gas velocity. A higher gas velocity delays the onset of the boundary depletion layer in the

gas phase (Figure 4) and increases the mass transfer coefficient across the interface. Our analysis indicates that the

rate of uptake is in the range of 1.0 kg/day for each absorber, and with higher gas velocity or higher surface area this

condition is attainable.

Figure 4: Variation of CO2 uptake in series of channels with change in air velocity and area of interface for

DGA inlet velocity of 4 mm/s.

IV. Process Modeling

Sub-scale contactor simulation lends insight into the expected performance of the contactor with respect to CO2

capture. However, for the technology to be viable for life support systems, dynamic process modeling was used to

understand the material and energy demands for the technology in a system context. This exercise assists in

understanding the overall process requirements as well as aiding in the identification of high technical risk components

deserving additional scrutiny. To that end, the commercial Aspen Technology, Inc. (Bedford, MA) simulation suite

was used for process modeling. Aspen Plus is the best-in-class industrial simulation tool with myriad unit operations

already built-in for a user to rapidly generate and evaluate a process-specific flow sheet. However, the technologies

relevant to spacecraft life support systems are often at a much smaller scale and with non-traditional components than

one would find programmed in a chemical process simulation software. Consequently, Aspen Custom Modeler,

Version 9.0, was used to develop a dynamic process model with custom components. The combination of sophisticated

dynamic numerical methods and pre-programmed physical properties routines enables the user to accelerate model

development by focusing on the application rather than thermodynamics or mathematical numerics.

The general process flow diagram for a liquid amine absorption system is displayed in Figure 5. Aspen Plus,

Version 9.0, was used to select components, physical properties and property methods. An electrolyte non-random

two-liquid model for CO2 capture in diglycolamine (DGA) previously developed by Aspen Technology10 was used in

developing physical properties for pure components and mixtures. The Aspen Technology model was also used to

determine water uptake/release in the evaporator which serves to provide some cabin humidity control in addition to

CO2 removal. Properties and modeling techniques for the DGA model are also explained in a similar liquid amine

scrubber model prepared by Aspen Technology11 for methyl diethanolamine (MDEA) and monoethanolamine (MEA)

which was compared against a pilot-scale industrial model12.

0.0

0.5

1.0

1.5

2.0

2.5

0 0.5 1 1.5 2 2.5

CO

2 u

pta

ke,

kg/d

ay

Air velocity,m/s

237 mm² per channel

320 mm² per channel


6

Figure 5: Schematic representation of the liquid amine CO2 capture process.

Each block within the process flow diagram in Figure 5 is summarized below in Table 1. The original model

prepared for NASA at Johnson Space Center (JSC) focuses on CO2 capture using diglycolamine (DGA) which is an

attractive sorbent given its low vapor pressure and capability to be thermally-regenerated within a continuous

process13. However, the model can be readily reconfigured for other sorbents given a number of liquid amines exist

within the AspenTech databases. The table provides a brief description of the components while more detailed

explanations on the modeling approach is described in other sections of this manuscript.

Table 1. Summary of unit operations associated with Figure 5.

Submodel Description

Make-up Air Provides necessary oxygen to maintain oxygen within 20-21 vol%

Out-Leak Leakage to space can be simulated by the user if desired; an infinitesimally low value was used in this

modeling effort (1×10-12 m3/hr)

Cabin Model capturing the dynamics of O2 consumption, and CO2/H2O production by a crew of four under various

metabolic conditions

Amine Scrubber Accepts cabin gas and regenerated amine as inputs calculating the composition and flow of scrubbed-gas

and CO2-laden DGA based on subscale COMSOL modeling results; water uptake is also modeled

RegenHX Generic heat exchanger model allowing thermal energy transfer between hot/cold streams though;

performance is based on a commercially available shell and tube model

Heater Simple heater model which calculates the power requirements for an electric heater

Evaporator Predicts the separation of CO2 and H2O from DGA at elevated temperatures

Chiller

Allows for regenerated DGA to be cooled back to a level that is acceptable in the Amine Scrubber for

efficient CO2 capture; this model was not needed for any runs reported within this report and it is simply a

placeholder in case a need for the chiller arises at later phases in the development process

Make-up

Amine/Water

Allows for additional DGA or water to be added if they are depleted; DGA recirculation rate is set in this

module at 2.4 L/hr and the DGA:H2O volumetric ratio was maintained at 65:35

Amine Tank

Amine tank model accounts for a time delay in composition and temperature associated with fluid flowing

through the plumbing of the system; currently, a delay of 0.01 hours (0.6 minutes) was used to represent

system plumbing

Vented Gas Final stream providing the steady-state CO2 removal rate as well as H

2O and DGA removal

Air Conditioning Calculates the heat rejection requirement necessary to reduce the gas temperature from the scrubber

temperature back to 21.1°C before it is returned to the cabin

Make-up Air

Out-leak

Cabin

Am

ine S

cru

bb

er

Eva

po

rato

r

Amine TankRegenHX

Heater

Chiller

Make-up Amine/Water

Air Conditioning

Evaporated Materials

O2, N2,

High CO2, H2O

O2, N2,

Low CO2, H2O

Lean DGA and H2O

CO2-laden DGA

and H2O

Cool lean

DGA and H2O

Hot lean

DGA and H2O

Hot CO2-laden DGA

and H2O

O2, H2O


7

Lastly, the process flow diagram in Figure 5 does not show any methods for water recovery. However, given the

regeneration temperatures discussed in later sections of this report, it is clear that it would be prudent to include some

method of water recovery such as a condensing heat exchanger located on the vented gas line. Condensed water would

then be used to reduce the make-up water requirement.

The cabin model is where carbon dioxide and water vapor accumulation are of the greatest importance with regard

to crew safety. This model works by first selecting the number of crew and their average metabolic rate. The model is

simulated as a continuous stirred-tank (CSTR) type model as shown in the equation below where 𝑁𝑐 are the total

moles of gas in the cabin and 𝑧𝑖 is the mole fraction of gas component 𝑖 in the cabin with 𝑖 representing CO2, H2O,

N2, O2, or DGA (note that trace metabolic byproducts have been ignored herein).

𝑑(𝑁𝑐)

𝑑𝑡= �̇�𝐼 + �̇�𝑆 − �̇�𝑂 − �̇�𝐴 + ∑ 𝑟𝑖

𝑖 (1)

𝑑(𝑧𝑖𝑁𝑐)

𝑑𝑡= 𝑧𝐼,𝑖�̇�𝐼 + 𝑧𝑆,𝑖�̇�𝑆 − 𝑧𝑖�̇�𝑂 − 𝑧𝑖�̇�𝐴 + 𝑟𝑖 (2)

In the above equations, �̇�𝐼 , �̇�𝑆, �̇�𝑂, �̇�𝐴 denote the molar flows for make-up air/oxygen in, scrubbed-gas return, out-

leak, and gas to the amine scrubber, respectively. The composition of the make-up air in 𝑧𝐼,𝑖, and scrubbed gas return

𝑧𝑆,𝑖, are known or dynamically calculated.

Oxygen consumption was calculated, in kmole/hr, as a function of metabolic rate 𝑀𝑅 in Btu/hr, number of crew

members 𝐶𝑀, and dimensionless respiratory quotient 𝑅𝑄 based on experimental data regressed to a statistical model14.

A respiratory quotient of 0.90 was used in this analysis.

𝑟𝑂2 = −MR

32.0∙

CM

2.2046[1.708 × 10−4 − 1.32 × 10−5

(RQ − 0.707)

0.293] (3)

Carbon dioxide generation is related directly to oxygen consumption.

𝑟𝐶𝑂2 = −𝑟𝑂2 ∙ RQ (4)

Water generation is calculated from the following relationship.

𝑟𝐻2𝑂 =CM

18.0 ∙ 2.2046[0.07 − 2.3 × 10−4MR] (5)

For most calculations, a nominal metabolic rate of 474 Btu/hr was used in the model (nominal metabolic rate per

the NASA Human Integration Design Handbook15, Table 6.2-10) resulting in oxygen consumption at 2.3319 g/min

and CO2 and H2O generation at 2.8866 g/min and 5.4180 g/min, respectively. However, a daily schedule including

sleep and exercise was also simulated in some cases (which was again specified by reference15, Table 6.2-10).

The pressure 𝑃𝑐, in the cabin is reconciled as materials are generated and consumed using the ideal gas equation

of state for the cabin of a defined volume, 𝑉𝑐. Temperature 𝑇 is in absolute units (Kelvin) and 𝑅𝑔 is the universal gas

constant (0.08206 atm-m3/kmole-K).

𝑃𝑐𝑉𝑐 = 𝑁𝑐𝑅𝑔𝑇 (6)

Oxygen was added through the Make-up Air block at the flow rate necessary to maintain the breathable atmosphere

at an O2 concentration of 20-21 vol%.

The sub-scale COMSOL Multiphysics model was used to calculate the CO2 flux as a function of gas flow rate, 𝑞𝑐,

for a single groove in the contactor. The COMSOL simulations are computationally expensive. As an alternative, the

COMSOL model output was regressed to a power law for flux as a function of flow for use in the process model.

These data are shown in Figure 6 providing the power low relation for flux as a function of flow velocity.


8

Figure 6 also illustrates a notional computer-aided drafting (CAD) design for a sub-scale contactor which

demonstrates how a contactor is specified in the process model. The sub-scale design has dimensions as provided

within the figure which accommodates sixteen trays – each of which has 12 grooves in the flow direction per side (or

24 total grooves per tray). The velocity associated with 26 acfm is 0.427 m/s. Since each groove has a surface area for

gas-liquid contact of 𝐴𝑔 which is 237 mm2, at least 71 total trays, 𝑛𝑡, would be necessary to meet a metabolic CO2

production rate of 2.8866 g/min. At higher gas flow rates, the flux is increased and less trays are required. Conversely,

lower flow rates decrease the flux meaning more trays are required to meet metabolic requirements. This is

commensurate with a larger contactor volume but also requires less fan power to move the gas. Changes in gas flow

also affect the system thermal balance in a non-linear fashion. Many of these trade-offs are explored in later sections

of this report. In summary, a process flow throughput is set by requirements and then the data in Figure 6 are used to

determine the flow velocity and the flux per groove. Thereafter, the total number of grooves are calculated which meet

the metabolic CO2 removal requirement.

Figure 6. Amine contactor dimensions and flux per groove.

Alkanolamine sorbents react with carbon dioxide forming carbamate salts. The theoretical maximum uptake, 𝜓max,

is thereby 0.5 moles CO2 per mole of amine sorbent. With DGA, the overall reaction is represented below in Figure 7

which shows the amine carbamate salt formation reaction. In this reaction, a non-bonded electron pair on the amine

functional group can interact with carbon dioxide while a proton is exchanged with a nearby amine group.

2DGA + CO2 ⇌ DGA ∙ COO− + DGA ∙ H+ (7)

0

10

20

30

40

50

60

70

80

90

100

0.0 0.5 1.0 1.5 2.0 2.5

Flu

x q

c, m

ole

/cm

2-s

Flow Velocity v, m/s

Am

ine I

nA

min

e O

ut

Side

Top

Isometric

6.87”

7.75”

2.75”

Air

qc = 50.86 vc0.7251


9

Figure 7: Liquid amine carbamate salt formation net reaction, R represents -CH2CH2OCH2CH2OH.

In reality, the overall reaction is the outcome of several simultaneous competing reactions outlined elsewhere10.

Other reactions include direct amine protonation through interactions with carbonic acid and a variety of acid-base

equilibria reactions as materials lose protons or as water ionizes forming hydroxide and hydronium. However, the

theoretical maximum still holds. For the contactor model, the flow of CO2 into the absorbent was based on the flux

and a linear driving force term (the departure from equilibrium) which reflects that as the sorbent approaches capacity,

the uptake rate is diminished.

𝑑𝑁𝐶𝑂2

𝑑𝑡= 𝑛𝑡𝐴𝑔𝑞𝑐 (1 −

𝜓

𝜓max) (8)

In the above expression, the differential on the left side is the molar flow of CO2 into the absorbent and 𝜓 is the

current loading (moles CO2 per mole of DGA) where 𝜓max denotes saturation, 𝑛𝑡 is the number of trays, 𝐴𝑔 is the

groove area per tray, and 𝑞𝑐 is the CO2 flux into the contactor based on Figure 6. Component off-gassing in the

evaporator (CO2, H2O, DGA) or additional humidity capture in the contactor, were modeled using a thermodynamic

flash.

The evaporator unit is modeled as a single-stage thermodynamic flash process. The Redlich-Kwong equation of

state with the modification by Soave16 (that is, the SRK equation of state) was used for the flash calculation partition

coefficients. The rates of vented CO2, water, and diglycolamine were all dynamically monitored to evaluate the CO2

capture rate as well as absorbent losses.

The heat of absorption was accounted for in both the absorber and evaporator blocks using a thermodynamic

identity.

𝜕 ln 𝑃𝐶𝑂2

𝜕(1/𝑇)= −

Δ𝐻𝑎𝑏𝑠,𝐶𝑂2

𝑅𝑔 (9)

Data for the heat of absorption for this system has been measured as a function of temperature and partial pressure

CO217. The data were regressed to the following relationship with 𝑃𝐶𝑂2 in pascals and 𝑇 in Kelvin where 𝜓 again

represents CO2 loading in DGA (moles CO2 per mole DGA).

ln 𝑃𝐶𝑂2 = 28.1 −7572

𝑇+ 67.8𝜓 − 25,209

𝜓

𝑇− 115𝜓2 + 50,113

𝜓2

𝑇 (10)

The amine tank model accounts for a time delay in composition and temperature associated with fluid flowing

through the plumbing of the system; currently, a delay of 0.01 hours (0.6 minutes) was used to represent system

plumbing which is a rough estimate of the residence time for the sorbent flow of 2.4 liters/hour. The make-up

HN

R H

H

N

R H

H

N+

R H

N

R H

H+

(RNHCOO-) (RNH3+)

O O

(RNH2)

(CO2)

(RNH2)

Non-bonded

electron pair


10

amine/water block adds water and DGA at a rate required to both maintain 2.4 liters/hr flow and to maintain the

volumetric ratio of 65:35 DGA to water.

The heater, air conditioning, and chiller blocks all use the same modeling approach. In these units, a setpoint

temperature is specified by the user and then the enthalpy rate (i.e., flow rate multiplied by mixture enthalpy) to

achieve the temperature change is calculated. The difference in the inlet and outlet enthalpy rate provide the

heating/cooling requirement in kW. Aspen Technology FORTRAN subroutines were invoked for the mixture enthalpy

calculation which uses the SRK equation of state.

It should be noted that for all cases within this study, the chiller was found not to be necessary and all cooling

requirements were 0 kW for the cases reported herein. However, the submodel has been maintained within the overall

process for use at a later time if necessary.

The RegenHX model is simulated as a traditional counter-flow heat exchanger in accordance with information and

data supplied for a commercially available heat exchanger (Model 00540-2 counter-flow heat exchanger by Exergy,

LLC; Garden City, NJ). The heat exchanged was calculated with a log-mean temperature difference expression for

counter-flow heat exchangers (see reference18, pages 293-294).

𝑄 = 𝑈𝐴

[(𝑇ℎ,𝑜 − 𝑇𝑐,𝑓) − (𝑇ℎ,𝑓 − 𝑇𝑐,𝑜)]

ln [(𝑇ℎ,𝑜 − 𝑇𝑐,𝑓)

(𝑇ℎ,𝑓 − 𝑇𝑐,𝑜)]

(11)

Subscripts 𝑜 and 𝑓 denote input and output while ℎ and 𝑐 denote hot-side and cold-side. The heat exchange area

(0.89 ft2) and delta-temperature across all sections of the exchanger were supplied by the vendor. The delta temperature

at the hot-side outlet and cold-side inlet was used in the model (7.47°C based on the vendor data). The vendor data

shows the heat exchanger as rated for 170 W of heat transfer under the flow conditions for the process which is

comparable to calculated values in the counter-flow heat exchange model. Some differences in the vendor calculation

and in the model calculation are that the model rigorously accounts for enthalpy as a function of composition while

the vendor calculation only provides a single heat exchange rate (rather than a range) indicating the vendor must have

presumed a fixed, rather than dynamic, composition.

The process model is solved using version 9.0 of the Aspen Custom Modeler (ACM) software package. ACM

converts all model equations into a linear algebra formulation that is solved numerically until residual expressions are

within a specified tolerance. Residual expressions were cast in an error-squared form where variable values were

sought in which the difference in the left- and right-side squared become approximately zero for all equations within

the model. This was numerically achieved in ACM using the default mixed Newton method solver. Transient

expressions were time-integrated using the Gear formulae19. As implemented in ACM, the Gear integration method is

a time-adaptive technique which is capable of resolving rapid variable changes with good numerical stability. For the

Gear method, an initial time step of 0.001 hours was used and was allowed to vary between 1×10-9 – 0.01 hours

depending on whether the numerical error threshold is achieved for a given time step.

The streams represented in Figure 5 are what are referred to as mole fractions ports in the lexicon of ACM.

Specifically, each mole fraction port transfers inlet/outlet streams which are thermodynamically specified by their

respective temperature, pressure, composition, and molar flow. The enthalpy rate and molar volume are also

transferred through the mole fraction port but these values are also calculable through the stream temperature, pressure

and composition. Though the information is redundant, it serves to simplify the model blocks which would otherwise

require code for calls to ACM subroutines or to custom relationships.

The process model was then used to explore a variety of performance characteristics as well as operational

scenarios. First, the influence of performance on regeneration temperature was evaluated. Secondly, a trade study was

performed for contactor size using equivalent system mass to provide insight into design for transit to the moon and

to Mars. Third, the influence on cabin volume was varied to determine the time-scales associated with processing gas

as well as steady-state concentrations. Lastly, the impact of metabolic rate transients, accounting for sleep, wake, and

exercise, have been simulated to investigate the dynamics of the model in a more realistic operational scenario.

A. Regeneration Temperature

Given knowledge of attainable flux rates which were generated from first principles modeling via COMSOL

Multiphysics finite element method model, the contactor can be sized to achieve removal rates commensurate with

metabolic production rates. Whether the process is viable though depends on whether the liquid sorbent can be


11

regenerated. The evaporator design is still under development though it is presumed that the microgravity geometry

will also rely on a v-groove style design.

Regeneration was simulated in the evaporator as a single-stage thermodynamic flash. Bear in mind that this is a

reactive system and that the process is more complicated than a vapor liquid equilibrium approach provides. However,

at higher temperatures, the formation of dissolved CO2 is favored and this assumes carbonate and bicarbonate

concentrations are minimized. The findings herein are potentially overly optimistic but it is worth noting the findings

have been corroborated in other modeling and pilot-scale testing studies11-13. The implicit assumption is that through

judicious design, the contactor will be capable of achieving performance close to the thermodynamic limit of a mixture

of CO2/DGA/H2O. Experimental work has been initiated though work is still underway to validate this assumption.

The equilibrium condition for component 𝑖 is that the gas-phase (𝑔) and liquid phase (𝑙) chemical potentials (𝜇)

are equal, 𝜇𝑔,𝑖 = 𝜇𝑙,𝑖. This relationship can be expressed in terms of molar composition (𝑥𝑖 for liquid-phase mole

fractions and 𝑦𝑖 for vapor-phase mole fractions) and fugacity coefficients, 𝜙𝑖.

𝜙𝑙,𝑖𝑥𝑖 = 𝜙𝑔,𝑖𝑦𝑖 (12)

The model calculates fugacity coefficients from the SRK equation of state. Thereafter, a partition coefficient is

defined for component, 𝐾𝑖, such that 𝑦𝑖 = 𝐾𝑖𝑥𝑖.

𝐾𝑖 =𝜙𝑙,𝑖

𝜙𝑔,𝑖 (13)

For an ideal solution, the partition coefficient can be cast in terms of Raoult’s law where 𝐾𝑖 = 𝑃𝑖∗/𝑃 with 𝑃𝑖

∗ as

the vapor pressure for component 𝑖. The solution herein is definitely non-ideal by virtue of the ability of the dissolved

CO2 to form electrolyte species with water and liquid amines (e.g., carbonates and carbamate salts as well as hydroxide

and hydronium ions). Nonetheless, the Raoult’s law formulation illustrates an important feature that still holds for the

fugacity-based partition coefficient. Specifically, vapor pressure is a highly non-linear function of temperature.

Likewise, fugacity shows a similar trend which means small changes in temperature can have dramatic effects.

Prior to performing any parametric and design calculations, it was first of interest to determine the regeneration

temperature to achieve an overall CO2 removal rate that meets performance requirements. The criteria for the optimal

temperature is the minimum temperature necessary to achieve a CO2 removal flow rate equal to that of metabolic

carbon dioxide production. A parametric investigation was performed for this analysis assuming a cabin pressurized

volume of 691 ft3 (the pressurized volume of a small spacecraft such as Orion20) so that steady-state performance

would be quickly achieved. Three regeneration temperatures were considered as demonstrated by Figure 8.

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Figure 8. CO2 partial pressure (PCO2) and relative humidity as a function of regeneration temperature for a

cabin volume of 691 ft3.

As seen in Figure 8, humidity levels stabilize in the cabin in around 2 hours. The system carbon dioxide removal

rate stabilizes in the same time frame though the cabin concentration of CO2 continues to rise in all but the 115°C case

in which the CO2 removal rate meets the metabolic production rate of 2.8866 g/minute. At 115°C, the concentration

reaches a value of 1.6 mm Hg which is well beneath the 1000-day SMAC of 3.6 mm Hg2. For temperatures less than

115°C , the CO2 removal rate is less than the metabolic production rate. Thus, the CO2 partial pressure continues to

rise as CO2 accumulates (i.e., the process is undersized for the CO2 challenge). The humidity removal rates are actually

much higher than metabolic output since the evaporator is operated near the boiling point of water. Therefore, all of

the humidity curves collapse onto a single curve since some of the water removed has to be replenished in the contactor

system during operation. Whether water is replenished from another source or recovered from the system is not

addressed directly in this model since it likely depends on the overall architecture of a craft not defined herein. Lastly,

to achieve the desired set points during operation, the regenerative heat exchanger is capable of significant heat

recuperation though the heater does need to be operated at a level of several watts. Some relevant system metrics for

this analysis are presented in Table 2.

Table 2. Ventilation removal rates and heating and cooling requirements for the temperature parametric.

The target CO2 production rate is 2.8866 g/min and H2O is 5.4180 g/min based on four crew at an average

metabolic rate of 474 Btu/hr15.

Regen. Temp. Flow Volume Heater RegenHX Return Cooling CO2 Rate H2O Rate

°C acfm m3 W W W g/min g/min

95 26 691 12.3 126.5 279.4 1.4622 5.8542

105 26 691 45.8 159.9 302.6 2.6121 17.3444

115 26 691 89.5 167.8 302.8 2.8866 21.1720

B. Contactor Sizing Analysis

First principles modeling of a sub-scale contactor indicated that the flux into the absorbent is a function of flow

velocity (see Figure 6). So long as the flux multiplied by the contact area meets the metabolic CO2 output, the contactor

will maintain a safe cabin atmosphere. However, a high flow system has a higher flux meaning some mass and volume

can be saved using a higher flow rate. Conversely, a low flow system may require a larger contactor but this can also

make sense under the right set of conditions.

It should be noted that the model dynamically calculates the cabin CO2 concentration based on effectively the

scrubber system works at a given process gas flow rate. That is, the process flow rate is a system input and was varied

from 13-52 acfm. The flux into the contactor was a function of flow rate (see Figure 6). For low flow rates, the final

CO2 cabin concentration tended to be higher in order to meet the 2.8866 gram/minute removal requirement. Higher

flow rates resulted in lower cabin CO2 concentrations. All in all though, if the system can meet the metabolic challenge

for a given flow rate, the final cabin concentration will rise to a time-averaged concentration in which the throughput

of the metabolic CO2 is achieved.

The contactor design data in Figure 6 was used as a basis to investigate these relationships in more detail. Again,

the Figure 6 data relied on the COMSOL Multiphysics model to predict the flux per groove within a contactor as a

function of process throughput. The COMSOL model does not assume perfect instantaneous transport but instead

solves species transport equations and accounts for reaction kinetics. This data relies on a nominal design assuming

16 trays with 24 v-grooves per tray and is able to be packaged in a contactor of that is 6.87 inches wide, 7.75 inches

tall, and 2.75 inches deep which leads to a mass of 0.821 kg. Scaling calculations show an equivalent 72 trays are

necessary to achieve the desired flux at 26 acfm (a scale increase of 4.5). Similarly, it is found 44 trays are needed for

52 acfm (scale increase of 2.75) and 119 trays for 13 acfm (scale increase of 7.44).

The optimal configuration and flow rate depends on mission requirements and the constraints on the spacecraft

developed to achieve the requirements. Levri, et al., pioneered the use of an equivalent system mass to make decisions

regarding system optimization for specific mission requirements21,22. Equivalent system mass is akin to a

‘transportation cost’. That is, the cost to transport a payload is proportional to the mass of the payload and supporting

infrastructure required to operate the life support equipment within the payload21. This method aims to not only


13

quantify the size and demands of a system but to also quantify the infrastructure mass and volume to fix the equipment

in place and to provide additional spacecraft shielding to protect the hardware. Equivalent system mass, 𝐸𝑆𝑀, is

calculated based on the mass of a payload along with several cost factors, viz.

𝐸𝑆𝑀 = 𝑀 + 𝑐𝑝𝑃 + 𝑐𝑐𝐶 + 𝑐𝑣𝑉 + 𝑐𝑡𝐶𝑇 (14)

In the equation above, 𝑀 represents the raw mass of the hardware, 𝑃, 𝐶, 𝑉, and 𝐶𝑇, all represent the power, cooling,

volume, and crew time requirements for the hardware. The 𝑐𝑗 parameters are mission-specific cost factors which map

a requirement back to an equivalent system mass. Cost factors are derived by the infrastructure required to operate a

life support system component23. Lastly, it is worth noting the caveat that 𝐸𝑆𝑀 alone should not be the only metric

used in selecting a design. Other criteria should be considered such as reliability and safety that are not directly

addressed by this metric. However, reliability and safety are not expected to change upon resizing the hardware

relevant to this study so they have been neglected herein. In addition, some crew time to maintain the system is

expected but it is not expected to change considerably as the contactor is rescaled, and thus, it has been neglected.

The volume and mass of the contactor were based on re-scaling the sub-scale contactor. The power and cooling

requirements are calculated by the model. Two sets of 𝐸𝑆𝑀 cost factors were considered based on two mission

scenarios: (1) transit to the moon and (2) transit to Mars. The cost factors, and their associated infrastructure

assumptions, are presented below in Table 3 which were collected from the NASA Baseline Values and Assumptions

document2, or BVAD (see tables 3.3, 3.4, 3.13, 3.14, and 3.17). It should be noted that the motivation for this analysis

is to provide perspective on how (1) cost factors influence optimum configuration and that (2) the liquid sorbent CO2

capture process can be reconfigured to meet mission demands. For that reason, cost factors for heritage technologies,

known with good precision, were selected rather than cost factors projected for exploration mission technologies

which may still have some error associated with the actual values.

Table 3. Equivalent system mass cost factors for missions to the moon and Mars from the NASA BVAD2.

Mission Lunar transit

Power 136 kg/kW Nuclear fission with thermoelectric static conversion

Thermal 323.9 kg/kW Aluminum anti-sun tracking radiators with Z93 surface coating*

Volume 80.8 kg/m3 Long duration shielded lunar transit value

Mission Mars transit

Power 23 kg/kW Stirling nuclear reactor

Thermal 40 kg/kW Flow through radiators with supplemental expendable cooling subsystem

Volume 215.5 kg/m3 Shielded Mars transit vehicle

* Current technology used on the International Space Station

Myriad scenarios can be constructed from the cost factors provided in the relevant reference2. Because of the

variety of possibilities, cost factors were selected in this analysis that were (A) relevant to the long term objectives of

NASA and (B) had substantial variability such that insight can be garnered into the sensitivity of the analysis to these

cost factors. The results from this analysis for a craft of 1900 ft3 (akin to a medium craft or module of a space station)

are presented in Table 4. The mass and volume requirements were developed based on a 26 tray test prototype for a

contactor at NASA-JSC which includes the contactor and the flow manifolds to and from the unit. The power costs

are related to the heating additional heating required to reach the regeneration temperature after the hot CO2-laden

flow exists the regenerative heat exchanger. The cooling requirement is for the air conditioning unit used to cool the

gas before returning it to the cabin (note that even though the flow rate is doubled, the temperature rise of cabin gas

through the contactor is calculated via an energy balance which means that higher flow rates experiencing a lower

temperature, and thus, cooling requirements do not necessarily double). Since liquid flow rates were held constant,

the regenerative heat exchanger and the degasser were not included in this analysis assuming they should be relatively

constant. As such, these 𝐸𝑆𝑀 figures are for relative comparison to configurations addressed within this manuscript

only and they are not appropriate for comparison to other system figures one could find in myriad NASA life support

literature.


14

Table 4. Equivalent System Mass analysis results for lunar and Mars transit.

Flow Number of CO2 Mass Volume Power Cooling Lunar 𝐸𝑆𝑀 Mars 𝐸𝑆𝑀

acfm Trays mm Hg kg m3 kW kW kg Kg

13 119 2.7 6.1043 0.0178 88.3 -269.4 106.8 22.8

26 72 1.5 3.6933 0.0108 89.5 -302.8 114.8 20.2

52 44 0.8 2.2570 0.0066 90.5 -309.3 115.3 18.1

For lunar transit, the thermal cost factor is substantial. As the CO2-laden gas flow rate is increased, the air

conditioning system becomes responsible for increased heat rejection which ultimately leads to a systematic increase

in the overall 𝐸𝑆𝑀 value. As a result, lower flow rates are more economical from an 𝐸𝑆𝑀 perspective though the

steady-state CO2 level does increase.

Conversely, for the Mars mission, the volume requirement to contain and shield the scrubber system is driving the

𝐸𝑆𝑀 calculation and the impact of scaling up a contactor to achieve the necessary flux at lower flow rates becomes

much more unfavorable. Therefore, the 𝐸𝑆𝑀 analysis suggests higher flow rates are preferred.

Note that pressure-flow curves for the contactor have not been determined, and therefore, the delta-pressure a

blower must overcome is not known as tray designs and packaging continue to evolve. Such data will likely influence

these calculations though the pressure drop through the contactor is expected to be orders of magnitude lower than a

solid-media packed bed. However, this exercise demonstrates that the technology remains adaptable and can be

optimized against mission constraints.

C. Cabin Volume Analysis

As previously discussed, optimal equipment for life support systems should be adaptable and scalable to meet

mission objectives as scenarios and destinations change. Moreover, having a tailorable technology allows for

commonality across transit vehicles, outposts, landing vehicles, and rovers. Therefore, it is of interest to understand

how the technology performs for cabin volumes of various sizes. In this analysis, again we consider a crew of four at

an average metabolic rate of 474 Btu/hr. The cabin composition was initialized at 20% relative humidity and 400 ppmv

of CO2 (0.3 mm Hg).

Three cabin sizes were considered for this analysis. A 691 ft3 cabin was assumed for transport vehicle which is

associated with the pressurized volume of Orion20. The volume was increased roughly three-fold to 1900 ft3 which is

the order of magnitude for a module for a larger spacecraft. Finally, a volume of 6900 ft3 was also considered (roughly

ten times the Orion volume) which would be what you would expect for a space station type of a craft with multiple

modules. The results for this analysis are displayed in Figure 9.


15

Figure 9. Parametric analysis on cabin volume.

As demonstrated in Figure 9, the volume of the vehicle influences the time required to achieve steady-state. The

small craft reaches steady-state in around 2 hours while the moderately-sized vehicle takes between 4-6 hours. The

large craft did not quite reach steady-state during the 12-hour simulation but is close to a steady-state value. The final

steady-state composition of all crafts reached was 1.6 mm Hg and 40.0% relative humidity (at a temperature of 21°C).

This is expected since removal rates are equal to metabolic production rates when steady-state is achieved. The

difference in time requirements are also expected since the cabin volume influences the residence time and thereby

the accumulation rate within the cabin. All in all, these results are encouraging as the final CO2 concentration is well

below the 1000-day SMAC and relative humidity is low enough that condensation is not expected. Also, the humidity

level is sufficient that broncho-pulmonary dehydration risks are mitigated.

At steady-state, the rate at which the system captures and segregates CO2 is 2.89 g/min (i.e., the metabolic

production rate). However, the water losses are much higher than metabolic production given the regeneration

temperature at 115°C exceeds the boiling point of water. Water removal rates are predicted to be 21.20 g/min (versus

5.42 g/minute for metabolic production). This shows the necessity for water recovery within this system. The water

recovery system could entail a variety of methods not considered herein (e.g., condensing heat exchanger, desiccant

dryer, membrane dryer).

Because the overall architecture of the spacecraft has not been identified, the drying technology has not been

evaluated. However, two cases were considered in Aspen Plus where (1) the evaporated gas stream is cooled to cabin

temperature (21.2°C) and (2) where it was assumed a process stream of cooled water exists through which a

condensing heat exchanger can be setup (assumed water temperature of 45°F or 7.2°C with an approach temperature

of 5°C such that the water is cooled to 12.2°C). In the first case at 21.2°C, 21.18 g/minute of water was condensed

which is associated with 99.86% recovery. For the second case at 12.2°C, 21.19 g/minute of water was condensed for

99.92% recovery. These calculations presume the mass transfer kinetics for condensation are fast and that

thermodynamic equilibrium is achieved which will only occur if careful attention is provided during the system design.

D. Impact of Exercise

Exercise is necessary during long duration transit in microgravity to prevent muscular atrophy as well as to manage

osteoporosis. The metabolic impact of exercise are temporary increases in both water and carbon dioxide production

rates. The carbon dioxide removal technology must be capable of handling such transients without putting the crew at

risk.

To evaluate the impact of exercise, an exercise profile was collected from the Human Integration Design

Document15 (see Table 6.2-10). The exercise routine in the referenced document does not specify an activity but it

does specify that exercise is 30 minutes long and is performed at 75% VO2 max where VO2 max is the maximum

attainable oxygen consumption for a crew member during physical activity. During exercise, CO2 and water

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16

production rates increase dramatically. After exercise is complete, the carbon dioxide production rate drops back down

to its previous level. Water production remains high for a period following exercise before decreasing back to its

original value over the course of around an hour and fifteen minutes. It should be noted that other exercise routines

are under consideration by NASA24 although none of the alternative routines has been formally accepted at this point.

The different routines include activity at different levels of VO2 max for different durations which results in different

CO2 and water productions. Therefore, if the exercise routine is changed, then this analysis may need to be updated.

For this analysis, all four crew members were assumed to exercise daily. To minimize the amount of equipment

needed, as well as the impact on the life support systems, exercise was assumed to be staggered evenly throughout the

daytime hours (i.e., it is presumed impractical for simultaneous crew exercise from both standpoint on requiring

redundant exercise equipment and larger life support systems). The imposed exercise schedules have exercise starting

at 11:00 AM with each crew member staggered with 1-hour increments. The schedule is demonstrated in Figure 10

and the results from the analysis are shown in Figure 11.

Figure 10. Exercise schedule and component production rates simulated herein based on NASA documents6.

02468

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17

Figure 11. Results for 72-hour simulation with sleep, wake, and exercise periods, with various flow rates and

cabin volumes. Dashed lines demarcate days 1, 2, and 3.

As illustrated by Figure 11Figure 11, cabin volumes and flow rates do influence the dynamics of the process.

Smaller cabins are susceptible to larger variations in CO2 and humidity. Higher flow rates result in higher CO2 flux

which maintains overall lower CO2 levels. In all cases, the carbon dioxide concentration is below the 180-day SMAC

of 5.2 mm Hg. In most cases, the CO2 concentration is less than the 1000-day SMAC of 3.6 mm Hg. It is worth noting

that environmental requirements are still evolving for exploration missions which may revise a target concentration

as low as 2 mm Hg25,26. If this indeed occurs, then the system did not meet the requirement for the smaller cabin

volumes though the exercise schedule could potentially be revised and spaced in such a way that would enable the

system to achieve the revised goal. The highest risk scenario is for a low flow and low cabin volume where exercise

results in humidity near 90% and CO2 partial pressure around 5 mm Hg. These data indicate the ability of the liquid

amine CO2 capture technology to rapidly respond to metabolic transients but also suggest that higher process flow

rates are more appropriate for smaller cabin volumes.

V. Conclusions and Future Work

Carbon dioxide accumulation within a closed environment remains a critical risk to crewmembers aboard

spacecraft. To address such issues, a water-tolerant CO2 capture process has been devised and analyzed which uses a

thermally-regenerable liquid amine absorbent.

Sub-scale finite element method simulations provided the expected CO2 flux into the absorbent as a function of

flow through the gas-liquid contactor. The sub-scale simulation results were used to simulate the overall system in a

chemical process simulator. Several salient aspects of the process were evaluated with promising results.

First, a parametric on regeneration temperature indicated a temperature of 115°C allowed the system to achieve

CO2 removal rates meeting time-averaged metabolic requirements. The final CO2 concentration in the analysis was

far below the NASA 180-day SMAC for carbon dioxide.

Secondly, the trade between contactor volume/mass and the CO2-laden gas flow rate was performed. An equivalent

system mass method was used as the metric to indicate an optimal configuration. 𝐸𝑆𝑀 cost factors were established

based on transit to the moon or Mars. The results of the analysis show the importance of designing the process to meet

mission requirements. That is, transit to the moon was most practical with a low gas flow rate and larger volume gas-

liquid contactor. Conversely, transit to Mars indicated a higher flow rate with a lower volume contactor was more

favorable.

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18

Next, the impact of spacecraft volume was analyzed. As the spacecraft volume increases, system transients become

slower but the overall final composition was the same. These data currently assume well-mixed conditions within the

cabin. Such an assumption may need to be revisited at a later stage when the spacecraft under consideration has been

further defined.

The impact of exercise was investigated. Exercise resulted in much higher CO2 and humidity levels though all

maximums were within allowable SMAC ranges. The final compositions for the exercise cases were different which

is reflective of the fact that the times-scale for transients in exercise (on the order of minutes) are less than the time-

scale to achieve steady-state (hours to a few days).

All in all, these results are encouraging for the liquid amine process. Much experimental work is still needed to

validate that flux rates and desorption rates are in fact achievable. The thermodynamics of the process show great

promise but process inefficiencies can arise unless the components within the process are judiciously designed. For

example, the necessary regeneration temperature should be confirmed experimentally and is underway in collaboration

with NASA Ames Research Center. Water evaporation rates should also be confirmed which may also inform

reasonable methods for water recovery. These results are also dependent upon achieving the model-predicted CO2

flux into the contactor which needs to be validated and is underway at the NASA Johnson Space Center. Finally, much

work is still to be done at the interfaced system-level and how the overall system behaves to steady and transient CO2

introduction rates.

Lastly, water evaporation rates from the evaporator are significant and need to be addressed. The strategy to

address evaporation will depend on the architecture of the spacecraft (whether cooling water is available or whether

other technologies must be considered). Some simple calculations indicate a substantial opportunity for water recovery

(greater than 99%) if there are temperature sources in the range of 12.2-22.2°C. This work will be updated as

experimental efforts continue at Johnson Space Center and Ames Research Center.

Acknowledgments

The authors thank the Advanced Exploration Services Program for funding the modeling efforts. The authors also

thank the leadership of the Crew and Thermal System Division for support and software capabilities used to perform

this work. Finally, the authors thank Giraldo Alvarez and Geoff DeGraff for assistance on translating computer aided

drafting (CAD) models to the sub-scale and process model inputs.

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Pennsylvania, 1971. 2Anderson, M.S., “Life Support Baseline Values and Assumptions Document.” National Aeronautics and Space

Administration, Doc. No. NASA/TP-2015–218570, March 2015. 3Satish, U., Mendell, M.J., Shekhar, K., Hotchi, T., Sullivan, D., Streufert, S., Fisk, W.J., “Is CO2 an indoor pollutant? Direct

effects of low-to-moderate CO2 concentrations on human decision-making performance.” Environmental Health Perspectives, Vol.

120.12, 2012. 4James, J.T., Macatangay, A., “Carbon Dioxide – Our Common Enemy.” National Aeronautics and Space Administration, Doc.

No. JSC-CN-18669, January 2009. 5James, J.T., “Carbon Dioxide: Surprising Effects on Decision Making and Neurocognitive Performance.” Submarine Air

Monitoring and Air Purification (SAMAP) Symposium, New Orleans, Louisiana, October 14-17, 2013. 6Chermiti I, Hidouri N, Ben Brahim A. “Entropy Generation Analysis of a Chemical Absorption Process Where Carbon

Dioxide is Absorbed by Falling Monoethanolamine Solution Film.” ASME. J. Heat Transfer,136(12):123001-123001-10, 2014. 7Al-Juaied, M., G. T. Rochelle. “Absorption of CO2 in Aqueous Diglycolamine.” Ind. Eng. Chem. Res., 45, 2473–2482, 2006. 8Wang, Z., G. Mayank, S. S. Warudkar, K. R. Cox, G. J. Hirasaki, M. S. Wong. “Improved CO2 Absorption in a Gas Liquid

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