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REVIEW 4 major objections 5 minor 42 references

A simplified digital twin of a pressure swing adsorption plant for air separation

T0 review · 4 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read A full-plant digital twin of a PSA oxygen plant matches experiment without fitted parameters.

desk verdict Useful full-plant PSA digital twin; the matched-optimum claim needs tighter uncertainty support. read the letter →

arxiv 2502.02268 v1 pith:4N5OSR3A submitted 2025-02-04 physics.flu-dyn

Abhijit Dhamanekar , Ritwik Das , Santosh Ansumali , Raviraju Vysyaraju , Arvind Rajendran , Diwakar S. V. This is my paper · ORCID
classification physics.flu-dyn PACS 47.56.+r47.11.-j
keywords pressureswingadsorptiondigitaltwinairseparationoxygenproductionaxisymmetricCFDSkarstromcycleporouszonemodellineardrivingforce
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that a 2D axisymmetric computational model can act as a digital twin of an entire pressure swing adsorption (PSA) plant—not just the adsorbent columns—and reproduce the measured performance of a real pilot unit producing oxygen from air. The model represents the reservoir, two zeolite columns, product tank, solenoid valves, pressure regulators, and mesh filters as coupled subdomains, and emulates valve cycling by switching boundary conditions between wall, interface, and outlet states. The authors show that the predicted column pressures and outlet oxygen purity agree closely with experiments over a range of cycle timings, and that both simulation and experiment identify the same optimal pressurization, purge, and equalization times. They also state that the model contains no fitted closure parameters. If correct, this provides a predictive design and optimization tool for PSA systems, extendable to hydrogen purification and carbon capture, at much lower cost than building and testing physical prototypes.

What carries the argument

The central mechanism is an axisymmetric 2D representation of the entire plant in which each physical component is mapped to a subdomain and the six solenoid valves are emulated by switching the boundary conditions of mating faces between wall, interface, and outlet states according to the six-step modified Skarstrom cycle. Pressure drops through valves, regulators, and filters are represented by porous-zone inertial resistance terms computed from manufacturer flow coefficients and literature correlations, while the zeolite beds use Ergun's equation and a radially varying porosity to capture wall channelling. Adsorption is modeled by the linear driving force equation with a multisite Langmuir isotherm, and the sources of mass, species, and energy are added through user-defined functions. The whole set of conservation equations is solved with the Ansys-Fluent SST k-omega framework and periodic face-pair mappings for the interface states.

What would settle it

Run the same digital-twin code on a geometrically different PSA plant of the same cycle type—e.g., different pipe lengths, valve flow coefficients, or a single-column unit with known breakthrough data—and compare predicted cyclic steady-state purity and column pressure traces against experiment without retuning any geometry or resistance parameters; a systematic offset in optimum purge or equalization time would falsify the no-fitting claim.

Watch

Extended reading notes

Core claim

The paper's central claim is that a simplified axisymmetric CFD model of the complete PSA plant closely replicates the dynamic behavior of a physical pilot plant producing oxygen at roughly 93% purity, including pressure transients in both columns and the output purity at cyclic steady state. The model integrates mass, momentum, energy, and species conservation with adsorption kinetics through a linear driving force model, using porous-zone approximations for valves, regulators, and filters, and dynamically switching boundary conditions to represent the six-step modified Skarstrom cycle. The authors report that both numerical and experimental results identify the same optimum performance at a pressurization time of 26 s, purge time of 2 s, and equalization time of 4 s. They further claim that the model has no parameters requiring fitting for closure, meaning all resistances, isotherm parameters, and transport coefficients are taken from physical correlations, manufacturer data, or literature.

Load-bearing premise

The model assumes that a 2D axisymmetric geometry, with porous-zone stand-ins for solenoid valves, pressure regulators, and filters, and parallel periodic boundary faces for the connecting pipes, faithfully represents the real three-dimensional piping and valve network, so that the matched pressures and purity optima are predictive rather than coincidental.

Editorial extensions

If this is right

  • PSA plant designers can use the model to screen cycle timings and valve sequences computationally before building hardware, since the model identifies the same optimal pressurization, purge, and equalization times as the physical plant.
  • The model supplies spatially resolved information that experiments cannot easily give, such as the nonplanar oxygen front caused by wall channelling, and can therefore suggest design changes like avoiding bottom-bottom equalization that lets nitrogen re-enter the columns.
  • Because the same framework couples reservoirs, valves, filters, and product tanks, it can be adapted to other PSA applications such as hydrogen purification and carbon capture with changes only in adsorbent properties and cycle definitions.
  • The absence of fitted parameters means the model can be applied to different plant scales and geometries with the same constitutive relations, provided the geometric surrogates for valves and piping remain faithful.
  • The cost of simulation is kept low by the axisymmetric approximation and a 4000-cell column grid, making design-space exploration over many cycle timings tractable within a few tens of repeated cycles.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural testable extension is to apply the same valve-as-boundary-condition approach to a three-dimensional or full-plant model and check whether the matched pressure transients persist when the assumption of rotational symmetry is relaxed for the T-joints and valve manifolds.
  • The claimed absence of fitted parameters could be falsified in practice by applying the model to a different adsorbent (e.g., LiX zeolite) or a different plant configuration: if the same isotherm and resistance correlations no longer reproduce purity optima, then some hidden tuning exists in the geometry adjustments or porous-zone coefficients.
  • The observation that valve opening and closing are abrupt in the model while experimental pressure probes show latency suggests that an explicit finite-time valve response model could improve agreement during the purge and equalization stages without changing the rest of the framework.
  • The sensitivity to equalization configuration illustrated for top-only, bottom-only, and combined equalization offers a concrete lever for plant retrofit: switching to top-only equalization would likely raise product purity at slightly lower nitrogen rejection, a proposition directly testable by modifying the valve sequence in the pilot plant.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This manuscript builds a 2D-axisymmetric CFD digital twin of a complete two-column PSA oxygen plant: the domain includes the air reservoir, adsorbent columns, product buffer tank, pressure regulators, six solenoid valves, and mesh filters, with valves emulated by switching face boundary conditions and with porous-zone models for regulator, valve, and filter losses. Adsorption is treated with the LDF model and a single-site Langmuir isotherm, implemented through UDF source terms in Ansys-Fluent. The model is first checked against the CO2/N2 breakthrough benchmark of Wilkins and Rajendran and of Ramos et al., then compared with pilot-plant column pressure traces and O2 purity for a modified Skarstrom cycle. Parameter sweeps over pressurization, purge, and equalization times identify an optimum at tpr=26 s, tpu=2 s, teq=4 s, which the authors state coincides with the experimental optimum and requires no fitted parameters.

Significance. If the claims hold, this is a useful full-plant modeling route: instead of 1D column models, it provides spatial resolution of adsorption fronts, wall effects, and component pressure drops, and it can screen cycle timings at CFD fidelity. The breakthrough reproduction and the good pressurization/depressurization pressure match give independent support to the UDF implementation. However, the validation is incomplete in respects that matter for the optimization claims: the tpr optimum is not statistically resolved, the tpu/teq sweeps are validated against phases the model concedes it does not reproduce, and the 'no fitted parameters' statement overstates the status of the isotherm and transport constants. The UDF code is not provided, so the digital twin is not directly reproducible from the manuscript alone.

major comments (4)
  1. [IV.C, Figs. 15-16] The abstract and conclusion claim that numerical and experimental results yield the same optimum tpr=26 s. The numerical CSS purities reported in Fig. 15 are 91.6% at 22 s, 92.3% at 26 s, and 92.2% at 30 s. The 26 s versus 30 s difference is 0.1 percentage points, which is smaller than the ±0.5% O2-sensor uncertainty quoted in Fig. 16, and no numerical uncertainty is reported. A plateau within error cannot identify a distinguished optimum; this part of the central claim is therefore under-resolved as stated. Please provide numerical error estimates, such as cycle-to-cycle variation, mesh sensitivity at CSS, or replicate runs, and either demonstrate that the optimum is outside the combined uncertainty or soften the claim to an optimum within a flat region.
  2. [IV.B, Fig. 10] The paper concedes that during purge and equalization the experimental pressure traces have a reduced slope, do not reach the numerical peaks, and take the full equalization time to equilibrate, whereas the numerical columns approach equalization promptly. These are precisely the phases whose durations, tpu and teq, are varied in the optimization sweeps of Sections IV.D and IV.E. Consequently, the matched optimum at tpu=2 s and teq=4 s is validated against the least-accurate parts of the cycle. The authors should quantify the mismatch, for example the peak pressure error and the equalization-time lag, and show that the ranking of simulated tpu and teq candidates is robust to the known probe latency and valve dynamics, or limit the optimization claim to tpr.
  3. [V; Table IV; Section III.C] The conclusion that 'the model does not have any parameters that need to be fitted for its closure' is too strong as written. The isotherm constants in Table IV come from fitting empirical isotherm data; the tortuosity tau=2.5 is selected from the range 2-5 in Section III.C; h_infinity=8 W/m2K is assumed in Section III.B.4; and the radial porosity parameters in Section III.D are fixed inputs. It is a meaningful and defensible claim that no parameters were fitted to the pilot-plant pressure and purity data, but that is not the same as parameter-free closure. Please restate the claim accordingly and list the externally calibrated parameters and their sources.
  4. [III.B.5] A fourth species, helium, is introduced 'to compensate for mass/volume imbalance arising from the arithmetic round-offs during the computations.' Since helium is a transported species with its own diffusivity, viscosity, and thermal conductivity, as listed in Table VI, any accumulation or spatial segregation of this dummy species will alter the mixture properties and could affect the reported O2 purities. The paper reports no helium mass fractions and no check that the dummy species remains negligible. Please show that the helium fraction stays below a small tolerance in the product stream and throughout the domain, or implement a strict species closure that does not add a physical species.
minor comments (5)
  1. [Fig. 10] Fig. 10 uses MPa while the text quotes pressures in bar; please harmonize units across the figures and the text.
  2. [Throughout] The name 'Skarstrom' is spelled as 'Skarstorm' and 'Skartsorm' in several places, including the Section IV heading; please standardize the spelling.
  3. [IV.A, Figs. 8-9] The grid-independence study reports only velocity profiles at one time instant; the statement that 'the corresponding inference also holds for other parameters' should be supported by at least one composition or temperature profile, since purity is the main output variable.
  4. [III.B.5, IV] The UDF source-term definitions are described in words, but no code or detailed numerical implementation of Eqs. (24)-(27) is given; adding the UDF listing or a supplementary file would materially improve reproducibility.
  5. [III.A, IV] The inlet mass-flow correction chain is not fully consistent: Section III.A mentions a 15% dryer purge loss, while Section IV says the corrected FAD includes a 12% temperature loss and a 15% dryer loss; please clarify the exact correction chain used to obtain 0.006341 kg/s.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the digital twin is validated against an independent in-house pilot plant, and the reported optima arise from parameter sweeps rather than from fitted or self-cited inputs.

full rationale

The derivation chain is not circular. The digital twin's predictions of pressure transients and O2 purity are compared with an in-house pilot plant, and the timed-step optima (tpr = 26 s, tpu ≈ 2 s, teq = 4 s) are obtained from parameter sweeps in both experiment and simulation; no pilot-plant purity or pressure data are used to calibrate the model. Boundary conditions such as the 4.89 × 10^-4 kg/s outlet flow and the corrected-FAD compressor inflow are stated operating conditions, not fitted parameters. The SSL isotherm constants in Table IV are fitted to independent adsorption equilibrium data ('obtained by fitting the empirical data'), not to the predicted purity or pressure traces; this is a material-property input, not a circular target. The one-column CO2/N2 breakthrough check reproduces the Ramos et al. simulation and the Wilkins–Rajendran experiment, and although those references share co-authors with the present paper, the comparison is against externally collected breakthrough data and serves only to verify the UDF implementation; the central full-plant claim stands on the separate pilot-plant validation. No equation is defined in terms of the quantity it is said to predict, and no fitted parameter is renamed as a prediction. The conclusion's phrase 'does not have any parameters that need to be fitted for its closure' is stronger than the Table IV isotherm fitting supports, but that is an overstatement about parameter provenance, not a circular derivation.

Assumptions & free parameters 5 free parameters · 6 assumptions · 1 invented entities

The central model rests on many empirical inputs taken from prior measurements and manufacturer data. The most important are the 13X isotherm parameters (fitted to equilibrium data), the assumed tortuosity and wall heat transfer coefficient, and the porous-zone representations of valves and regulators. The paper's claim that the model has no fitted parameters is only true in the narrow sense that none were fitted to the pilot-plant performance data.

free parameters (5)
  • 13X isotherm parameters (qsat, b0, delta H) for Ar, O2, N2 = qsat=3.252 mol/kg; b0: Ar 1.588e-09, O2 2.022e-09, N2 5.042e-10 1/Pa; delta H: Ar -13161.89, O2 -12685.50, N2…
    Table IV; obtained by fitting empirical adsorption data to the single-site Langmuir model. These constants set equilibrium loadings in every column and directly determine the predicted purity.
  • Macropore tortuosity factor tau = 2.5
    Section III C; chosen within the literature range 2 to 5. It scales the macropore diffusivity Dp_i and therefore the LDF mass transfer coefficient kLi.
  • External wall heat transfer coefficient h_infinity = 8 W/m2K
    Section III B4; assumed constant. It controls heat loss to ambient, which affects bed temperature and adsorption kinetics.
  • Radial bed porosity correlation and mean bed porosity = eps_bo = 0.363 with Mueller/de Klerk correlation parameters
    Section III D; these packed-bed wall channelling parameters come from the literature and affect velocity profiles, dispersion, and front shape.
  • Valve flow coefficient Kv and pressure regulator/dryer loss constants = Kv = 1.5 (manufacturer); dryer purge loss 15%; compressor temperature loss 12%
    Section III A and Eq. 8-10; empirical and manufacturer inputs determine pressure drops across solenoid valves and regulators.
assumptions (6)
  • domain assumption Ideal gas behavior holds throughout the flow domain.
    Stated in Section III B before Eq. 1 and used in Eq. 31 for gas density.
  • domain assumption Gas and adsorbent are in local thermal equilibrium because particles and velocities are small.
    Section III B, before Eq. 14; allows a single temperature equation in porous zones.
  • domain assumption Axisymmetric geometry with translational periodic face pairs and porous-zone valve surrogates represents the 3D plant.
    Section III A; this is the main modeling assumption, and the text acknowledges that it requires careful geometric adjustment.
  • domain assumption Adsorption kinetics are macropore-diffusion controlled and follow the LDF model with tortuosity 2.5.
    Section III C, Eqs. 24 and 27-29; assumes molecular diffusion in macropores controls mass transfer for air separation on 13X zeolite.
  • domain assumption Extended single-site Langmuir isotherm is valid for the multi-component gas mixture.
    Section III C, Eq. 25; assumes homogeneous adsorption sites, no interactions between adsorbed molecules, and a fixed number of sites.
  • standard math Standard conservation equations, Ergun equation, Fuller diffusivities, and the SST k-omega turbulence closure in Fluent are applicable.
    Section III B, Eqs. 1-23; these are standard engineering correlations and conservation laws used throughout the model.
invented entities (1)
  • Fourth dummy gas species 'helium' in the PSA simulations
    purpose: Compensate for mass or volume imbalance arising from arithmetic round-off in the species conservation equations.
    Section III B5, around Eq. 19; helium is not part of the dry air feed (N2/O2/Ar 78:21:1) but is solved as a species. It is an artificial numerical device rather than a physical component, and its mass fraction should remain negligible for the model to be sound.

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Pith. "Pith review of A simplified digital twin of a pressure swing adsorption plant for air separation." pith.science (2026). https://pith.science/paper/4N5OSR3A

@misc{pith2026250202268,
  author       = {Pith},
  title        = {Pith review of: A simplified digital twin of a pressure swing adsorption plant for air separation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4N5OSR3A}},
  note         = {Machine review of arXiv:2502.02268}
}
read the original abstract

The pressure swing adsorption (PSA) process is one of the widely utilized techniques for air separation. Operating on the Skarstrom cycle, the porous adsorbent columns of a PSA system alternate between adsorption and desorption phases to selectively enrich the desired component in a gas mixture. The current work presents a robust and generalizable digital twin CFD model of a PSA system that can significantly help in design and device characterization. Using an axisymmetric representation, the digital twin accurately mimics all the key components of an air separation plant, including the air reservoir, adsorbent columns, product buffer tank, pressure regulator, solenoidal valves, and mesh filters. The model simulates the flow and adsorption processes in the system by solving the conservation equations for mass, momentum, energy, and species, along with the equation for adsorption kinetics. The cyclic operation of the PSA plants, typically controlled by solenoid valves, is emulated by dynamically modifying the boundary conditions of different subdomains. Such an integrated approach is shown here to closely replicate the performance of an in-house PSA pilot setup producing oxygen in terms of purity and pressure transience. Also, both the numerical and the experimental results yield an optimum performance for the same process parameters, such as pressurization time (26 s), purge time (2 s), and equalization time (4 s). The proposed numerical model is versatile and can be adapted to various industrial applications of PSA technology, such as hydrogen purification and carbon capture. Thus, it offers a cost-effective tool for designing and optimizing PSA systems.

Figures

Figures reproduced from arXiv: 2502.02268 by the authors.

Figure 1
Figure 1. FIG. 1: The schematic of the experimental PSA pilot plant [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2: Modified Skarstrom cycle [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3: The meshed geometry of digital twin model [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (15 more)
Figure 4
Figure 4. Figure 4: FIG. 4: Magnified sub-sections of the digital twin indicating pressure regulator (PR), [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5: Digital valve sequence and the resulting flow paths for different steps of the [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6: Schematic representation of the CO [PITH_FULL_IMAGE:figures/full_fig_p025_6.png]
Figure 7
Figure 7. Figure 7: FIG. 7: A single column test case pertaining to CO [PITH_FULL_IMAGE:figures/full_fig_p027_7.png]
Figure 8
Figure 8. Figure 8: FIG. 8: A sample grid of the adsorbent column [PITH_FULL_IMAGE:figures/full_fig_p030_8.png]
Figure 9
Figure 9. Figure 9: FIG. 9: Grid independency study [PITH_FULL_IMAGE:figures/full_fig_p030_9.png]
Figure 10
Figure 10. Figure 10: FIG. 10: Transient comparison of adsorbent column pressures for a cycle with t [PITH_FULL_IMAGE:figures/full_fig_p030_10.png]
Figure 11
Figure 11. Figure 11: FIG. 11: Contours of oxygen volume fraction: end of a) Pressurization-1, b) Purge-1, c) [PITH_FULL_IMAGE:figures/full_fig_p031_11.png]
Figure 12
Figure 12. Figure 12: FIG. 12: Contours of oxygen volume fraction at the end of equalization step for a) [PITH_FULL_IMAGE:figures/full_fig_p032_12.png]
Figure 13
Figure 13. Figure 13: FIG. 13: Contours of nitrogen volume fraction: end of a) Pressurization-1, b) Purge-1, [PITH_FULL_IMAGE:figures/full_fig_p033_13.png]
Figure 14
Figure 14. Figure 14: FIG. 14: Contours of temperature: end of a) Pressurization-1, b) Purge-1, c) [PITH_FULL_IMAGE:figures/full_fig_p034_14.png]
Figure 15
Figure 15. Figure 15: FIG. 15: Pressure variation in the adsorbent column and species concentration at the [PITH_FULL_IMAGE:figures/full_fig_p037_15.png]
Figure 16
Figure 16. Figure 16: FIG. 16: Effect of pressurization time on purity [PITH_FULL_IMAGE:figures/full_fig_p038_16.png]
Figure 17
Figure 17. Figure 17: FIG. 17: Effect of purge time on purity [PITH_FULL_IMAGE:figures/full_fig_p038_17.png]
Figure 18
Figure 18. Figure 18: FIG. 18: Effect of equalization time on purity [PITH_FULL_IMAGE:figures/full_fig_p039_18.png]

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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.