Tools · Fluids & Thermodynamics

VLE / Flash

Multicomponent vapor–liquid phase equilibrium with a cubic equation of state and UNIFAC or NRTL activity coefficients — flashes, bubble and dew points, and Txy or Pxy diagrams.

Updated October 1, 2026

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VLE / Flash computes vapor–liquid phase equilibrium for mixtures of up to eight components: how a feed splits between vapor and liquid, the bubble and dew points, and Txy or Pxy diagrams for binaries. For gas-phase chemical equilibrium, use Cantera instead. It runs on CPU with the open-source thermo and chemicals libraries.

How it works

It uses a gamma–phi model: an activity-coefficient model for the liquid and a cubic equation of state for the vapor.

eos: peng-robinson (default) or srk, built from each component's critical temperature, critical pressure, and acentric factor.
activity_model: unifac (default) or nrtl. NRTL uses ChemSep interaction parameters unless you supply binary_params, fitted values of τᵢⱼ = a + b/T and α, usually from VLE Parameter Regression. Pairs you don't supply fall back to ChemSep.

`mode`	Needs	Finds
`tp_flash`	`temperature_k`, `pressure_pa`	Phase split and compositions
`ph_flash`	`pressure_pa`, `enthalpy_j_mol`	Temperature and phase split
`bubble_t` / `dew_t`	`pressure_pa`	Bubble or dew temperature
`bubble_p` / `dew_p`	`temperature_k`	Bubble or dew pressure
`txy`	`pressure_pa`	Isobaric diagram for a binary
`pxy`	`temperature_k`	Isothermal diagram for a binary

Inputs

components: up to 8, each with a name and mole fraction z (the fractions must sum to 1). Names (or CAS numbers) are looked up in the chemicals database; for anything it doesn't know, give tc_k, pc_pa, and omega together. A component table can come from an earlier step, such as Group-Contribution Properties estimates.
mode and the conditions it needs, from the table above.
diagram_points: grid size for txy and pxy (default 21, up to 51).
binary_params (NRTL only): rows of component_i, component_j, tau_a, tau_b, and alpha, for both directions of each pair.

Outputs

File	Contents
`result.json`	The equilibrium result: conditions, phase fractions, and the model used
`compositions.csv`	`phase`, `component`, `z`, `x`, `y`: the overall, liquid, and vapor compositions
`diagram.csv`	The Txy or Pxy diagram (diagram modes only)

Not covered

Chemical equilibrium (use Cantera) and process flowsheets.
PC-SAFT and other non-cubic equations of state.

VLE Thermodynamic Consistency Checker to audit measured data, VLE Parameter Regression to fit NRTL parameters to it, and CoolProp for pure-fluid properties.

Run it from the API

Submit with Submit a job and the job_type below. Price it first with Estimate job reservation cost: submitting reserves that amount from your wallet, and the charge settles at the actual runtime.

VLE / Flash `vle-flash`

Job type: vle-flash
Hardware: cpu (default)
Typical runtime: 5 min on CPU

Payload

Payload fields
Field	Type	Description
`mode`required	`string`	One of: `"tp_flash", "ph_flash", "bubble_t", "bubble_p", "dew_t", "dew_p", "txy", "pxy"`
`eos`	`string`	One of: `"peng-robinson", "srk"`
`activity_model`	`string`	One of: `"unifac", "nrtl"`
`temperature_k`	`number`	Limits: `> 0`
`pressure_pa`	`number`	Limits: `> 0`
`enthalpy_j_mol`	`number`
`diagram_points`	`integer`	Limits: `≥ 2, ≤ 51`
`components[]`required	`object[]`	Limits: `min items 1, max items 8`
components.`name`required	`string`	Limits: `min length 1`
components.`z`required	`number`	Limits: `> 0`
components.`tc_k`	`number`	Limits: `> 0`
components.`pc_pa`	`number`	Limits: `> 0`
components.`omega`	`number`
`binary_params`	`tableValue`

Example

from cognichem_client import CogniChem

client = CogniChem.from_env()  # reads COGNICHEM_API_KEY
payload = {
    "mode": "tp_flash",
    "eos": "peng-robinson",
    "activity_model": "unifac",
    "temperature_k": 353.15,
    "pressure_pa": 101325,
    "components": [
        {
            "name": "ethanol",
            "z": 0.5,
            "tc_k": 513.9,
            "pc_pa": 6148000,
            "omega": 0.645,
        },
        {
            "name": "water",
            "z": 0.5,
            "tc_k": 647.1,
            "pc_pa": 22064000,
            "omega": 0.344,
        },
    ],
}

estimate = client.jobs.estimate(job_type="vle-flash", payload=payload, resource="cpu")
print(f"Reserves ${estimate.cost:.2f}")

job = client.jobs.submit(
    job_name="my-vle-flash-run",
    job_type="vle-flash",
    payload=payload,
    resource="cpu",
)
status = client.jobs.wait(job.process_id)
if status.status == "completed":
    client.jobs.result(job.process_id, save_path=".")

curl -X POST "https://api.cognichem.com/api/v1/jobs/submit" \
  -H "X-Api-Key: $COGNICHEM_API_KEY" \
  -H "Idempotency-Key: $(uuidgen)" \
  -H "Content-Type: application/json" \
  -d '{
  "job_name": "my-vle-flash-run",
  "job_type": "vle-flash",
  "payload": {
    "mode": "tp_flash",
    "eos": "peng-robinson",
    "activity_model": "unifac",
    "temperature_k": 353.15,
    "pressure_pa": 101325,
    "components": [
      {
        "name": "ethanol",
        "z": 0.5,
        "tc_k": 513.9,
        "pc_pa": 6148000,
        "omega": 0.645
      },
      {
        "name": "water",
        "z": 0.5,
        "tc_k": 647.1,
        "pc_pa": 22064000,
        "omega": 0.344
      }
    ]
  },
  "resource": "cpu"
}'

{
  "mode": "tp_flash",
  "eos": "peng-robinson",
  "activity_model": "unifac",
  "temperature_k": 353.15,
  "pressure_pa": 101325,
  "components": [
    {
      "name": "ethanol",
      "z": 0.5,
      "tc_k": 513.9,
      "pc_pa": 6148000,
      "omega": 0.645
    },
    {
      "name": "water",
      "z": 0.5,
      "tc_k": 647.1,
      "pc_pa": 22064000,
      "omega": 0.344
    }
  ]
}

Sample data from the job catalog; long values are shortened here. Each job_name must be unique among your jobs.

Workflow inputs

Table (list) · optionalCSV, JSON
Table (list) · optionalCSV, JSON

Workflow outputs

ArchiveZIP
TableCSV
TableCSV