fugacio-sim 0.1.0

Differentiable steady-state and dynamic process flowsheet simulator (Fugacio).

fugacio-sim

Differentiable process-simulation layer for the Fugacio stack: flowsheet and unit-operation models built on top of fugacio.thermo.

The core abstraction is the Stream, a JAX pytree whose molar flows, temperature, and pressure are differentiable leaves (component names are static metadata). Because the underlying EOS phase equilibrium is differentiable, unit operations are too: you can take a gradient of any downstream quantity (a product flow, a recovery, a purity) with respect to feed conditions or operating variables, which is the basis for gradient-based flowsheet optimisation.

Stream properties

Any Stream has a two-phase-aware enthalpy and entropy (via the fugacio.thermo energy core), so unit operations close energy balances, not just material balances: molar_enthalpy, molar_entropy, enthalpy_flow, entropy_flow, mass_flow, molar_mass.

Unit operations (rigorous material + energy balances)

• flash_drum: isothermal-isobaric vapour/liquid separator.
• heater: heater/cooler on a temperature or a duty specification.
• valve: isenthalpic (Joule-Thomson) pressure letdown.
• pump: incompressible-liquid pump with an efficiency.
• compressor / turbine: isentropic machines with an efficiency.
• mix: adiabatic, energy-balanced mixer (exact material balance).
• splitter / component_separator: flow split and idealised component split.
• bubble_pressure / antoine_psat: lightweight modified-Raoult helpers.

Flowsheets with recycle

tear_solve closes a recycle by solving the tear fixed point tear = g(tear, theta) with a Wegstein-accelerated iteration, and differentiates the converged flowsheet by the implicit function theorem: a gradient through the recycle costs one adjoint solve regardless of iteration count. Flowsheet is a small declarative builder on top of it.

Distillation

• Shortcut (Fenske-Underwood-Gilliland): fenske_min_stages, underwood_min_reflux, gilliland_stages, kirkbride_feed_stage, and the shortcut_column wrapper.
• Rigorous solve_column: a multistage equilibrium-stage column (Wang-Henke bubble-point, constant molar overflow) with EOS K-values on every stage, differentiable through the fixed-point iteration.

Non-ideal separations & diagrams

Built on the fugacio.thermo property system (via the eos_model_for, nrtl_model_for, uniquac_model_for, unifac_model_for, and saft_model_for bridges, the last building a molecular PC-SAFT model from component names):

• flash_vle, decanter, three_phase_flash: activity-based VLE / LLE / VLLE drums for real, non-ideal mixtures.
• pxy_diagram, txy_diagram, azeotrope_pressure, azeotrope_temperature: binary phase diagrams and azeotrope finders.
• residue_curve, residue_curve_map: ternary open-evaporation trajectories for laying out distillation boundaries.

Reactors

Energy-balanced reactor unit operations over one or more fugacio.thermo Reactions, each runnable isothermally (reporting the heat duty) or adiabatically (solving the outlet temperature) and returning a ReactorResult: equilibrium_reactor (chemical equilibrium), stoichiometric_reactor (specified extent or conversion), and kinetic cstr, pfr, and batch_reactor sized by volume (and time). conversion is a small helper on the inlet/outlet streams.

Reactive separations

Reaction coupled to phase separation, both differentiable through the joint solve: reactive_flash (simultaneous chemical + vapour-liquid equilibrium in a drum) and reactive_distillation (a rate-based column with per-stage reaction source terms).

Example: differentiate a flash drum

import jax
import jax.numpy as jnp
from fugacio.sim import Stream, flash_drum

feed = Stream.from_fractions(
    ("methane", "propane", "n-pentane"),
    jnp.array([0.5, 0.3, 0.2]),
    flow=100.0, t=320.0, p=20e5,
)
vapor, liquid = flash_drum(feed, 320.0, 20e5)
vapor.total, liquid.total  # ~74.7 and ~25.3 mol/s

# Sensitivity of vapour product flow to drum temperature:
d_vapor_dT = jax.grad(lambda T: flash_drum(feed, T, 20e5)[0].total)
d_vapor_dT(320.0)

Example: a recycle, differentiated end-to-end

import jax.numpy as jnp
from fugacio.sim import Stream, flash_drum, mix, splitter, tear_solve

components = ("methane", "propane", "n-pentane")
fresh = Stream.from_fractions(components, jnp.array([0.5, 0.3, 0.2]), 100.0, 320.0, 20e5)

def one_pass(recycle, theta):
    mixed = mix([fresh, recycle], t=320.0)
    _vapor, liquid = flash_drum(mixed, theta["T"], theta["P"])
    recycled, _purge = splitter(liquid, jnp.array([theta["r"], 1.0 - theta["r"]]))
    return recycled

guess = Stream.from_fractions(components, jnp.array([0.1, 0.3, 0.6]), 30.0, 320.0, 20e5)
recycle = tear_solve(one_pass, guess, {"T": 320.0, "P": 20e5, "r": 0.5})

Example: a rigorous distillation column

import jax
import jax.numpy as jnp
from fugacio.sim import Stream, solve_column

feed = Stream.from_fractions(("propane", "n-butane"), jnp.array([0.5, 0.5]), 100.0, 320.0, 10e5)
col = solve_column(feed, n_stages=12, feed_stage=6, reflux=2.0, distillate_rate=50.0)
col.distillate.z  # ~[0.97, 0.03] propane overhead

# Exact gradient of distillate purity w.r.t. reflux ratio:
jax.grad(
    lambda r: solve_column(feed, 12, 6, r, 50.0).distillate.z[0]
)(2.0)

Part of the fugacio namespace; installs independently: pip install fugacio-sim.