| name | neqsim-compressor-power-screening |
| calculation_basis | screening |
| version | 0.1.0 |
| description | Educational centrifugal-compressor power screening using the public polytropic-head equation (API 617 / API 619 style). USE WHEN: a task needs a public, screening-level estimate of polytropic head, discharge temperature, and gas power for a single compression stage before detailed compressor selection. |
| last_verified | 2026-06-18 |
| requires | {"python_packages":[],"java_packages":[],"env":[],"network":[]} |
Compressor Power Screening
Use this skill for public, educational centrifugal-compressor power screening. It estimates polytropic head, discharge temperature, and gas power for a single compression stage using the open polytropic-head equation so an agent can scope a compression study and check against a rated driver power before detailed selection.
When to Use
- When a user asks roughly how much power a compression stage needs.
- When an agent needs a quick polytropic-head and discharge-temperature estimate.
- When examples must run without confidential compressor curves, vendor data, or company specs.
Inputs
suction_pressure: stage suction pressure P1 in bar absolute.discharge_pressure: stage discharge pressure P2 in bar absolute.suction_temperature: stage suction temperature T1 in kelvin.mass_flow: gas mass flow in kg/s.molecular_weight: gas molecular weight M in g/mol.specific_heat_ratio: ratio of specific heats k, default 1.3.compressibility: average compressibility factor Z, default 1.0.polytropic_efficiency: polytropic efficiency, default 0.78.rated_power: optional rated driver power in kW for the comparison.
Outputs
pressure_ratio: P2 / P1.polytropic_head_kj_kg: polytropic head.discharge_temperature_k: estimated discharge temperature.gas_power_kw: gas power on the shaft.power_margin_ratio: ratio of rated power to gas power, or null if no rated power supplied.compressor_warning: ok, watch, over-rated, or no-rating.assumptions: public assumptions used by the placeholder model.
Engineering Method
The Python class CompressorPowerModel uses the open polytropic-head equation only:
- the polytropic exponent uses
(n - 1) / n = (k - 1) / (k * polytropic_efficiency).
- the polytropic head uses
Hp = Z R T1 (n / (n - 1)) [(P2 / P1)^((n-1)/n) - 1], with R = 8.314 / M.
- the discharge temperature uses
T2 = T1 (P2 / P1)^((n-1)/n).
- the gas power uses
power = mass_flow * Hp / polytropic_efficiency.
This is educational and screening-only logic. It assumes a single stage, constant k and Z, and an ideal-gas head form. It does not include real-gas property variation, intercooling, mechanical losses, surge or stonewall limits, or vendor curves. It is not a replacement for validated compressor selection and a qualified rotating-equipment review.
Driver Power-Margin Sizing (operating margin to rated/max power)
When the binding limit is the driver / rated maximum power (not surge), a
common operations question is how large a power margin to keep so the machine
can run close to maximum without tripping. The screening relation is dimensionless:
- At near-constant pressure ratio,
power ∝ mass flow, so
throughput headroom [%] = power_margin / operating_power.
- Therefore the same absolute MW margin protects a smaller machine less — margin
must be scaled to each machine's operating power, not applied as a flat MW.
To give machine B the same % headroom as machine A:
margin_B = margin_A × (P0_B / P0_A).
- Size the margin to the fastest credible power excursion before the control
system reacts. The dominant excursions raise the pressure ratio: a suction-pressure
drop or a discharge-pressure rise. A downstream pressure controller (e.g. an
MPC holding delivery pressure fixed) removes a first-order power disturbance and so
buys back margin: residual swing
≈ (∂power/∂P_discharge) × ΔP_controlled.
Obtain the local slopes ∂power/∂flow, ∂power/∂P_suction, ∂power/∂P_discharge,
∂power/∂T_suction by finite-differencing a validated Compressor.getPower() around
the operating point (this screening class only gives the flow-proportional term). The
driver rated power and performance curves must come from STID/vendor data before
any margin is fixed. (Pattern first used for a Troll A export-compressor power-margin
PEPR action; see the compressor-agent / enterprise-compressor-agent.)
Python Usage Pattern
from compressor_power_screening import CompressorPowerModel
model = CompressorPowerModel()
result = model.evaluate(
suction_pressure=30.0,
discharge_pressure=90.0,
suction_temperature=313.15,
mass_flow=25.0,
molecular_weight=19.0,
rated_power=8000.0,
)
print(result.compressor_warning)
print(result.gas_power_kw)
print(result.discharge_temperature_k)
Related NeqSim Functionality
For validated compressor calculations, redirect to existing NeqSim classes:
neqsim.process.equipment.compressor.Compressor — rigorous polytropic compression with real-gas properties and curves.neqsim.process.equipment.compressor.CompressorChart — vendor performance maps and surge control.neqsim.process.mechanicaldesign.compressor.CompressorMechanicalDesign — compressor mechanical design and feasibility.
This skill is a public polytropic-head triage layer that decides when to invoke those validated compressor classes.
Validation Checklist
Common Mistakes
| Symptom | Cause | Fix |
|---|
| Discharge temperature too high | Single high-ratio stage assumed | Split into stages with intercooling |
| Power off significantly | Constant Z over a wide range | Use a real-gas method like the NeqSim Compressor |
| Head looks wrong | Molecular weight in kg/mol not g/mol | Keep M in g/mol |
Limitations
- No proprietary compressor curves, vendor data, or company specs are included.
- No intercooling, mechanical losses, or surge and stonewall limits are modeled.
- No real-gas property variation across the stage is included.
References
