Quick Start

This guide walks through a complete techno-economic assessment in under 10 minutes using OpenPyTEA’s Python API.

Step 1 — Import the library

from openpytea import Equipment, Plant
from openpytea import sensitivity_data, plot_sensitivity
from openpytea import fixed_capital_data, plot_stacked_bar

Step 2 — Define equipment

Create an Equipment object by specifying the equipment category, type, size parameter, and material. To check which equipment categories and types are available, open the cost correlation database. Here is a snippet of how the database is structured:

key	category	type	units	s_lower	s_upper	form	cost_year	source
impeller_mixer_turton_2001	Agitators, blenders, & mixers	Impeller mixer	power, kW	5	150	quad log-log	2001	Turton et al. (2018)
boilers_packaged_towler_2010	Boilers, heaters, & furnaces	Boiler, packaged	steam, kg/h	5000	200000	power-law	2010	Towler & Sinnott (2010)
solid_bowl_centrifuge_turton_2001	Centrifuges	Solid bowl	diameter, m	0.3	2	quad log-log	2001	Turton et al. (2018)
centrifugal_compressor_towler_2010	Compressors, fans, & blowers	Compressor, centrifugal	driver power, kW	75	30000	power-law	2010	Towler & Sinnott (2010)
…	…	…	…	…	…	…	…	…

For example, let’s define a centrifugal compressor with a shaft power of 5000 kW, made of carbon steel:

compressor = Equipment(
    name="COMP-01",
    param=5000,            # driver power in kW
    process_type="Fluids",
    category="Compressors, fans, & blowers",
    type="Compressor, centrifugal",
    material="Carbon steel",
)

print(compressor)

The purchased cost and direct (installed) cost are computed automatically from cost correlations and adjusted to 2024 USD using the Chemical Engineering Plant Cost Index (CEPCI).

Step 3 — Configure the plant

Pass equipment and financial parameters to Plant:

plant = Plant({
    "plant_name": "Ammonia Plant",
    "process_type": "Fluids",
    "country": "Netherlands",
    "equipment": [compressor],

    # Financial assumptions
    "interest_rate": 0.09,
    "project_lifetime": 20,
    "tax_rate": 0.25,

    # Product: ammonia production and selling price
    "plant_products": {
        "ammonia": {
            "production": 125_000,   # t/yr
            "price": 500,            # USD/t
        }
    },

    # Variable OPEX: electricity consumption and price
    "variable_opex_inputs": {
        "electricity": {
            "consumption": 110,      # GWh/yr
            "price": 75,             # USD/MWh
        }
    },
})

To learn more about plant configuration inputs, see the Plant user guide.

Step 4 — Run the calculation

plant.calculate_all(print_results=True)

This prints a summary table of all costs and financial metrics:

Capital cost estimation
===================================
ISBL: 9,874,560.00 USD
OSBL: 987,456.00 USD
Design and engineering: 1,184,947.20 USD
Contingency: 1,184,947.20 USD
===================================
Fixed capital investment: 13,231,910.40 USD
Variable production costs estimation
===================================
  - Electricity: 8,250,000.00 USD per year
===================================
Total Variable OPEX: 8,250,000.00 USD per year
Fixed production costs estimation
===================================
Operating labor costs: 220,000.00 USD per year
Supervision costs: 44,000.00 USD per year
...
===================================
Fixed OPEX: 1,123,456.00 USD per year
Revenue estimation
===================================
  - Ammonia: 62,500,000.00 USD per year
===================================
Total Revenue: 62,500,000.00 USD per year
Year | Present Value [USD] | Cumulative NPV [USD]
-------------------------------------------
   1 |     48,623,853.21 |      48,623,853.21
   2 |     44,609,498.36 |      93,233,351.57
   3 |     40,926,145.28 |     134,159,496.85
   ...
  20 |     17,284,565.91 |     411,847,293.42
Levelized cost: 166.123 USD/unit
Payback time: 2.09 years
Return of investment: 47.83%
Internal Rate of Return: 62.31%

Step 5 — Visualise the CAPEX breakdown

capex = fixed_capital_data(plant)
ax = plot_stacked_bar(capex)
ax.get_figure().savefig("capex.png", dpi=150)

For more visualisation options, see the Plotting user guide.

Step 6 — Sensitivity analysis

See how the levelized cost of ammonia changes as the electricity price varies ±50 %:

sens = sensitivity_data(
    plant,
    parameter="electricity",   # variable OPEX item
    metric="LCOP", # levelized cost of product
    plus_minus_value=0.5,
    n_points=30,
    label="LCOA (USD/t)",
)
ax = plot_sensitivity(sens)

For more sensitivity and uncertainty analysis options, see the Analysis user guide.

Step 7 — What’s next?

User Guide

Deep dives into every module — equipment costing, plant configuration, Monte Carlo simulation, JSON workflows, and more.

User Guide

Case Studies

Real-world examples: hydrogen production, liquefaction, and geothermal energy.

Examples & Case Studies