openpytea.plant

class openpytea.plant.Plant(configuration)

Bases: object

Plant class for techno-economic analysis of industrial processing plants. This class models capital costs, operating expenses, revenue, and financial metrics for chemical/process plants. It supports multiple process types (Solids, Fluids, Mixed), geographic locations with regional cost factors, and comprehensive cash flow analysis. Attributes:

processTypes (dict): Cost multipliers for different process categories

(OS, DE, X).

locFactors (dict): Geographic location factors for capital cost

adjustments.

Configuration Parameters:

name (str): Plant name identifier. process_type (str): Type of process

(“Solids”, “Fluids”, or “Mixed”).

country (str): Country location for cost factor lookup.

Default: “United States”.

region (str): Regional area within country. Default: “Gulf Coast”. currency (str): Currency code (e.g., “USD”). Default: “USD”. exchange_rate (float): Conversion factor to base currency.

Default: 1.0.

interest_rate (float): Discount rate for NPV calculations.

Default: 0.09.

project_lifetime (int or array): Plant operating life in years.

Default: 20.

plant_utilization (float): Capacity utilization factor (0-1).

Default: 1.

tax_rate (float): Corporate tax rate for cash flow. Default: 0. working_capital (float or None): Working capital requirement.

Auto-calculated if None.

depreciation (dict or None): Depreciation method configuration.

Cash Flow Profiles:

capex_ramp (list or None): Fraction of fixed capital spent in each

construction year. Must be a 1-D list of non-negative numbers that sum to 1.0. Length must be less than project_lifetime. Working capital is drawn in the final construction year and released at the end of the project. Default: [0.3, 0.6, 0.1] (3-year build).

production_ramp (list or None): Nameplate capacity utilisation

fraction for each project year (0–1). Values must be between 0 and 1. If shorter than project_lifetime the remaining years are set to 1.0 (full capacity). Length must not exceed project_lifetime. Default: [0, 0, 0.4, 0.8] (full capacity from year 4 on).

Capital Cost Factors:

loc_factor (float or None): Location factor applied to ISBL.

Overrides country/region lookup when set. Default: None.

fixed_capital_factors (dict): Override the multipliers used to

calculate individual fixed capital components. Any subset of keys may be supplied; omitted keys fall back to processTypes defaults. Keys and defaults (process-type dependent):

“osbl” – fraction of ISBL (e.g. 0.3 for Fluids) “de” – fraction of ISBL+OSBL (e.g. 0.3 for Fluids) “contingency” – fraction of ISBL+OSBL (e.g. 0.1 for all types)

fixed_capital_components (dict): Override the computed cost value

of individual fixed capital components directly. Takes precedence over fixed_capital_factors for the same component. Keys match attribute names:

“osbl”, “dne”, “contingency”

Labor & Operations:

operators_per_shift (int or None): Manual input or auto-calculated. operators_hired (int or None): Total operators needed;

auto-calculated if None.

operator_hourly_rate (dict or float): Wage rate for operators. working_weeks_per_year (int): Annual working weeks. Default: 49. working_shifts_per_week (int): Shifts per week. Default: 5. operating_shifts_per_day (int): Daily operating shifts. Default: 3.

Equipment & Economics:

equipment_list (list): Equipment objects with cost data. variable_opex_inputs (dict): Variable operating cost inputs

(consumption, price).

plant_products (dict): Product specifications

(production rate, price).

fc (float): Fixed capital cost multiplier for installed costs. fp (float): Fixed OPEX cost multiplier.

Fixed OPEX Customisation:

fixed_opex_factors (dict): Override the multipliers used to

calculate individual fixed OPEX components. Any subset of keys may be supplied; omitted keys fall back to defaults. Keys and defaults:

“supervision” – 0.25 × operating_labor_costs “direct_salary_overhead”– 0.50 × (labor + supervision) “laboratory_charges” – 0.10 × operating_labor_costs “maintenance” – 0.05 × ISBL “taxes_insurance” – 0.015 × ISBL “rent_of_land” – 0.015 × (ISBL + OSBL) “environmental_charges” – 0.01 × (ISBL + OSBL) “operating_supplies” – 0.009 × ISBL “general_plant_overhead”– 0.65 × (labor + supervision

• direct_salary_overhead)

“working_capital” – 0.15 × fixed_capital “patents_royalties” – 0.02 × cash cost of production “distribution_selling” – 0.02 × cash cost of production “rnd” – 0.03 × cash cost of production

fixed_opex_components (dict): Override the computed cost value of

individual fixed OPEX components directly. Takes precedence over fixed_opex_factors for the same component. Downstream components that depend on an overridden value use the overridden value in their own calculation. Keys match attribute names:

“supervision_costs”, “direct_salary_overhead”, “laboratory_charges”, “maintenance_costs”, “taxes_insurance_costs”, “rent_of_land_costs”, “environmental_charges”, “operating_supplies”, “general_plant_overhead”, “patents_royalties”, “distribution_selling_costs”, “RnD_costs”

Additional Capex:

additional_capex_years (array): Years when additional capex occurs. additional_capex_cost (array): Corresponding capex amounts.

Monte Carlo:

project_uncertainties (dict): Per-parameter uncertainty settings

for Monte Carlo simulation. Each key maps to a sub-dict with optional fields std, min, and max. Omitting a key uses the built-in default distribution. Setting std=0 disables sampling for that parameter (default for plant_utilization and tax_rate). Supported keys:

“fixed_capital_factor” – std=0.3, min=0.25, max=1.75 “fixed_opex_factor” – std=0.3, min=0.25, max=1.75 “project_lifetime” – std=5, min/max auto (±2σ, ≥5) “interest_rate” – std=0.03, min/max auto (±2σ, ≥0.02) “plant_utilization” – std=0 (fixed unless overridden) “tax_rate” – std=0 (fixed unless overridden)

monte_carlo_inputs (dict or None): Stochastic input distributions

populated after running monte_carlo().

monte_carlo_metrics (dict or None): Distribution results populated

after running monte_carlo().

Example:

>>> config = {
...     "plant_name": "Example Plant",
...     "process_type": "Fluids",
...     "country": "United States",
...     "region": "Gulf Coast",
...     "equipment": [equipment_obj],
...     "plant_products": {"product_A": {"production": 100,
                            "price": 50}},
... }
>>> plant = Plant(config)
>>> plant.calculate_all(print_results=True)
>>> npv = plant.calculate_npv()

Parameters:

configuration (dict)

processTypes = {'Fluids': {'DE': 0.3, 'OS': 0.3, 'X': 0.1}, 'Mixed': {'DE': 0.25, 'OS': 0.4, 'X': 0.1}, 'Solids': {'DE': 0.2, 'OS': 0.4, 'X': 0.1}}

locFactors = {'Australia': 1.21, 'Brazil': 1.14, 'Canada': {'Fort McMurray': 1.6, 'Ontario': 1.0}, 'China': {'imported': 1.12, 'indigenous': 0.61}, 'France': 1.13, 'Germany': 1.11, 'India': 1.02, 'Italy': 1.14, 'Japan': 1.26, 'Mexico': 1.03, 'Middle East': 1.07, 'Netherlands': 1.19, 'Russia': 1.53, 'Southeast Asia': 1.12, 'United Kingdom': 1.02, 'United States': {'East Coast': 1.04, 'Gulf Coast': 1.0, 'Midwest': 1.02, 'West Coast': 1.07}}

update_configuration(configuration)

Update plant parameters while preserving nested structures.

Top-level scalar keys overwrite existing values. Nested dicts (variable_opex_inputs, plant_products, operator_hourly_rate, project_uncertainties, fixed_opex_factors, fixed_capital_factors) are deep-merged rather than replaced.

Parameters:

configuration (dict) – Partial or full plant configuration. Only supplied keys are updated.

calculate_purchased_cost(print_results=False)

Sum equipment purchased costs with exchange rate conversion.

Parameters:

print_results (bool, optional) – Print a per-equipment cost breakdown. Default is False.

Returns:

Total purchased cost in plant currency.

Return type:

float

calculate_isbl(fc=1.0, print_results=False)

Calculate Inside Battery Limits (ISBL) cost.

Sums direct equipment costs and applies the location factor and the installed cost multiplier fc.

Parameters:

• fc (float, optional) – Installed cost multiplier. Default is 1.0.

• print_results (bool, optional) – Print a per-equipment cost breakdown. Default is False.

Returns:

ISBL cost in plant currency.

Return type:

float

Raises:

ValueError – If the plant’s country or region is not found in locFactors and no explicit loc_factor is set.

calculate_fixed_capital(fc=None, additional_capex=False, print_results=False)

Calculate total fixed capital investment.

Includes ISBL, OSBL, design & engineering, and contingency. Factors can be overridden via fixed_capital_factors / fixed_capital_components set on the plant.

Parameters:

• fc (float or None, optional) – Installed cost multiplier. Defaults to 1.0 if None.

• additional_capex (bool, optional) – Include additional CAPEX items in the printed summary. Default is False.

• print_results (bool, optional) – Print a cost breakdown. Default is False.

Returns:

Total fixed capital cost in plant currency.

Return type:

float

Raises:

ValueError – If process_type is not one of the supported process types.

calculate_variable_opex(print_results=False)

Calculate annual variable operating costs.

Iterates over variable_opex_inputs and computes cost as consumption × price × 365 × plant_utilization for each item.

Parameters:

print_results (bool, optional) – Print a per-item cost breakdown. Default is False.

Returns:

Total annual variable OPEX in plant currency.

Return type:

float

calculate_revenue(print_results=False)

Calculate annual revenue from plant products.

Iterates over plant_products and computes revenue as production × price × 365 × plant_utilization for each product.

Parameters:

print_results (bool, optional) – Print a per-product revenue breakdown. Default is False.

Returns:

Total annual revenue in plant currency.

Return type:

float

count_process_steps(equipments, target_process_types, excluded_cats=None)

Count equipment units matching a set of process types.

Parameters:

• equipments (list) – List of Equipment objects to scan.

• target_process_types (set) – Process type labels to match (e.g. {"Fluids", "Mixed"}).

• excluded_cats (set or None, optional) – Equipment categories to skip. Default is None (no exclusions).

Returns:

Number of matching equipment units.

Return type:

int

calculate_operators_per_shift(no_fluid_process=None, no_solid_process=None)

Calculate the number of operators required per shift.

Uses the empirical correlation from Turton et al. based on fluid and solid process step counts. Returns operators_per_shift directly if it was set manually on the plant.

Parameters:

• no_fluid_process (int or None, optional) – Number of fluid/mixed process steps. Auto-counted if None.

• no_solid_process (int or None, optional) – Number of solid/mixed process steps (max 2). Auto-counted if None.

Returns:

Estimated operators per shift.

Return type:

float

Raises:

ValueError – If no_solid_process exceeds 2.

calculate_operators_hired(no_fluid_process=None, no_solid_process=None)

Calculate the total number of operators to hire.

Accounts for the ratio of operating shifts per year to working shifts per year. Returns operators_hired directly if set manually.

Parameters:

• no_fluid_process (int or None, optional) – Number of fluid/mixed process steps. Passed to calculate_operators_per_shift if needed.

• no_solid_process (int or None, optional) – Number of solid/mixed process steps. Passed to calculate_operators_per_shift if needed.

Returns:

Total operators to hire.

Return type:

int

calculate_operating_labor(no_fluid_process=None, no_solid_process=None)

Calculate total annual operating labor costs.

Parameters:

• no_fluid_process (int or None, optional) – Number of fluid/mixed process steps. Auto-counted if None.

• no_solid_process (int or None, optional) – Number of solid/mixed process steps. Auto-counted if None.

Returns:

Annual operating labor cost in plant currency.

Return type:

float

calculate_fixed_opex(fp=None, print_results=False)

Calculate fixed operating expenses (OPEX).

Computes supervision, salary overhead, laboratory charges, maintenance, taxes & insurance, rent, environmental charges, operating supplies, general plant overhead, working capital interest, patents & royalties, distribution & selling, and R&D costs. Factors and individual component values can be overridden via fixed_opex_factors and fixed_opex_components set on the plant.

Parameters:

• fp (float or None, optional) – Fixed OPEX multiplier applied to the total. Defaults to 1.0 if None.

• print_results (bool, optional) – Print a full fixed OPEX breakdown. Default is False.

Returns:

Total annual fixed OPEX in plant currency.

Return type:

float

calculate_cash_flow(print_results=False)

Build a year-by-year cash flow table.

Applies the CAPEX ramp, production ramp, depreciation schedule, and tax lag to produce annual capital cost, revenue, cash cost, gross profit, depreciation, taxable income, tax paid, and net cash flow arrays. Supports vectorised (Monte Carlo) inputs when project_lifetime, interest_rate, etc. are arrays.

Parameters:

print_results (bool, optional) – Return a formatted pd.DataFrame.style for scalar scenarios. Default is False.

Returns:

Styled cash flow table when print_results=True and inputs are scalar; None otherwise (results stored as instance arrays).

Return type:

pd.DataFrame.style or None

Raises:

ValueError – If project_lifetime < 3, capex_ramp or production_ramp are invalid, or no plant products are defined.

calculate_npv(print_results=False)

Calculate Net Present Value (NPV) of the project cash flows.

Discounts each year’s cash flow at interest_rate and returns the cumulative NPV at the end of the project lifetime. Supports vectorised inputs for Monte Carlo scenarios.

Parameters:

print_results (bool, optional) – Print a year-by-year present value and cumulative NPV table. Default is False.

Returns:

Final NPV (scalar) or array of NPVs across scenarios.

Return type:

float or np.ndarray

Raises:

ValueError – If interest_rate is an array whose length does not match the number of cash flow scenarios.

calculate_levelized_cost(print_results=False)

Calculate the levelized cost of production (LCOP).

Discounts capital costs, operating costs, and production over the project lifetime at interest_rate. Side-product revenues are subtracted before dividing by discounted production.

Parameters:

print_results (bool, optional) – Print the mean levelized cost. Default is False.

Returns:

Levelized cost per unit of main product (scalar or array).

Return type:

float or np.ndarray

calculate_payback_time(additional_capex=False, print_results=False)

Calculate simple payback time.

Divides total fixed capital (optionally including additional CAPEX) by the mean annual cash flow across revenue-generating years.

Parameters:

• additional_capex (bool, optional) – Include additional CAPEX in the total investment. Default is False.

• print_results (bool, optional) – Print the payback time. Default is False.

Returns:

Payback time in years (nan if no revenue-generating years exist).

Return type:

float or np.ndarray

calculate_roi(additional_capex=False, print_results=False)

Calculate Return on Investment (ROI).

Computes total net profit over the project lifetime as a percentage of total investment (fixed capital + working capital, optionally including additional CAPEX), annualised by project lifetime.

Parameters:

• additional_capex (bool, optional) – Include additional CAPEX in total investment. Default is False.

• print_results (bool, optional) – Print the ROI value. Default is False.

Returns:

ROI as a percentage (scalar or array across scenarios).

Return type:

float or np.ndarray

calculate_irr(print_results=False)

Calculate the Internal Rate of Return (IRR).

Finds the discount rate at which NPV equals zero using Brent’s method. Returns nan if no sign change is found (no valid IRR exists).

Parameters:

print_results (bool, optional) – Print the IRR value. Default is False.

Returns:

IRR as a fraction (e.g. 0.15 = 15%), or nan if undefined.

Return type:

float or np.ndarray

calculate_all(additional_capex=False, print_results=False)

Run all financial calculations sequentially.

Calls calculate_fixed_capital, calculate_variable_opex, calculate_fixed_opex, calculate_revenue, calculate_cash_flow, calculate_npv, calculate_levelized_cost, calculate_payback_time, calculate_roi, and calculate_irr.

Parameters:

• additional_capex (bool, optional) – Pass through to calculate_fixed_capital, calculate_payback_time, and calculate_roi. Default is False.

• print_results (bool, optional) – Print results from each sub-calculation. Default is False.

to_dict()

Serialize plant configuration and all computed metrics to a dict.

Returns:

Nested dictionary with sections: plant_configuration, equipment_summary, capital_costs, variable_opex, fixed_opex, revenue, cash_flow, and metrics.

Return type:

dict

class openpytea.plant.DepreciationConfig

Bases: object

Configuration for asset depreciation calculations.

This class defines the parameters needed to compute depreciation using various methods.

Attributes:

method (DepMethod): The depreciation method to use. Defaults to “straight_line”. Options: “straight_line”, “declining_balance”, “macrs”. life (Optional[int]): The useful life of the asset in years.

Used by straight_line and declining_balance methods. Defaults to None.

db_factor (float): The declining balance factor (multiplier).

Only used by the declining_balance method. Defaults to 2.0.

salvage_fraction (float): The salvage value as a fraction

of the initial cost. Used by straight_line and declining_balance methods. Defaults to 0.0.

macrs_class (int): The MACRS property class (1-20).

Only used by the macrs method. Defaults to 7.

convention (str): The depreciation convention for MACRS.

Only used by the macrs method. Defaults to “half_year”.

service_start_year (int): The year index (starting from 0)

when the asset is placed in service. Defaults to 2.

method: Literal['straight_line', 'declining_balance', 'macrs'] = 'straight_line'

life: int | None = None

db_factor: float = 2.0

salvage_fraction: float = 0.0

macrs_class: int = 7

convention: str = 'half_year'

service_start_year: int = 2

openpytea.plant.build_depreciation_array(project_life, capex_by_year, dep_cfg=None)

Build a depreciation schedule array over the project lifecycle. Calculates annual depreciation amounts for capital expenditures using the specified depreciation method. Supports multiple depreciation methods including straight-line, declining balance, and MACRS. :param project_life: The total duration of the project in years. :type project_life: int :param capex_by_year: Dictionary mapping year to capital expenditure amount for that year. :type capex_by_year: Dict[int, float] :param dep_cfg: Depreciation configuration dictionary containing method, life,

salvage fraction, and method-specific parameters. If None, uses normalized default configuration. Default is None.

Returns:

1D array of shape (project_life,) containing annual depreciation amounts. Values are floats representing depreciation in each year.

Return type:

np.ndarray

Raises:

ValueError – If the depreciation method specified in dep_cfg is not one of the supported methods: ‘straight_line’, ‘declining_balance’, or ‘macrs’.

Parameters:

• project_life (int)

• capex_by_year (Dict[int, float])

• dep_cfg (Optional[dict], optional)

Notes

• Capital expenditures are placed in service starting at the configured

  service start year.

• Depreciation schedules respect the project horizon after placement

  in service.

• Zero amounts and expired horizons are skipped without error.

Classes

class openpytea.plant.Plant(configuration)

Bases: object

Plant class for techno-economic analysis of industrial processing plants. This class models capital costs, operating expenses, revenue, and financial metrics for chemical/process plants. It supports multiple process types (Solids, Fluids, Mixed), geographic locations with regional cost factors, and comprehensive cash flow analysis. Attributes:

processTypes (dict): Cost multipliers for different process categories

(OS, DE, X).

locFactors (dict): Geographic location factors for capital cost

adjustments.

Configuration Parameters:

name (str): Plant name identifier. process_type (str): Type of process

(“Solids”, “Fluids”, or “Mixed”).

country (str): Country location for cost factor lookup.

Default: “United States”.

region (str): Regional area within country. Default: “Gulf Coast”. currency (str): Currency code (e.g., “USD”). Default: “USD”. exchange_rate (float): Conversion factor to base currency.

Default: 1.0.

interest_rate (float): Discount rate for NPV calculations.

Default: 0.09.

project_lifetime (int or array): Plant operating life in years.

Default: 20.

plant_utilization (float): Capacity utilization factor (0-1).

Default: 1.

tax_rate (float): Corporate tax rate for cash flow. Default: 0. working_capital (float or None): Working capital requirement.

Auto-calculated if None.

depreciation (dict or None): Depreciation method configuration.

Cash Flow Profiles:

capex_ramp (list or None): Fraction of fixed capital spent in each

construction year. Must be a 1-D list of non-negative numbers that sum to 1.0. Length must be less than project_lifetime. Working capital is drawn in the final construction year and released at the end of the project. Default: [0.3, 0.6, 0.1] (3-year build).

production_ramp (list or None): Nameplate capacity utilisation

fraction for each project year (0–1). Values must be between 0 and 1. If shorter than project_lifetime the remaining years are set to 1.0 (full capacity). Length must not exceed project_lifetime. Default: [0, 0, 0.4, 0.8] (full capacity from year 4 on).

Capital Cost Factors:

loc_factor (float or None): Location factor applied to ISBL.

Overrides country/region lookup when set. Default: None.

fixed_capital_factors (dict): Override the multipliers used to

calculate individual fixed capital components. Any subset of keys may be supplied; omitted keys fall back to processTypes defaults. Keys and defaults (process-type dependent):

“osbl” – fraction of ISBL (e.g. 0.3 for Fluids) “de” – fraction of ISBL+OSBL (e.g. 0.3 for Fluids) “contingency” – fraction of ISBL+OSBL (e.g. 0.1 for all types)

fixed_capital_components (dict): Override the computed cost value

of individual fixed capital components directly. Takes precedence over fixed_capital_factors for the same component. Keys match attribute names:

“osbl”, “dne”, “contingency”

Labor & Operations:

operators_per_shift (int or None): Manual input or auto-calculated. operators_hired (int or None): Total operators needed;

auto-calculated if None.

operator_hourly_rate (dict or float): Wage rate for operators. working_weeks_per_year (int): Annual working weeks. Default: 49. working_shifts_per_week (int): Shifts per week. Default: 5. operating_shifts_per_day (int): Daily operating shifts. Default: 3.

Equipment & Economics:

equipment_list (list): Equipment objects with cost data. variable_opex_inputs (dict): Variable operating cost inputs

(consumption, price).

plant_products (dict): Product specifications

(production rate, price).

fc (float): Fixed capital cost multiplier for installed costs. fp (float): Fixed OPEX cost multiplier.

Fixed OPEX Customisation:

fixed_opex_factors (dict): Override the multipliers used to

calculate individual fixed OPEX components. Any subset of keys may be supplied; omitted keys fall back to defaults. Keys and defaults:

“supervision” – 0.25 × operating_labor_costs “direct_salary_overhead”– 0.50 × (labor + supervision) “laboratory_charges” – 0.10 × operating_labor_costs “maintenance” – 0.05 × ISBL “taxes_insurance” – 0.015 × ISBL “rent_of_land” – 0.015 × (ISBL + OSBL) “environmental_charges” – 0.01 × (ISBL + OSBL) “operating_supplies” – 0.009 × ISBL “general_plant_overhead”– 0.65 × (labor + supervision

• direct_salary_overhead)

“working_capital” – 0.15 × fixed_capital “patents_royalties” – 0.02 × cash cost of production “distribution_selling” – 0.02 × cash cost of production “rnd” – 0.03 × cash cost of production

fixed_opex_components (dict): Override the computed cost value of

individual fixed OPEX components directly. Takes precedence over fixed_opex_factors for the same component. Downstream components that depend on an overridden value use the overridden value in their own calculation. Keys match attribute names:

“supervision_costs”, “direct_salary_overhead”, “laboratory_charges”, “maintenance_costs”, “taxes_insurance_costs”, “rent_of_land_costs”, “environmental_charges”, “operating_supplies”, “general_plant_overhead”, “patents_royalties”, “distribution_selling_costs”, “RnD_costs”

Additional Capex:

additional_capex_years (array): Years when additional capex occurs. additional_capex_cost (array): Corresponding capex amounts.

Monte Carlo:

project_uncertainties (dict): Per-parameter uncertainty settings

for Monte Carlo simulation. Each key maps to a sub-dict with optional fields std, min, and max. Omitting a key uses the built-in default distribution. Setting std=0 disables sampling for that parameter (default for plant_utilization and tax_rate). Supported keys:

“fixed_capital_factor” – std=0.3, min=0.25, max=1.75 “fixed_opex_factor” – std=0.3, min=0.25, max=1.75 “project_lifetime” – std=5, min/max auto (±2σ, ≥5) “interest_rate” – std=0.03, min/max auto (±2σ, ≥0.02) “plant_utilization” – std=0 (fixed unless overridden) “tax_rate” – std=0 (fixed unless overridden)

monte_carlo_inputs (dict or None): Stochastic input distributions

populated after running monte_carlo().

monte_carlo_metrics (dict or None): Distribution results populated

after running monte_carlo().

Example:

>>> config = {
...     "plant_name": "Example Plant",
...     "process_type": "Fluids",
...     "country": "United States",
...     "region": "Gulf Coast",
...     "equipment": [equipment_obj],
...     "plant_products": {"product_A": {"production": 100,
                            "price": 50}},
... }
>>> plant = Plant(config)
>>> plant.calculate_all(print_results=True)
>>> npv = plant.calculate_npv()

Parameters:

configuration (dict)

processTypes = {'Fluids': {'DE': 0.3, 'OS': 0.3, 'X': 0.1}, 'Mixed': {'DE': 0.25, 'OS': 0.4, 'X': 0.1}, 'Solids': {'DE': 0.2, 'OS': 0.4, 'X': 0.1}}

locFactors = {'Australia': 1.21, 'Brazil': 1.14, 'Canada': {'Fort McMurray': 1.6, 'Ontario': 1.0}, 'China': {'imported': 1.12, 'indigenous': 0.61}, 'France': 1.13, 'Germany': 1.11, 'India': 1.02, 'Italy': 1.14, 'Japan': 1.26, 'Mexico': 1.03, 'Middle East': 1.07, 'Netherlands': 1.19, 'Russia': 1.53, 'Southeast Asia': 1.12, 'United Kingdom': 1.02, 'United States': {'East Coast': 1.04, 'Gulf Coast': 1.0, 'Midwest': 1.02, 'West Coast': 1.07}}

update_configuration(configuration)

Update plant parameters while preserving nested structures.

Top-level scalar keys overwrite existing values. Nested dicts (variable_opex_inputs, plant_products, operator_hourly_rate, project_uncertainties, fixed_opex_factors, fixed_capital_factors) are deep-merged rather than replaced.

Parameters:

configuration (dict) – Partial or full plant configuration. Only supplied keys are updated.

calculate_purchased_cost(print_results=False)

Sum equipment purchased costs with exchange rate conversion.

Parameters:

print_results (bool, optional) – Print a per-equipment cost breakdown. Default is False.

Returns:

Total purchased cost in plant currency.

Return type:

float

calculate_isbl(fc=1.0, print_results=False)

Calculate Inside Battery Limits (ISBL) cost.

Sums direct equipment costs and applies the location factor and the installed cost multiplier fc.

Parameters:

• fc (float, optional) – Installed cost multiplier. Default is 1.0.

• print_results (bool, optional) – Print a per-equipment cost breakdown. Default is False.

Returns:

ISBL cost in plant currency.

Return type:

float

Raises:

ValueError – If the plant’s country or region is not found in locFactors and no explicit loc_factor is set.

calculate_fixed_capital(fc=None, additional_capex=False, print_results=False)

Calculate total fixed capital investment.

Includes ISBL, OSBL, design & engineering, and contingency. Factors can be overridden via fixed_capital_factors / fixed_capital_components set on the plant.

Parameters:

• fc (float or None, optional) – Installed cost multiplier. Defaults to 1.0 if None.

• additional_capex (bool, optional) – Include additional CAPEX items in the printed summary. Default is False.

• print_results (bool, optional) – Print a cost breakdown. Default is False.

Returns:

Total fixed capital cost in plant currency.

Return type:

float

Raises:

ValueError – If process_type is not one of the supported process types.

calculate_variable_opex(print_results=False)

Calculate annual variable operating costs.

Iterates over variable_opex_inputs and computes cost as consumption × price × 365 × plant_utilization for each item.

Parameters:

print_results (bool, optional) – Print a per-item cost breakdown. Default is False.

Returns:

Total annual variable OPEX in plant currency.

Return type:

float

calculate_revenue(print_results=False)

Calculate annual revenue from plant products.

Iterates over plant_products and computes revenue as production × price × 365 × plant_utilization for each product.

Parameters:

print_results (bool, optional) – Print a per-product revenue breakdown. Default is False.

Returns:

Total annual revenue in plant currency.

Return type:

float

count_process_steps(equipments, target_process_types, excluded_cats=None)

Count equipment units matching a set of process types.

Parameters:

• equipments (list) – List of Equipment objects to scan.

• target_process_types (set) – Process type labels to match (e.g. {"Fluids", "Mixed"}).

• excluded_cats (set or None, optional) – Equipment categories to skip. Default is None (no exclusions).

Returns:

Number of matching equipment units.

Return type:

int

calculate_operators_per_shift(no_fluid_process=None, no_solid_process=None)

Calculate the number of operators required per shift.

Uses the empirical correlation from Turton et al. based on fluid and solid process step counts. Returns operators_per_shift directly if it was set manually on the plant.

Parameters:

• no_fluid_process (int or None, optional) – Number of fluid/mixed process steps. Auto-counted if None.

• no_solid_process (int or None, optional) – Number of solid/mixed process steps (max 2). Auto-counted if None.

Returns:

Estimated operators per shift.

Return type:

float

Raises:

ValueError – If no_solid_process exceeds 2.

calculate_operators_hired(no_fluid_process=None, no_solid_process=None)

Calculate the total number of operators to hire.

Accounts for the ratio of operating shifts per year to working shifts per year. Returns operators_hired directly if set manually.

Parameters:

• no_fluid_process (int or None, optional) – Number of fluid/mixed process steps. Passed to calculate_operators_per_shift if needed.

• no_solid_process (int or None, optional) – Number of solid/mixed process steps. Passed to calculate_operators_per_shift if needed.

Returns:

Total operators to hire.

Return type:

int

calculate_operating_labor(no_fluid_process=None, no_solid_process=None)

Calculate total annual operating labor costs.

Parameters:

• no_fluid_process (int or None, optional) – Number of fluid/mixed process steps. Auto-counted if None.

• no_solid_process (int or None, optional) – Number of solid/mixed process steps. Auto-counted if None.

Returns:

Annual operating labor cost in plant currency.

Return type:

float

calculate_fixed_opex(fp=None, print_results=False)

Calculate fixed operating expenses (OPEX).

Computes supervision, salary overhead, laboratory charges, maintenance, taxes & insurance, rent, environmental charges, operating supplies, general plant overhead, working capital interest, patents & royalties, distribution & selling, and R&D costs. Factors and individual component values can be overridden via fixed_opex_factors and fixed_opex_components set on the plant.

Parameters:

• fp (float or None, optional) – Fixed OPEX multiplier applied to the total. Defaults to 1.0 if None.

• print_results (bool, optional) – Print a full fixed OPEX breakdown. Default is False.

Returns:

Total annual fixed OPEX in plant currency.

Return type:

float

calculate_cash_flow(print_results=False)

Build a year-by-year cash flow table.

Applies the CAPEX ramp, production ramp, depreciation schedule, and tax lag to produce annual capital cost, revenue, cash cost, gross profit, depreciation, taxable income, tax paid, and net cash flow arrays. Supports vectorised (Monte Carlo) inputs when project_lifetime, interest_rate, etc. are arrays.

Parameters:

print_results (bool, optional) – Return a formatted pd.DataFrame.style for scalar scenarios. Default is False.

Returns:

Styled cash flow table when print_results=True and inputs are scalar; None otherwise (results stored as instance arrays).

Return type:

pd.DataFrame.style or None

Raises:

ValueError – If project_lifetime < 3, capex_ramp or production_ramp are invalid, or no plant products are defined.

calculate_npv(print_results=False)

Calculate Net Present Value (NPV) of the project cash flows.

Discounts each year’s cash flow at interest_rate and returns the cumulative NPV at the end of the project lifetime. Supports vectorised inputs for Monte Carlo scenarios.

Parameters:

print_results (bool, optional) – Print a year-by-year present value and cumulative NPV table. Default is False.

Returns:

Final NPV (scalar) or array of NPVs across scenarios.

Return type:

float or np.ndarray

Raises:

ValueError – If interest_rate is an array whose length does not match the number of cash flow scenarios.

calculate_levelized_cost(print_results=False)

Calculate the levelized cost of production (LCOP).

Discounts capital costs, operating costs, and production over the project lifetime at interest_rate. Side-product revenues are subtracted before dividing by discounted production.

Parameters:

print_results (bool, optional) – Print the mean levelized cost. Default is False.

Returns:

Levelized cost per unit of main product (scalar or array).

Return type:

float or np.ndarray

calculate_payback_time(additional_capex=False, print_results=False)

Calculate simple payback time.

Divides total fixed capital (optionally including additional CAPEX) by the mean annual cash flow across revenue-generating years.

Parameters:

• additional_capex (bool, optional) – Include additional CAPEX in the total investment. Default is False.

• print_results (bool, optional) – Print the payback time. Default is False.

Returns:

Payback time in years (nan if no revenue-generating years exist).

Return type:

float or np.ndarray

calculate_roi(additional_capex=False, print_results=False)

Calculate Return on Investment (ROI).

Computes total net profit over the project lifetime as a percentage of total investment (fixed capital + working capital, optionally including additional CAPEX), annualised by project lifetime.

Parameters:

• additional_capex (bool, optional) – Include additional CAPEX in total investment. Default is False.

• print_results (bool, optional) – Print the ROI value. Default is False.

Returns:

ROI as a percentage (scalar or array across scenarios).

Return type:

float or np.ndarray

calculate_irr(print_results=False)

Calculate the Internal Rate of Return (IRR).

Finds the discount rate at which NPV equals zero using Brent’s method. Returns nan if no sign change is found (no valid IRR exists).

Parameters:

print_results (bool, optional) – Print the IRR value. Default is False.

Returns:

IRR as a fraction (e.g. 0.15 = 15%), or nan if undefined.

Return type:

float or np.ndarray

calculate_all(additional_capex=False, print_results=False)

Run all financial calculations sequentially.

Calls calculate_fixed_capital, calculate_variable_opex, calculate_fixed_opex, calculate_revenue, calculate_cash_flow, calculate_npv, calculate_levelized_cost, calculate_payback_time, calculate_roi, and calculate_irr.

Parameters:

• additional_capex (bool, optional) – Pass through to calculate_fixed_capital, calculate_payback_time, and calculate_roi. Default is False.

• print_results (bool, optional) – Print results from each sub-calculation. Default is False.

to_dict()

Serialize plant configuration and all computed metrics to a dict.

Returns:

Nested dictionary with sections: plant_configuration, equipment_summary, capital_costs, variable_opex, fixed_opex, revenue, cash_flow, and metrics.

Return type:

dict

class openpytea.plant.DepreciationConfig

Bases: object

Configuration for asset depreciation calculations.

This class defines the parameters needed to compute depreciation using various methods.

Attributes:

method (DepMethod): The depreciation method to use. Defaults to “straight_line”. Options: “straight_line”, “declining_balance”, “macrs”. life (Optional[int]): The useful life of the asset in years.

Used by straight_line and declining_balance methods. Defaults to None.

db_factor (float): The declining balance factor (multiplier).

Only used by the declining_balance method. Defaults to 2.0.

salvage_fraction (float): The salvage value as a fraction

of the initial cost. Used by straight_line and declining_balance methods. Defaults to 0.0.

macrs_class (int): The MACRS property class (1-20).

Only used by the macrs method. Defaults to 7.

convention (str): The depreciation convention for MACRS.

Only used by the macrs method. Defaults to “half_year”.

service_start_year (int): The year index (starting from 0)

when the asset is placed in service. Defaults to 2.

method: Literal['straight_line', 'declining_balance', 'macrs'] = 'straight_line'

life: int | None = None

db_factor: float = 2.0

salvage_fraction: float = 0.0

macrs_class: int = 7

convention: str = 'half_year'

service_start_year: int = 2