openpytea.equipment

openpytea.equipment.inflation_adjustment(equipment_cost, cost_year, target_year=2024)[source]

Adjust equipment cost from one year to another using the Chemical Engineering Plant Cost Index (CEPCI).

This function uses historical CEPCI values to convert equipment costs between different years, accounting for inflation in the chemical engineering industry.

Parameters:

  • equipment_cost (float) – The cost of the equipment in the cost_year (in USD).

  • cost_year (int) – The year in which the equipment_cost is valued. Must be available in CEPCI_DF index.

  • target_year (int, optional) – The year to adjust the cost to. Default is 2024. Must be available in CEPCI_DF index.

Returns:

The inflation-adjusted equipment cost in target_year (in USD).

Return type:

float

Raises:

  • ValueError – If cost_year is not found in CEPCI_DF.

  • ValueError – If target_year is not found in CEPCI_DF.

Notes

The adjustment factor is calculated as: adjusted_cost = equipment_cost * (CEPCI[target_year] / CEPCI[cost_year])

Examples

>>> # Adjust from 2015 to 2023
>>> new_cost = inflation_adjustment(50000, 2015, 2023)
class openpytea.equipment.CostCorrelationDB(df=                                              key  ...                                        doi /  link 0                      impeller_mixer_turton_2001  ...  https://books.google.nl/books/about/Analysis_S... 1                     kneader_blender_turton_2001  ...  https://books.google.nl/books/about/Analysis_S... 2                     propeller_mixer_towler_2010  ...         doi.org/10.1016/B978-0-12-821179-3.00007-8 3                     propeller_mixer_turton_2001  ...  https://books.google.nl/books/about/Analysis_S... 4                      ribbon_blender_turton_2001  ...  https://books.google.nl/books/about/Analysis_S... ..                                            ...  ...                                                ... 160           cooling_tower_and_pumps_towler_2010  ...         doi.org/10.1016/B978-0-12-821179-3.00007-8 161  packaged_mechanical_refrigerator_towler_2010  ...         doi.org/10.1016/B978-0-12-821179-3.00007-8 162          water_ion_exchange_plant_towler_2010  ...         doi.org/10.1016/B978-0-12-821179-3.00007-8 163         internal_coils_vaporizers_turton_2001  ...  https://books.google.nl/books/about/Analysis_S... 164       jacketed_vessels_vaporizers_turton_2001  ...  https://books.google.nl/books/about/Analysis_S...  [165 rows x 21 columns])[source]

Bases: object

Database interface for equipment cost correlations.

Manages cost estimation correlations for equipment based on size/capacity parameters. Supports multiple correlation forms (power-law, quad log-log) and handles equipment parallelization when capacity limits are exceeded.

df

Cost correlation data with columns: key, category, type, form, s_lower, s_upper, upper_parallel, a, b, n, k1, k2, k3, cost_year.

Type:

pd.DataFrame

evaluate(key, s)[source]

Calculate purchased equipment cost based on correlation key and size.

Parameters:

  • key (str) – Unique identifier for the cost correlation.

  • s (float) – Equipment size/capacity parameter.

Returns:

(total_cost, number_of_units, cost_year).

Return type:

tuple[float, int, int]

Raises:

  • KeyError – If correlation key not found in database.

  • ValueError – If size is below the lower bound or the correlation form is unsupported.

key_for_category_type(eq_category, type)[source]

Look up correlation key by equipment category and optional type.

Parameters:

  • eq_category (str) – Equipment category name.

  • type (str | None) – Equipment sub-type (optional).

Returns:

Correlation key if found, None otherwise.

Return type:

str | None

class openpytea.equipment.Equipment(name, param, process_type, category, type=None, material='Carbon steel', num_units=None, purchased_cost=None, cost_year=None, cost_func=None, target_year=2024, erection_factor=None, piping_factor=None, instrumentation_factor=None, electrical_factor=None, civil_factor=None, structural_factor=None, lagging_factor=None, material_factor=None)[source]

Bases: object

Equipment cost estimation class for process equipment.

Manages cost calculation of process equipment based on process type, material, and equipment parameters. Supports both direct cost input and calculated costs from a cost correlation database.

process_factors

Process type factors affecting cost calculation. Keys are process types (“Solids”, “Fluids”, “Mixed”, “Electrical”). Values are dicts with factors: fer, fp, fi, fel, fc, fs, fl.

Type:

dict

material_factors

Material type multipliers mapping material names to cost factors (1.0 to 1.7).

Type:

dict

Parameters:

  • name (str) – Equipment identifier/name.

  • param (float) – Equipment parameter (size, capacity) for cost correlation lookup.

  • process_type (str) – Type of process (“Solids”, “Fluids”, “Mixed”, or “Electrical”).

  • category (str) – Equipment category for database lookup.

  • type (str | None, optional) – Equipment sub-type for database lookup. Default is None.

  • material (str, optional) – Material of construction. Default is “Carbon steel”.

  • num_units (int | None, optional) – Number of identical units. Default is None (set to 1 when purchased_cost is provided).

  • purchased_cost (float | None, optional) – Direct purchased cost input. If provided, param is ignored. Default is None.

  • cost_year (int | None, optional) – Year of the purchased_cost quote for inflation adjustment. Default is None.

  • cost_func (str | None, optional) – Explicit cost correlation key from the database. Default is None (auto-resolved from category/type).

  • target_year (int, optional) – Target year for inflation adjustment. Default is 2024.

  • erection_factor (float | None, optional) – Erection factor override. Default is None (use process_type table).

  • piping_factor (float | None, optional) – Piping factor override. Default is None (use process_type table).

  • instrumentation_factor (float | None, optional) – Instrumentation & controls factor override. Default is None.

  • electrical_factor (float | None, optional) – Electrical factor override. Default is None (use process_type table).

  • civil_factor (float | None, optional) – Civil factor override. Default is None (use process_type table).

  • structural_factor (float | None, optional) – Structural steel factor override. Default is None (use process_type table).

  • lagging_factor (float | None, optional) – Lagging & painting factor override. Default is None (use process_type table).

  • material_factor (float | None, optional) – Material factor override. Default is None (use material table).

Raises:

  • ValueError – If process_type or material is not found in the factor dictionaries.

  • KeyError – If the category/type combination is not found in the database and cost_func is not specified.

Examples

>>> eq = Equipment(
...     name="Reactor",
...     param=100,
...     process_type="Fluids",
...     category="Reactor",
...     material="304 stainless steel"
... )
>>> print(eq.direct_cost)
process_factors = {'Electrical': {'fc': 0.2, 'fel': 0.7, 'fer': 0.4, 'fi': 0.7, 'fl': 0.1, 'fp': 0.1, 'fs': 0.1}, 'Fluids': {'fc': 0.3, 'fel': 0.2, 'fer': 0.3, 'fi': 0.3, 'fl': 0.1, 'fp': 0.8, 'fs': 0.2}, 'Mixed': {'fc': 0.3, 'fel': 0.2, 'fer': 0.5, 'fi': 0.3, 'fl': 0.1, 'fp': 0.6, 'fs': 0.2}, 'Solids': {'fc': 0.2, 'fel': 0.15, 'fer': 0.6, 'fi': 0.2, 'fl': 0.05, 'fp': 0.2, 'fs': 0.1}}
material_factors = {'304 stainless steel': 1.3, '316 stainless steel': 1.3, '321 stainless steel': 1.5, 'Aluminum': 1.07, 'Bronze': 1.07, 'Carbon steel': 1.0, 'Cast steel': 1.1, 'Hastelloy C': 1.55, 'Inconel': 1.7, 'Monel': 1.65, 'Nickel': 1.7}
calculate_direct_cost()[source]

Calculate total direct cost including process and material factors.

Applies erection, piping, instrumentation, electrical, civil, structural, lagging, and material factors to the purchased cost.

Returns:

Total direct installed cost.

Return type:

float

to_dict()[source]

Convert equipment specifications and costs to a dictionary.

Returns:

Keys: name, category, type, material, process_type, param, num_units, cost_year, target_year, purchased_cost, direct_cost.

Return type:

dict

Functions

openpytea.equipment.inflation_adjustment(equipment_cost, cost_year, target_year=2024)[source]

Adjust equipment cost from one year to another using the Chemical Engineering Plant Cost Index (CEPCI).

This function uses historical CEPCI values to convert equipment costs between different years, accounting for inflation in the chemical engineering industry.

Parameters:

  • equipment_cost (float) – The cost of the equipment in the cost_year (in USD).

  • cost_year (int) – The year in which the equipment_cost is valued. Must be available in CEPCI_DF index.

  • target_year (int, optional) – The year to adjust the cost to. Default is 2024. Must be available in CEPCI_DF index.

Returns:

The inflation-adjusted equipment cost in target_year (in USD).

Return type:

float

Raises:

  • ValueError – If cost_year is not found in CEPCI_DF.

  • ValueError – If target_year is not found in CEPCI_DF.

Notes

The adjustment factor is calculated as: adjusted_cost = equipment_cost * (CEPCI[target_year] / CEPCI[cost_year])

Examples

>>> # Adjust from 2015 to 2023
>>> new_cost = inflation_adjustment(50000, 2015, 2023)

Classes

class openpytea.equipment.CostCorrelationDB(df=                                              key  ...                                        doi /  link 0                      impeller_mixer_turton_2001  ...  https://books.google.nl/books/about/Analysis_S... 1                     kneader_blender_turton_2001  ...  https://books.google.nl/books/about/Analysis_S... 2                     propeller_mixer_towler_2010  ...         doi.org/10.1016/B978-0-12-821179-3.00007-8 3                     propeller_mixer_turton_2001  ...  https://books.google.nl/books/about/Analysis_S... 4                      ribbon_blender_turton_2001  ...  https://books.google.nl/books/about/Analysis_S... ..                                            ...  ...                                                ... 160           cooling_tower_and_pumps_towler_2010  ...         doi.org/10.1016/B978-0-12-821179-3.00007-8 161  packaged_mechanical_refrigerator_towler_2010  ...         doi.org/10.1016/B978-0-12-821179-3.00007-8 162          water_ion_exchange_plant_towler_2010  ...         doi.org/10.1016/B978-0-12-821179-3.00007-8 163         internal_coils_vaporizers_turton_2001  ...  https://books.google.nl/books/about/Analysis_S... 164       jacketed_vessels_vaporizers_turton_2001  ...  https://books.google.nl/books/about/Analysis_S...  [165 rows x 21 columns])[source]

Bases: object

Database interface for equipment cost correlations.

Manages cost estimation correlations for equipment based on size/capacity parameters. Supports multiple correlation forms (power-law, quad log-log) and handles equipment parallelization when capacity limits are exceeded.

df

Cost correlation data with columns: key, category, type, form, s_lower, s_upper, upper_parallel, a, b, n, k1, k2, k3, cost_year.

Type:

pd.DataFrame

evaluate(key, s)[source]

Calculate purchased equipment cost based on correlation key and size.

Parameters:

  • key (str) – Unique identifier for the cost correlation.

  • s (float) – Equipment size/capacity parameter.

Returns:

(total_cost, number_of_units, cost_year).

Return type:

tuple[float, int, int]

Raises:

  • KeyError – If correlation key not found in database.

  • ValueError – If size is below the lower bound or the correlation form is unsupported.

key_for_category_type(eq_category, type)[source]

Look up correlation key by equipment category and optional type.

Parameters:

  • eq_category (str) – Equipment category name.

  • type (str | None) – Equipment sub-type (optional).

Returns:

Correlation key if found, None otherwise.

Return type:

str | None

class openpytea.equipment.Equipment(name, param, process_type, category, type=None, material='Carbon steel', num_units=None, purchased_cost=None, cost_year=None, cost_func=None, target_year=2024, erection_factor=None, piping_factor=None, instrumentation_factor=None, electrical_factor=None, civil_factor=None, structural_factor=None, lagging_factor=None, material_factor=None)[source]

Bases: object

Equipment cost estimation class for process equipment.

Manages cost calculation of process equipment based on process type, material, and equipment parameters. Supports both direct cost input and calculated costs from a cost correlation database.

process_factors

Process type factors affecting cost calculation. Keys are process types (“Solids”, “Fluids”, “Mixed”, “Electrical”). Values are dicts with factors: fer, fp, fi, fel, fc, fs, fl.

Type:

dict

material_factors

Material type multipliers mapping material names to cost factors (1.0 to 1.7).

Type:

dict

Parameters:

  • name (str) – Equipment identifier/name.

  • param (float) – Equipment parameter (size, capacity) for cost correlation lookup.

  • process_type (str) – Type of process (“Solids”, “Fluids”, “Mixed”, or “Electrical”).

  • category (str) – Equipment category for database lookup.

  • type (str | None, optional) – Equipment sub-type for database lookup. Default is None.

  • material (str, optional) – Material of construction. Default is “Carbon steel”.

  • num_units (int | None, optional) – Number of identical units. Default is None (set to 1 when purchased_cost is provided).

  • purchased_cost (float | None, optional) – Direct purchased cost input. If provided, param is ignored. Default is None.

  • cost_year (int | None, optional) – Year of the purchased_cost quote for inflation adjustment. Default is None.

  • cost_func (str | None, optional) – Explicit cost correlation key from the database. Default is None (auto-resolved from category/type).

  • target_year (int, optional) – Target year for inflation adjustment. Default is 2024.

  • erection_factor (float | None, optional) – Erection factor override. Default is None (use process_type table).

  • piping_factor (float | None, optional) – Piping factor override. Default is None (use process_type table).

  • instrumentation_factor (float | None, optional) – Instrumentation & controls factor override. Default is None.

  • electrical_factor (float | None, optional) – Electrical factor override. Default is None (use process_type table).

  • civil_factor (float | None, optional) – Civil factor override. Default is None (use process_type table).

  • structural_factor (float | None, optional) – Structural steel factor override. Default is None (use process_type table).

  • lagging_factor (float | None, optional) – Lagging & painting factor override. Default is None (use process_type table).

  • material_factor (float | None, optional) – Material factor override. Default is None (use material table).

Raises:

  • ValueError – If process_type or material is not found in the factor dictionaries.

  • KeyError – If the category/type combination is not found in the database and cost_func is not specified.

Examples

>>> eq = Equipment(
...     name="Reactor",
...     param=100,
...     process_type="Fluids",
...     category="Reactor",
...     material="304 stainless steel"
... )
>>> print(eq.direct_cost)
process_factors = {'Electrical': {'fc': 0.2, 'fel': 0.7, 'fer': 0.4, 'fi': 0.7, 'fl': 0.1, 'fp': 0.1, 'fs': 0.1}, 'Fluids': {'fc': 0.3, 'fel': 0.2, 'fer': 0.3, 'fi': 0.3, 'fl': 0.1, 'fp': 0.8, 'fs': 0.2}, 'Mixed': {'fc': 0.3, 'fel': 0.2, 'fer': 0.5, 'fi': 0.3, 'fl': 0.1, 'fp': 0.6, 'fs': 0.2}, 'Solids': {'fc': 0.2, 'fel': 0.15, 'fer': 0.6, 'fi': 0.2, 'fl': 0.05, 'fp': 0.2, 'fs': 0.1}}
material_factors = {'304 stainless steel': 1.3, '316 stainless steel': 1.3, '321 stainless steel': 1.5, 'Aluminum': 1.07, 'Bronze': 1.07, 'Carbon steel': 1.0, 'Cast steel': 1.1, 'Hastelloy C': 1.55, 'Inconel': 1.7, 'Monel': 1.65, 'Nickel': 1.7}
calculate_direct_cost()[source]

Calculate total direct cost including process and material factors.

Applies erection, piping, instrumentation, electrical, civil, structural, lagging, and material factors to the purchased cost.

Returns:

Total direct installed cost.

Return type:

float

to_dict()[source]

Convert equipment specifications and costs to a dictionary.

Returns:

Keys: name, category, type, material, process_type, param, num_units, cost_year, target_year, purchased_cost, direct_cost.

Return type:

dict