Location factor

idaes/core/base/costing_base.py

@@ -20,10 +20,14 @@
 # This plays with some private attributes - most are necessary
 # pylint: disable=protected-access
 
+import json
+import os
+
 from functools import partial
 
 import pyomo.environ as pyo
 from pyomo.common.config import ConfigBlock, ConfigValue
+from pyomo.common.fileutils import this_file_dir
 from pyomo.util.calc_var_value import calculate_variable_from_constraint
 from pyomo.contrib.fbbt.fbbt import compute_bounds_on_expr
 
@@ -98,6 +102,64 @@ def register_idaes_currency_units():
         )
 
 
+def load_location_factors():
+    """
+    Estimate the cost of constructing the same plant in different geographic regions using location factors.
+
+    This method uses a location (or investment site) factor to adjust the total permanent investment (TPI)
+    based on regional differences in labor costs, workforce efficiency, local regulations and customs,
+    union status, and other local economic conditions.
+
+    Reference:
+    Seider, Warren D., et al. *Product and Process Design Principles: Synthesis, Analysis, and Evaluation.*
+    John Wiley & Sons, 2016.
+
+    The conversion equation is given by:
+
+    .. math::
+        C_{TPI, corrected} = F_{ISF} \times C_{TPI}
+
+    where:
+    - :math:`C` represents cost,
+    - :math:`F` represents a factor,
+    - :math:`TPI` is the total plant investment, and
+    - :math:`ISF` is the investment site factor (i.e., location factor).
+
+    Location factors for 139 countries are sourced from
+    Compass International, Inc. (2017). 2017 worldwide industrial [Sample edition].
+    https://www.compassinternational.net/wp-content/uploads/2017/01/Worldwide-Industrial.pdf
+    (Accessed March 25, 2025).
+
+    Location factors for U.S. regions are sourced from Table 16.13 in the Seider reference above. The values
+    are converted appropriately assuming the base factor of 1.0 corresponds to Washington D.C. and U.S. Northeast.
+
+    Note: For some countries, multiple city-specific location factors are provided.
+    The benchmark location is Washington, D.C., USA.
+    """
+    directory = this_file_dir()
+    with open(os.path.join(directory, "location_factors.json"), "r") as file:
+        location_data = json.load(file)
+
+    # convert to a dictionary format
+    location_factors = {}
+    for item in location_data:
+        country = item["country"]
+        city = item["city"]
+
+        # create entry if new country, otherwise it already exists
+        if country not in location_factors:
+            location_factors[country] = {}
+
+        # create entry for city
+        location_factors[country][city] = {}
+
+        # populate min, max, average
+        for val in ["min", "max", "average"]:
+            location_factors[country][city][val] = item["location_factor"][val]
+
+    return location_factors
+
+
 class DefaultCostingComponents(StrEnum):
     """
     Costing components Enum