Revised vapor composition constraints fix ZeroDivisionError when gdp.hull reformulation is applied

.github/workflows/typos.toml

@@ -3,3 +3,4 @@
 hda = "hda"
 HDA = "HDA"
 equil = "equil"
+gam = "gam"

benchmark.py

@@ -1,7 +1,7 @@
 import os
 import json
 import time
-import sys
+from contextlib import redirect_stdout
 from datetime import datetime
 from importlib import import_module
 from pyomo.environ import *
@@ -28,14 +28,13 @@ def benchmark(model, strategy, timelimit, result_dir, subsolver="scip"):
     None
     """
     model = model.clone()
-    stdout = sys.stdout
+    log_path = os.path.join(result_dir, f"{strategy}_{subsolver}.log")
+    json_path = os.path.join(result_dir, f"{strategy}_{subsolver}.json")
     if strategy in ["gdp.bigm", "gdp.hull"]:
         transformation_start_time = time.time()
         TransformationFactory(strategy).apply_to(model)
         transformation_end_time = time.time()
-        with open(
-            result_dir + "/" + strategy + "_" + subsolver + ".log", "w"
-        ) as sys.stdout:
+        with open(log_path, "w") as log_file, redirect_stdout(log_file):
             results = SolverFactory(subsolver).solve(
                 model, tee=True, timelimit=timelimit
             )
@@ -50,9 +49,7 @@ def benchmark(model, strategy, timelimit, result_dir, subsolver="scip"):
         "gdpopt.lbb",
         "gdpopt.ric",
     ]:
-        with open(
-            result_dir + "/" + strategy + "_" + subsolver + ".log", "w"
-        ) as sys.stdout:
+        with open(log_path, "w") as log_file, redirect_stdout(log_file):
             results = SolverFactory(strategy).solve(
                 model,
                 tee=True,
@@ -63,9 +60,10 @@ def benchmark(model, strategy, timelimit, result_dir, subsolver="scip"):
                 time_limit=timelimit,
             )
             print(results)
+    else:
+        raise ValueError(f"Unknown benchmark strategy: {strategy}")
 
-    sys.stdout = stdout
-    with open(result_dir + "/" + strategy + "_" + subsolver + ".json", "w") as f:
+    with open(json_path, "w") as f:
         json.dump(results.json_repn(), f)
     return None
 

gdplib/biofuel/model.py

@@ -4,7 +4,7 @@
 
 The model enforces constraints to ensure that raw material supplies do not exceed available amounts, product shipments meet market demands exactly, and production at each site matches outgoing shipments and available resources.
 It also optimizes transportation costs by managing both variable and fixed costs associated with active transportation routes.
-The disjunctions in the model define the operational modes for facility sites (modular, conventional, or inactive) and the activity status of supply and product routes (active or inactive). 
+The disjunctions in the model define the operational modes for facility sites (modular, conventional, or inactive) and the activity status of supply and product routes (active or inactive).
 These elements allow the model to simulate different operational scenarios and strategic decisions, optimizing the network's layout and logistics based on economic and market conditions.
 The objective of the model is to optimize the network layout and production allocation to minimize total costs, which include setup and teardown of facilities, production costs, and transportation costs.
 

gdplib/gdp_col/initialize.py

@@ -72,9 +72,9 @@ def set_value_if_not_fixed(var, val):
             for i in range(1, num_active_trays)
         ]
         for tray in range(2, num_active_trays):
-            indx = new_indices[tray - 1]
-            lower = floor(indx)
-            frac_above = indx - lower
+            index = new_indices[tray - 1]
+            lower = floor(index)
+            frac_above = index - lower
             # Take linear combination of values
             tray_indexed_data.loc[tray] = (
                 tray_indexed_data.loc[lower] * (1 - frac_above)

gdplib/hda/HDA_GDP_gdpopt.py

@@ -1416,7 +1416,7 @@ def Relvol(_m, dist_):
                 return m.avevlt[dist] == sqrt(divided1 * divided2)
             return Constraint.Skip
 
-        b.relvol = Constraint([dist], rule=Relvol, doc='average relative volatilty')
+        b.relvol = Constraint([dist], rule=Relvol, doc='average relative volatility')
 
         def Undwood(_m, dist_):
             if dist_ == dist:

gdplib/kaibel/README.md

@@ -6,6 +6,10 @@ Source paper:
 
 > Rawlings, E. S., Chen, Q., Grossmann, I. E., & Caballero, J. A. (2019). Kaibel Column: Modeling, optimization, and conceptual design of multi-product dividing wall columns. *Computers and Chemical Engineering*, 125, 31–39. https://doi.org/10.1016/j.compchemeng.2019.03.006
 
+Heat-capacity and enthalpy calculations use the Ruzicka-Domalski group-additivity heat-capacity method:
+
+> Ruzicka, V. Jr., & Domalski, E. S. Estimation of the heat capacities of organic liquids as a function of temperature using group additivity. *Journal of Physical and Chemical Reference Data*, 22(3). https://www.osti.gov/biblio/6251468
+
 ## Problem Details
 
 ### Solution
@@ -30,4 +34,3 @@ Best known objective value: 99,709
 - ``disjtn``: disjunctions
 
 
-