Add Air-Water Equilibrium Property Package

docs/how_to_guides/how_to_use_parameter_sweep.rst

@@ -140,7 +140,13 @@ future versions. The preferred way is to pass in generating functions as shown b
 
 .. testcode::
 
-    parameter_sweep(build_model, build_sweep_params, build_outputs, csv_results_file_name='outputs_results.csv', h5_results_file_name='outputs_results.h5')
+    parameter_sweep(
+        build_model,
+        build_sweep_params,
+        build_outputs,
+        csv_results_file_name="outputs_results.csv",
+        h5_results_file_name="outputs_results.h5",
+    )
 
 .. testcleanup::
 

docs/how_to_guides/how_to_use_parameter_sweep_monte_carlo.rst

@@ -1,5 +1,5 @@
-Monte Carlo testing with the Parameter Sweep
-============================================
+How to do Monte Carlo testing with Parameter Sweep
+==================================================
 
 Overview
 --------
@@ -106,9 +106,17 @@ With the generating functions defined and suitably initialized, we can call the
     seed = None
 
     # Run the parameter sweep
-    global_results = parameter_sweep(build_model, build_sweep_params, build_outputs, csv_results_file_name='monte_carlo_results.csv',
-        optimize_function=RO_flowsheet.optimize, debugging_data_dir=debugging_data_dir, num_samples=num_samples, seed=seed,
-        build_sweep_params_kwargs=dict(num_samples=num_samples))
+    global_results = parameter_sweep(
+        build_model,
+        build_sweep_params,
+        build_outputs,
+        csv_results_file_name="monte_carlo_results.csv",
+        optimize_function=RO_flowsheet.optimize,
+        debugging_data_dir=debugging_data_dir,
+        num_samples=num_samples,
+        seed=seed,
+        build_sweep_params_kwargs=dict(num_samples=num_samples),
+    )
 
 Note that ``num_samples`` must be provided for any of the random sample classes.  For the very small problem size and simple model used here, parallel hardware is almost certainly not necessary.  However, for larger total numbers of samples or more computationally demanding models, a significant speedup may be attained on a multi-core workstation or high performance computing (HPC) cluster.  To distribute the workload between more than one worker, simply call the scipt using the ``mpirun`` command from the command line
 

docs/how_to_guides/how_to_use_ui_api.rst

@@ -1,7 +1,7 @@
 .. _howto_ui-api:
 
-Add a flowsheet to the UI
-==========================
+How to add a flowsheet to the UI
+=================================
 .. py:currentmodule:: idaes_flowsheet_processor.api
 
 This API is intended for model developers who would like to connect their flowsheets to the UI.

docs/technical_reference/property_models/index.rst

@@ -15,3 +15,4 @@ Property Models
    modified_ADM1
    modified_ASM2D
    mc_aq_sol
+   air_water_equil