NatLabRockies · kbrunik · Apr 11, 2026 · Apr 11, 2026 · Apr 11, 2026 · Apr 11, 2026
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -51,6 +51,7 @@
   - The `FlexibleDemandOpenLoopConverterController` has been renamed to `FlexibleDemandComponent`
   - The `DemandOpenLoopConverterController` has been renamed to `GenericDemandComponent`
 - Modified CI setup so Windows is temporarily disabled and also so unit, regression, and integration tests are run in separate jobs to speed up testing and provide more information on test failures. [PR 668](https://github.com/NatLabRockies/H2Integrate/pull/668)
+- Change commodity in DRI and EAF model from pig iron to sponge iron based on likely carbon content [PR 670](https://github.com/NatLabRockies/H2Integrate/pull/670)
 
 ## 0.7.2 [April 9, 2026]
 

diff --git a/docs/technology_models/iron_dri.md b/docs/technology_models/iron_dri.md
@@ -12,6 +12,10 @@ The models implemented in H2I are:
   - `HydrogenIronReductionPlantPerformanceComponent`
   - `HydrogenIronReductionPlantCostComponent`
 
+```{note}
+The DRI model outputs sponge iron, which is low in carbon content. The LBNL model calls the outputs pig iron, but that's typically produced using a blast furnace rather than through the DRI process and has higher carbon impurities.
+```
+
 Citation:
 ```bibtex
 @article{rosner2023green,

diff --git a/docs/technology_models/steel_eaf.md b/docs/technology_models/steel_eaf.md
@@ -3,8 +3,9 @@
 H2I contains two steel electric arc furnace (EAF) models, one is for a facility that utilizes iron pellets from natural gas (NG) direct iron reduction and another facility that utilizes iron pellets from hydrogen (H2)direct iron reduction.
 
 The main difference is the required feedstocks for processing the pellets.
-- NG-EAF requires: natural gas, water, pig iron and electricity.
-- H2-EAF requires: natural gas, water, carbon, lime, pig iron and electricity.
+- NG-EAF requires: natural gas, water, sponge iron and electricity.
+- H2-EAF requires: natural gas, water, carbon, lime, sponge iron and electricity.
+
 
 The original models were constructed in Aspen Pro and translated into Python and added to H2I. The models were developed in conjunction with [Lawrence Berkeley National Laboratory (LBNL)](https://www.lbl.gov/).
 
@@ -16,6 +17,10 @@ The models implemented in H2I are:
   - `HydrogenEAFPlantPerformanceComponent`
   - `HydrogenEAFPlantCostComponent`
 
+```{note}
+The EAF model use sponge iron as an input rather than pig iron, which is lower in carbon content. The LBNL model calls the input to the EAF pig iron, but that's typically produced using a blast furnace rather than through the DRI process and has higher carbon impurities.
+```
+
 Citation:
 ```bibtex
 @article{rosner2023green,

diff --git a/examples/21_iron_examples/iron_dri/plant_config.yaml b/examples/21_iron_examples/iron_dri/plant_config.yaml
@@ -23,7 +23,7 @@ technology_interconnections:
   - [eaf_grid_feedstock, steel_plant, electricity, cable]
   - [eaf_water_feedstock, steel_plant, water, pipe]
   - [eaf_natural_gas_feedstock, steel_plant, natural_gas, pipe]
-  - [iron_plant, steel_plant, pig_iron, pig_iron_transport]
+  - [iron_plant, steel_plant, sponge_iron, sponge_iron_transport]
 plant:
   plant_life: 30
   simulation:
@@ -64,8 +64,8 @@ finance_parameters:
         - iron_mine
         - mine_electricity_feedstock
         - mine_crude_ore_feedstock
-    pig_iron:
-      commodity: pig_iron
+    sponge_iron:
+      commodity: sponge_iron
       commodity_stream: iron_plant
       technologies:
         - processed_ore_feedstock

diff --git a/examples/21_iron_examples/iron_dri/run_iron.py b/examples/21_iron_examples/iron_dri/run_iron.py
@@ -3,11 +3,11 @@
 In this example, iron ore pellets are produced at different iron mine locations in NE Minnesota.
 These mines send processed ore pellets to a separate iron DRI plant located outside Chicago.
 Four different cases are generated for four different iron mine setups in the `test_inputs.csv`.
-The first two cases generate standard blast furnace gradepellets at two different mine locations.
+The first two cases generate standard blast furnace grade pellets at two different mine locations.
 The second two cases generate DR grade pellets at the same location, with the output capacity
 varied to show how the capacity of the mine does not affect the levelized cost of iron_ore pellets
-(LCOI), nor does it affect the final cost of the pig_iron produced by DRI (LCOP) or the cost of the
-steel produced by the EAF (LCOS).
+(LCOI), nor does it affect the final cost of the sponge_iron produced by DRI (LCOS) or the cost
+of the steel produced by the EAF (LCOS).
 
 """
 
@@ -78,22 +78,26 @@
         )
     )
     lcois_iron.append(
-        float(model.model.get_val("finance_subgroup_pig_iron.price_pig_iron", units="USD/kg")[0])
+        float(
+            model.model.get_val("finance_subgroup_sponge_iron.price_sponge_iron", units="USD/kg")[0]
+        )
     )
     capexes_iron.append(
-        float(model.model.get_val("finance_subgroup_pig_iron.total_capex_adjusted", units="USD")[0])
+        float(
+            model.model.get_val("finance_subgroup_sponge_iron.total_capex_adjusted", units="USD")[0]
+        )
     )
     fopexes_iron.append(
         float(
-            model.model.get_val("finance_subgroup_pig_iron.total_opex_adjusted", units="USD/year")[
-                0
-            ]
+            model.model.get_val(
+                "finance_subgroup_sponge_iron.total_opex_adjusted", units="USD/year"
+            )[0]
         )
     )
     vopexes_iron.append(
         float(
             model.model.get_val(
-                "finance_subgroup_pig_iron.total_varopex_adjusted", units="USD/year"
+                "finance_subgroup_sponge_iron.total_varopex_adjusted", units="USD/year"
             )[0]
         )
     )

diff --git a/examples/21_iron_examples/iron_dri/tech_config.yaml b/examples/21_iron_examples/iron_dri/tech_config.yaml
@@ -163,18 +163,18 @@ technologies:
       model: NaturalGasIronReductionPlantCostComponent
     model_inputs:
       shared_parameters:
-        pig_iron_production_rate_tonnes_per_hr: 161.8829908675799  # equivalent to 1418095 t/yr
+        sponge_iron_production_rate_tonnes_per_hr: 161.8829908675799  # equivalent to 1418095 t/yr
       performance_parameters:
         water_density: 1000  # kg/m3
       cost_parameters:
         skilled_labor_cost: 40.85  # 2022 USD/hr
         unskilled_labor_cost: 30.0  # 2022 USD/hr
-  pig_iron_transport:
+  sponge_iron_transport:
     performance_model:
       model: GenericTransporterPerformanceModel
     model_inputs:
       performance_parameters:
-        commodity: pig_iron
+        commodity: sponge_iron
         commodity_rate_units: kg/h
   eaf_grid_feedstock:  # electricity feedstock for EAF
     performance_model:

diff --git a/examples/21_iron_examples/iron_mapping/driver_config.yaml b/examples/21_iron_examples/iron_mapping/driver_config.yaml
@@ -34,10 +34,10 @@ design_variables:
       lower: 0
       upper: 10
 objective:
-  name: finance_subgroup_pig_iron.LCOP
+  name: finance_subgroup_sponge_iron.LCOS
 recorder:
   file: cases.sql
   overwrite_recorder: true
   flag: true
-  includes: [site.latitude, site.longitude, finance_subgroup_pig_iron.LCOP]
+  includes: [site.latitude, site.longitude, finance_subgroup_sponge_iron.LCOS]
   excludes: ['*']
diff --git a/examples/21_iron_examples/iron_mapping/ex_out/cases.csv b/examples/21_iron_examples/iron_mapping/ex_out/cases.csv
@@ -1,4 +1,4 @@
-,site.latitude (deg),site.longitude (deg),dri_grid_feedstock.price (USD/MW/h),hydrogen_feedstock.price (USD/kg),finance_subgroup_pig_iron.LCOP (USD/kg)
+,site.latitude (deg),site.longitude (deg),dri_grid_feedstock.price (USD/MW/h),hydrogen_feedstock.price (USD/kg),finance_subgroup_sponge_iron.LCOS (USD/kg)
 0,39.179,-94.644,58.24602573,7.819127689,0.7850112572903336
 1,40.639,-94.179,51.41859144,6.486845574,0.7039548783993137
 2,42.135,-74.231,54.87195632,9.087358988,0.8611145933067907
@@ -45,7 +45,7 @@
 43,46.295,-97.814,50.4352344,6.861520616,0.726439613726291
 44,41.415,-84.014,57.72454279,7.748746212,0.7807120287164648
 45,40.169,-88.725,55.9963289,6.822567835,0.7246859586288446
-46,41.603,-93.416,50.76824359,6.498809353,0.7046066294238851
+46,41.603,-93.416,50.76824359,6.498809353,0.7046066294238849
 47,44.258,-96.218,48.08888229,6.484302926,0.7034453872616688
 48,42.783,-75.227,58.89441507,8.518178578,0.8272278321191734
 49,43.47,-94.398,48.64393387,6.650122757,0.7135023812546356
@@ -70,7 +70,7 @@
 68,40.403,-92.166,51.16250247,6.74221526,0.7193242468559962
 69,38.862,-93.101,52.61488688,6.996040576,0.7347832629480767
 70,41.717,-88.398,58.02462706,7.10284254,0.7418012700736446
-71,44.056,-72.423,57.60315341,9.361197001,0.8779170147170843
+71,44.056,-72.423,57.60315341,9.361197001,0.877917014717084
 72,45.586,-87.582,57.64037358,7.851745274,0.786913049961754
 73,44.721,-93.281,54.55981386,7.286968665,0.7525319703228306
 74,40.227,-85.106,55.34456603,7.546306027,0.7682519002330839
@@ -140,7 +140,7 @@
 138,42.209,-95.172,49.93455623,6.639947411,0.7130269507479543
 139,40.386,-79.688,63.86411489,9.17986164,0.8676536883118218
 140,41.225,-97.815,48.29404304,6.887453274,0.727774096858826
-141,43.101,-88.679,53.03067508,6.615076123,0.7118586114493456
+141,43.101,-88.679,53.03067508,6.615076123,0.7118586114493455
 142,46.066,-92.269,61.42938904,8.143522162,0.8049102012304439
 143,45.91,-90.383,57.83685565,7.028809928,0.7373176052605699
 144,43.227,-75.538,75.42836767,11.27986223,0.9955041042695535
@@ -180,7 +180,7 @@
 178,42.24,-76.323,69.36892469,11.02959371,0.9797667028885654
 179,40.26,-95.917,48.50109937,6.553833735,0.7076816387944875
 180,45.441,-88.545,55.8526599,6.659344602,0.7148295257785313
-181,41.361,-72.544,53.88391198,7.614258476,0.7721926421678088
+181,41.361,-72.544,53.88391198,7.614258476,0.7721926421678089
 182,43.881,-74.532,58.37302204,7.950019899,0.7929166027975763
 183,46.375,-93.045,55.3918541,7.021579046,0.7366200908703626
 184,39.137,-85.003,61.51175413,8.628449283,0.8341562646653646
@@ -206,15 +206,15 @@
 204,40.577,-81.398,62.90874286,9.207572255,0.8692222190216685
 205,46.361,-84.431,60.67660851,8.526102871,0.8278962509744834
 206,44.053,-97.747,49.14434791,7.507503977,0.7652491876919038
-207,40.592,-75.951,52.92795272,7.774752517,0.7817668956631634
+207,40.592,-75.951,52.92795272,7.774752517,0.7817668956631636
 208,39.147,-79.282,50.22245241,7.648694799,0.7738772007716359
 209,38.644,-76.981,60.48685661,9.107058567,0.8629029652934552
 210,42.699,-95.913,47.57093505,6.650574037,0.7134148077286483
 211,40.276,-80.446,64.19364001,9.581061709,0.8918781152361044
 212,46.002,-94.17,55.75347218,7.786657918,0.7827869516303637
 213,43.769,-95.151,47.31078488,6.411883727,0.6989958493683212
 214,42.828,-83.285,54.80541876,7.452960164,0.7625662120288659
-215,42.306,-85.339,58.19811541,7.738857599,0.7801664835008522
+215,42.306,-85.339,58.19811541,7.738857599,0.7801664835008524
 216,45.817,-96.539,52.01958202,7.034215719,0.7370212390937694
 217,40.981,-72.087,47.38085805,7.581109738,0.769498384106217
 218,43.651,-84.949,58.31734328,7.714266851,0.7786966109612123
@@ -364,7 +364,7 @@
 362,42.695,-97.11,47.29014174,6.582336603,0.7092705952544859
 363,46.317,-86.678,50.57965136,7.544514147,0.7676341459041081
 364,39.278,-85.55,55.89197093,8.15175868,0.8048144424536288
-365,45.259,-87.002,49.62045856,6.510959335,0.7052163945598876
+365,45.259,-87.002,49.62045856,6.510959335,0.7052163945598875
 366,45.913,-83.725,51.24562465,7.70481277,0.7773701205726504
 367,38.913,-91.406,53.51277698,6.99251437,0.7346667109256176
 368,43.752,-90.126,58.17486708,7.480069934,0.7645611564600783
@@ -399,7 +399,7 @@
 397,38.392,-88.5,57.86180863,8.146670896,0.8047184093570208
 398,44.244,-90.231,58.69672689,7.387475541,0.7590342752558699
 399,43.469,-97.73,49.2252985,7.077334545,0.7393220482476153
-400,38.105,-94.806,49.62168947,6.735856204,0.7187760205954808
+400,38.105,-94.806,49.62168947,6.735856204,0.7187760205954807
 401,41.24,-85.519,53.29636267,7.026106974,0.7366689275159279
 402,39.257,-77.09,60.22997497,9.200428081,0.8685049256999342
 403,45.223,-83.711,57.52598123,7.826185595,0.7853597674770538
@@ -416,7 +416,7 @@
 414,41.059,-88.915,56.09943255,7.088687211,0.7407418719709922
 415,38.728,-75.52,55.35219581,8.633813323,0.8338207659543093
 416,39.353,-90.096,58.12244541,7.782775604,0.7828062956046264
-417,45.818,-95.751,48.45444571,6.964379638,0.7324293042045387
+417,45.818,-95.751,48.45444571,6.964379638,0.7324293042045384
 418,41.765,-83.501,57.41290156,8.084847126,0.800942906196559
 419,41.709,-90.317,58.05576703,6.956987333,0.7330106910756611
 420,41.223,-88.31,59.46589028,7.512207926,0.7666369269279907
@@ -491,7 +491,7 @@
 489,46.583,-87.917,52.02200649,6.6587893,0.7143862682010519
 490,38.862,-80.212,64.90563199,10.12406768,0.9246932250882576
 491,46.506,-90.645,67.55517351,9.152558169,0.8664023503170806
-492,44.595,-84.463,60.91517064,7.760417109,0.7817570027709664
+492,44.595,-84.463,60.91517064,7.760417109,0.7817570027709662
 493,45.955,-85.624,49.64725022,7.121041155,0.7420023471036234
 494,42.325,-79.325,60.74805435,9.191121493,0.8679992329984643
 495,44.135,-83.625,56.50144774,7.908995333,0.7902429391098073

diff --git a/examples/21_iron_examples/iron_mapping/plant_config.yaml b/examples/21_iron_examples/iron_mapping/plant_config.yaml
@@ -59,8 +59,8 @@ finance_parameters:
         - iron_mine
         - mine_electricity_feedstock
         - mine_crude_ore_feedstock
-    pig_iron:
-      commodity: pig_iron
+    sponge_iron:
+      commodity: sponge_iron
       commodity_stream: iron_plant
       technologies:
         - processed_ore_feedstock

diff --git a/examples/21_iron_examples/iron_mapping/run_iron.py b/examples/21_iron_examples/iron_mapping/run_iron.py
@@ -42,7 +42,7 @@
 # NOTE: you can swap './ex_28_out/cases.sql' with './ex_28_out/cases.csv' to read results from csv
 fig, ax, lcoi_layer_gdf = plot_geospatial_point_heat_map(
     case_results_fpath=case_results_filepath,
-    metric_to_plot="finance_subgroup_pig_iron.LCOP (USD/kg)",
+    metric_to_plot="finance_subgroup_sponge_iron.LCOS (USD/kg)",
     map_preferences={
         "figsize": (10, 8),
         "colorbar_label": "Levelized Cost of\nIron [$/kg]",

diff --git a/examples/21_iron_examples/iron_mapping/tech_config.yaml b/examples/21_iron_examples/iron_mapping/tech_config.yaml
@@ -156,16 +156,16 @@ technologies:
       model: HydrogenIronReductionPlantCostComponent
     model_inputs:
       shared_parameters:
-        pig_iron_production_rate_tonnes_per_hr: 161.8829908675799  # equivalent to 1418095 t/yr
+        sponge_iron_production_rate_tonnes_per_hr: 161.8829908675799  # equivalent to 1418095 t/yr
       performance_parameters:
         water_density: 1000  # kg/m3
       cost_parameters:
         skilled_labor_cost: 40.85  # 2022 USD/hr
         unskilled_labor_cost: 30.0  # 2022 USD/hr
-  pig_iron_transport:
+  sponge_iron_transport:
     performance_model:
       model: GenericTransporterPerformanceModel
     model_inputs:
       performance_parameters:
-        commodity: pig_iron
+        commodity: sponge_iron
         commodity_rate_units: kg/h
diff --git a/examples/test/test_all_examples.py b/examples/test/test_all_examples.py
@@ -1966,7 +1966,7 @@ def test_iron_mapping_example(subtests, temp_copy_of_example):
     # Plot LCOI results from cases.sql file, save sql data to csv
     fig, ax, lcoi_layer_gdf = plot_geospatial_point_heat_map(
         case_results_fpath=cases_csv_fpath,
-        metric_to_plot="finance_subgroup_pig_iron.LCOP (USD/kg)",
+        metric_to_plot="finance_subgroup_sponge_iron.LCOS (USD/kg)",
         map_preferences={
             "figsize": (10, 8),
             "colorbar_label": "Levelized Cost of\nIron [$/kg]",
@@ -2237,9 +2237,9 @@ def test_iron_dri_eaf_example(subtests, temp_copy_of_example):
         lcoi = h2i.model.get_val("finance_subgroup_iron_ore.LCOI", units="USD/t")[0]
         assert pytest.approx(lcoi, rel=1e-4) == 135.3741358811098
 
-    with subtests.test("Value check on LCOP"):
-        lcop = h2i.model.get_val("finance_subgroup_pig_iron.LCOP", units="USD/t")[0]
-        assert pytest.approx(lcop, rel=1e-4) == 359.670379351
+    with subtests.test("Value check on LCOS"):
+        lcos = h2i.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/t")[0]
+        assert pytest.approx(lcos, rel=1e-4) == 359.670379351
 
     with subtests.test("Value check on LCOS"):
         lcos = h2i.model.get_val("finance_subgroup_steel.LCOS", units="USD/t")[0]