diff --git a/CHANGELOG.md b/CHANGELOG.md
index a8899c821..9f30e981b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -13,6 +13,7 @@
 - Fixed error message that is thrown in `check_inputs` and updated testing of `check_inputs` to actually test the error messages [PR #846](https://github.com/NatLabRockies/H2Integrate/pull/846)
 - Fixed several tests where the assert statements paired with `pytest.raises` were indented inside the context manager block and never ran, and corrected the now-active expected error strings. [PR #846](https://github.com/NatLabRockies/H2Integrate/pull/846)
 - Replaces all custom attrs validators in `h2integrate.core.validators` with built in attrs validators. [PR 835](https://github.com/NatLabRockies/H2Integrate/pull/835)
+- Add `NRRIIronMinePerformanceModel` and `NRRIIronMineCostModel`. [PR 840](https://github.com/NatLabRockies/H2Integrate/pull/840)
 - Exempted demand components from the tech interconnections checking, added unit test. [PR 850](https://github.com/NatLabRockies/H2Integrate/pull/850)
 
 ## 0.9 [August 10, 2026]
diff --git a/docs/_static/class_hierarchy.html b/docs/_static/class_hierarchy.html
index 9f9b81d2d..ec8719109 100644
--- a/docs/_static/class_hierarchy.html
+++ b/docs/_static/class_hierarchy.html
@@ -380,8 +380,8 @@ <h1></h1>
 
 
                   // parsing and collecting nodes and edges from the python
-                  nodes = new vis.DataSet([{"borderWidth": 5.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SiteBaseComponent", "label": "SiteBaseComponent", "shape": "ellipse", "size": 18.710526315789473, "title": "SiteBaseComponent\ncore/sites.py\n[Core / General]", "x": -79.5125603737886, "y": 584.9567473090942}, {"borderWidth": 4.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SiteLocationComponent", "label": "SiteLocationComponent", "shape": "ellipse", "size": 18.0, "title": "SiteLocationComponent\ncore/sites.py\n[Core / General]", "x": -51.99787538017026, "y": 668.8874099443384}, {"borderWidth": 5.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PerformanceModelBaseClass", "label": "PerformanceModelBaseClass", "shape": "ellipse", "size": 40.73684210526316, "title": "PerformanceModelBaseClass\ncore/model_baseclasses.py\n[Core / General]", "x": -137.54209445136647, "y": 722.897904528822}, {"borderWidth": 5.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "CostModelBaseClass", "label": "CostModelBaseClass", "shape": "ellipse", "size": 45.0, "title": "CostModelBaseClass\ncore/model_baseclasses.py\n[Core / General]", "x": -243.0155271316636, "y": 685.2630296209401}, {"borderWidth": 4.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ResizeablePerformanceModelBaseClass", "label": "ResizeablePerformanceModelBaseClass", "shape": "ellipse", "size": 19.42105263157895, "title": "ResizeablePerformanceModelBaseClass\ncore/model_baseclasses.py\n[Core / General]", "x": -288.0485916668164, "y": 575.9204299065049}, {"borderWidth": 5.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "CacheBaseClass", "label": "CacheBaseClass", "shape": "ellipse", "size": 18.710526315789473, "title": "CacheBaseClass\ncore/model_baseclasses.py\n[Core / General]", "x": -237.4418960911029, "y": 464.62515055513364}, {"borderWidth": 4.0, "color": {"background": "#F5C542", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GenericConverterCostModel", "label": "GenericConverterCostModel", "shape": "dot", "size": 18.0, "title": "GenericConverterCostModel\nconverters/generic_converter_cost.py\n[Converter / Other]", "x": 428.09388111524015, "y": 469.0988894808179}, {"borderWidth": 3.0, "color": {"background": "#4A90D9", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PYSAMSolarPlantPerformanceModel", "label": "PYSAMSolarPlantPerformanceModel", "shape": "dot", "size": 18.0, "title": "PYSAMSolarPlantPerformanceModel\nconverters/solar/solar_pysam.py\n[Converter / Solar]", "x": 455.6085661088585, "y": 553.0295521160621}, {"borderWidth": 4.0, "color": {"background": "#4A90D9", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ATBUtilityPVCostModel", "label": "ATBUtilityPVCostModel", "shape": "dot", "size": 18.0, "title": "ATBUtilityPVCostModel\nconverters/solar/atb_utility_pv_cost.py\n[Converter / Solar]", "x": 370.0643470376623, "y": 607.0400467005456}, {"borderWidth": 4.0, "color": {"background": "#4A90D9", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ATBResComPVCostModel", "label": "ATBResComPVCostModel", "shape": "dot", "size": 18.0, "title": "ATBResComPVCostModel\nconverters/solar/atb_res_com_pv_cost.py\n[Converter / Solar]", "x": 264.59091435736514, "y": 569.4051717926637}, {"borderWidth": 4.0, "color": {"background": "#4A90D9", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SolarPerformanceBaseClass", "label": "SolarPerformanceBaseClass", "shape": "dot", "size": 18.710526315789473, "title": "SolarPerformanceBaseClass\nconverters/solar/solar_baseclass.py\n[Converter / Solar]", "x": 219.55784982221238, "y": 460.06257207822847}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ElectrolyzerPerformanceBaseClass", "label": "ElectrolyzerPerformanceBaseClass", "shape": "dot", "size": 19.42105263157895, "title": "ElectrolyzerPerformanceBaseClass\nconverters/hydrogen/electrolyzer_baseclass.py\n[Converter / Hydrogen]", "x": 270.16454539792585, "y": 348.7672927268573}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ElectrolyzerCostBaseClass", "label": "ElectrolyzerCostBaseClass", "shape": "dot", "size": 20.842105263157894, "title": "ElectrolyzerCostBaseClass\nconverters/hydrogen/electrolyzer_baseclass.py\n[Converter / Hydrogen]", "x": 388.2687164324306, "y": 307.72022284941175}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SingliticoCostModel", "label": "SingliticoCostModel", "shape": "dot", "size": 18.0, "title": "SingliticoCostModel\nconverters/hydrogen/singlitico_cost_model.py\n[Converter / Hydrogen]", "x": 501.4805766605487, "y": 365.4133442102892}, {"borderWidth": 1, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "WOMBATElectrolyzerModel", "label": "WOMBATElectrolyzerModel", "shape": "dot", "size": 18.0, "title": "WOMBATElectrolyzerModel\nconverters/hydrogen/wombat_model.py\n[Converter / Hydrogen]", "x": 538.9625776291001, "y": 488.44605748599986}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "LinearH2FuelCellPerformanceModel", "label": "LinearH2FuelCellPerformanceModel", "shape": "dot", "size": 18.0, "title": "LinearH2FuelCellPerformanceModel\nconverters/hydrogen/h2_fuel_cell.py\n[Converter / Hydrogen]", "x": 475.93189116793957, "y": 601.9588898891661}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "H2FuelCellCostModel", "label": "H2FuelCellCostModel", "shape": "dot", "size": 18.0, "title": "H2FuelCellCostModel\nconverters/hydrogen/h2_fuel_cell.py\n[Converter / Hydrogen]", "x": 349.5705117805884, "y": 635.8507248662497}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SteamMethaneReformerPerformanceModel", "label": "SteamMethaneReformerPerformanceModel", "shape": "dot", "size": 18.0, "title": "SteamMethaneReformerPerformanceModel\nconverters/hydrogen/steam_methane_reformer.py\n[Converter / Hydrogen]", "x": 236.61361523178945, "y": 568.2423536759715}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SteamMethaneReformerCostModel", "label": "SteamMethaneReformerCostModel", "shape": "dot", "size": 18.0, "title": "SteamMethaneReformerCostModel\nconverters/hydrogen/steam_methane_reformer.py\n[Converter / Hydrogen]", "x": 206.39396475153276, "y": 439.409697663304}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "BasicElectrolyzerCostModel", "label": "BasicElectrolyzerCostModel", "shape": "dot", "size": 18.0, "title": "BasicElectrolyzerCostModel\nconverters/hydrogen/basic_cost_model.py\n[Converter / Hydrogen]", "x": 278.165573095184, "y": 327.54056326717114}, {"borderWidth": 2.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ECOElectrolyzerPerformanceModel", "label": "ECOElectrolyzerPerformanceModel", "shape": "dot", "size": 18.710526315789473, "title": "ECOElectrolyzerPerformanceModel\nconverters/hydrogen/pem_electrolyzer.py\n[Converter / Hydrogen]", "x": 408.929515746538, "y": 301.0719911584519}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "CustomElectrolyzerCostModel", "label": "CustomElectrolyzerCostModel", "shape": "dot", "size": 18.0, "title": "CustomElectrolyzerCostModel\nconverters/hydrogen/custom_electrolyzer_cost_model.py\n[Converter / Hydrogen]", "x": 519.3422437620703, "y": 376.741275969993}, {"borderWidth": 2.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "HTSEPerformanceModel", "label": "HTSEPerformanceModel", "shape": "dot", "size": 18.0, "title": "HTSEPerformanceModel\nconverters/hydrogen/htse_electrolyzer.py\n[Converter / Hydrogen]", "x": 541.9933368656093, "y": 509.0547616483423}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "HTSECostModel", "label": "HTSECostModel", "shape": "dot", "size": 18.0, "title": "HTSECostModel\nconverters/hydrogen/htse_electrolyzer.py\n[Converter / Hydrogen]", "x": 462.61867180963463, "y": 617.7338834509918}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GeoH2SubsurfaceCostModel", "label": "GeoH2SubsurfaceCostModel", "shape": "dot", "size": 18.0, "title": "GeoH2SubsurfaceCostModel\nconverters/hydrogen/geologic/mathur_modified.py\n[Converter / Hydrogen]", "x": 329.04975630639217, "y": 636.513453478361}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GeoH2SubsurfacePerformanceBaseClass", "label": "GeoH2SubsurfacePerformanceBaseClass", "shape": "dot", "size": 19.42105263157895, "title": "GeoH2SubsurfacePerformanceBaseClass\nconverters/hydrogen/geologic/h2_well_subsurface_baseclass.py\n[Converter / Hydrogen]", "x": 222.32576716621537, "y": 553.5863417347226}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GeoH2SubsurfaceCostBaseClass", "label": "GeoH2SubsurfaceCostBaseClass", "shape": "dot", "size": 18.710526315789473, "title": "GeoH2SubsurfaceCostBaseClass\nconverters/hydrogen/geologic/h2_well_subsurface_baseclass.py\n[Converter / Hydrogen]", "x": 207.46113153897124, "y": 419.00371238109653}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "AspenGeoH2SurfacePerformanceModel", "label": "AspenGeoH2SurfacePerformanceModel", "shape": "dot", "size": 18.0, "title": "AspenGeoH2SurfacePerformanceModel\nconverters/hydrogen/geologic/aspen_surface_processing.py\n[Converter / Hydrogen]", "x": 293.81016729091453, "y": 314.4201619012048}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "AspenGeoH2SurfaceCostModel", "label": "AspenGeoH2SurfaceCostModel", "shape": "dot", "size": 18.0, "title": "AspenGeoH2SurfaceCostModel\nconverters/hydrogen/geologic/aspen_surface_processing.py\n[Converter / Hydrogen]", "x": 429.1980303433203, "y": 303.5048975398027}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "NaturalGeoH2PerformanceModel", "label": "NaturalGeoH2PerformanceModel", "shape": "dot", "size": 18.0, "title": "NaturalGeoH2PerformanceModel\nconverters/hydrogen/geologic/simple_natural_geoh2.py\n[Converter / Hydrogen]", "x": 531.4807512394786, "y": 393.1585470237545}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "StimulatedGeoH2PerformanceModel", "label": "StimulatedGeoH2PerformanceModel", "shape": "dot", "size": 18.0, "title": "StimulatedGeoH2PerformanceModel\nconverters/hydrogen/geologic/templeton_serpentinization.py\n[Converter / Hydrogen]", "x": 538.4198729733245, "y": 529.1652694271473}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GeoH2SurfacePerformanceBaseClass", "label": "GeoH2SurfacePerformanceBaseClass", "shape": "dot", "size": 18.710526315789473, "title": "GeoH2SurfacePerformanceBaseClass\nconverters/hydrogen/geologic/h2_well_surface_baseclass.py\n[Converter / Hydrogen]", "x": 445.5710237882473, "y": 629.0047614736493}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GeoH2SurfaceCostBaseClass", "label": "GeoH2SurfaceCostBaseClass", "shape": "dot", "size": 18.710526315789473, "title": "GeoH2SurfaceCostBaseClass\nconverters/hydrogen/geologic/h2_well_surface_baseclass.py\n[Converter / Hydrogen]", "x": 309.11651798504033, "y": 631.947653855694}, {"borderWidth": 3.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ReverseOsmosisPerformanceModel", "label": "ReverseOsmosisPerformanceModel", "shape": "dot", "size": 18.0, "title": "ReverseOsmosisPerformanceModel\nconverters/water/desal/desalination.py\n[Converter / Water]", "x": 211.84908461781183, "y": 536.0083507662598}, {"borderWidth": 3.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ReverseOsmosisCostModel", "label": "ReverseOsmosisCostModel", "shape": "dot", "size": 18.0, "title": "ReverseOsmosisCostModel\nconverters/water/desal/desalination.py\n[Converter / Water]", "x": 212.91655044595416, "y": 399.26617741803875}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DesalinationPerformanceBaseClass", "label": "DesalinationPerformanceBaseClass", "shape": "dot", "size": 18.710526315789473, "title": "DesalinationPerformanceBaseClass\nconverters/water/desal/desalination_baseclass.py\n[Converter / Water]", "x": 311.84419223616084, "y": 304.68898638961775}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DesalinationCostBaseClass", "label": "DesalinationCostBaseClass", "shape": "dot", "size": 18.710526315789473, "title": "DesalinationCostBaseClass\nconverters/water/desal/desalination_baseclass.py\n[Converter / Water]", "x": 448.7220200189815, "y": 309.77552217810415}, {"borderWidth": 4.0, "color": {"background": "#1B3A5C", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SimpleThermalNuclearReactorPerformanceModel", "label": "SimpleThermalNuclearReactorPerformanceModel", "shape": "dot", "size": 18.0, "title": "SimpleThermalNuclearReactorPerformanceModel\nconverters/nuclear/nuclear_plant_thermal.py\n[Converter / Nuclear]", "x": 540.4994435124524, "y": 411.5906925910052}, {"borderWidth": 4.0, "color": {"background": "#1B3A5C", "border": "#555555", "highlight": {"background": "#FF6B6B", 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diff --git a/docs/technology_models/iron_mine.md b/docs/technology_models/iron_mine.md
index c1fca3d94..f8601de9f 100644
--- a/docs/technology_models/iron_mine.md
+++ b/docs/technology_models/iron_mine.md
@@ -1,6 +1,10 @@
 # Iron mine model
 
-H2I contains an iron mine model that simulates the extraction of crude ore and its processing into iron ore pellets.
+H2I contains 2 iron mine models that simulate the extraction of crude ore and its processing into iron ore pellets:
+    - `SimpleIronMine`: Models only the flow of `crude_ore` in and `iron_ore` out, with costs all lumped together
+    - `NRRIIronMine`: Models mass flows and electricity/fuel consumption at intermediate steps, with costs broken out
+
+## SimpleIronMine
 The main input feedstock is `crude_ore`, i.e. the unprocessed ore in the earth containing iron oxide.
 The output commodity is `iron_ore` in the form of pellets that can be shipped to other plants (e.g. `iron_plant`) for further processing.
 
@@ -14,7 +18,7 @@ There are two potential grades of ore produced from an iron mine in this model:
 
 It was determined that 3 of these mines (Northshore, United, and Hibbing) had crude reserves sufficient to produce DR-grade pellets, although only one (Northshore) reported production data on DR-grade pellets, with the rest reporting their data strictly on standard ore pellets.
 The increases in cost and energy usage reported at the Northshore mine were used to project the potential performance and cost of DR-grade production at United and Hibbing.
-The results of this analysis are compiled in the directory `h2integrate/converters/iron/martin_ore/`.
+The results of this analysis are compiled in the directory `h2integrate/converters/iron/simple_ore/`.
 Performance data are included in `perf_inputs.csv` with cost data in `cost_inputs.csv`.
 
 These data were compiled from two sources:
@@ -27,8 +31,50 @@ These data were compiled from two sources:
     - [Minorca Mine](https://minedocs.com/22/Minorca-TR-12312021.pdf)
     - [Tilden Mine](https://minedocs.com/22/Tilden-TR-12312021.pdf)
 
-To use this model, specify `"MartinIronMinePerformanceComponent"` as the performance model and `"MartinIronMineCostComponent"` as the cost model.
+To use this model, specify `"SimpleIronMinePerformanceComponent"` as the performance model and `"SimpleIronMineCostComponent"` as the cost model.
 Currently, no complex calculations occur beyond importing performance and costs.
 In the performance model, the "wet long tons" (wlt) that ore production is typically reported in are converted to dry metric tons for use in H2I.
 In the cost model, the total capex costs for a plant are scaled by the amount of are produced annually.
 Besides these calculations, previously-calculated performance and cost metrics are simply loaded from the input spreadsheets.
+
+## NRRIIronMine
+The main inputs are `electricity` and `fuel`.
+The output commodity is `iron_ore` in the form of pellets that can be shipped to other plants (e.g. `iron_plant`) for further processing.
+
+This model was developed in conjunction with the [University of Minnesota's Natural Resource Research Institute (NRRI)](https://www.nrri.umn.edu/), led by Kimberly Anderson <kimander@d.umn.edu>
+
+This model splits out the separate processes to produce iron ore pellets at a mine (mining, comminution, beneficiation, pelletization) and tracks the material flows.
+
+Sources (2021 costs):
+    - [SEC S-K 1300 Tilden Mining Company](https://www.sec.gov/Archives/edgar/data/764065/000076406522000037/clf-2021123110xkex965.htm)
+    - [SEC S-K 1300 Hibbing Taconite](https://www.sec.gov/Archives/edgar/data/764065/000076406522000033/a20220211-8xkxex961.htm)
+    - [SEC S-K 1300 United Taconite](https://www.sec.gov/Archives/edgar/data/764065/000076406522000033/a20220211-8xkxex964.htm)
+    - [SEC S-K 1300 Minorca Mine](https://www.sec.gov/Archives/edgar/data/764065/000076406522000033/a20220211-8xkxex962.htm)
+    - [SEC S-K 1300 Northshore Mining Company](https://www.sec.gov/Archives/edgar/data/764065/000076406522000033/a20220211-8xkxex963.htm)
+
+Local electricity costs (2012):
+    - [Tilden Estimated Electrical Costs](https://www.electricitylocal.com/states/michigan/marquette/)
+    - [Hibtac Estimate Electrical Costs](https://www.electricitylocal.com/states/minnesota/hibbing/)
+    - [Utac Estimated Electrical Costs](https://www.electricitylocal.com/states/minnesota/virginia/)
+    - [Minora Estimated Electrical Costs](https://www.electricitylocal.com/states/minnesota/virginia/)
+    - [Northshore - Babbitt Electrical Costs](https://www.electricitylocal.com/states/minnesota/babbitt/)
+    - [Northshore - Silver Bay Electrical Costs](https://www.electricitylocal.com/states/minnesota/silver-bay/)
+    - All inflated to 2021 costs using electricity [CPI](https://fred.stlouisfed.org/series/CUUR0000SEHF01):
+        - 2012 average electricity CPI: 196.6298
+        - 2021 average electricity CPI: 223.8915833
+
+Estimated electrical breakdown data synthesized using Gemini and ChatGPT and verified using:
+    - Prediction of fuel consumption of mining dump trucks: A neural networks approach Elnaz Siami-Irdemoosa, Saeid Dindarloo, Applied Energy 151, 2015, pp. 77-84
+    - US DOE - Critical Minerals & Energy Innovation under Lawrence Berkeley National Laboratory under Contract DE-AC02-05CH11231 (January 2026)
+    - The Effects of Increasing Costs of the Future Relation Between Open Pit and Underground Mining, Dan Nilsson, 1982. US Dept of Interior - Office of Surface Mining - Bureau of Mines under Grand No. OSM G5105032
+
+Fuel Costs (2021):
+    - Natural Gas:
+        - MN industrial NG $5.47/tcf [EIA](https://www.eia.gov/dnav/ng/ng_pri_sum_dcu_smn_a.htm)
+        - 1.037 MMBtu/tcf
+        - Final NG price: $5.275/MMBtu
+    - Diesel:
+        - MN pump diesel $3.208/gal [MN Dept. of Revenue](https://www.revenue.state.mn.us/petroleum-tax-eia-average-retail-fuel-price)
+        - minus est. $0.285/gal MN excise tax [MN Dept. of Revenue](https://www.revenue.state.mn.us/petroleum-tax-fuel-excise-tax-rates-and-fees)
+        - minus $0.244/gal federal tax [EIA](https://www.eia.gov/tools/faqs/faq.php?id=10&t=5)
+        - Final off road diesel price: $2.679/gal
diff --git a/docs/user_guide/model_overview.md b/docs/user_guide/model_overview.md
index 2494e28ee..1d54273b1 100644
--- a/docs/user_guide/model_overview.md
+++ b/docs/user_guide/model_overview.md
@@ -130,6 +130,8 @@ auto-generated API page.
     + {py:class}`~h2integrate.converters.water.desal.desalination.ReverseOsmosisCostModel` - An OpenMDAO component that computes the cost of a reverse osmosis desalination system.
 
 - `generic`: generic converter components
+  - performance models:
+    + {py:class}`~h2integrate.converters.combustion_machines.turbine_simple_cycle.SimpleCycleTurbinePerformanceModel` - Performance model for simple Brayton-cycle turbines.
   - cost models:
     + {py:class}`~h2integrate.converters.generic_converter_cost.GenericConverterCostModel`
 
@@ -168,14 +170,16 @@ auto-generated API page.
   - performance models:
     + {py:class}`~h2integrate.converters.iron.humbert_ewin_perf.HumbertEwinPerformanceComponent` - OpenMDAO component for the Humbert iron electrowinning performance model.
     + {py:class}`~h2integrate.converters.iron.iron_dri_plant.HydrogenIronReductionPlantPerformanceComponent` - Performance component for hydrogen-based direct reduced iron (DRI) plant using the Rosner performance model.
-    + {py:class}`~h2integrate.converters.iron.martin_mine_perf_model.MartinIronMinePerformanceComponent`
+    + {py:class}`~h2integrate.converters.iron.nrri_iron_mine.NRRIIronMinePerformanceComponent`
     + {py:class}`~h2integrate.converters.iron.iron_dri_plant.NaturalGasIronReductionPlantPerformanceComponent` - Performance component for natural gas-based direct reduced iron (DRI) plant using the Rosner performance model.
+    + {py:class}`~h2integrate.converters.iron.simple_mine_perf_model.SimpleIronMinePerformanceComponent`
   - cost models:
     + {py:class}`~h2integrate.converters.iron.humbert_stinn_ewin_cost.HumbertStinnEwinCostComponent` - OpenMDAO component for the Humbert/Stinn iron electrowinning cost model.
     + {py:class}`~h2integrate.converters.iron.iron_dri_plant.HydrogenIronReductionPlantCostComponent` - Cost component for hydrogen-based direct reduced iron (DRI) plant using the Rosner cost model.
     + {py:class}`~h2integrate.converters.iron.iron_transport.IronTransportCostComponent`
-    + {py:class}`~h2integrate.converters.iron.martin_mine_cost_model.MartinIronMineCostComponent`
+    + {py:class}`~h2integrate.converters.iron.nrri_iron_mine.NRRIIronMineCostComponent`
     + {py:class}`~h2integrate.converters.iron.iron_dri_plant.NaturalGasIronReductionPlantCostComponent` - Cost component for natural gas-based direct reduced iron (DRI) plant using the Rosner cost model.
+    + {py:class}`~h2integrate.converters.iron.simple_mine_cost_model.SimpleIronMineCostComponent`
   - other components:
     + {py:class}`~h2integrate.converters.iron.iron_transport.IronTransportPerformanceComponent`
 
diff --git a/examples/21_iron_examples/iron_dri/tech_config.yaml b/examples/21_iron_examples/iron_dri/tech_config.yaml
index f919d80ef..373b1c6ab 100644
--- a/examples/21_iron_examples/iron_dri/tech_config.yaml
+++ b/examples/21_iron_examples/iron_dri/tech_config.yaml
@@ -42,9 +42,9 @@ technologies:
         commodity_rate_units: t/h
   iron_mine:
     performance_model:
-      model: MartinIronMinePerformanceComponent
+      model: SimpleIronMinePerformanceComponent
     cost_model:
-      model: MartinIronMineCostComponent
+      model: SimpleIronMineCostComponent
     model_inputs:
       shared_parameters:
         mine: Northshore
diff --git a/examples/21_iron_examples/iron_dri_nrri/driver_config.yaml b/examples/21_iron_examples/iron_dri_nrri/driver_config.yaml
new file mode 100644
index 000000000..1f8d10de9
--- /dev/null
+++ b/examples/21_iron_examples/iron_dri_nrri/driver_config.yaml
@@ -0,0 +1,4 @@
+name: driver_config
+description: Simply setting up an outputs folder, nothing fancy
+general:
+  folder_output: outputs
diff --git a/examples/21_iron_examples/iron_dri_nrri/plant_config.yaml b/examples/21_iron_examples/iron_dri_nrri/plant_config.yaml
new file mode 100644
index 000000000..1dbeebb19
--- /dev/null
+++ b/examples/21_iron_examples/iron_dri_nrri/plant_config.yaml
@@ -0,0 +1,92 @@
+name: plant_config
+description: This plant is located in MN, USA...
+sites:
+  site:
+    latitude: 41.717
+    longitude: -88.398
+technology_interconnections:
+  # Connect feedstocks to iron mine.
+  - [mine_electricity_feedstock, iron_mine, electricity, cable]
+  - [mine_natural_gas_feedstock, iron_mine, natural_gas, pipe]
+  - [mine_diesel_feedstock, iron_mine, diesel, pipe]
+  # Connect iron_ore price and processed_ore_feedstock to iron_transport
+  # Connect the LCOI of iron ore to the price of a feedstock component representing the ore to be used in DRI
+  - [finance_subgroup_iron_ore, processed_ore_feedstock, [price_iron_ore, price]]
+  # Connect the ore feedstock available to the iron plant
+  - [processed_ore_feedstock, iron_plant, iron_ore, iron_ore_transport]
+  - [dri_grid_feedstock, iron_plant, electricity, cable]
+  - [catalyst_feedstock, iron_plant, reformer_catalyst, catalyst_transport]
+  - [water_feedstock, iron_plant, water, pipe]
+  - [natural_gas_feedstock, iron_plant, natural_gas, pipe]
+  # Use the iron ore consumed by the iron plant to calculate transport costs
+  - [iron_plant, iron_transport, [iron_ore_consumed, iron_ore_in]]
+  # Connect feedstocks to steel plant
+  - [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, sponge_iron, sponge_iron_transport]
+plant:
+  plant_life: 30
+  simulation:
+    n_timesteps: 8760
+    dt: 3600
+    timezone: 0
+finance_parameters:
+  finance_groups:
+    finance_model: ProFastLCO
+    model_inputs:
+      params:
+        analysis_start_year: 2032
+        installation_time: 36  # months
+        inflation_rate: 0.0  # 0 for nominal analysis
+        discount_rate: 0.09  # nominal return based on 2024 ATB baseline workbook for land-based wind
+        debt_equity_ratio: 2.62  # 2024 ATB uses 72.4% debt for land-based wind
+        property_tax_and_insurance: 0.03  # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf  https://www.house.mn.gov/hrd/issinfo/clsrates.aspx
+        total_income_tax_rate: 0.257  # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8)
+        capital_gains_tax_rate: 0.15  # H2FAST default
+        sales_tax_rate: 0.07375  # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/
+        debt_interest_rate: 0.07  # based on 2024 ATB nominal interest rate for land-based wind
+        debt_type: Revolving debt  # can be "Revolving debt" or "One time loan". Revolving debt is H2FAST default and leads to much lower LCOH
+        loan_period_if_used: 0  # H2FAST default, not used for revolving debt
+        cash_onhand_months: 1  # H2FAST default
+        admin_expense: 0.00  # percent of sales H2FAST default
+      capital_items:
+        depr_type: MACRS   # can be "MACRS" or "Straight line"
+        depr_period: 5  # 5  years - for clean energy facilities as specified by the IRS MACRS schedule https://www.irs.gov/publications/p946#en_US_2020_publink1000107507
+        refurb: [0.]
+  cost_adjustment_parameters:
+    cost_year_adjustment_inflation: 0.025
+    target_dollar_year: 2022
+  finance_subgroups:
+    iron_ore:
+      commodity: iron_ore
+      commodity_stream: iron_mine
+      technologies:
+        - iron_mine
+        - mine_electricity_feedstock
+    sponge_iron:
+      commodity: sponge_iron
+      commodity_stream: iron_plant
+      technologies:
+        - processed_ore_feedstock
+        - iron_transport
+        - iron_plant
+        - dri_grid_feedstock
+        - catalyst_feedstock
+        - water_feedstock
+        - natural_gas_feedstock
+    steel:
+      commodity: steel
+      commodity_stream: steel_plant
+      technologies:
+        - processed_ore_feedstock
+        - iron_transport
+        - iron_plant
+        - dri_grid_feedstock
+        - catalyst_feedstock
+        - water_feedstock
+        - natural_gas_feedstock
+        - eaf_water_feedstock
+        - eaf_natural_gas_feedstock
+        - eaf_grid_feedstock
+        - steel_plant
diff --git a/examples/21_iron_examples/iron_dri_nrri/run_iron.py b/examples/21_iron_examples/iron_dri_nrri/run_iron.py
new file mode 100644
index 000000000..467401c94
--- /dev/null
+++ b/examples/21_iron_examples/iron_dri_nrri/run_iron.py
@@ -0,0 +1,156 @@
+"""Example of an iron mine sending processed ore pellets to a separate DRI iron plant and EAF
+
+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 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 sponge_iron produced by DRI (LCOS) or the cost
+of the steel produced by the EAF (LCOS).
+
+"""
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from h2integrate import H2IntegrateModel
+from h2integrate.tools.run_cases import modify_tech_config, load_tech_config_cases
+
+
+# Create H2Integrate model
+model = H2IntegrateModel("single_site_iron.yaml")
+
+# Load cases
+case_file = Path("test_inputs.csv")
+cases = load_tech_config_cases(case_file)
+
+# Modify and run the model for different cases
+casenames = [
+    "Standard Iron - Hibbing",
+    "Standard Iron - Northshore",
+    "DR Grade Iron - Northshore",
+    "DR Grade Iron - Northshore (adjusted)",
+]
+
+# Create empty lists to store the costs
+lcois_ore = []
+capexes_ore = []
+fopexes_ore = []
+vopexes_ore = []
+production_ore = []
+lcois_iron = []
+capexes_iron = []
+fopexes_iron = []
+vopexes_iron = []
+production_ore = []
+lcois_steel = []
+capexes_steel = []
+fopexes_steel = []
+vopexes_steel = []
+
+model.run()
+model.post_process()
+
+for casename in casenames:
+    model = modify_tech_config(model, cases[casename])
+    model.run()
+    lcois_ore.append(
+        float(model.model.get_val("finance_subgroup_iron_ore.price_iron_ore", units="USD/kg")[0])
+    )
+    capexes_ore.append(
+        float(model.model.get_val("finance_subgroup_iron_ore.total_capex_adjusted", units="USD")[0])
+    )
+    fopexes_ore.append(
+        float(
+            model.model.get_val("finance_subgroup_iron_ore.total_opex_adjusted", units="USD/year")[
+                0
+            ]
+        )
+    )
+    vopexes_ore.append(
+        float(
+            model.model.get_val(
+                "finance_subgroup_iron_ore.total_varopex_adjusted", units="USD/year"
+            )[0]
+        )
+    )
+    lcois_iron.append(
+        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_sponge_iron.total_capex_adjusted", units="USD")[0]
+        )
+    )
+    fopexes_iron.append(
+        float(
+            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_sponge_iron.total_varopex_adjusted", units="USD/year"
+            )[0]
+        )
+    )
+    lcois_steel.append(
+        float(model.model.get_val("finance_subgroup_steel.price_steel", units="USD/kg")[0])
+    )
+    capexes_steel.append(
+        float(model.model.get_val("finance_subgroup_steel.total_capex_adjusted", units="USD")[0])
+    )
+    fopexes_steel.append(
+        float(
+            model.model.get_val("finance_subgroup_steel.total_opex_adjusted", units="USD/year")[0]
+        )
+    )
+    vopexes_steel.append(
+        float(
+            model.model.get_val("finance_subgroup_steel.total_varopex_adjusted", units="USD/year")[
+                0
+            ]
+        )
+    )
+
+# Compare the Capex, Fixed Opex, and Variable Opex across the 4 cases
+columns = pd.MultiIndex.from_tuples(
+    [
+        ("Levelized Cost", "[USD/kg]"),
+        ("Capex", "[USD]"),
+        ("Fixed Opex", "[USD/year]"),
+        ("Variable Opex", "[USD/year]"),
+    ]
+)
+print()
+
+df_ore = pd.DataFrame(
+    np.transpose(np.vstack([lcois_ore, capexes_ore, fopexes_ore, vopexes_ore])),
+    index=casenames,
+    columns=columns,
+)
+print(df_ore)
+
+print()
+df_iron = pd.DataFrame(
+    np.transpose(np.vstack([lcois_iron, capexes_iron, fopexes_iron, vopexes_iron])),
+    index=casenames,
+    columns=columns,
+)
+print(df_iron)
+
+print()
+df_steel = pd.DataFrame(
+    np.transpose(np.vstack([lcois_steel, capexes_steel, fopexes_steel, vopexes_steel])),
+    index=casenames,
+    columns=columns,
+)
+df_steel = df_steel.iloc[2:]
+print(df_steel)
diff --git a/examples/21_iron_examples/iron_dri_nrri/single_site_iron.yaml b/examples/21_iron_examples/iron_dri_nrri/single_site_iron.yaml
new file mode 100644
index 000000000..2403fa15c
--- /dev/null
+++ b/examples/21_iron_examples/iron_dri_nrri/single_site_iron.yaml
@@ -0,0 +1,5 @@
+name: H2Integrate_config
+system_summary: An iron mine producing ore pellets and a separately-located iron plant performing direct reduction.
+driver_config: driver_config.yaml
+technology_config: tech_config.yaml
+plant_config: plant_config.yaml
diff --git a/examples/21_iron_examples/iron_dri_nrri/tech_config.yaml b/examples/21_iron_examples/iron_dri_nrri/tech_config.yaml
new file mode 100644
index 000000000..bdcf8f9ec
--- /dev/null
+++ b/examples/21_iron_examples/iron_dri_nrri/tech_config.yaml
@@ -0,0 +1,251 @@
+name: technology_config
+description: This hybrid plant produces iron
+technologies:
+  mine_electricity_feedstock:  # electricity feedstock for iron ore
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: electricity
+        commodity_rate_units: MW
+      performance_parameters:
+        rated_capacity: 30
+      cost_parameters:
+        cost_year: 2021
+        price: 105.097
+        annual_cost: 0.
+        start_up_cost: 0.
+  mine_natural_gas_feedstock:  # natural gas feedstock for iron ore
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: natural_gas
+        commodity_rate_units: MMBtu/h
+      performance_parameters:
+        rated_capacity: 1000.
+      cost_parameters:
+        cost_year: 2021
+        price: 5.275
+        annual_cost: 0.
+        start_up_cost: 0.
+  mine_diesel_feedstock:  # diesel feedstock for iron ore
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: diesel
+        commodity_rate_units: galUS/h
+      performance_parameters:
+        rated_capacity: 1000.
+      cost_parameters:
+        cost_year: 2021
+        price: 2.679
+        annual_cost: 0.
+        start_up_cost: 0.
+  iron_mine:
+    performance_model:
+      model: NRRIIronMinePerformanceComponent
+    cost_model:
+      model: NRRIIronMineCostComponent
+    model_inputs:
+      performance_parameters:
+        max_ore_production_rate_tonnes_per_hr: 221.2592636
+      cost_parameters:
+        taconite_pellet_type: drg
+        cost_year: 2021
+      shared_parameters:
+        mine: Northshore
+  iron_ore_transport:
+    performance_model:
+      model: GenericTransporterPerformanceModel
+    model_inputs:
+      performance_parameters:
+        commodity: iron_ore
+        commodity_rate_units: kg/h
+  processed_ore_feedstock:  # iron ore feedstock
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: iron_ore
+        commodity_rate_units: t/h
+      performance_parameters:
+        rated_capacity: 250.  # need 828.50385048 t/h
+      cost_parameters:
+        cost_year: 2022
+        price: 0.0
+        annual_cost: 0.
+        start_up_cost: 0.
+  iron_transport:
+    performance_model:
+      model: IronTransportPerformanceComponent
+    cost_model:
+      model: IronTransportCostComponent
+    model_inputs:
+      performance_parameters:
+        find_closest_ship_site: false
+        shipment_site: Chicago
+      cost_parameters:
+        transport_year: 2022
+        cost_year: 2022
+  natural_gas_feedstock:
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: natural_gas
+        commodity_rate_units: MMBtu/h
+      performance_parameters:
+        rated_capacity: 1270.  # need 1268.934 MMBtu/h
+      cost_parameters:
+        cost_year: 2022
+        price: 4.0  # USD 4.0/MMBtu
+        annual_cost: 0.
+        start_up_cost: 0.
+  water_feedstock:  # for iron reduction
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: water
+        commodity_rate_units: galUS/h
+      performance_parameters:
+        rated_capacity: 40000.  # need 38710.49649 galUS/h
+      cost_parameters:
+        cost_year: 2022
+        price: 0.0016700004398318847  # cost is USD 0.441167535/t, converted to USD/gal
+        annual_cost: 0.
+        start_up_cost: 0.
+  catalyst_feedstock:  # for NG iron reduction
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: reformer_catalyst
+        commodity_rate_units: (m**3)  # m**3/h
+      performance_parameters:
+        rated_capacity: 0.001  # need 0.00056546 m**3/h
+      cost_parameters:
+        cost_year: 2022
+        price: 17515.14  # USD 17515.14/m**3
+        annual_cost: 0.
+        start_up_cost: 0.
+  catalyst_transport:
+    performance_model:
+      model: GenericTransporterPerformanceModel
+    model_inputs:
+      performance_parameters:
+        commodity: reformer_catalyst
+        commodity_rate_units: (m**3)
+  dri_grid_feedstock:  # electricity feedstock for iron dri
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: electricity
+        commodity_rate_units: kW
+      performance_parameters:
+        rated_capacity: 27000.  # need 26949.46472431 kW
+      cost_parameters:
+        cost_year: 2022
+        price: 0.05802  # USD/kW
+        annual_cost: 0.
+        start_up_cost: 0.
+  iron_plant:
+    performance_model:
+      model: NaturalGasIronReductionPlantPerformanceComponent
+    cost_model:
+      model: NaturalGasIronReductionPlantCostComponent
+    model_inputs:
+      shared_parameters:
+        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
+  sponge_iron_transport:
+    performance_model:
+      model: GenericTransporterPerformanceModel
+    model_inputs:
+      performance_parameters:
+        commodity: sponge_iron
+        commodity_rate_units: kg/h
+  eaf_grid_feedstock:  # electricity feedstock for EAF
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: electricity
+        commodity_rate_units: kW
+      performance_parameters:
+        rated_capacity: 56650.  # need 56642.327357 kW
+      cost_parameters:
+        cost_year: 2022
+        price: 0.05802  # USD/kW
+        annual_cost: 0.
+        start_up_cost: 0.
+  eaf_water_feedstock:  # for EAF
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: water
+        commodity_rate_units: galUS/h
+      performance_parameters:
+        rated_capacity: 10000.  # need 9083.687924154146 galUS/h
+      cost_parameters:
+        cost_year: 2022
+        price: 0.0016700004398318847  # cost is USD 0.441167535/t, converted to USD/gal
+        annual_cost: 0.
+        start_up_cost: 0.
+  eaf_natural_gas_feedstock:
+    performance_model:
+      model: FeedstockPerformanceModel
+    cost_model:
+      model: FeedstockCostModel
+    model_inputs:
+      shared_parameters:
+        commodity: natural_gas
+        commodity_rate_units: MMBtu/h
+      performance_parameters:
+        rated_capacity: 280.  # need 276.5242929731515 MMBtu/h
+      cost_parameters:
+        cost_year: 2022
+        price: 4.0  # USD 4.0/MMBtu
+        annual_cost: 0.
+        start_up_cost: 0.
+  steel_plant:
+    performance_model:
+      model: NaturalGasEAFPlantPerformanceComponent
+    cost_model:
+      model: NaturalGasEAFPlantCostComponent
+    model_inputs:
+      shared_parameters:
+        steel_production_rate_tonnes_per_hr: 135.8187214611872  # equivalent to 1189772 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
diff --git a/examples/21_iron_examples/iron_dri_nrri/test_inputs.csv b/examples/21_iron_examples/iron_dri_nrri/test_inputs.csv
new file mode 100644
index 000000000..3d992b498
--- /dev/null
+++ b/examples/21_iron_examples/iron_dri_nrri/test_inputs.csv
@@ -0,0 +1,4 @@
+Index 0,Index 1,Index 2,Index 3,Index 4,Type,Standard Iron - Hibbing,Standard Iron - Northshore,DR Grade Iron - Northshore,DR Grade Iron - Northshore (adjusted)
+technologies,iron_mine,model_inputs,shared_parameters,mine,str,Hibbing,Northshore,Northshore,Northshore
+technologies,iron_mine,model_inputs,cost_parameters,taconite_pellet_type,str,std,std,drg,drg
+technologies,processed_ore_feedstock,model_inputs,performance_parameters,rated_capacity,float,250,250,250,221.2592636
diff --git a/examples/21_iron_examples/iron_electrowinning/tech_config.yaml b/examples/21_iron_examples/iron_electrowinning/tech_config.yaml
index c1beda77b..e30a4fec0 100644
--- a/examples/21_iron_examples/iron_electrowinning/tech_config.yaml
+++ b/examples/21_iron_examples/iron_electrowinning/tech_config.yaml
@@ -43,9 +43,9 @@ technologies:
         commodity_rate_units: t/h
   iron_mine:  # iron mine - turns crude_iron into iron_ore
     performance_model:
-      model: MartinIronMinePerformanceComponent
+      model: SimpleIronMinePerformanceComponent
     cost_model:
-      model: MartinIronMineCostComponent
+      model: SimpleIronMineCostComponent
     model_inputs:
       shared_parameters:
         mine: Northshore
diff --git a/examples/21_iron_examples/iron_mapping/run_iron.py b/examples/21_iron_examples/iron_mapping/run_iron.py
index 767c66da5..33fd1bf88 100644
--- a/examples/21_iron_examples/iron_mapping/run_iron.py
+++ b/examples/21_iron_examples/iron_mapping/run_iron.py
@@ -34,7 +34,7 @@
 save_plot_filepath.unlink(missing_ok=True)
 case_results_filepath = ex_out_dir / "cases.csv"
 ore_prices_filepath = ex_dir / "example_ore_prices.csv"
-shipping_coords_filepath = ROOT_DIR / "converters/iron/martin_transport/shipping_coords.csv"
+shipping_coords_filepath = ROOT_DIR / "converters/iron/simple_transport/shipping_coords.csv"
 shipping_prices_filepath = ex_dir / "example_shipping_prices.csv"
 
 # Plot the LCOI results with geopandas and contextily
diff --git a/examples/21_iron_examples/iron_mapping/tech_config.yaml b/examples/21_iron_examples/iron_mapping/tech_config.yaml
index 0605323bb..776b7d415 100644
--- a/examples/21_iron_examples/iron_mapping/tech_config.yaml
+++ b/examples/21_iron_examples/iron_mapping/tech_config.yaml
@@ -42,9 +42,9 @@ technologies:
         commodity_rate_units: t/h
   iron_mine:
     performance_model:
-      model: MartinIronMinePerformanceComponent
+      model: SimpleIronMinePerformanceComponent
     cost_model:
-      model: MartinIronMineCostComponent
+      model: SimpleIronMineCostComponent
     model_inputs:
       shared_parameters:
         mine: Northshore
diff --git a/examples/test/test_all_examples.py b/examples/test/test_all_examples.py
index ce2e2b995..fc106ebfc 100644
--- a/examples/test/test_all_examples.py
+++ b/examples/test/test_all_examples.py
@@ -2276,7 +2276,7 @@ def test_iron_mapping_example(subtests, temp_copy_of_example):
     ex_dir = example_folder
     ex_out_dir = ex_dir / "ex_out"
     ore_prices_filepath = ex_dir / "example_ore_prices.csv"
-    shipping_coords_filepath = ROOT_DIR / "converters/iron/martin_transport/shipping_coords.csv"
+    shipping_coords_filepath = ROOT_DIR / "converters/iron/simple_transport/shipping_coords.csv"
     shipping_prices_filepath = ex_dir / "example_shipping_prices.csv"
     cases_csv_fpath = ex_out_dir / "cases.csv"
     ex_png_fpath = ex_out_dir / "example_iron_map.png"
@@ -2565,6 +2565,30 @@ def test_iron_dri_eaf_example(subtests, temp_copy_of_example):
         assert pytest.approx(lcos, rel=1e-4) == 531.5842266865
 
 
+@pytest.mark.integration
+@pytest.mark.parametrize(
+    "example_folder,resource_example_folder", [("21_iron_examples/iron_dri_nrri", None)]
+)
+def test_iron_dri_nrri_example(subtests, temp_copy_of_example):
+    example_folder = temp_copy_of_example
+
+    h2i = H2IntegrateModel(example_folder / "single_site_iron.yaml")
+
+    h2i.run()
+
+    with subtests.test("Value check on LCOI"):
+        lcoi = h2i.model.get_val("finance_subgroup_iron_ore.LCOI", units="USD/t")[0]
+        assert pytest.approx(lcoi, rel=1e-4) == 129.083
+
+    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) == 350.302
+
+    with subtests.test("Value check on LCOS"):
+        lcos = h2i.model.get_val("finance_subgroup_steel.LCOS", units="USD/t")[0]
+        assert pytest.approx(lcos, rel=1e-4) == 520.417
+
+
 @pytest.mark.integration
 @pytest.mark.parametrize(
     "example_folder,resource_example_folder", [("21_iron_examples/iron_electrowinning", None)]
diff --git a/h2integrate/converters/iron/__init__.py b/h2integrate/converters/iron/__init__.py
index db279ef89..3df499479 100644
--- a/h2integrate/converters/iron/__init__.py
+++ b/h2integrate/converters/iron/__init__.py
@@ -1,7 +1,7 @@
-from h2integrate.converters.iron.martin_mine_perf_model import (
-    MartinIronMinePerformanceComponent,
+from h2integrate.converters.iron.simple_mine_perf_model import (
+    SimpleIronMinePerformanceComponent,
 )
-from h2integrate.converters.iron.martin_mine_cost_model import MartinIronMineCostComponent
+from h2integrate.converters.iron.simple_mine_cost_model import SimpleIronMineCostComponent
 from h2integrate.converters.iron.iron_dri_plant import (
     NaturalGasIronReductionPlantPerformanceComponent,
     NaturalGasIronReductionPlantCostComponent,
@@ -14,3 +14,7 @@
     IronTransportPerformanceComponent,
     IronTransportCostComponent,
 )
+from h2integrate.converters.iron.nrri_iron_mine import (
+    NRRIIronMinePerformanceComponent,
+    NRRIIronMineCostComponent,
+)
diff --git a/h2integrate/converters/iron/iron_transport.py b/h2integrate/converters/iron/iron_transport.py
index 82bb4205c..97866873b 100644
--- a/h2integrate/converters/iron/iron_transport.py
+++ b/h2integrate/converters/iron/iron_transport.py
@@ -86,7 +86,7 @@ def compute(self, inputs, outputs):
         lon = self.options["plant_config"]["sites"].get("site", {}).get("longitude")
         site_location = (lat, lon)
         shipping_coord_fpath = (
-            ROOT_DIR / "converters" / "iron" / "martin_transport" / "shipping_coords.csv"
+            ROOT_DIR / "converters" / "iron" / "simple_transport" / "shipping_coords.csv"
         )
         shipping_locations = pd.read_csv(shipping_coord_fpath, index_col="Unnamed: 0")
 
diff --git a/h2integrate/converters/iron/nrri_iron_mine.py b/h2integrate/converters/iron/nrri_iron_mine.py
new file mode 100644
index 000000000..08b414aa2
--- /dev/null
+++ b/h2integrate/converters/iron/nrri_iron_mine.py
@@ -0,0 +1,534 @@
+import copy
+import warnings
+
+import numpy as np
+import pandas as pd
+from attrs import field, define, validators
+from openmdao.utils import units
+
+from h2integrate import ROOT_DIR
+from h2integrate.core.utilities import BaseConfig, merge_shared_inputs
+from h2integrate.core.model_baseclasses import CostModelBaseClass, PerformanceModelBaseClass
+from h2integrate.tools.inflation.inflate import inflate_cpi
+
+
+@define(kw_only=True)
+class NRRIIronMinePerformanceConfig(BaseConfig):
+    """Configuration class for NRRIIronMinePerformanceComponent.
+
+    Attributes:
+        mine (str): name of ore mine. Must be "Hibbing", "Northshore", "United",
+            "Minorca" or "Tilden"
+        max_ore_production_rate_tonnes_per_hr (float): capacity of the pellet plant
+            in units of metric tonnes of pellets produced per hour.
+
+    """
+
+    max_ore_production_rate_tonnes_per_hr: float = field()
+    mine: str = field(
+        validator=validators.in_(["Hibbing", "Northshore", "United", "Minorca", "Tilden"])
+    )
+
+
+class NRRIIronMinePerformanceComponent(PerformanceModelBaseClass):
+    _time_step_bounds = (
+        3600,
+        3600,
+    )  # (min, max) time step lengths (in seconds) compatible with this model
+    _control_classifier = "flexible"
+
+    def initialize(self):
+        super().initialize()
+        self.commodity = "iron_ore"
+        self.commodity_rate_units = "t/h"
+        self.commodity_amount_units = "t"
+
+    def setup(self):
+        self.config = NRRIIronMinePerformanceConfig.from_dict(
+            merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"),
+            strict=True,
+            additional_cls_name=self.__class__.__name__,
+        )
+        super().setup()
+
+        self.add_input(
+            "system_capacity",
+            val=self.config.max_ore_production_rate_tonnes_per_hr,
+            units="t/h",
+            desc="Ore production capacity",
+        )
+
+        # Add electricity input, default to 0 --> set using feedstock component
+        self.add_input(
+            "electricity_in",
+            val=0.0,
+            shape=self.n_timesteps,
+            units="kW",
+            desc="Electricity available for iron ore processing",
+        )
+
+        # Add natural_gas input, default to 0 --> set using feedstock component
+        self.add_input(
+            "natural_gas_in",
+            val=0.0,
+            shape=self.n_timesteps,
+            units="MMBtu/h",
+            desc="Natural_gas feedstock into iron mine",
+        )
+
+        # Add diesel input, default to 0 --> set using feedstock component
+        self.add_input(
+            "diesel_in",
+            val=0.0,
+            shape=self.n_timesteps,
+            units="galUS/h",
+            desc="Diesel feedstock into iron mine",
+        )
+
+        self.add_output(
+            "electricity_consumed",
+            val=0.0,
+            shape=self.n_timesteps,
+            units="kW",
+            desc="Electricity consumed",
+        )
+
+        self.add_output(
+            "natural_gas_consumed",
+            val=0.0,
+            shape=self.n_timesteps,
+            units="MMBtu/h",
+            desc="Natural gas consumed",
+        )
+
+        self.add_output(
+            "diesel_consumed",
+            val=0.0,
+            shape=self.n_timesteps,
+            units="galUS/h",
+            desc="Diesel consumed",
+        )
+
+        self.add_output(
+            "tailings_out", val=0.0, shape=self.n_timesteps, units="t/h", desc="Tailings produced"
+        )
+
+        output_dict = {
+            "raw_ore": {"units": "t/h", "desc": "Raw ore mass flow"},
+            "crushed_ore": {"units": "t/h", "desc": "Crushed ore mass flow"},
+            "concentrated_ore": {"units": "t/h", "desc": "Concentrated ore mass flow"},
+            "mining_electricity": {"units": "kW", "desc": "Electricity consumed in mining process"},
+            "crushing_electricity": {
+                "units": "kW",
+                "desc": "Electricity consumed in crushing process",
+            },
+            "concentration_electricity": {
+                "units": "kW",
+                "desc": "Electricity consumed in beneficiation process",
+            },
+            "pelletization_electricity": {
+                "units": "kW",
+                "desc": "Electricity consumed in pelletization process",
+            },
+            "mining_diesel": {"units": "galUS/h", "desc": "Diesel consumed in mining process"},
+            "concentration_natural_gas": {
+                "units": "MMBtu/h",
+                "desc": "Natural gas consumed in beneficiation process",
+            },
+            "pelletization_natural_gas": {
+                "units": "MMBtu/h",
+                "desc": "Natrual gas consumed in pelletization process",
+            },
+        }
+        for key, val in output_dict.items():
+            self.add_output(
+                f"{key}",
+                val=0.0,
+                shape=self.n_timesteps,
+                units=val["units"],
+                desc=val["desc"],
+            )
+
+        coeff_fpath = ROOT_DIR / "converters" / "iron" / "nrri_ore" / "perf_coeffs.csv"
+        # nrri ore performance model
+        coeff_df = pd.read_csv(coeff_fpath)
+        self.coeff_df = self.format_coeff_df(coeff_df, self.config.mine)
+
+    def format_coeff_df(self, coeff_df, mine):
+        """Update the coefficient dataframe such that values are adjusted to standard units
+            and units are compatible with OpenMDAO units. Also filter the dataframe to include
+            only the data necessary for a given mine and pellet type.
+
+        Args:
+            coeff_df (pd.DataFrame): cost coefficient dataframe.
+            mine (str): name of mine that ore is extracted from.
+
+        Returns:
+            pd.DataFrame: cost coefficient dataframe
+        """
+        data_cols = ["units", "process", mine]
+        coeff_df = coeff_df[data_cols]
+        coeff_df = coeff_df.rename(columns={mine: "value"})
+
+        # convert wet to dry
+        moisture_percent = 2.0
+        dry_fraction = (100 - moisture_percent) / 100
+
+        # convert wet long tons per year to dry long tons per year
+        i_wlt = coeff_df[coeff_df["units"] == "WLT/Yr"].index.to_list()
+        coeff_df.loc[i_wlt, "value"] = coeff_df.loc[i_wlt, "value"] * dry_fraction
+        coeff_df.loc[i_wlt, "units"] = "lt/yr"
+
+        # convert kWh/wet long ton to kWh/dry long ton
+        i_per_wlt = coeff_df[coeff_df["units"] == "kWh/LTP"].index.to_list()
+        coeff_df.loc[i_per_wlt, "value"] = coeff_df.loc[i_per_wlt, "value"]
+        coeff_df.loc[i_per_wlt, "units"] = "kWh/lt"
+        coeff_df.loc[i_per_wlt, "Type"] = "energy use/pellet"
+
+        # convert MMBtu/wet long ton to MMBtu/dry long ton
+        i = coeff_df[coeff_df["units"] == "MMBtu/LTP"].index.to_list()
+        coeff_df.loc[i, "value"] = coeff_df.loc[i, "value"]
+        coeff_df.loc[i, "units"] = "MMBtu/lt"
+        coeff_df.loc[i, "Type"] = "natural gas use/pellet"
+
+        # convert gal/wet long ton to gal/dry long ton
+        i = coeff_df[coeff_df["units"] == "gal/LTP"].index.to_list()
+        coeff_df.loc[i, "value"] = coeff_df.loc[i, "value"]
+        coeff_df.loc[i, "units"] = "galUS/lt"
+        coeff_df.loc[i, "Type"] = "diesel use/pellet"
+
+        # convert units to standardized units
+        unit_rename_mapper = {}
+        old_units = list(set(coeff_df["units"].to_list()))
+        for ii, old_unit in enumerate(old_units):
+            if "kWh" in old_unit:
+                old_unit = old_unit.replace("kWh", "(kW*h)")
+            if "lt" in old_unit:  # dry long tons
+                old_unit = old_unit.replace("lt", "(2240*lb)")
+            unit_rename_mapper.update({old_units[ii]: old_unit})
+        coeff_df["units"] = coeff_df["units"].replace(to_replace=unit_rename_mapper)
+
+        convert_units_dict = {
+            "(kW*h)/(2240*lb)": "(kW*h)/t",
+            "MMBtu/(2240*lb)": "MMBtu/t",
+            "galUS/(2240*lb)": "galUS/t",
+            "(2240*lb)": "t",
+            "(2240*lb)/yr": "t/yr",
+        }
+        for i in coeff_df.index.to_list():
+            if coeff_df.loc[i, "units"] in convert_units_dict:
+                current_units = coeff_df.loc[i, "units"]
+                desired_units = convert_units_dict[current_units]
+                coeff_df.loc[i, "value"] = units.convert_units(
+                    coeff_df.loc[i, "value"], current_units, desired_units
+                )
+                coeff_df.loc[i, "units"] = desired_units
+
+        return coeff_df
+
+    def compute(self, inputs, outputs):
+        energy_per_process = {}
+        natural_gas_per_process = {}
+        diesel_per_process = {}
+
+        system_capacity = inputs["system_capacity"][0]  # t/h pellets
+
+        ref_pellets = self.coeff_df[self.coeff_df["process"] == "Iron Ore Pellets"]["value"].values
+        # User warning if system capacity * 8760 is above ref pellets
+        if system_capacity * 8760 > ref_pellets:
+            msg = (
+                f"System capacity of {system_capacity} t/yr exceeds the reference pellet"
+                f" production of {ref_pellets} t/yr."
+                f" This may lead to unrealistic results."
+            )
+            warnings.warn(msg, UserWarning)
+
+        #### Mining
+        ref_raw_ore = self.coeff_df[self.coeff_df["process"] == "ROM Ore"]["value"].values
+        energy_per_process["mining"] = self.coeff_df[
+            (self.coeff_df["process"] == "Mining") & (self.coeff_df["units"] == "(kW*h)/t")
+        ]["value"].values
+        diesel_per_process["mining"] = self.coeff_df[
+            (self.coeff_df["process"] == "Mining") & (self.coeff_df["units"] == "galUS/t")
+        ]["value"].values
+
+        #### Crushing (Comminution)
+        ref_crushed_ore = self.coeff_df[self.coeff_df["process"] == "Crushed Ore"]["value"].values
+        energy_per_process["crushing"] = self.coeff_df[
+            (self.coeff_df["process"] == "Comminution (Crushing)")
+            & (self.coeff_df["units"] == "(kW*h)/t")
+        ]["value"].values
+
+        #### Beneficiation (Concentration)
+        ref_conc_ore = self.coeff_df[self.coeff_df["process"] == "Concentrated Ore"]["value"].values
+        energy_per_process["concentration"] = self.coeff_df[
+            (self.coeff_df["process"] == "Beneficiation (Concentration)")
+            & (self.coeff_df["units"] == "(kW*h)/t")
+        ]["value"].values
+        natural_gas_per_process["concentration"] = self.coeff_df[
+            (self.coeff_df["process"] == "Beneficiation (Concentration)")
+            & (self.coeff_df["units"] == "MMBtu/t")
+        ]["value"].values
+
+        # Byproduct of beneficiation
+        ref_tailings = self.coeff_df[self.coeff_df["process"] == "Tailings"]["value"].values
+
+        #### Pelletization
+        ref_pellets = self.coeff_df[self.coeff_df["process"] == "Iron Ore Pellets"]["value"].values
+        energy_per_process["pelletization"] = self.coeff_df[
+            (self.coeff_df["process"] == "Pelletization") & (self.coeff_df["units"] == "(kW*h)/t")
+        ]["value"].values
+        natural_gas_per_process["pelletization"] = self.coeff_df[
+            (self.coeff_df["process"] == "Pelletization") & (self.coeff_df["units"] == "MMBtu/t")
+        ]["value"].values
+
+        # max feedstock consumption
+        max_elec_consumed = sum(energy_per_process.values()) * system_capacity  # kW
+        max_natural_gas_consumed = sum(natural_gas_per_process.values()) * system_capacity  # MMBtu
+        max_diesel_consumed = sum(diesel_per_process.values()) * system_capacity  # gal
+
+        # available feedstocks, saturated at maximum system feedstock consumption
+        electricity_available = np.where(
+            inputs["electricity_in"] > max_elec_consumed,
+            max_elec_consumed,
+            inputs["electricity_in"],
+        )
+        natural_gas_available = np.where(
+            inputs["natural_gas_in"] > max_natural_gas_consumed,
+            max_natural_gas_consumed,
+            inputs["natural_gas_in"],
+        )
+        diesel_available = np.where(
+            inputs["diesel_in"] > max_diesel_consumed,
+            max_diesel_consumed,
+            inputs["diesel_in"],
+        )
+
+        # how much output can be produced from each of the feedstocks
+        processed_ore_from_electricity = (
+            electricity_available / max_elec_consumed
+        ) * system_capacity  # t/h pellets
+        processed_ore_from_natural_gas = (
+            natural_gas_available / max_natural_gas_consumed
+        ) * system_capacity  # t/h pellets
+        processed_ore_from_diesel = (
+            diesel_available / max_diesel_consumed
+        ) * system_capacity  # t/h pellets
+
+        # output is minimum between available feedstocks and output command value
+        processed_ore_production = np.minimum.reduce(
+            [
+                processed_ore_from_diesel,
+                processed_ore_from_natural_gas,
+                processed_ore_from_electricity,
+            ]
+        )
+        outputs["iron_ore_out"] = processed_ore_production
+        outputs["total_iron_ore_produced"] = np.sum(processed_ore_production)
+        outputs["annual_iron_ore_produced"] = outputs["total_iron_ore_produced"] * (
+            1 / self.fraction_of_year_simulated
+        )
+        outputs["rated_iron_ore_production"] = inputs["system_capacity"]
+        outputs["capacity_factor"] = outputs["total_iron_ore_produced"] / (
+            outputs["rated_iron_ore_production"] * self.n_timesteps
+        )
+
+        # mass flow through mining process
+        outputs["raw_ore"] = processed_ore_production * ref_raw_ore / ref_pellets
+        outputs["crushed_ore"] = processed_ore_production * ref_crushed_ore / ref_pellets
+        outputs["concentrated_ore"] = processed_ore_production * ref_conc_ore / ref_pellets
+        outputs["tailings_out"] = processed_ore_production * ref_tailings / ref_pellets
+
+        # energy and fuel consumption per process
+        outputs["mining_electricity"] = energy_per_process["mining"] * processed_ore_production
+        outputs["crushing_electricity"] = energy_per_process["crushing"] * processed_ore_production
+        outputs["concentration_electricity"] = (
+            energy_per_process["concentration"] * processed_ore_production
+        )
+        outputs["pelletization_electricity"] = (
+            energy_per_process["pelletization"] * processed_ore_production
+        )
+
+        outputs["mining_diesel"] = diesel_per_process["mining"] * processed_ore_production
+        outputs["concentration_natural_gas"] = (
+            natural_gas_per_process["concentration"] * processed_ore_production
+        )
+        outputs["pelletization_natural_gas"] = (
+            natural_gas_per_process["pelletization"] * processed_ore_production
+        )
+
+        # feedstock consumption
+        outputs["electricity_consumed"] = (
+            sum(energy_per_process.values()) * processed_ore_production
+        )
+        outputs["natural_gas_consumed"] = (
+            sum(natural_gas_per_process.values()) * processed_ore_production
+        )
+        outputs["diesel_consumed"] = sum(diesel_per_process.values()) * processed_ore_production
+
+        # Apply curtailment based on set_point
+        self.apply_curtailment(outputs)
+
+
+@define(kw_only=True)
+class NRRIIronMineCostConfig(BaseConfig):
+    """Configuration class for NRRIIronMineCostComponent.
+
+    Attributes:
+        mine (str): name of ore mine. Must be "Hibbing", "Northshore", "United",
+            "Minorca" or "Tilden"
+        taconite_pellet_type (str): type of taconite pellets, options are "std" or "drg".
+        cost_year (int): target dollar year to convert costs to.
+    """
+
+    mine: str = field(
+        validator=validators.in_(["Hibbing", "Northshore", "United", "Minorca", "Tilden"])
+    )
+    taconite_pellet_type: str = field(
+        converter=(str.lower, str.strip), validator=validators.in_(["std", "drg"])
+    )
+    # the cost model is based on costs from 2021 and can be adjusted to another cost year
+    # using CPI adjustment.
+    cost_year: int = field(converter=int, validator=(validators.ge(2010), validators.le(2024)))
+
+
+class NRRIIronMineCostComponent(CostModelBaseClass):
+    _time_step_bounds = (
+        3600,
+        3600,
+    )  # (min, max) time step lengths (in seconds) compatible with this model
+
+    def setup(self):
+        # merge inputs from performance parameters and cost parameters
+        config_dict = merge_shared_inputs(
+            copy.deepcopy(self.options["tech_config"]["model_inputs"]), "cost"
+        )
+
+        if "cost_year" in config_dict:
+            if config_dict.get("cost_year", 2021) != 2021:
+                msg = (
+                    "This cost model is based on 2021 costs and adjusts costs using CPI. "
+                    "The cost year cannot be modified for this cost model. "
+                )
+                raise ValueError(msg)
+
+        target_dollar_year = self.options["plant_config"]["finance_parameters"][
+            "cost_adjustment_parameters"
+        ]["target_dollar_year"]
+
+        if target_dollar_year <= 2024 and target_dollar_year >= 2010:
+            # adjust costs from 2021 to target dollar year using CPI adjustment
+            self.target_dollar_year = target_dollar_year
+
+        elif target_dollar_year < 2010:
+            # adjust costs from 2021 to 2010 using CPI adjustment
+            self.target_dollar_year = 2010
+
+        elif target_dollar_year > 2024:
+            # adjust costs from 2021 to 2024 using CPI adjustment
+            self.target_dollar_year = 2024
+
+        config_dict.update({"cost_year": self.target_dollar_year})
+
+        self.config = NRRIIronMineCostConfig.from_dict(
+            config_dict,
+            strict=True,
+            additional_cls_name=self.__class__.__name__,
+        )
+
+        super().setup()
+
+        self.add_input(
+            "annual_iron_ore_produced",
+            val=0.0,
+            shape=self.plant_life,
+            units="t/yr",
+            desc="Annual iron ore production",
+        )
+
+        coeff_fpath = ROOT_DIR / "converters" / "iron" / "nrri_ore" / "cost_coeffs.csv"
+        # nrri ore cost model
+        coeff_df = pd.read_csv(coeff_fpath)
+        self.coeff_df = self.format_coeff_df(coeff_df, self.config.mine)
+
+    def format_coeff_df(self, coeff_df, mine):
+        """Update the coefficient dataframe such that values are adjusted to standard units
+            and units are compatible with OpenMDAO units. Also filter the dataframe to include
+            only the data necessary for a given mine and pellet type.
+
+        Args:
+            coeff_df (pd.DataFrame): cost coefficient dataframe.
+            mine (str): name of mine that ore is extracted from.
+
+        Returns:
+            pd.DataFrame: cost coefficient dataframe
+        """
+        data_cols = ["units", "process", mine]
+        coeff_df = coeff_df[data_cols]
+        coeff_df = coeff_df.rename(columns={mine: "value"})
+
+        # convert wet to dry
+        moisture_percent = 2.0
+        dry_fraction = (100 - moisture_percent) / 100
+
+        # convert wet long tons per year to dry long tons per year
+        i_wlt = coeff_df[coeff_df["units"] == "WLT/Yr"].index.to_list()
+        coeff_df.loc[i_wlt, "value"] = coeff_df.loc[i_wlt, "value"] * dry_fraction
+        coeff_df.loc[i_wlt, "units"] = "lt/yr"
+
+        i_per_wlt = coeff_df[coeff_df["units"] == "USD/LTP"].index.to_list()
+        coeff_df.loc[i_per_wlt, "value"] = coeff_df.loc[i_per_wlt, "value"]
+        coeff_df.loc[i_per_wlt, "units"] = "USD/lt"
+
+        i_per_wlt = coeff_df[coeff_df["units"] == "USD/LT"].index.to_list()
+        coeff_df.loc[i_per_wlt, "value"] = coeff_df.loc[i_per_wlt, "value"]
+        coeff_df.loc[i_per_wlt, "units"] = "USD/lt"
+
+        # convert units to standardized units
+        unit_rename_mapper = {}
+        old_units = list(set(coeff_df["units"].to_list()))
+        for ii, old_unit in enumerate(old_units):
+            if "lt" in old_unit:  # dry long tons
+                old_unit = old_unit.replace("lt", "(2240*lb)")
+            unit_rename_mapper.update({old_units[ii]: old_unit})
+        coeff_df["units"] = coeff_df["units"].replace(to_replace=unit_rename_mapper)
+
+        convert_units_dict = {
+            "USD/(2240*lb)": "USD/t",
+            "(2240*lb)": "t",
+            "(2240*lb)/yr": "t/yr",
+        }
+        for i in coeff_df.index.to_list():
+            if coeff_df.loc[i, "units"] in convert_units_dict:
+                current_units = coeff_df.loc[i, "units"]
+                desired_units = convert_units_dict[current_units]
+                coeff_df.loc[i, "value"] = units.convert_units(
+                    coeff_df.loc[i, "value"], current_units, desired_units
+                )
+                coeff_df.loc[i, "units"] = desired_units
+
+        return coeff_df
+
+    def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):
+        pellet_type = self.config.taconite_pellet_type
+
+        # Get the capital cost for the reference design and scale to the modeled mine
+        ref_Oreproduced = self.coeff_df[self.coeff_df["process"] == "capacity"]["value"].values
+        capex_index = "capex_" + pellet_type
+        ref_tot_capex = self.coeff_df[self.coeff_df["process"] == capex_index]["value"].values
+        ref_capex_per_processed_ore = ref_tot_capex / ref_Oreproduced  # USD/t/yr
+        tot_capex_2021USD = (
+            inputs["annual_iron_ore_produced"][0] * ref_capex_per_processed_ore
+        )  # USD
+
+        # OpEx is calculated from the total opex minus energy costs calculated from SEC reports
+        # Variable energy cost is then considered from electricity, NG, and diesel feedstocks
+        opex_index = "opex_" + pellet_type
+        om_2021USD = (
+            inputs["annual_iron_ore_produced"][0]
+            * self.coeff_df.loc[self.coeff_df["process"] == opex_index, "value"].values
+        )
+
+        # adjust costs to cost year
+        outputs["CapEx"] = inflate_cpi(tot_capex_2021USD, 2021, self.config.cost_year)
+        outputs["OpEx"] = inflate_cpi(om_2021USD, 2021, self.config.cost_year)
diff --git a/h2integrate/converters/iron/nrri_ore/cost_coeffs.csv b/h2integrate/converters/iron/nrri_ore/cost_coeffs.csv
new file mode 100644
index 000000000..605890f13
--- /dev/null
+++ b/h2integrate/converters/iron/nrri_ore/cost_coeffs.csv
@@ -0,0 +1,6 @@
+units,process,Tilden,Hibbing,United,Minorca,Northshore
+WLT/Yr,capacity,7457805,7400230,4841000,2787480,4541386
+USD/LTP,opex_std,48.66003278,49.31140012,53.73089275,64.92175978,54.64185122
+USD/LTP,opex_drg,77.20462954,68.45029261,89.36613254,77.20462954,73.79746347
+USD,capex_std,1497234175,1204722994,873562057.6,616834719.1,896467052.9
+USD,capex_drg,1146741705,1060343358,1248848844,1146741705,1131032914
diff --git a/h2integrate/converters/iron/nrri_ore/perf_coeffs.csv b/h2integrate/converters/iron/nrri_ore/perf_coeffs.csv
new file mode 100644
index 000000000..9fc333d2b
--- /dev/null
+++ b/h2integrate/converters/iron/nrri_ore/perf_coeffs.csv
@@ -0,0 +1,13 @@
+units,process,Tilden,Hibbing,United,Minorca,Northshore
+WLT/Yr,ROM Ore,60360000,60466666.67,38012500,16566666.67,28960000
+WLT/Yr,Crushed Ore,20500000,28083000,14920000,8460000,16860000
+WLT/Yr,Concentrated Ore,8770640,7400230,5209000,2823400,4454221
+WLT/Yr,Tailings,12593329,6266000,9239000,6320300,3258928.05
+WLT/Yr,Iron Ore Pellets,7457805,7400230,4841000,2787480,4541386
+kWh/LTP,Mining,10.56,5.419861815,6.17,9.57,8.43
+kWh/LTP,Comminution (Crushing),8.64,1.699663584,10.57,9.09,9.79
+kWh/LTP,Beneficiation (Concentration),58.5495405,107.89,67.45,67.63,72.68
+kWh/LTP,Pelletization,23.62080378,47.46,32.75,49.51,40.88
+gal/LTP,Mining,1.20563222,0.89641912,0.851549267,0.732247048,0.540185089
+MMBtu/LTP,Beneficiation (Concentration),1.294814579,0,0,0,0
+MMBtu/LTP,Pelletization,1.1192,0.4,0.742912828,0.55,0.62
diff --git a/h2integrate/converters/iron/martin_mine_cost_model.py b/h2integrate/converters/iron/simple_mine_cost_model.py
similarity index 96%
rename from h2integrate/converters/iron/martin_mine_cost_model.py
rename to h2integrate/converters/iron/simple_mine_cost_model.py
index d7e80f26d..7b3b0f688 100644
--- a/h2integrate/converters/iron/martin_mine_cost_model.py
+++ b/h2integrate/converters/iron/simple_mine_cost_model.py
@@ -11,8 +11,8 @@
 
 
 @define(kw_only=True)
-class MartinIronMineCostConfig(BaseConfig):
-    """Configuration class for MartinIronMineCostComponent.
+class SimpleIronMineCostConfig(BaseConfig):
+    """Configuration class for SimpleIronMineCostComponent.
 
     Attributes:
         taconite_pellet_type (str): type of taconite pellets, options are "std" or "drg".
@@ -39,7 +39,7 @@ class MartinIronMineCostConfig(BaseConfig):
     cost_year: int = field(converter=int, validator=(validators.ge(2010), validators.le(2024)))
 
 
-class MartinIronMineCostComponent(CostModelBaseClass):
+class SimpleIronMineCostComponent(CostModelBaseClass):
     _time_step_bounds = (
         3600,
         3600,
@@ -76,7 +76,7 @@ def setup(self):
             self.target_dollar_year = 2024
 
         config_dict.update({"cost_year": self.target_dollar_year})
-        self.config = MartinIronMineCostConfig.from_dict(
+        self.config = SimpleIronMineCostConfig.from_dict(
             config_dict,
             strict=True,
             additional_cls_name=self.__class__.__name__,
@@ -99,7 +99,7 @@ def setup(self):
             desc="Iron ore pellets produced",
         )
 
-        coeff_fpath = ROOT_DIR / "converters" / "iron" / "martin_ore" / "cost_coeffs.csv"
+        coeff_fpath = ROOT_DIR / "converters" / "iron" / "simple_ore" / "cost_coeffs.csv"
         # martin ore performance model
         coeff_df = pd.read_csv(coeff_fpath, index_col=0)
         self.coeff_df = self.format_coeff_df(coeff_df, self.config.mine)
diff --git a/h2integrate/converters/iron/martin_mine_perf_model.py b/h2integrate/converters/iron/simple_mine_perf_model.py
similarity index 96%
rename from h2integrate/converters/iron/martin_mine_perf_model.py
rename to h2integrate/converters/iron/simple_mine_perf_model.py
index 2363fd597..955c553c5 100644
--- a/h2integrate/converters/iron/martin_mine_perf_model.py
+++ b/h2integrate/converters/iron/simple_mine_perf_model.py
@@ -9,8 +9,8 @@
 
 
 @define(kw_only=True)
-class MartinIronMinePerformanceConfig(BaseConfig):
-    """Configuration class for MartinIronMinePerformanceComponent.
+class SimpleIronMinePerformanceConfig(BaseConfig):
+    """Configuration class for SimpleIronMinePerformanceComponent.
 
     Attributes:
         taconite_pellet_type (str): type of taconite pellets, options are "std" or "drg".
@@ -31,7 +31,7 @@ class MartinIronMinePerformanceConfig(BaseConfig):
     )
 
 
-class MartinIronMinePerformanceComponent(PerformanceModelBaseClass):
+class SimpleIronMinePerformanceComponent(PerformanceModelBaseClass):
     _time_step_bounds = (
         3600,
         3600,
@@ -46,7 +46,7 @@ def initialize(self):
 
     def setup(self):
         super().setup()
-        self.config = MartinIronMinePerformanceConfig.from_dict(
+        self.config = SimpleIronMinePerformanceConfig.from_dict(
             merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"),
             strict=True,
             additional_cls_name=self.__class__.__name__,
@@ -102,7 +102,7 @@ def setup(self):
             desc="Electricity consumed",
         )
 
-        coeff_fpath = ROOT_DIR / "converters" / "iron" / "martin_ore" / "perf_coeffs.csv"
+        coeff_fpath = ROOT_DIR / "converters" / "iron" / "simple_ore" / "perf_coeffs.csv"
         # martin ore performance model
         coeff_df = pd.read_csv(coeff_fpath, index_col=0)
         self.coeff_df = self.format_coeff_df(coeff_df, self.config.mine)
diff --git a/h2integrate/converters/iron/martin_ore/cost_coeffs.csv b/h2integrate/converters/iron/simple_ore/cost_coeffs.csv
similarity index 100%
rename from h2integrate/converters/iron/martin_ore/cost_coeffs.csv
rename to h2integrate/converters/iron/simple_ore/cost_coeffs.csv
diff --git a/h2integrate/converters/iron/martin_ore/perf_coeffs.csv b/h2integrate/converters/iron/simple_ore/perf_coeffs.csv
similarity index 100%
rename from h2integrate/converters/iron/martin_ore/perf_coeffs.csv
rename to h2integrate/converters/iron/simple_ore/perf_coeffs.csv
diff --git a/h2integrate/converters/iron/martin_transport/shipping_coords.csv b/h2integrate/converters/iron/simple_transport/shipping_coords.csv
similarity index 100%
rename from h2integrate/converters/iron/martin_transport/shipping_coords.csv
rename to h2integrate/converters/iron/simple_transport/shipping_coords.csv
diff --git a/h2integrate/converters/iron/test/test_nrri_mine.py b/h2integrate/converters/iron/test/test_nrri_mine.py
new file mode 100644
index 000000000..66aecfbeb
--- /dev/null
+++ b/h2integrate/converters/iron/test/test_nrri_mine.py
@@ -0,0 +1,141 @@
+import numpy as np
+import pytest
+import openmdao.api as om
+from pytest import fixture
+
+from h2integrate.converters.iron.nrri_iron_mine import (
+    NRRIIronMineCostComponent,
+    NRRIIronMinePerformanceComponent,
+)
+
+
+@fixture
+def iron_ore_config_martin_om():
+    shared_params = {
+        "mine": "Tilden",
+    }
+    tech_config = {
+        "model_inputs": {
+            "shared_parameters": shared_params,
+            "performance_parameters": {
+                "max_ore_production_rate_tonnes_per_hr": (7457805 * 0.98 * 1.016)
+                / 8760,  # convert from WLT/yr to LT/yr to t/yr and then hourly,
+            },
+            "cost_parameters": {
+                "cost_year": 2021,
+                "taconite_pellet_type": "std",
+            },
+        }
+    }
+    return tech_config
+
+
+@pytest.mark.unit
+def test_iron_mine_performance_outputs(
+    plant_config, driver_config, iron_ore_config_martin_om, subtests
+):
+    prob = om.Problem()
+    iron_ore_perf = NRRIIronMinePerformanceComponent(
+        plant_config=plant_config,
+        tech_config=iron_ore_config_martin_om,
+        driver_config=driver_config,
+    )
+    prob.model.add_subsystem("comp", iron_ore_perf, promotes=["*"])
+    prob.setup()
+
+    hourly_electricity = 85795.22689
+    hourly_fuel = 2134.768277
+    hourly_diesel = 1e6
+    ore_rated_capacity = 7457805 * 0.98 * 1.016
+
+    prob.set_val("comp.electricity_in", [hourly_electricity] * 8760, units="kW")
+    prob.set_val("comp.natural_gas_in", [hourly_fuel] * 8760, units="MMBtu/h")
+    prob.set_val("comp.diesel_in", [hourly_diesel] * 8760, units="galUS/h")
+    prob.set_val("comp.iron_ore_command_value", [ore_rated_capacity], units="t/h")
+
+    prob.run_model()
+    commodity_rate_units = "t/h"
+
+    # check pellet production
+    with subtests.test("iron_ore_out"):
+        iron_ore_out = prob.get_val("comp.iron_ore_out", units=commodity_rate_units)
+        # 0.98 is converting from WLT to LT, 1.016 is converting from LT to t
+        assert np.sum(iron_ore_out) == pytest.approx(7457805 * 0.98 * 1.016, rel=1e-3)
+
+    with subtests.test("pelletization elec"):
+        pel_elec = prob.get_val("comp.pelletization_electricity", units="kW")
+        assert np.sum(pel_elec) == pytest.approx(23.62080378 * 7457805 * 0.98, rel=1e-3)
+
+
+@pytest.mark.regression
+def test_iron_pellet_cost_outputs(plant_config, driver_config, iron_ore_config_martin_om, subtests):
+    plant_config["finance_parameters"]["cost_adjustment_parameters"]["target_dollar_year"] = 2021
+    prob = om.Problem()
+    iron_ore_cost = NRRIIronMineCostComponent(
+        plant_config=plant_config,
+        tech_config=iron_ore_config_martin_om,
+        driver_config=driver_config,
+    )
+    prob.model.add_subsystem("comp", iron_ore_cost, promotes=["*"])
+    prob.setup()
+
+    prob.set_val("comp.annual_iron_ore_produced", [7457805 * 1.016], units="t/yr")
+
+    prob.run_model()
+
+    with subtests.test("total_capex"):
+        total_capex = prob.get_val("comp.CapEx", units="USD")
+        assert total_capex == pytest.approx(1527719439.562, rel=1e-3)
+
+    # check total opex for year 1
+    with subtests.test("total_opex"):
+        total_opex = prob.get_val("comp.OpEx", units="USD/yr")
+        assert total_opex == pytest.approx(7457805.0 * 48.66003278, rel=1e-3)
+
+
+@pytest.mark.regression
+def test_iron_mine_cost_outputs(plant_config, driver_config, iron_ore_config_martin_om, subtests):
+    iron_ore_config_martin_om["model_inputs"]["shared_parameters"]["mine"] = "United"
+    iron_ore_config_martin_om["model_inputs"]["cost_parameters"]["taconite_pellet_type"] = "drg"
+    plant_config["finance_parameters"]["cost_adjustment_parameters"]["target_dollar_year"] = 2021
+    prob = om.Problem()
+    iron_ore_cost = NRRIIronMineCostComponent(
+        plant_config=plant_config,
+        tech_config=iron_ore_config_martin_om,
+        driver_config=driver_config,
+    )
+    prob.model.add_subsystem("comp", iron_ore_cost, promotes=["*"])
+    prob.setup()
+
+    prob.set_val("comp.annual_iron_ore_produced", [7457805 * 1.016], units="t/yr")
+
+    prob.run_model()
+
+    # check total opex for year 1
+    with subtests.test("total_opex"):
+        total_opex = prob.get_val("comp.OpEx", units="USD/yr")
+        assert total_opex == pytest.approx(7457805.0 * 89.3661325, rel=1e-3)
+
+
+@pytest.mark.regression
+def test_adjusting_cost_year(plant_config, driver_config, iron_ore_config_martin_om, subtests):
+    iron_ore_config_martin_om["model_inputs"]["shared_parameters"]["mine"] = "United"
+    iron_ore_config_martin_om["model_inputs"]["cost_parameters"]["taconite_pellet_type"] = "drg"
+    prob = om.Problem()
+    iron_ore_cost = NRRIIronMineCostComponent(
+        plant_config=plant_config,
+        tech_config=iron_ore_config_martin_om,
+        driver_config=driver_config,
+    )
+    prob.model.add_subsystem("comp", iron_ore_cost, promotes=["*"])
+    prob.setup()
+
+    prob.set_val("comp.annual_iron_ore_produced", [7457805 * 1.016], units="t/yr")
+
+    prob.run_model()
+
+    # check total opex for year 1
+    with subtests.test("total_opex"):
+        total_opex = prob.get_val("comp.OpEx", units="USD/yr")
+        # greater than 2021 because the cost year is adjusted to 2022, which has a higher CPI
+        assert total_opex == pytest.approx(719809158.0098612, rel=1e-3)
diff --git a/h2integrate/converters/iron/test/test_martin_mine.py b/h2integrate/converters/iron/test/test_simple_mine.py
similarity index 93%
rename from h2integrate/converters/iron/test/test_martin_mine.py
rename to h2integrate/converters/iron/test/test_simple_mine.py
index 052a51179..82a8face1 100644
--- a/h2integrate/converters/iron/test/test_martin_mine.py
+++ b/h2integrate/converters/iron/test/test_simple_mine.py
@@ -3,8 +3,8 @@
 import openmdao.api as om
 from pytest import fixture
 
-from h2integrate.converters.iron.martin_mine_cost_model import MartinIronMineCostComponent
-from h2integrate.converters.iron.martin_mine_perf_model import MartinIronMinePerformanceComponent
+from h2integrate.converters.iron.simple_mine_cost_model import SimpleIronMineCostComponent
+from h2integrate.converters.iron.simple_mine_perf_model import SimpleIronMinePerformanceComponent
 
 
 @fixture
@@ -27,7 +27,7 @@ def test_iron_mine_performance_outputs(
     plant_config, driver_config, iron_ore_config_martin_om, subtests
 ):
     prob = om.Problem()
-    iron_ore_perf = MartinIronMinePerformanceComponent(
+    iron_ore_perf = SimpleIronMinePerformanceComponent(
         plant_config=plant_config,
         tech_config=iron_ore_config_martin_om,
         driver_config=driver_config,
@@ -124,17 +124,17 @@ def test_iron_mine_performance_outputs(
 
 @pytest.mark.regression
 def test_baseline_iron_ore_costs(plant_config, driver_config, iron_ore_config_martin_om, subtests):
-    martin_ore_capex = 1221599018.626594
-    martin_ore_fixed_om = 0.0
+    simple_ore_capex = 1221599018.626594
+    simple_ore_fixed_om = 0.0
 
     prob = om.Problem()
-    iron_ore_perf = MartinIronMinePerformanceComponent(
+    iron_ore_perf = SimpleIronMinePerformanceComponent(
         plant_config=plant_config,
         tech_config=iron_ore_config_martin_om,
         driver_config=driver_config,
     )
 
-    iron_ore_cost = MartinIronMineCostComponent(
+    iron_ore_cost = SimpleIronMineCostComponent(
         plant_config=plant_config,
         tech_config=iron_ore_config_martin_om,
         driver_config=driver_config,
@@ -162,12 +162,12 @@ def test_baseline_iron_ore_costs(plant_config, driver_config, iron_ore_config_ma
     with subtests.test("CapEx"):
         assert (
             pytest.approx(prob.get_val("ore_cost.CapEx", units="USD")[0], rel=1e-6)
-            == martin_ore_capex
+            == simple_ore_capex
         )
     with subtests.test("OpEx"):
         assert (
             pytest.approx(prob.get_val("ore_cost.OpEx", units="USD/year")[0], rel=1e-6)
-            == martin_ore_fixed_om
+            == simple_ore_fixed_om
         )
     with subtests.test("VarOpEx"):
         varopex_per_t = prob.get_val("ore_cost.VarOpEx", units="USD/year")[0] / annual_ore_produced
diff --git a/h2integrate/core/supported_models.py b/h2integrate/core/supported_models.py
index d1e2b798a..a99961239 100644
--- a/h2integrate/core/supported_models.py
+++ b/h2integrate/core/supported_models.py
@@ -90,8 +90,11 @@ def copy(self):
         "SteamMethaneReformerCostModel": "converters.hydrogen:SteamMethaneReformerCostModel",
         "SimpleASUCostModel": "converters.nitrogen:SimpleASUCostModel",
         "SimpleASUPerformanceModel": "converters.nitrogen:SimpleASUPerformanceModel",
-        "MartinIronMinePerformanceComponent": "converters.iron:MartinIronMinePerformanceComponent",
-        "MartinIronMineCostComponent": "converters.iron:MartinIronMineCostComponent",
+        "HOPPComponent": "converters.hopp:HOPPComponent",
+        "SimpleIronMinePerformanceComponent": "converters.iron:SimpleIronMinePerformanceComponent",
+        "SimpleIronMineCostComponent": "converters.iron:SimpleIronMineCostComponent",
+        "NRRIIronMinePerformanceComponent": "converters.iron:NRRIIronMinePerformanceComponent",
+        "NRRIIronMineCostComponent": "converters.iron:NRRIIronMineCostComponent",
         "NaturalGasIronReductionPlantPerformanceComponent": "converters.iron:NaturalGasIronReductionPlantPerformanceComponent",
         "NaturalGasIronReductionPlantCostComponent": "converters.iron:NaturalGasIronReductionPlantCostComponent",
         "HydrogenIronReductionPlantPerformanceComponent": "converters.iron:HydrogenIronReductionPlantPerformanceComponent",
diff --git a/h2integrate/transporters/pipe.py b/h2integrate/transporters/pipe.py
index 5ad3a92c1..2c115a4d4 100644
--- a/h2integrate/transporters/pipe.py
+++ b/h2integrate/transporters/pipe.py
@@ -24,6 +24,7 @@ def initialize(self):
                 "wellhead_gas",
                 "water",
                 "oxygen",
+                "diesel",
             ],
         )
         self.options.declare("plant_config", types=dict)
@@ -37,7 +38,7 @@ def setup(self):
 
         if transport_item == "natural_gas":
             units = "MMBtu/h"
-        elif transport_item == "water":
+        elif transport_item == "water" or transport_item == "diesel":
             units = "galUS/h"
         elif transport_item == "co2":
             units = "kg/h"