diff --git a/CHANGELOG.md b/CHANGELOG.md
index b5e276524..e0e118d74 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,7 @@
 - Enable `PySAMWindPlantPerformanceModel` to accept more than 300 turbines by overriding the default maximum in the PySAM model. [PR 831](https://github.com/NatLabRockies/H2Integrate/pull/831)
 - Add `PySAMWavePerformanceModel` and `WaveResource` to wrap PySAM MhkWave as an H2I performance model, replacing the HOPP wave module in example 09. [PR 825](https://github.com/NatLabRockies/H2Integrate/pull/825)
 - Replace HOPP with native H2I wind, solar, and battery models in example 11. Adds `percent_load_missed` and `curtailment_percent` outputs to `DemandComponentBase`, allows zero capacity in wind/solar/battery performance models. [PR 826](https://github.com/NatLabRockies/H2Integrate/pull/826)
+- Add `NRRIIronMinePerformanceModel` and `NRRIIronMineCostModel`. [PR 840](https://github.com/NatLabRockies/H2Integrate/pull/840)
 
 ## 0.9 [August 10, 2026]
 
diff --git a/docs/_static/class_hierarchy.html b/docs/_static/class_hierarchy.html
index c0a7b1382..9701bd428 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": 19.42105263157895, "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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+                  edges = new vis.DataSet([{"arrows": "to", "from": "SiteBaseComponent", "to": "SiteLocationComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "ResizeablePerformanceModelBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SolarPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "LinearH2FuelCellPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SteamMethaneReformerPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "GeoH2SubsurfacePerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "GeoH2SurfacePerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "DesalinationPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleThermalNuclearReactorPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "QuinnNuclearPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "CMUElectricArcFurnaceScrapOnlyPerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "ElectricArcFurnacePlantBasePerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "CMUElectricArcFurnaceDRIPerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SteelPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleASUPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "WindPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "WindArdPerformanceCompatibilityComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "MethanolPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "HumbertEwinPerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "NRRIIronMinePerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "IronReductionPlantBasePerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleIronMinePerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "HOPPComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "PySAMTidalPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "PySAMWavePerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "RunOfRiverHydroPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleAmmoniaPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "DOCPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "OAEPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "GridPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "NaturalGasPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleGasProducerPerformance"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleGasConsumerPerformance"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "StoragePerformanceBase"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "DemandComponentBase"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GenericConverterCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ATBUtilityPVCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ATBResComPVCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ElectrolyzerCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "H2FuelCellCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SteamMethaneReformerCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GeoH2SubsurfaceCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GeoH2SurfaceCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "DesalinationCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleThermalNuclearReactorCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "QuinnNuclearCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "CMUElectricArcFurnaceCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ElectricArcFurnacePlantBaseCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SteelCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleASUCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ATBWindPlantCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "WindArdCostCompatibilityComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "MethanolCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "NRRIIronMineCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleIronMineCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "IronTransportCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "IronReductionPlantBaseCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "HumbertStinnEwinCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "PySAMMarineCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "RunOfRiverHydroCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "AmmoniaSynLoopCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleAmmoniaCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "DOCCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "OAECostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "OAECostAndFinancialModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GridCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "NaturalGasCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleGasProducerCost"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleGasConsumerCost"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GenericStorageCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "MCHTOLStorageCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "HydrogenStorageBaseCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ATBBatteryCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "FeedstockCostModel"}, {"arrows": "to", "from": "ResizeablePerformanceModelBaseClass", "to": "ElectrolyzerPerformanceBaseClass"}, {"arrows": "to", "from": "ResizeablePerformanceModelBaseClass", "to": "AmmoniaSynLoopPerformanceModel"}, {"arrows": "to", "from": "CacheBaseClass", "to": "FlorisWindPlantPerformanceModel"}, {"arrows": "to", "from": "CacheBaseClass", "to": "HOPPComponent"}, {"arrows": "to", "from": "SolarPerformanceBaseClass", "to": "PYSAMSolarPlantPerformanceModel"}, {"arrows": "to", "from": "ElectrolyzerPerformanceBaseClass", "to": "ECOElectrolyzerPerformanceModel"}, {"arrows": "to", "from": "ElectrolyzerPerformanceBaseClass", "to": "HTSEPerformanceModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "SingliticoCostModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "BasicElectrolyzerCostModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "CustomElectrolyzerCostModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "HTSECostModel"}, {"arrows": "to", "from": "ECOElectrolyzerPerformanceModel", "to": "WOMBATElectrolyzerModel"}, {"arrows": "to", "from": "GeoH2SubsurfacePerformanceBaseClass", "to": "NaturalGeoH2PerformanceModel"}, {"arrows": "to", "from": "GeoH2SubsurfacePerformanceBaseClass", "to": "StimulatedGeoH2PerformanceModel"}, {"arrows": "to", "from": "GeoH2SubsurfaceCostBaseClass", "to": "GeoH2SubsurfaceCostModel"}, {"arrows": "to", "from": "GeoH2SurfacePerformanceBaseClass", "to": "AspenGeoH2SurfacePerformanceModel"}, {"arrows": "to", "from": "GeoH2SurfaceCostBaseClass", "to": "AspenGeoH2SurfaceCostModel"}, {"arrows": "to", "from": "DesalinationPerformanceBaseClass", "to": "ReverseOsmosisPerformanceModel"}, {"arrows": "to", "from": "DesalinationCostBaseClass", "to": "ReverseOsmosisCostModel"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBasePerformanceComponent", "to": "HydrogenEAFPlantPerformanceComponent"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBasePerformanceComponent", "to": "NaturalGasEAFPlantPerformanceComponent"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBaseCostComponent", "to": "HydrogenEAFPlantCostComponent"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBaseCostComponent", "to": "NaturalGasEAFPlantCostComponent"}, {"arrows": "to", "from": "SteelPerformanceBaseClass", "to": "SteelPerformanceModel"}, {"arrows": "to", "from": "SteelCostBaseClass", "to": "SteelCostAndFinancialModel"}, {"arrows": "to", "from": "WindPerformanceBaseClass", "to": "PYSAMWindPlantPerformanceModel"}, {"arrows": "to", "from": "WindPerformanceBaseClass", "to": "FlorisWindPlantPerformanceModel"}, {"arrows": "to", "from": "MethanolPerformanceBaseClass", "to": "SMRMethanolPlantPerformanceModel"}, {"arrows": "to", "from": "MethanolPerformanceBaseClass", "to": "CO2HMethanolPlantPerformanceModel"}, {"arrows": "to", "from": "MethanolCostBaseClass", "to": "SMRMethanolPlantCostModel"}, {"arrows": "to", "from": "MethanolCostBaseClass", "to": "CO2HMethanolPlantCostModel"}, {"arrows": "to", "from": "MethanolFinanceBaseClass", "to": "SMRMethanolPlantFinanceModel"}, {"arrows": "to", "from": "MethanolFinanceBaseClass", "to": "CO2HMethanolPlantFinanceModel"}, {"arrows": "to", "from": "IronReductionPlantBasePerformanceComponent", "to": "HydrogenIronReductionPlantPerformanceComponent"}, {"arrows": "to", "from": "IronReductionPlantBasePerformanceComponent", "to": "NaturalGasIronReductionPlantPerformanceComponent"}, {"arrows": "to", "from": "IronReductionPlantBaseCostComponent", "to": "HydrogenIronReductionPlantCostComponent"}, {"arrows": "to", "from": "IronReductionPlantBaseCostComponent", "to": "NaturalGasIronReductionPlantCostComponent"}, {"arrows": "to", "from": "StoragePerformanceBase", "to": "StoragePerformanceModel"}, {"arrows": "to", "from": "StoragePerformanceBase", "to": "StorageAutoSizingModel"}, {"arrows": "to", "from": "StoragePerformanceBase", "to": "PySAMBatteryPerformanceModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "LinedRockCavernStorageCostModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "SaltCavernStorageCostModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "PipeStorageCostModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "CompressedGasStorageCostModel"}, {"arrows": "to", "from": "FeedstockCostModel", "to": "EIANaturalGasFeedstockCostModel"}, {"arrows": "to", "from": "ProFastBase", "to": "ProFastLCO"}, {"arrows": "to", "from": "ProFastBase", "to": "ProFastNPV"}, {"arrows": "to", "from": "ResourceBaseAPIModel", "to": "SolarResourceBaseAPIModel"}, {"arrows": "to", "from": "ResourceBaseAPIModel", "to": "WindResourceBaseAPIModel"}, {"arrows": "to", "from": "SolarResourceBaseAPIModel", "to": "OpenMeteoHistoricalSolarResource"}, {"arrows": "to", "from": "SolarResourceBaseAPIModel", "to": "NLRDeveloperAPISolarResourceBase"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "MeteosatPrimeMeridianSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "MeteosatPrimeMeridianTMYSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "Himawari7SolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "Himawari8SolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "HimawariTMYSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESAggregatedSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESConusSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESFullDiscSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESTMYSolarAPI"}, {"arrows": "to", "from": "WindResourceBaseAPIModel", "to": "OpenMeteoHistoricalWindResource"}, {"arrows": "to", "from": "WindResourceBaseAPIModel", "to": "WTKNLRDeveloperAPIWindResource"}, {"arrows": "to", "from": "DemandComponentBase", "to": "GenericDemandComponent"}, {"arrows": "to", "from": "DemandComponentBase", "to": "FlexibleDemandComponent"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "PeakLoadManagementOptimizedStorageController"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "HeuristicLoadFollowingStorageController"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "OptimizedDispatchStorageController"}, {"arrows": "to", "from": "OpenLoopControlBase", "to": "DemandOpenLoopStorageController"}, {"arrows": "to", "from": "OpenLoopControlBase", "to": "SimpleStorageOpenLoopController"}, {"arrows": "to", "from": "OpenLoopControlBase", "to": "PeakLoadManagementHeuristicOpenLoopStorageController"}, {"arrows": "to", "from": "SystemLevelControlBase", "to": "ProfitMaximizationControl"}, {"arrows": "to", "from": "SystemLevelControlBase", "to": "DemandFollowingControl"}, {"arrows": "to", "from": "SystemLevelControlBase", "to": "CostMinimizationControl"}, {"arrows": "to", "from": "PyomoRuleBaseClass", "to": "PyomoDispatchGenericConverter"}, {"arrows": "to", "from": "PyomoRuleBaseClass", "to": "PyomoRuleStorageBaseclass"}]);
 
                   nodeColors = {};
                   allNodes = nodes.get({ returnType: "Object" });
diff --git a/docs/technology_models/iron_mine.md b/docs/technology_models/iron_mine.md
index c1fca3d94..6c2d02c0c 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 +31,33 @@ 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:
+    - [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)
+    - [Tilden Estimated Electrical Costs](https://www.electricitylocal.com/states/michigan/marquette/)
+    - [Hibtac Estimate Electrical Costs](https://www.electricitylocal.com/states/minnesota/hibbing/)
+    - [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/)
+
+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
diff --git a/docs/user_guide/model_overview.md b/docs/user_guide/model_overview.md
index 6d5c80dcc..36b1dfea4 100644
--- a/docs/user_guide/model_overview.md
+++ b/docs/user_guide/model_overview.md
@@ -171,13 +171,13 @@ 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.simple_mine_perf_model.SimpleIronMinePerformanceComponent`
     + {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.
   - 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.simple_mine_cost_model.SimpleIronMineCostComponent`
     + {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.
   - 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_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 3bdce6441..c57eafa3f 100644
--- a/examples/test/test_all_examples.py
+++ b/examples/test/test_all_examples.py
@@ -2275,7 +2275,7 @@ 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"
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 fcff1019b..d7a4f6758 100644
--- a/h2integrate/converters/iron/iron_transport.py
+++ b/h2integrate/converters/iron/iron_transport.py
@@ -87,7 +87,7 @@ 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..22987afe1
--- /dev/null
+++ b/h2integrate/converters/iron/nrri_iron_mine.py
@@ -0,0 +1,432 @@
+import warnings
+
+import numpy as np
+import pandas as pd
+from attrs import field, define
+from openmdao.utils import units
+
+from h2integrate import ROOT_DIR
+from h2integrate.core.utilities import BaseConfig, merge_shared_inputs
+from h2integrate.core.validators import contains, must_equal
+from h2integrate.core.model_baseclasses import CostModelBaseClass, PerformanceModelBaseClass
+
+
+@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=contains(["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 fuel input, default to 0 --> set using feedstock component
+        self.add_input(
+            "fuel_in",
+            val=0.0,
+            shape=self.n_timesteps,
+            units="MMBtu/h",
+            desc="Fuel feedstock into iron mine",
+        )
+
+        self.add_output(
+            "electricity_consumed",
+            val=0.0,
+            shape=self.n_timesteps,
+            units="kW",
+            desc="Electricity consumed",
+        )
+
+        self.add_output(
+            "fuel_consumed", val=0.0, shape=self.n_timesteps, units="MMBtu/h", desc="Fuel 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_fuel": {"units": "MMBtu/h", "desc": "Fuel consumed in mining process"},
+            "concentration_fuel": {
+                "units": "MMBtu/h",
+                "desc": "Fuel consumed in beneficiation process",
+            },
+            "pelletization_fuel": {
+                "units": "MMBtu/h",
+                "desc": "Fuel 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 kWh/wet long ton to kWh/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"] = "fuel 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",
+            "(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 = {}
+        fuel_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
+        fuel_per_process["mining"] = self.coeff_df[
+            (self.coeff_df["process"] == "Mining") & (self.coeff_df["units"] == "MMBtu/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
+        fuel_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
+        fuel_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_fuel_consumed = sum(fuel_per_process.values()) * system_capacity  # MMBtu
+
+        # available feedstocks, saturated at maximum system feedstock consumption
+        electricity_available = np.where(
+            inputs["electricity_in"] > max_elec_consumed,
+            max_elec_consumed,
+            inputs["electricity_in"],
+        )
+        fuel_available = np.where(
+            inputs["fuel_in"] > max_fuel_consumed,
+            max_fuel_consumed,
+            inputs["fuel_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_fuel = (
+            fuel_available / max_fuel_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_fuel,
+                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_fuel"] = fuel_per_process["mining"] * processed_ore_production
+        outputs["concentration_fuel"] = fuel_per_process["concentration"] * processed_ore_production
+        outputs["pelletization_fuel"] = fuel_per_process["pelletization"] * processed_ore_production
+
+        # feedstock consumption
+        outputs["electricity_consumed"] = (
+            sum(energy_per_process.values()) * processed_ore_production
+        )
+        outputs["fuel_consumed"] = sum(fuel_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"
+        cost_year (int): target dollar year to convert costs to.
+    """
+
+    mine: str = field(validator=contains(["Hibbing", "Northshore", "United", "Minorca", "Tilden"]))
+    cost_year: int = field(
+        converter=int, validator=must_equal(2025)
+    )  # TODO: check what year SEC reports are from
+
+
+class NRRIIronMineCostComponent(CostModelBaseClass):
+    _time_step_bounds = (
+        3600,
+        3600,
+    )  # (min, max) time step lengths (in seconds) compatible with this model
+
+    def setup(self):
+        self.config = NRRIIronMineCostConfig.from_dict(
+            merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"),
+            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",
+        )
+
+        self.add_input(
+            "raw_ore",
+            val=0.0,
+            shape=self.n_timesteps,
+            units="t/h",
+            desc="Raw ore mass flow",
+        )
+
+        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"})
+
+        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",
+        }
+        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):
+        outputs["CapEx"] = 0.0  # assumed that it's all included in annualized fixed operating costs
+
+        # OpEx is either on a per LT pellet basis or LT crude ore basis, depending on the mine
+        # Both are included in OpEx calculation, but only one will be non-zero depending on the mine
+        outputs["OpEx"] = (
+            inputs["annual_iron_ore_produced"][0]
+            * self.coeff_df.loc[self.coeff_df["process"] == "pellet", "value"].values
+            + sum(inputs["raw_ore"])
+            * self.coeff_df.loc[self.coeff_df["process"] == "mined", "value"].values
+        )
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..5e58eee30
--- /dev/null
+++ b/h2integrate/converters/iron/nrri_ore/cost_coeffs.csv
@@ -0,0 +1,3 @@
+units,process,Tilden,Hibbing,United,Minorca,Northshore
+USD/LTP,pellet,15.3,19.87,0,16.89,0
+USD/LT,mined,0,0,3.72,0,3.22
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..5e787986e
--- /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,12.02,5.419861815,7.1,10.9,8.43
+kWh/LTP,Comminution (Crushing),9.84,1.699663584,10.57,9.09,9.79
+kWh/LTP,Beneficiation (Concentration),57.35163458,107.89,67.45,67.63,72.68
+kWh/LTP,Pelletization,23.62080378,47.46,32.75,49.51,40.88
+MMBtu/LTP,Mining,0.144675866,0.144675866,0.144675866,0.144675866,0.144675866
+MMBtu/LTP,Beneficiation (Concentration),1.294814579,0.29,0.312045032,0.632286537,0.632286537
+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 d9ba84f21..b5b979f75 100644
--- a/h2integrate/converters/iron/martin_mine_cost_model.py
+++ b/h2integrate/converters/iron/simple_mine_cost_model.py
@@ -12,8 +12,8 @@
 
 
 @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".
@@ -38,7 +38,7 @@ class MartinIronMineCostConfig(BaseConfig):
     cost_year: int = field(converter=int, validator=range_val(2010, 2024))
 
 
-class MartinIronMineCostComponent(CostModelBaseClass):
+class SimpleIronMineCostComponent(CostModelBaseClass):
     _time_step_bounds = (
         3600,
         3600,
@@ -75,7 +75,7 @@ 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__,
@@ -98,7 +98,7 @@ 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 2c33fcd87..8550af8cf 100644
--- a/h2integrate/converters/iron/martin_mine_perf_model.py
+++ b/h2integrate/converters/iron/simple_mine_perf_model.py
@@ -10,8 +10,8 @@
 
 
 @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".
@@ -30,7 +30,7 @@ class MartinIronMinePerformanceConfig(BaseConfig):
     mine: str = field(validator=contains(["Hibbing", "Northshore", "United", "Minorca", "Tilden"]))
 
 
-class MartinIronMinePerformanceComponent(PerformanceModelBaseClass):
+class SimpleIronMinePerformanceComponent(PerformanceModelBaseClass):
     _time_step_bounds = (
         3600,
         3600,
@@ -45,7 +45,7 @@ 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__,
@@ -101,7 +101,7 @@ 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..2b0fcbf02
--- /dev/null
+++ b/h2integrate/converters/iron/test/test_nrri_mine.py
@@ -0,0 +1,109 @@
+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": 2025,
+            },
+        }
+    }
+    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
+    ore_rated_capacity = 7457805 * 0.98 * 1.016
+
+    prob.set_val("comp.electricity_in", [hourly_electricity] * 8760, units="kW")
+    prob.set_val("comp.fuel_in", [hourly_fuel] * 8760, units="MMBtu/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):
+    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.set_val("comp.raw_ore", [1e6] * 8760, units="t/h")
+
+    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 * 15.3, 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"
+    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.set_val("comp.raw_ore", [10] * 8760, units="t/h")
+
+    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(87600 * 3.72 / 1.016, 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 d80d5e1ec..2f0648f45 100644
--- a/h2integrate/core/supported_models.py
+++ b/h2integrate/core/supported_models.py
@@ -90,8 +90,10 @@ def copy(self):
         "SimpleASUCostModel": "converters.nitrogen:SimpleASUCostModel",
         "SimpleASUPerformanceModel": "converters.nitrogen:SimpleASUPerformanceModel",
         "HOPPComponent": "converters.hopp:HOPPComponent",
-        "MartinIronMinePerformanceComponent": "converters.iron:MartinIronMinePerformanceComponent",
-        "MartinIronMineCostComponent": "converters.iron:MartinIronMineCostComponent",
+        "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",