Iron: Adding electrowinning capabilities

CHANGELOG.md

@@ -5,6 +5,7 @@
 - Updates models for NumPy version 2.4.0
 - Update test values for WOMBAT update to 0.13.0
 - Added standalone iron DRI and steel EAF performance and cost models
+- Added iron electrowinning model
 - Added capability to have transport models that require user input parameters
 - Add geologic hydrogen surface processing converter
 - Add baseclass for caching functionality

docs/_toc.yml

@@ -43,6 +43,7 @@ parts:
   - file: technology_models/geologic_hydrogen.md
   - file: technology_models/grid
   - file: technology_models/hydrogen_storage.md
+  - file: technology_models/iron_ewin.md
 
 - caption: Resource Models
   chapters:

docs/technology_models/iron_ewin.md

@@ -0,0 +1,58 @@
+# Iron electrowinning models
+
+H2I contains iron electrowinning models to simulate the reduction of iron oxide to pure iron and removal of impurities.
+The main input feedstock is iron ore, while the output commodity is "sponge iron", i.e. iron that is typically brittle ("spongey") and contains less carbon than most steel alloys.
+This sponge iron can then be used in an electric arc furnace (EAF) to produce steel.
+
+There are currently three iron electrowinning processes modeled in H2I:
+    - Aqueous Hydroxide Electrolysis (AHE)
+    - Molten Salt Electrolysis (MSE)
+    - Molten Oxide Electrolysis (MOE)
+
+In reality, the exact composition and structure of the resulting sponge iron will differ depending on the process and the conditions.
+Currently, H2I models do not make these distinctions, as the technology is new and we are still building out the capability.
+Instead, the models in their current form are based on two recent studies of electrowinning technology as a whole.
+
+The first study is by [Humbert et al.](doi.org/10.1007/s40831-024-00878-3), who focus specifically on iron and the three technologies above.
+These authors gather information on the specific energy required for electrolysis and associated pretreatments needed, which is applied in the `humbert_electrowinning_performance` performance model.
+In their supporting information, they also model the full operational expenditures for each process, which is applied in the `humbert_stinn_electrowinning_cost` cost model.
+
+The second study is by [Stinn & Allanore](doi.org/10.1149.2/2.F06202IF), who present a generalized capital cost model for electrowinning of many different metals.
+These authors use both cost data and physical parameters from existing studies to fit the model to be applicable to any metal, including iron.
+This model is applied in the `humbert_stinn_electrowinning_cost` cost model.
+
+To use this model, specify `"humbert_electrowinning_performance"` as the performance model and `"humbert_stinn_electrowinning_cost"` as the cost model.
+The performance model will Humbert et al.'s energy consumption data to consume electricity as a feedstock and feed this information to the cost model.
+The cost model will calculate capex costs based on the Stinn correlations and opex costs based on the Humbert SI.
+
+## Performance Model
+
+```{eval-rst}
+.. autoclass:: h2integrate.converters.iron.humbert_ewin_perf.HumbertEwinConfig
+   :members:
+   :undoc-members:
+   :show-inheritance:
+```
+
+```{eval-rst}
+.. autoclass:: h2integrate.converters.iron.humbert_ewin_perf.HumbertEwinPerformanceComponent
+   :members:
+   :undoc-members:
+   :show-inheritance:
+```
+
+## Cost Model
+
+```{eval-rst}
+.. autoclass:: h2integrate.converters.iron.humbert_stinn_ewin_cost.HumbertStinnEwinCostConfig
+   :members:
+   :undoc-members:
+   :show-inheritance:
+```
+
+```{eval-rst}
+.. autoclass:: h2integrate.converters.iron.humbert_stinn_ewin_cost.HumbertStinnEwinCostComponent
+   :members:
+   :undoc-members:
+   :show-inheritance:
+```

docs/user_guide/model_overview.md

@@ -44,7 +44,7 @@ The inputs, outputs, and corresponding technology that are currently available i
 | `hopp`           |  electricity  | N/A |
 | `electrolyzer`   |  hydrogen     | electricity |
 | `geoh2`          |  hydrogen     | rock type |
-| `steel`          |  steel        | hydrogen |
+| `steel`          |  steel        | iron ore |
 | `ammonia`        |  ammonia      | nitrogen, hydrogen |
 | `doc`   |  co2     | electricity |
 | `oae`   |  co2     | electricity |
@@ -225,13 +225,18 @@ Below summarizes the available performance, cost, and financial models for each
         + `'iron_mine_performance_martin'`
     - cost models:
         + `'iron_mine_cost_martin'`
-- `iron_dri`: iron ore direct reduction
+- `iron_dri`: direct reduced iron
     - performance models:
         + `'ng_dri_performance_rosner'`
         + `'h2_dri_performance_rosner'`
     - cost models:
         + `'ng_dri_cost_rosner'`
         + `'h2_dri_cost_rosner'`
+- `iron_ewin`: iron electrowinning
+    - performance models:
+        + `'humbert_electrowinning_performance'`
+    - cost models:
+        + `'humbert_stinn_electrowinning_cost'`
 
 (transport-models)=
 ## Transport Models

examples/27_iron_electrowinning/27_iron_electrowinning.yaml

@@ -0,0 +1,7 @@
+name: "H2Integrate_config"
+
+system_summary: "An iron plant using processed ore pellets for electrowinning."
+
+driver_config: "driver_config.yaml"
+technology_config: "tech_config.yaml"
+plant_config: "plant_config.yaml"

examples/27_iron_electrowinning/driver_config.yaml

@@ -0,0 +1,5 @@
+name: "driver_config"
+description: "Simply setting up an outputs folder, nothing fancy"
+
+general:
+  folder_output: outputs

examples/27_iron_electrowinning/plant_config.yaml

@@ -0,0 +1,65 @@
+name: "plant_config"
+description: "Configures an iron electrowinning system."
+
+sites:
+  ewin_site:
+    latitude: 41.717
+    longitude: -88.398
+
+technology_interconnections: [
+  # Connect feedstocks to iron mine.
+  # Requires `electricity` and `crude ore`.
+  # Both are set up as generic feedstock components - see tech_config.
+  ["grid_feedstock","iron_mine","electricity","cable"],
+  ["mine_feedstock","iron_mine","crude_ore","pipe"],
+  # Connect feedstocks to iron electrowinning plant.
+  # Requires `electricity` and `iron_ore`.
+  # Electricity is set up as generic feedstock component,
+  # but iron_ore is set up as an output of an iron_mine.
+  ["iron_mine","iron_plant","iron_ore","iron_transport"],
+  ["ewin_grid_feedstock","iron_plant","electricity","cable"],
+]
+
+plant:
+  plant_life: 30
+finance_parameters:
+  finance_groups:
+    finance_model: "ProFastComp"
+    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.nrel.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", "grid_feedstock", "mine_feedstock"]
+    sponge_iron:
+      commodity: "sponge_iron"
+      commodity_stream: "iron_plant"
+      technologies:
+      - "iron_mine"
+      - "grid_feedstock"
+      - "mine_feedstock"
+      - "iron_transport"
+      - "iron_plant"
+      - "ewin_grid_feedstock"

examples/27_iron_electrowinning/run_iron.py

@@ -0,0 +1,43 @@
+"""Comparing three different iron electrowinning technologies
+
+This script runs an end-to-end iron production system (including the mine) and compares the
+levelized cost of sponge_iron across three different iron electrowinning technologies to see
+how their costs compare:
+    - Aqueous Hydroxide Electrolysis (AHE)
+    - Molten Salt Electrolysis (MSE)
+    - Molten Oxide Electrolysis (MOE)
+
+New users may find it helpful to look at the tech_config.yaml (particularly the iron_plant) to see
+how the technologies were set up, as well as the  plant_config.yaml (particularly the
+technology_interconnections) to see how the technologies were connected.
+
+"""
+
+from h2integrate.core.h2integrate_model import H2IntegrateModel
+
+
+# Create H2Integrate model
+model = H2IntegrateModel("27_iron_electrowinning.yaml")
+
+# Define the electrowinning types as a list
+electrolysis_types = ["ahe", "mse", "moe"]
+lcois = []
+
+for electrolysis_type in electrolysis_types:
+    # Set the technology config value directly
+    model.technology_config["technologies"]["iron_plant"]["model_inputs"]["shared_parameters"][
+        "electrolysis_type"
+    ] = electrolysis_type
+    model.setup()  # re-setup the model after changing config
+    model.run()
+    model.post_process()
+    lcois.append(
+        float(
+            model.model.get_val("finance_subgroup_sponge_iron.price_sponge_iron", units="USD/kg")[0]
+        )
+    )
+
+# Compare the LCOIs from each electrowinning type
+print("Levelized Cost of Iron (LCOI) by Electrowinning Type:")
+for electrolysis_type, lcoi in zip(electrolysis_types, lcois):
+    print(f"  {electrolysis_type.upper()}: ${lcoi:,.2f} per kg of sponge iron")

examples/27_iron_electrowinning/tech_config.yaml

@@ -0,0 +1,90 @@
+name: "technology_config"
+description: "Set up technology for an iron mine/iron electrowinning system"
+
+technologies:
+  grid_feedstock: #electricity feedstock for iron ore - comes from grid
+    performance_model:
+      model: "feedstock_performance"
+    cost_model:
+      model: "feedstock_cost"
+    model_inputs:
+      shared_parameters:
+        feedstock_type: "electricity"
+        units: "MW"
+      performance_parameters:
+        rated_capacity: 30.
+      cost_parameters:
+        cost_year: 2022
+        price: 58.02 #USD/MW
+        annual_cost: 0.
+        start_up_cost: 0.
+
+  mine_feedstock: #crude iron ore feedstock - comes from the ground
+    performance_model:
+      model: "feedstock_performance"
+    cost_model:
+      model: "feedstock_cost"
+    model_inputs:
+      shared_parameters:
+        feedstock_type: "crude_ore"
+        units: "t/h"
+      performance_parameters:
+        rated_capacity: 2000.
+      cost_parameters:
+        cost_year: 2022
+        price: 0.0
+        annual_cost: 0.
+        start_up_cost: 0.
+
+  iron_mine: # iron mine - turns crude_iron into iron_ore
+    performance_model:
+      model: "iron_mine_performance_martin"
+    cost_model:
+      model: "iron_mine_cost_martin"
+    model_inputs:
+      shared_parameters:
+        mine: "Northshore"
+        taconite_pellet_type: "drg"
+        max_ore_production_rate_tonnes_per_hr: 250
+
+  iron_transport: # ship iron_ore from iron_mine to iron_plant
+    performance_model:
+      model: "iron_transport_performance"
+    cost_model:
+      model: "iron_transport_cost"
+    model_inputs:
+      performance_parameters:
+        find_closest_ship_site: False
+        shipment_site: "Chicago"
+      cost_parameters:
+        transport_year: 2022
+        cost_year: 2022
+
+  ewin_grid_feedstock: #electricity feedstock for iron electrowinning
+    performance_model:
+      model: "feedstock_performance"
+    cost_model:
+      model: "feedstock_cost"
+    model_inputs:
+      shared_parameters:
+        feedstock_type: "electricity"
+        units: "kW"
+      performance_parameters:
+        rated_capacity: 600000.
+      cost_parameters:
+        cost_year: 2022
+        price: 0.05802 #USD/kW
+        annual_cost: 0.
+        start_up_cost: 0.
+
+  iron_plant: # iron plant - turns iron_ore into sponge_iron
+    performance_model:
+      model: "humbert_electrowinning_performance"
+    cost_model:
+      model: "humbert_stinn_electrowinning_cost"
+    model_inputs:
+      shared_parameters:
+        electrolysis_type: "ahe"
+      performance_parameters:
+        ore_fe_wt_pct: 65
+        capacity_mw: 600

examples/test/test_all_examples.py

@@ -1795,6 +1795,39 @@ def test_21_iron_dri_eaf_example(subtests):
         assert pytest.approx(lcos, rel=1e-4) == 524.8228189073025
 
 
+def test_27_iron_electrowinning_example(subtests):
+    os.chdir(EXAMPLE_DIR / "27_iron_electrowinning")
+
+    model = H2IntegrateModel("27_iron_electrowinning.yaml")
+
+    with subtests.test("Value check on AHE"):
+        model.technology_config["technologies"]["iron_plant"]["model_inputs"]["shared_parameters"][
+            "electrolysis_type"
+        ] = "ahe"
+        model.setup()
+        model.run()
+        lcoi = model.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/kg")[0]
+        assert pytest.approx(lcoi, rel=1e-4) == 2.187928233525775
+
+    with subtests.test("Value check on MSE"):
+        model.technology_config["technologies"]["iron_plant"]["model_inputs"]["shared_parameters"][
+            "electrolysis_type"
+        ] = "mse"
+        model.setup()
+        model.run()
+        lcoi = model.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/kg")[0]
+        assert pytest.approx(lcoi, rel=1e-4) == 3.3410182461323226
+
+    with subtests.test("Value check on MOE"):
+        model.technology_config["technologies"]["iron_plant"]["model_inputs"]["shared_parameters"][
+            "electrolysis_type"
+        ] = "moe"
+        model.setup()
+        model.run()
+        lcoi = model.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/kg")[0]
+        assert pytest.approx(lcoi, rel=1e-4) == 2.2832248695268893
+
+
 def test_sweeping_different_resource_sites_doe(subtests):
     os.chdir(EXAMPLE_DIR / "27_site_doe_diff")
     import pandas as pd