Iron: Adding electrowinning capabilities

examples/27_iron_electrowinning/27_iron_electrowinning.yaml

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+name: "H2Integrate_config"
+
+system_summary: "This reference hybrid plant is located in Minnesota and contains wind, solar, and battery storage technologies. The system is designed to produce hydrogen using an electrolyzer and also produce steel using a grid-connected plant."
+
+driver_config: "driver_config.yaml"
+technology_config: "tech_config.yaml"
+plant_config: "plant_config.yaml"

examples/27_iron_electrowinning/driver_config.yaml

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+name: "driver_config"
+description: "This analysis runs a hybrid plant to match the first example in H2Integrate"
+
+general:
+  folder_output: outputs

examples/27_iron_electrowinning/plant_config.yaml

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+name: "plant_config"
+description: "This plant is located in MN, USA..."
+
+site:
+  latitude: 41.717
+  longitude: -88.398
+
+  # array of polygons defining boundaries with x/y coords
+  boundaries: [
+    {
+      x: [0.0, 1000.0, 1000.0, 0.0],
+      y: [0.0, 0.0, 100.0, 1000.0],
+    },
+    {
+      x: [2000.0, 2500.0, 2000.0],
+      y: [2000.0, 2000.0, 2500.0],
+    }
+  ]
+
+
+# array of arrays containing left-to-right technology
+# interconnections; can support bidirectional connections
+# with the reverse definition.
+# this will naturally grow as we mature the interconnected tech
+technology_interconnections: [
+  # connect feedstocks to iron mine
+  ["grid_feedstock","iron_mine","electricity","cable"],
+  ["mine_feedstock","iron_mine","crude_ore","pipe"],
+  # connect feedstocks to iron plant
+  ["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

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+from pathlib import Path
+
+from h2integrate.tools.run_cases import modify_tech_config, load_tech_config_cases
+from h2integrate.core.h2integrate_model import H2IntegrateModel
+
+
+# Create H2Integrate model
+model = H2IntegrateModel("27_iron_electrowinning.yaml")
+
+# Load cases
+case_file = Path("test_inputs.csv")
+cases = load_tech_config_cases(case_file)
+
+# Modify and run the model for different cases
+casenames = [
+    "AHE",
+    "MSE",
+    "MOE",
+]
+lcois = []
+
+for casename in casenames:
+    model = modify_tech_config(model, cases[casename])
+    model.run()
+    model.post_process()
+    lcois.append(float(model.model.get_val("finance_subgroup_sponge_iron.price_sponge_iron")[0]))
+
+# Compare the LCOIs from each electrowinning type
+print(lcois)

examples/27_iron_electrowinning/tech_config.yaml

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+name: "technology_config"
+description: "This hybrid plant produces iron"
+
+technologies:
+  grid_feedstock: #electricity feedstock for iron ore
+    performance_model:
+      model: "feedstock_performance"
+    cost_model:
+      model: "feedstock_cost"
+    model_inputs:
+      shared_parameters:
+        feedstock_type: "electricity"
+        units: "MW"
+      performance_parameters:
+        rated_capacity: 30. # MW, need 27.913 MW per timestep for iron ore
+      cost_parameters:
+        cost_year: 2022
+        price: 58.02 #USD/MW
+        annual_cost: 0.
+        start_up_cost: 0.
+
+  mine_feedstock: #iron ore feedstock
+    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. # need 828.50385048 t/h
+      cost_parameters:
+        cost_year: 2022
+        price: 0.0
+        annual_cost: 0.
+        start_up_cost: 0.
+
+  iron_mine:
+    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:
+    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 dri
+    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:
+    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/27_iron_electrowinning/test_inputs.csv

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+Index 0,Index 1,Index 2,Index 3,Index 4,Type,AHE,MSE,MOE
+technologies,iron_plant,model_inputs,shared_parameters,electrolysis_type,str,ahe,mse,moe

h2integrate/converters/iron/humbert_ewin_perf.py

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+import numpy as np
+import openmdao.api as om
+from attrs import field, define
+
+from h2integrate.core.utilities import BaseConfig, merge_shared_inputs
+from h2integrate.core.validators import contains
+
+
+@define
+class HumbertEwinConfig(BaseConfig):
+    electrolysis_type: str = field(
+        kw_only=True, converter=(str.lower, str.strip), validator=contains(["ahe", "mse", "moe"])
+    )  # product selection
+    ore_fe_wt_pct: float = field(kw_only=True)
+    capacity_mw: float = field(kw_only=True)
+
+
+class HumbertEwinPerformanceComponent(om.ExplicitComponent):
+    """
+    Humbert: doi.org/10.1007/s40831-024-00878-3
+    """
+
+    def initialize(self):
+        self.options.declare("driver_config", types=dict)
+        self.options.declare("plant_config", types=dict)
+        self.options.declare("tech_config", types=dict)
+
+    def setup(self):
+        n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"]
+        self.config = HumbertEwinConfig.from_dict(
+            merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"),
+            strict=False,
+        )
+
+        ewin_type = self.config.electrolysis_type
+        # Lookup specific energy consumption from Humbert Table 10
+        if ewin_type == "ahe":
+            spec_energy_cons_lo = 2.781
+            spec_energy_cons_hi = 3.779
+        elif ewin_type == "mse":
+            spec_energy_cons_lo = 2.720
+            spec_energy_cons_hi = 3.138
+        elif ewin_type == "moe":
+            spec_energy_cons_lo = 2.89
+            spec_energy_cons_hi = 4.45
+        spec_energy_cons_fe = (spec_energy_cons_lo + spec_energy_cons_hi) / 2  # kWh/kg_Fe
+
+        self.add_input("electricity_in", val=0.0, shape=n_timesteps, units="kW")
+        self.add_input("iron_ore_in", val=0.0, shape=n_timesteps, units="kg/h")
+        self.add_input("ore_fe_wt_pct", val=self.config.ore_fe_wt_pct, units="percent")
+        self.add_input("spec_energy_cons_fe", val=spec_energy_cons_fe, units="kW*h/kg")
+        self.add_input("capacity", val=self.config.capacity_mw, units="MW")
+
+        self.add_output(
+            "electricity_consumed",
+            val=0.0,
+            shape=n_timesteps,
+            units="kW",
+            desc="Electricity consumed",
+        )
+        self.add_output("limiting_input", val=0.0, shape=n_timesteps, units=None)
+        self.add_output("sponge_iron_out", val=0.0, shape=n_timesteps, units="kg/h")
+        self.add_output("total_sponge_iron_produced", val=0.0, units="kg/year")
+        self.add_output("output_capacity", val=0.0, units="kg/year")
+
+    def compute(self, inputs, outputs):
+        n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"]
+
+        # Parse inputs
+        elec_in = inputs["electricity_in"]
+        ore_in = inputs["iron_ore_in"]
+        pct_fe = inputs["ore_fe_wt_pct"]
+        kwh_kg_fe = inputs["spec_energy_cons_fe"]
+        cap_kw = inputs["capacity"] * 1000
+
+        # If no connected input, set ore / electricity to max needed
+        if self.get_source("electricity_in")[:9] == "_auto_ivc":
+            elec_in = np.full(n_timesteps, cap_kw)
+        if self.get_source("iron_ore_in")[:9] == "_auto_ivc":
+            ore_in = np.full(n_timesteps, cap_kw / kwh_kg_fe / pct_fe * 100)
+
+        # Calculate max iron production for each input
+        fe_from_ore = ore_in * pct_fe / 100
+        fe_from_elec = elec_in / kwh_kg_fe
+
+        # Limiting iron production per hour by each input
+        fe_prod = np.minimum.reduce([fe_from_ore, fe_from_elec])
+        limiters = np.argmin([fe_from_ore, fe_from_elec], axis=0)
+
+        # Limiting iron production per hour by capacity
+        fe_prod = np.minimum.reduce([fe_prod, np.full(len(fe_prod), cap_kw / kwh_kg_fe)])
+        cap_lim = 1 - np.argmax([fe_prod, np.full(len(fe_prod), cap_kw / kwh_kg_fe)], axis=0)
+
+        # Determine what the limiting factor is for each hour
+        limiters = np.maximum.reduce([cap_lim * 2, limiters])
+        outputs["limiting_input"] = limiters
+
+        # Determine actual electricity consumption from iron consumption
+        elec_consume = fe_prod * kwh_kg_fe
+
+        # Return iron production
+        outputs["sponge_iron_out"] = fe_prod
+        outputs["electricity_consumed"] = elec_consume
+        outputs["total_sponge_iron_produced"] = np.sum(fe_prod)
+        outputs["output_capacity"] = cap_kw / kwh_kg_fe * 8760