Iron: Example with independently sized mine and iron/steel plant #433
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| # Iron mine model | ||
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| H2I contains an iron mine model that simulates the extraction of crude ore and its processing into iron ore pellets. | ||
| 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 for further processing (`iron_plant` for instance). | ||
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| This model was developed in conjunction with the University of Minnesota's Natural Resource Research Institute (NRRI). | ||
| NRRI compiled cost and production data from 5 existing mines and provided expertise for analysis at NLR to determine the energy input and cost trends across these mines. | ||
| Four of the mines (Northshore, United, Hibbing, and Minorca) are located in Minnesota, while one (Tilden) is located in Michigan. | ||
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| There are two potential grades of ore produced from an iron mine in this model: | ||
| - Standard or Blast Furnace (BF) grade pellets (62-65% Fe) | ||
| - Direct Reduction (DR) grade pellets (>67% Fe) | ||
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| 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 `h2integreate/converters/iron/martin_ore/`. | ||
| Performance data are included in `perf_inputs.csv`, with cost data in `cost_inputs.csv`. | ||
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| These data were compiled from two sources: | ||
| - The EPA's "Taconite Iron Ore NESHAP Economic Impact Analysis" by [Heller et al.](https://www.epa.gov/sites/default/files/2020-07/documents/taconite_eia_neshap_final_08-2003.pdf) - Capex estimates | ||
| - This document esitmated the total percentage of cost spent throughout the entire industry on capital as a percentage of total production costs - 5.4%. This percentage is applied to the total annual production costs of each plant to find the estimated Capex. | ||
| - Cleveland-Cliffs Inc.'s Technical Report Summaries for individual mines - Opex and performance data | ||
| - [Northshore Mine](https://minedocs.com/22/Northshore-TR-12312021.pdf) | ||
| - [United Mine](https://minedocs.com/22/United-Taconite-TR-12312021.pdf) | ||
| - [Hibbing Mine](https://minedocs.com/22/Hibbing-Taconite-TR-12312021.pdf) | ||
| - [Minorca Mine](https://minedocs.com/22/Minorca-TR-12312021.pdf) | ||
| - [Tilden Mine](https://minedocs.com/22/Tilden-TR-12312021.pdf) | ||
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| To use this model, specify `"iron_mine_performance_martin"` as the performance model and `"iron_mine_cost_martin"` 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 annualy. | ||
| Besides these calculations, previously-calculated performance and cost metrics are simply loaded from the input spreadsheets. |
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| name: "driver_config" | ||
| description: "Simply setting up an outputs folder, nothing fancy" | ||
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| general: | ||
| folder_output: outputs |
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@@ -6,21 +6,22 @@ sites: | |
| latitude: 41.717 | ||
| longitude: -88.398 | ||
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| technology_interconnections: [ | ||
| # Connect feedstocks to iron mine. | ||
| ["mine_electricity_feedstock","iron_mine","electricity","cable"], | ||
| ["mine_crude_ore_feedstock","iron_mine","crude_ore","pipe"], | ||
| # Connect iron_ore price and processed_ore_feedstock to iron_transport | ||
| # Connect the LCOI of iron ore to the price of a feedstock component representing the ore to be used in DRI | ||
| ["finance_subgroup_iron_ore","processed_ore_feedstock",["price_iron_ore","price"]], | ||
| # Connect the ore feedstock available to the iron plant | ||
| ["processed_ore_feedstock","iron_plant","iron_ore","pipe"], | ||
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Collaborator
There was a problem hiding this comment. I replaced iron_transport here with a pipe so that the iron_transport model is not input the total iron ore available |
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| ["dri_grid_feedstock","iron_plant","electricity","cable"], | ||
| ["catalyst_feedstock","iron_plant","reformer_catalyst","pipe"], | ||
| ["water_feedstock","iron_plant","water","pipe"], | ||
| ["natural_gas_feedstock","iron_plant","natural_gas","pipe"], | ||
| # Use the iron ore consumed by the iron plant to calculate transport costs | ||
| ["iron_plant","iron_transport", ["iron_ore_consumed","iron_ore_in"]], | ||
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Collaborator
There was a problem hiding this comment. @jmartin4u - this is where I'm connecting the iron ore consumed to the iron transport model |
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| # Connect feedstocks to steel plant | ||
| ["eaf_grid_feedstock","steel_plant","electricity","cable"], | ||
| ["eaf_water_feedstock","steel_plant","water","pipe"], | ||
| ["eaf_natural_gas_feedstock","steel_plant","natural_gas","pipe"], | ||
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@@ -63,14 +64,12 @@ finance_parameters: | |
| iron_ore: | ||
| commodity: "iron_ore" | ||
| commodity_stream: "iron_mine" | ||
| technologies: ["iron_mine", "mine_electricity_feedstock", "mine_crude_ore_feedstock"] | ||
| pig_iron: | ||
| commodity: "pig_iron" | ||
| commodity_stream: "iron_plant" | ||
| technologies: | ||
| - "processed_ore_feedstock" | ||
| - "iron_transport" | ||
| - "iron_plant" | ||
| - "dri_grid_feedstock" | ||
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@@ -81,9 +80,7 @@ finance_parameters: | |
| commodity: "steel" | ||
| commodity_stream: "steel_plant" | ||
| technologies: | ||
| - "processed_ore_feedstock" | ||
| - "iron_transport" | ||
| - "iron_plant" | ||
| - "dri_grid_feedstock" | ||
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| from pathlib import Path | ||
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| import numpy as np | ||
| import pandas as pd | ||
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| from h2integrate.tools.run_cases import modify_tech_config, load_tech_config_cases | ||
| from h2integrate.core.h2integrate_model import H2IntegrateModel | ||
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| # Create H2Integrate model | ||
| model = H2IntegrateModel("21_iron.yaml") | ||
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| # Load cases | ||
| case_file = Path("test_inputs.csv") | ||
| cases = load_tech_config_cases(case_file) | ||
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| # Modify and run the model for different cases | ||
| casenames = [ | ||
| "Standard Iron - Hibbing", | ||
| "Standard Iron - Northshore", | ||
| "DR Grade Iron - Northshore", | ||
| "DR Grade Iron - Northshore (adjusted)", | ||
| ] | ||
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| lcois_ore = [] | ||
| capexes_ore = [] | ||
| fopexes_ore = [] | ||
| vopexes_ore = [] | ||
| lcois_iron = [] | ||
| capexes_iron = [] | ||
| fopexes_iron = [] | ||
| vopexes_iron = [] | ||
| lcois_steel = [] | ||
| capexes_steel = [] | ||
| fopexes_steel = [] | ||
| vopexes_steel = [] | ||
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| for casename in casenames: | ||
| model = modify_tech_config(model, cases[casename]) | ||
| model.run() | ||
| model.post_process() | ||
| lcois_ore.append(float(model.model.get_val("finance_subgroup_iron_ore.price_iron_ore")[0])) | ||
| capexes_ore.append( | ||
| float(model.model.get_val("finance_subgroup_iron_ore.total_capex_adjusted")[0]) | ||
| ) | ||
| fopexes_ore.append( | ||
| float(model.model.get_val("finance_subgroup_iron_ore.total_opex_adjusted")[0]) | ||
| ) | ||
| vopexes_ore.append( | ||
| float(model.model.get_val("finance_subgroup_iron_ore.total_varopex_adjusted")[0]) | ||
| ) | ||
| lcois_iron.append(float(model.model.get_val("finance_subgroup_pig_iron.price_pig_iron")[0])) | ||
| capexes_iron.append( | ||
| float(model.model.get_val("finance_subgroup_pig_iron.total_capex_adjusted")[0]) | ||
| ) | ||
| fopexes_iron.append( | ||
| float(model.model.get_val("finance_subgroup_pig_iron.total_opex_adjusted")[0]) | ||
| ) | ||
| vopexes_iron.append( | ||
| float(model.model.get_val("finance_subgroup_pig_iron.total_varopex_adjusted")[0]) | ||
| ) | ||
| lcois_steel.append(float(model.model.get_val("finance_subgroup_steel.price_steel")[0])) | ||
| capexes_steel.append( | ||
| float(model.model.get_val("finance_subgroup_steel.total_capex_adjusted")[0]) | ||
| ) | ||
| fopexes_steel.append( | ||
| float(model.model.get_val("finance_subgroup_steel.total_opex_adjusted")[0]) | ||
| ) | ||
| vopexes_steel.append( | ||
| float(model.model.get_val("finance_subgroup_steel.total_varopex_adjusted")[0]) | ||
| ) | ||
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| # Compare the Capex, Fixed Opex, and Variable Opex across the 4 cases | ||
| columns = [ | ||
| "Levelized Cost\n[USD/kg]", | ||
| "Capex\n[USD]", | ||
| "Fixed Opex\n[USD/year]", | ||
| "Variable Opex\n[USD/year]", | ||
| ] | ||
| df_ore = pd.DataFrame( | ||
| np.transpose(np.vstack([lcois_ore, capexes_ore, fopexes_ore, vopexes_ore])), | ||
| index=casenames, | ||
| columns=columns, | ||
| ) | ||
| print(df_ore) | ||
| df_iron = pd.DataFrame( | ||
| np.transpose(np.vstack([lcois_iron, capexes_iron, fopexes_iron, vopexes_iron])), | ||
| index=casenames, | ||
| columns=columns, | ||
| ) | ||
| print(df_iron) | ||
| df_steel = pd.DataFrame( | ||
| np.transpose(np.vstack([lcois_steel, capexes_steel, fopexes_steel, vopexes_steel])), | ||
| index=casenames, | ||
| columns=columns, | ||
| ) | ||
| df_steel = df_steel.iloc[2:] | ||
| print(df_steel) |
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thanks for adding this doc page! It looks great!