Paper mill and synthetic aviation fuels

docs/user_guide/how_to_interface_with_user_defined_model.md

@@ -25,8 +25,8 @@ You can combine an existing H2Integrate model and a custom model for the same te
 
 To demonstrate this capability, we include a minimal example of a custom technology model: a **paper mill**. This example includes:
 
-- A `PaperMillPerformance` model that converts electricity input to paper output.
-- A `PaperMillCost` model that estimates capital and operational expenditures.
+- A `CustomPaperMillPerformance` model that converts electricity input to paper output.
+- A `CustomPaperMillCost` model that estimates capital and operational expenditures.
 - A `PaperMillFinance` technology finance model that computes the levelized cost of paper production (LCOP).
 
 Refer to the [Paper Mill Model Example](https://github.com/NatLabRockies/H2Integrate/tree/develop/examples/06_custom_tech/) for a complete walkthrough.

examples/06_custom_tech/tech_config.yaml

@@ -30,10 +30,10 @@ technologies:
         cost_year: 2019
   paper_mill:
     performance_model:
-      model: PaperMillPerformance
+      model: CustomPaperMillPerformance
       model_location: user_defined_model/paper_mill.py
     cost_model:
-      model: PaperMillCost
+      model: CustomPaperMillCost
       model_location: user_defined_model/paper_mill.py
     finance_model:
       model: PaperMillFinance

examples/06_custom_tech/user_defined_model/paper_mill.py

@@ -15,7 +15,7 @@ class PaperMillConfig(BaseConfig):
     electricity_usage_rate: float = field()
 
 
-class PaperMillPerformance(om.ExplicitComponent):
+class CustomPaperMillPerformance(om.ExplicitComponent):
     _time_step_bounds = (
         3600,
         3600,
@@ -57,7 +57,7 @@ class PaperMillCostConfig(CostModelBaseConfig):
     plant_capacity: float = field()
 
 
-class PaperMillCost(CostModelBaseClass):
+class CustomPaperMillCost(CostModelBaseClass):
     _time_step_bounds = (
         3600,
         3600,

examples/36_paper_mill/36_paper_mill_mn.yaml

@@ -0,0 +1,6 @@
+name: H2Integrate_config
+system_summary: This reference paper mill plant is located in Minnesota and for its first pass, it contains paper mill plant
+  powered by grid. The system is designed to produce paper at a constant rate throughout the year.
+driver_config: driver_config.yaml
+technology_config: tech_config.yaml
+plant_config: plant_config.yaml

examples/36_paper_mill/Breakdown_cost_plots.py

@@ -0,0 +1,185 @@
+# """
+# Created on Fri May 15 07:38:06 2026
+
+# @author: mkoleva
+# """
+
+# import pandas as pd
+# import matplotlib.pyplot as plt
+
+# # Load Excel file
+# file_path = "Breakdown_costs_per_scenario.xlsx"
+# df = pd.read_excel(file_path, sheet_name="Sheet1", header=None)
+
+# # Scenario labels — edit however you prefer
+# scenarios = [
+#     "Paper + Pulp",
+#     "SAF with H2",
+#     "SAF with low-carbon H2",
+#     "Paper + Pulp\nSAF with H2",
+#     "Paper + Pulp\nSAF with low-carbon H2"
+# ]
+
+# # Extract cost component names
+# components = df.iloc[3:, 0].values
+
+# # Build scenario value arrays (sum of appropriate columns)
+# records = {}
+# records["Paper + Pulp"] = df.iloc[3:, [1, 2, 3]].astype(float).sum(axis=1).values
+# records["SAF with H2"] = df.iloc[3:, [3]].astype(float).sum(axis=1).values
+# records["SAF with low-carbon H2"] = df.iloc[3:, [4]].astype(float).sum(axis=1).values
+# records["Paper + Pulp\nSAF with H2"] = df.iloc[3:, [5, 6, 7]].astype(float).sum(axis=1).values
+# results = df.iloc[3:, [8, 9, 10]]
+# records["Paper + Pulp\nSAF with low-carbon H2"] = results.astype(float).sum(axis=1).values
+
+# # Build DataFrame
+# plot_df = pd.DataFrame(records, index=components)
+# plot_df = plot_df[scenarios]  # order consistently
+
+# # Assign custom colors
+# colors = []
+# for comp in plot_df.index:
+#     if "CapEx" in comp:
+#         colors.append("navy")
+#     elif "OpEx" in comp:
+#         colors.append("orange")
+#     elif "Feedstock" in comp:
+#         colors.append("deepskyblue")
+#     elif "Taxes" in comp:
+#         colors.append("lightpink")
+#     elif "Finances" in comp:
+#         colors.append("yellowgreen")
+#     else:
+#         colors.append(None)   # Let matplotlib choose default
+
+# # Plotting
+# plt.figure(figsize=(10, 6))
+# bottom = [0] * len(scenarios)
+
+# for idx, comp in enumerate(plot_df.index):
+#     plt.bar(
+#         scenarios,
+#         plot_df.loc[comp],
+#         bottom=bottom,
+#         color=colors[idx],
+#         label=comp
+#     )
+#     bottom = [bottom[i] + plot_df.loc[comp][i] for i in range(len(scenarios))]
+
+# plt.xlabel("Scenario")
+# plt.ylabel("Cost ($/kg)")
+# plt.title("Cost Breakdown per Scenario")
+
+# # FORCE horizontal x-axis labels
+# plt.xticks(rotation=0, ha="center")
+
+# plt.legend()
+# plt.tight_layout()
+
+# plt.savefig("stacked_cost_breakdown_final.png", dpi=300)
+# plt.show()
+
+import textwrap
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+# -------------------------------------------------------------
+# LOAD EXCEL
+# -------------------------------------------------------------
+file_path = "Breakdown_costs_per_scenario.xlsx"
+df = pd.read_excel(file_path, sheet_name="Sheet1", header=None)
+
+# -------------------------------------------------------------
+# READ STRUCTURE
+# -------------------------------------------------------------
+scenario_row = df.iloc[0, 1:].tolist()
+product_row = df.iloc[1, 1:].tolist()
+components = df.iloc[2:, 0].astype(str).str.strip().tolist()
+values = df.iloc[2:, 1:].astype(float)
+
+# -------------------------------------------------------------
+# CLEAN NANS
+# -------------------------------------------------------------
+valid = [i for i, s in enumerate(scenario_row) if str(s) != "nan"]
+scenario_row = [scenario_row[i] for i in valid]
+product_row = [product_row[i] for i in valid]
+values = values.iloc[:, valid]
+
+# -------------------------------------------------------------
+# MULTILINE SCENARIO LABELS (automatic wrapping)
+# -------------------------------------------------------------
+scenario_row_wrapped = ["\n".join(textwrap.wrap(s, width=18)) for s in scenario_row]
+
+# -------------------------------------------------------------
+# BUILD MULTIINDEX
+# -------------------------------------------------------------
+tuples = list(zip(scenario_row_wrapped, product_row))
+df_plot = pd.DataFrame(values.values, index=components, columns=pd.MultiIndex.from_tuples(tuples))
+
+# -------------------------------------------------------------
+# FLATTENED PRODUCT LABELS
+# -------------------------------------------------------------
+flat_products = product_row
+
+# -------------------------------------------------------------
+# GROUP POSITIONS FOR SCENARIO LABELS
+# -------------------------------------------------------------
+scenario_groups = {}
+for idx, scen in enumerate(scenario_row_wrapped):
+    scenario_groups.setdefault(scen, []).append(idx)
+
+x = np.arange(len(flat_products))
+
+# -------------------------------------------------------------
+# COLOR MAP
+# -------------------------------------------------------------
+color_map = {
+    "CapEx ($/kg)": "navy",
+    "OpEx ($/kg)": "orange",
+    "Feedstock ($/kg)": "deepskyblue",
+    "Taxes ($/kg)": "lightpink",
+    "Finances ($/kg)": "yellowgreen",
+}
+
+# -------------------------------------------------------------
+# PLOT
+# -------------------------------------------------------------
+plt.figure(figsize=(18, 7))
+
+bottom = np.zeros(len(x))
+
+for comp in components:
+    y = df_plot.loc[comp].values
+    plt.bar(x, y, bottom=bottom, color=color_map[comp], label=comp)
+    bottom += y
+
+# -------------------------------------------------------------
+# X-AXIS LABELS (PRODUCT LEVEL)
+# -------------------------------------------------------------
+plt.xticks(x, flat_products, rotation=0, ha="center")
+
+# -------------------------------------------------------------
+# Y-AXIS LABEL
+# -------------------------------------------------------------
+plt.ylabel("Levelized cost ($/kg)")
+
+plt.title("Cost Breakdown by Product and Scenario")
+
+# -------------------------------------------------------------
+# SCENARIO LABELS (CENTERED ABOVE GROUPS)
+# -------------------------------------------------------------
+ymin, ymax = plt.ylim()
+for scen, idxs in scenario_groups.items():
+    center = np.mean(idxs)
+    plt.text(
+        center, ymax + ymax * 0.04, scen, ha="center", va="bottom", fontsize=11, fontweight="bold"
+    )
+
+plt.ylim(ymin, ymax * 1.25)
+
+plt.legend(title="Cost Component", bbox_to_anchor=(1.02, 1), loc="upper left")
+plt.tight_layout()
+plt.show()

examples/36_paper_mill/driver_config.yaml

@@ -0,0 +1,10 @@
+name: driver_config
+description: This analysis runs a paper mill plant and matches other examples in H2Integrate
+general:
+  folder_output: outputs
+recorder:
+  file: cases.sql
+  overwrite_recorder: true
+  flag: true
+  includes: ['*']
+  excludes: ['*_resource*']