{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71fda4a6",
   "metadata": {},
   "source": [
    "This notebook has been designed to complement the article: \n",
    "\n",
    "Freboeuf L., Sonnemann G., Magdalena R., Helmut R., Beylot A. Resource Assessment methods in Industrial Ecology and Process Engineering: a critical review towards improved consideration in LCA. Journal of Industrial Ecology.\n",
    "\n",
    "This online resource provides the Jupyter notebook used to produce Figures 4 and 5, and the code to obtain the CExD score used for comparison in the Discussion Section "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bfe1374",
   "metadata": {},
   "source": [
    "# Results Fig 4-5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67ba994f",
   "metadata": {},
   "source": [
    "## Input data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb9f6972",
   "metadata": {},
   "source": [
    "### General, python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "44cf705d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Librairies ---\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "from typing import Dict, List, Mapping, Optional\n",
    "\n",
    "# plots\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from itertools import combinations\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "# --- Unified Font & Text Settings ---\n",
    "plt.rcParams[\"font.family\"]    = \"monospace\"\n",
    "plt.rcParams[\"font.monospace\"] = [\"Lucida Console\", \"DejaVu Sans Mono\"]\n",
    "\n",
    "fontsize_title = 16\n",
    "fontsize_label = 14\n",
    "fontsize_ticks = 12\n",
    "fontsize_legend = 11"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2a0d048",
   "metadata": {},
   "source": [
    "### Thermodynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07213664",
   "metadata": {},
   "outputs": [],
   "source": [
    "species_involved = [\"Al2O3\", \"Al2SiO5\", \"Al2Si2O5(OH)4\", \"H2O\", \"SiO2\", \"Al\", \"Si\", \"O2\", \"H2\"]  # List of all species involved in the reactions\n",
    "resource = \"Al2O3\"  # Resource species (j) in this case, corundum\n",
    "reference_elements_i = [\"Al\", \"O2\", \"Si\", \"H2\"]  # Reference elements (i) for the reactions, chosen based on the species involved and their standard states\n",
    "\n",
    "# Standard state for elements\n",
    "standard_state_equivalencies = {\n",
    "    \"O\": \"O2\",\n",
    "    \"Al\": \"Al\",\n",
    "    \"Si\": \"Si\",\n",
    "    \"H\": \"H2\",\n",
    "    }  # Map atomic to molecular for standard states\n",
    "\n",
    "# reaction of formation from the reference chemical elements (i) to the resource (j)\n",
    "reaction_formation_jfromis_sc = { # \"sc\" stands for \"stoichiometric coefficients\"\n",
    "    \"Al2O3\": 1,\n",
    "    \"Al\": -2,\n",
    "    \"O2\": -1.5,\n",
    "}\n",
    "\n",
    "# Environment conditions\n",
    "Tenv = 298.15  # K\n",
    "penv = 1.015 * 10**5  # Pa, not needed for G° calculations but included for completeness\n",
    "\n",
    "# --- the reference environmental species can change => different dictionaries...\n",
    "# SZARGUT :\n",
    "# For Szargut, the values are taken from 🪢 https://www.researchgate.net/profile/Alicia-Valero/publication/271828138_main/data/54d2565d0cf28e069723e580/main.pdf (free access).\n",
    "# The original paper, where Szargut explains how chemical exergi is computed is 📖 https://doi.org/10.1016/0306-2619(89)90016-0, but I don't have access to it.\n",
    "\n",
    "# Al2O3 + SiO2 = Al2SiO5\n",
    "reference_environment_species_k_szargut = [\"Al2SiO5\", \"SiO2\"]  # Reference environmental species for Szargut\n",
    "\n",
    "reaction_formation_jfromks_sc_szargut = { # \"sc\" stands for \"stoichiometric coefficients\"\n",
    "    \"Al2O3\": 1,\n",
    "    \"Al2SiO5\": -1,\n",
    "    \"SiO2\": 1,\n",
    "}\n",
    "\n",
    "# SiO2 is an env species used,  and Al2SiO5 is an env species created\n",
    "szargut_chem_exergy_i = { # kj/mol\n",
    "    \"Al\": 790.34,   # Aluminium (i)\n",
    "    \"O2\": 3.97,  # Dioxygen (i)\n",
    "    \"Si\": 854.17,   # Silicon (i)\n",
    "    \"H2\": 236.10,  # Dihydrogen (i)\n",
    "}\n",
    "\n",
    "HSC_chem_exergy_i = { # kj/mol\n",
    "    \"Al\": 795.70,   # Aluminium (i)\n",
    "    \"O2\": 3.92,  # Dioxygen (i)\n",
    "    \"Si\": 855.00,   # Silicon (i)\n",
    "    \"H2\": 236.12,  # Dihydrogen (i)\n",
    "}\n",
    "\n",
    "staneck_chem_exergy_i = { # kj/mol # 📖 https://www.sudoc.fr/224722972,  ISBN : 978-3-319-48648-2 = szargut_chem_exergy_i\n",
    "    \"Al\": 790.34,   # Aluminium (i)\n",
    "    \"O2\": 3.97,  # Dioxygen (i)\n",
    "    \"Si\": 854.17,   # Silicon (i)\n",
    "    \"H2\": 236.1,  # Dihydrogen (i)\n",
    "}\n",
    "\n",
    "rivero_chem_exergy_i = { # kj/mol  # 📖 https://www-sciencedirect-com.docelec.u-bordeaux.fr/science/article/pii/S0360544206000569?via%3Dihub\n",
    "    \"Al\": 795.7,   # Aluminium (i)\n",
    "    \"O2\": 3.92,  # Dioxygen (i)\n",
    "    \"Si\": 855.00,   # Silicon (i)\n",
    "    \"H2\": 236.12,  # Dihydrogen (i)\n",
    "}\n",
    "\n",
    "szargut_chem_exergy_k = {\n",
    "    \"O2\": 3.97,  # Dioxygen (k)\n",
    "    \"Al2SiO5\": 3.79,\n",
    "    \"SiO2\": 1.44,\n",
    "    \"H2O\": 9.49,\n",
    "}\n",
    "\n",
    "HSC_chem_exergy_k = {\n",
    "    \"O2\": 3.92, # Dioxygen (k)\n",
    "    \"Al2SiO5\": 34.55,\n",
    "    \"SiO2\": 2.63,\n",
    "    \"H2O\": 0.94,\n",
    "    \"Al2Si2O5(OH)4\": -10.53 # 29.86, # Halloysite (k4)\n",
    "    # \"Al2Si2O5(OH)4\": -10.53, # Kaolinite (k1)\n",
    "}\n",
    "\n",
    "staneck_chem_exergy_k = { # = szargut_chem_exergy_k\n",
    "    \"O2\": 3.97,  # \n",
    "    \"Al2SiO5\": 3.79,\n",
    "    \"SiO2\": 1.44,\n",
    "    \"H2O\": 9.49\n",
    "}\n",
    "\n",
    "rivero_chem_exergy_k = { # = szargut_chem_exergy_k\n",
    "    \"O2\": 3.92,  # \n",
    "    \"Al2SiO5\": 15.32,\n",
    "    \"SiO2\": 2.23,\n",
    "    \"H2O\": 9.49\n",
    "}\n",
    "\n",
    "# Standard Gibbs free energy of formation (ΔGºf) in kJ/mol\n",
    "DeltafrG_std_szargut_brgm = {\n",
    "    \"Al2O3\": -1582.257,  # Corundum (j) 🪢 https://thermoddem.brgm.fr/species/corundumalpha\n",
    "    \"Al2SiO5\": -2440.990,  # Aluminite (k1) \n",
    "    \"SiO2\": -856.700,     # Quartz (k2)\n",
    "    \"H2O\": -228.59,\n",
    "}\n",
    "\n",
    "DeltafrG_std_staneck_brgm = {\n",
    "    \"Al2O3\": -1582.257,  # Corundum (j) 🪢 https://thermoddem.brgm.fr/species/corundumalpha\n",
    "    \"Al2SiO5\": -2440.990,  # Aluminite (k1) \n",
    "    \"SiO2\": -856.700,     # Quartz (k2)\n",
    "    \"H2O\": -228.59,\n",
    "}\n",
    "\n",
    "DeltafrG_std_rivero_brgm = {\n",
    "    \"Al2O3\": -1582.257,  # Corundum (j) 🪢 https://thermoddem.brgm.fr/species/corundumalpha\n",
    "    \"Al2SiO5\": -2440.99,  # Aluminite (k1) \n",
    "    \"SiO2\": -856.700,     # Quartz (k2)\n",
    "    \"H2O\": -228.59,\n",
    "}\n",
    "\n",
    "DeltafrG_std_HSC = {\n",
    "    \"Al2O3\": -1582.274,  # Corundum (j) \n",
    "    \"Al2SiO5\": -2421.647,  # Sillimanite # CAS: 12141-46-7 \n",
    "    \"SiO2\": -856.285,     # Quartz (k2)\n",
    "    \"H2O\": -237.141,      # Water (k3)\n",
    "    \"Al2Si2O5(OH)4\": -3801.813,  # Kaolinite (k4)\n",
    "}\n",
    "\n",
    "DeltafrG_std_FS = {\n",
    "    \"Al2O3\": -1582.2937,  # corundum (alpha) (j), FactPS\n",
    "    \"Al2SiO5\": -3023.6378,  # Al2SiO5_kyanite(s3), FactPS\n",
    "    \"SiO2\": -856.3181,     # Quartz(l) (k2), FactPS\n",
    "    \"H2O\": -237.1900,      # Water (k3), FactPS\n",
    "    \"Al2Si2O5(OH)4\": -3799.6558,  # Kaolinite (k4), FactPS\n",
    "}\n",
    "\n",
    "# 📖 DE MEESTER : doi/10.1021/es060167d\n",
    "# Al2O3 + 2*SiO2 + 2H2O = Al2Si2O5(OH)4\n",
    "# 2*SiO2 and 2*H2O are env species used, and Al2Si2O5(OH)4 is an env species created\n",
    "reference_environment_species_k_demeester = [\"Al2Si2O5(OH)4\", \"SiO2\", \"H2O\"]\n",
    "\n",
    "reaction_formation_jfromks_sc_demeester = { # \"sc\" stands for \"stoichiometric coefficients\"\n",
    "    \"Al2O3\": 1,\n",
    "    \"Al2Si2O5(OH)4\": -1,\n",
    "    \"SiO2\": 2,\n",
    "    \"H2O\": 2,\n",
    "}\n",
    "\n",
    "demeester_chem_exergy_i = { # kj/mol\n",
    "    \"Al\" : 1709.2975, # ⚠️ CORRECTED ⚠️ The real value reported is: 809.4 in https://pubs.acs.org/doi/suppl/10.1021/es060167d/suppl_file/es060167dsi20060522_074651.pdf\n",
    "    \"O2\": 3.97, # checked ☑️\n",
    "    \"Si\" : 853.8, # checked ☑️\n",
    "    \"H2\": 236.0, # checked ☑️\n",
    "}\n",
    "\n",
    "demeester_chem_exergy_k = { # kj/mol\n",
    "    \"Al2Si2O5(OH)4\": 12.26, # assume true\n",
    "    \"SiO2\": 1.37, # assume true\n",
    "    \"H2O\": 9.49, # assume true\n",
    "    \"O2\": 3.97, # \n",
    "}\n",
    "\n",
    "# Standard Gibbs free energy of formation (ΔGºf) in kJ/mol\n",
    "DeltafrG_std_demeester_brgm = {\n",
    "    \"Al2O3\": -1582.257,  # Corundum (j) 🪢 https://thermoddem.brgm.fr/species/corundumalpha\n",
    "    \"Al2Si2O5(OH)4\": -5603.8,  # Kaolinite (k1)  \n",
    "    \"SiO2\": -856.4,     # Quartz (k2)\n",
    "    \"H2O\": -228.5,    # Water (k3)\n",
    "}\n",
    "\n",
    "\n",
    "# Standard entropy (S°) in J/mol·K found in thermoddem database 🪢 https://thermoddem.brgm.fr/data \n",
    "S_std_brgm = {\n",
    "    \"Al\": 28.30,   # Aluminium (i)\n",
    "    \"O2\": 205.15,  # Dioxygen (i)\n",
    "    \"Si\": 18.81,   # Silicon (i)\n",
    "    \"H2\": 130.68,  # Dihydrogen (i)\n",
    "}\n",
    "\n",
    "S_std_HSC = {\n",
    "    \"Al\": 28.275,   # Aluminium (i)\n",
    "    \"O2\": 205.149,  # Dioxygen (i)\n",
    "    \"Si\": 18.820,   # Silicon (i)\n",
    "    \"H2\": 130.679,  # Dihydrogen (i)\n",
    "}\n",
    "\n",
    "S_std_FS = {\n",
    "    \"Al\": 28.275,   # Aluminium (i)\n",
    "    \"O2\": 205.0376,  # Dioxygen (i) \n",
    "    \"Si\": 18.82,   # Silicon (i)\n",
    "    \"H2\": 130.5706,  # Dihydrogen (i)\n",
    "}           \n",
    "\n",
    "# Standard enthalpy (H°) in kJ/mol (empty if not provided)\n",
    "H_std = {}\n",
    "\n",
    "# Standard Gibbs free energy (G°) in kJ/mol (empty if not provided)\n",
    "G_std = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aebb36a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The b value we get when we take the g value from holland reported by demeester:\n",
      " 1709.2975000000004\n",
      "the G value reported from Holland db is: \n",
      " -5603.8\n",
      " while it should be according to Ex value reported by Demeester:\n",
      " -3804.005\n"
     ]
    }
   ],
   "source": [
    "## ⚠️ Issue potential with Demeester G reported\n",
    "g_demeester = -5603.8 # kJ/mol\n",
    "chem_ex_si_dm = 853.8 # kJ/mol\n",
    "chem_ex_o2_dm = 3.97 # kJ/mol\n",
    "chem_ex_H2_dm = 236 # kJ/mol\n",
    "b_ch_kao = 12.26 # kJ/mol\n",
    "element = \"Al2Si2O5(OH)4\"\n",
    "formula_chem_al = (b_ch_kao - (g_demeester + 2*chem_ex_si_dm + 4.5*chem_ex_o2_dm + 2*chem_ex_H2_dm))/2\n",
    "print(f\"The b value we get when we take the g value from holland reported by demeester:\\n {formula_chem_al}\")\n",
    "chem_ex_al_reported = 809.4 # kJ/mol\n",
    "correct_g_compar_to_ex_al = (2*chem_ex_al_reported - b_ch_kao)*-1 - (2*chem_ex_si_dm + 4.5*chem_ex_o2_dm + 2*chem_ex_H2_dm)\n",
    "print(f\"the G value reported from Holland db is: \\n {g_demeester}\\n while it should be according to Ex value reported by Demeester:\\n {correct_g_compar_to_ex_al}\")\n",
    "# Standard Gibbs free energy of formation (ΔGºf) in kJ/mol\n",
    "DeltafrG_std_demeester_brgm = {\n",
    "    \"Al2O3\": -1582.257,  # Corundum (j) 🪢 https://thermoddem.brgm.fr/species/corundumalpha\n",
    "    \"Al2Si2O5(OH)4\": correct_g_compar_to_ex_al,  # Kaolinite (k1)  \n",
    "    \"SiO2\": -856.4,     # Quartz (k2)\n",
    "    \"H2O\": -228.5,    # Water (k3)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf0bec16",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1e8bd12b",
   "metadata": {},
   "outputs": [],
   "source": [
    "### we at least must used the b_chk of the reference environment species:\n",
    "\n",
    "# --- Helper Function: Parse Chemical Formula ---\n",
    "def parse_formula(formula: str, standard_state_equivalencies: Dict[str, str]) -> Dict[str, float]:\n",
    "    \"\"\"\n",
    "    Parse a chemical formula into a dictionary of standard state species and counts.\n",
    "    \"\"\"\n",
    "    # Handle (OH)4\n",
    "    formula = formula.replace(\"(OH)4\", \"O4 H4\")\n",
    "    formula = formula.replace(\"(\", \"\").replace(\")\", \"\")\n",
    "    elements = re.findall(r\"([A-Z][a-z]*)(\\d*)\", formula)\n",
    "    counts = {}\n",
    "    for (element, count) in elements:\n",
    "        if count == \"\":\n",
    "            count = 1\n",
    "        else:\n",
    "            count = int(count)\n",
    "        counts[element] = counts.get(element, 0) + count\n",
    "    \n",
    "    # Adjust for standard states\n",
    "    new_counts = {}\n",
    "    for element, count in counts.items():\n",
    "        if element in standard_state_equivalencies:\n",
    "            std = standard_state_equivalencies[element]\n",
    "            if std == \"O2\" and element == \"O\":\n",
    "                new_counts[std] = new_counts.get(std, 0) + count / 2.0\n",
    "            elif std == \"H2\" and element == \"H\":\n",
    "                new_counts[std] = new_counts.get(std, 0) + count / 2.0\n",
    "            else:\n",
    "                new_counts[std] = new_counts.get(std, 0) + count\n",
    "        else:\n",
    "            new_counts[element] = new_counts.get(element, 0) + count\n",
    "    return new_counts\n",
    "\n",
    "def compute_b_chi_sequential(\n",
    "    chem_exergy_k,\n",
    "    DeltafrG_std,\n",
    "    standard_state_equivalencies,\n",
    "    b_chk_init,\n",
    "    env_species_order\n",
    "):\n",
    "    \"\"\"\n",
    "    Strict thermodynamic back-calculation:\n",
    "\n",
    "    b_chk = ΔGf + Σ(n_i * b_chi_i)\n",
    "\n",
    "    RULES:\n",
    "    - ONLY O2 is known initially\n",
    "    - Si, H are solved (not given)\n",
    "    - Al is solved last via aluminosilicates\n",
    "    \"\"\"\n",
    "\n",
    "    # ------------------------------------------------------------\n",
    "    # INITIAL STATE: ONLY O2 IS KNOWN\n",
    "    # ------------------------------------------------------------\n",
    "    b_chi = {\n",
    "        \"O2\": b_chk_init[\"O2\"]\n",
    "    }\n",
    "\n",
    "    def parse(species):\n",
    "        return parse_formula(species, standard_state_equivalencies)\n",
    "\n",
    "    def solve(species):\n",
    "        if species not in chem_exergy_k or species not in DeltafrG_std:\n",
    "            return False\n",
    "\n",
    "        stoich = parse(species)\n",
    "\n",
    "        known_sum = 0.0\n",
    "        unknown = None\n",
    "\n",
    "        for el, coef in stoich.items():\n",
    "            if el in b_chi:\n",
    "                known_sum += coef * b_chi[el]\n",
    "            else:\n",
    "                if unknown is None:\n",
    "                    unknown = (el, coef)\n",
    "                else:\n",
    "                    return False  # too many unknowns\n",
    "\n",
    "        if unknown is None:\n",
    "            return False\n",
    "\n",
    "        el, coef = unknown\n",
    "\n",
    "        b_chi[el] = (\n",
    "            chem_exergy_k[species]\n",
    "            - DeltafrG_std[species]\n",
    "            - known_sum\n",
    "        ) / coef\n",
    "\n",
    "        return True\n",
    "\n",
    "    # ------------------------------------------------------------\n",
    "    # STEP 1 — O2 already known\n",
    "    # ------------------------------------------------------------\n",
    "\n",
    "    # ------------------------------------------------------------\n",
    "    # STEP 2 — derive Si and H FIRST (must be causal)\n",
    "    # ------------------------------------------------------------\n",
    "    primary_species = [\"SiO2\", \"H2O\"]\n",
    "\n",
    "    for sp in primary_species:\n",
    "        solve(sp)\n",
    "\n",
    "    # ------------------------------------------------------------\n",
    "    # STEP 3 — iterate full propagation for Al system\n",
    "    # ------------------------------------------------------------\n",
    "    for _ in range(5):\n",
    "        for sp in env_species_order:\n",
    "            solve(sp)\n",
    "\n",
    "    return b_chi\n",
    "\n",
    "# --- Function: Compute G° for Elements and Compounds ---\n",
    "def compute_G_std(\n",
    "    species: str,\n",
    "    DeltafrG_std: Dict[str, float],\n",
    "    S_std: Dict[str, float],\n",
    "    H_std: Dict[str, float],\n",
    "    G_std: Dict[str, float],\n",
    "    reference_elements: List[str],\n",
    "    standard_state_equivalencies: Dict[str, str],\n",
    "    Tenv: float\n",
    ") -> Optional[float]:\n",
    "    \"\"\"\n",
    "    Compute G° (kJ/mol) for a species at given temperature.\n",
    "    \"\"\"\n",
    "    if species in G_std:\n",
    "        return G_std[species]\n",
    "    \n",
    "    # Check if species is an element (in standard state)\n",
    "    if species in reference_elements:\n",
    "        if species in S_std:\n",
    "            return -Tenv * S_std[species] / 1000  # Convert J to kJ\n",
    "        else:\n",
    "            return None\n",
    "    \n",
    "    # For compounds, use ΔG°f + sum of elements' G°\n",
    "    if species in DeltafrG_std:\n",
    "        elements = parse_formula(species, standard_state_equivalencies)\n",
    "        G_elements = 0\n",
    "        for element, count in elements.items():\n",
    "            G_element = compute_G_std(\n",
    "                element, DeltafrG_std, S_std, H_std, G_std, \n",
    "                reference_elements, standard_state_equivalencies, Tenv\n",
    "            )\n",
    "            if G_element is None:\n",
    "                return None\n",
    "            G_elements += count * G_element\n",
    "        return DeltafrG_std[species] + G_elements\n",
    "    \n",
    "    # Try from H° and S° if available\n",
    "    if species in S_std and species in H_std:\n",
    "        return H_std[species] - Tenv * S_std[species] / 1000\n",
    "    \n",
    "    return None\n",
    "\n",
    "# --- Function: Compute G for Reference State sum_i (elements) ---\n",
    "def compute_std_G_sumi_in_j(\n",
    "    reaction_formation: Dict[str, float],\n",
    "    S_std: Dict[str, float],\n",
    "    Tenv: float\n",
    ") -> float:\n",
    "    \"\"\"\n",
    "    Compute G(∑i, °) = Σ (stoich_coef * G°_element)\n",
    "    \"\"\"\n",
    "    G_sum_i = 0.0\n",
    "    for species, coef in reaction_formation.items():\n",
    "        if coef < 0:  # Reactants (elements)\n",
    "            if species in S_std:\n",
    "                G_element = -Tenv * S_std[species] / 1000\n",
    "                G_sum_i += abs(coef) * G_element\n",
    "            else:\n",
    "                raise ValueError(f\"Missing S° for element {species}\")\n",
    "    return G_sum_i\n",
    "\n",
    "# --- Function: Compute G for Reference State sum_k (environmental species) ---\n",
    "def compute_std_G_sumk_in_j(\n",
    "    DeltafrG_std: Dict[str, float],\n",
    "    S_std: Dict[str, float],\n",
    "    H_std: Dict[str, float],\n",
    "    G_std: Dict[str, float],\n",
    "    reference_elements: List[str],\n",
    "    standard_state_equivalencies: Dict[str, str],\n",
    "    Tenv: float,\n",
    "    reaction_formation_j_from_k: Dict[str, float],\n",
    "    resource: str\n",
    ") -> float:\n",
    "    \"\"\"\n",
    "    Compute G(∑k, °) = Σ (-coef_k * G°_k_species) from the reaction j = Σ coef_k * k\n",
    "    \"\"\"\n",
    "    G_sum_k = 0.0\n",
    "    for species, coef in reaction_formation_j_from_k.items():\n",
    "        if species == resource:\n",
    "            continue\n",
    "        G_species = compute_G_std(\n",
    "            species, DeltafrG_std, S_std, H_std, G_std,\n",
    "            reference_elements, standard_state_equivalencies, Tenv\n",
    "        )\n",
    "        if G_species is None:\n",
    "            raise ValueError(f\"Cannot compute G° for {species}\")\n",
    "        G_sum_k += -coef * G_species\n",
    "    return G_sum_k\n",
    "\n",
    "# --- Function: Compute the value of chemical exergy of the sum of environment reference species k present in the resource j\n",
    "def compute_sumk_in_j(\n",
    "    chem_exergy_k: Dict[str, float],\n",
    "    reaction_formation_jfrom_k: Dict[str, float]\n",
    ") -> float:\n",
    "    \"\"\"\n",
    "    Compute Szargut(∑k, env) from chemical exergies of environmental species\n",
    "    Based on reaction: j = Al2O3 = Al2SiO5 - SiO2 for Szargut reference environment species\n",
    "        or j = Al2O3 = Al2Si2O5(OH)4 - 2*SiO2 - 2*H2O for De Meester reference environment species\n",
    "    \"\"\"\n",
    "    # For reaction: Al2O3 -> Al2SiO5 - SiO2\n",
    "    # B_sumk_in_j = Σ (coef_k * exergy_k) with signs opposite to formation\n",
    "    if reaction_formation_jfrom_k == reaction_formation_jfromks_sc_szargut:\n",
    "        szargut_value = 0.0\n",
    "        for species, coef in reaction_formation_jfrom_k.items():\n",
    "            if species in chem_exergy_k:\n",
    "                if species == \"Al2O3\":\n",
    "                    continue  # Skip the resource species\n",
    "                szargut_value += -coef * chem_exergy_k[species]\n",
    "        return szargut_value\n",
    "    if reaction_formation_jfrom_k == reaction_formation_jfromks_sc_demeester:\n",
    "        demeester_value = 0.0\n",
    "        for species, coef in reaction_formation_jfrom_k.items():\n",
    "            if species in chem_exergy_k:\n",
    "                if species == \"Al2O3\":\n",
    "                    continue  # Skip the resource species\n",
    "                demeester_value += -coef * chem_exergy_k[species]\n",
    "        return demeester_value\n",
    "\n",
    "# --- Function: Compute the value of chemical exergy of the sum of standard reference species i present in the resource j\n",
    "def compute_sumi_in_j(\n",
    "    chem_exergy_i: Dict[str, float],\n",
    "    reaction_formation_jfrom_i: Dict[str, float]\n",
    ") -> float:\n",
    "    \"\"\"\n",
    "    Compute Szargut(∑\\nν_i * B_ch,i) from chemical exergies of reference elements\n",
    "    Based on reaction: j = Al2O3 = 2Al + 1.5O2\n",
    "    \"\"\"\n",
    "    # For reaction: Al2O3 -> 2Al + 1.5O2\n",
    "    # B_sumi_in_j = Σ (coef_i * exergy_i) with signs opposite to formation\n",
    "    szargut_value = 0.0\n",
    "    for species, coef in reaction_formation_jfrom_i.items():\n",
    "        if species in chem_exergy_i:\n",
    "            if species == \"Al2O3\":\n",
    "                continue  # Skip the resource species\n",
    "            szargut_value += -coef * chem_exergy_i[species]\n",
    "    return szargut_value\n",
    "\n",
    "# --- Main Function: Compute All 6 G Values ---\n",
    "def compute_all_G_values(\n",
    "    # Thermodynamic data\n",
    "    DeltafrG_std: Dict[str, float],\n",
    "    S_std: Dict[str, float],\n",
    "    H_std: Dict[str, float],\n",
    "    G_std: Dict[str, float],\n",
    "    \n",
    "    # Chemical systems\n",
    "    resource: str,\n",
    "    reference_elements_i: List[str],\n",
    "    reference_species_k: List[str],\n",
    "    \n",
    "    # Reactions\n",
    "    reaction_formation_j_from_i: Mapping[str, float],\n",
    "    reaction_formation_j_from_k: Mapping[str, float],\n",
    "    \n",
    "    # Szargut data\n",
    "    chem_exergy_k: Dict[str, float],\n",
    "    chem_exergy_i: Dict[str, float],  # Chemical exergies of elements for Szargut(∑ν_i * B_ch,i)\n",
    "    \n",
    "    # Environment\n",
    "    Tenv: float,\n",
    "    \n",
    "    # Helpers\n",
    "    standard_state_equivalencies: Dict[str, str]\n",
    ") -> Dict[str, float]:\n",
    "    \"\"\"\n",
    "    Compute all 6 G values and related quantities.\n",
    "    Returns dictionary with all computed values.\n",
    "    \"\"\"\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    # Step 1: Compute G(∑i, °)\n",
    "    results[\"G_std_sumi_in_j\"] = compute_std_G_sumi_in_j(\n",
    "        reaction_formation_j_from_i, S_std, Tenv\n",
    "    )\n",
    "    \n",
    "    # Step 2: Compute G(j, °) for resource\n",
    "    G_std_resource = compute_G_std(\n",
    "        resource, DeltafrG_std, S_std, H_std, G_std,\n",
    "        reference_elements_i, standard_state_equivalencies, Tenv\n",
    "    )\n",
    "    if G_std_resource is None:\n",
    "        raise ValueError(f\"Cannot compute G° for {resource}\")\n",
    "    results[\"G_std_j\"] = G_std_resource\n",
    "    \n",
    "    # Step 3: Compute G(∑k, °)\n",
    "    results[\"G_std_sumk_in_j\"] = compute_std_G_sumk_in_j(\n",
    "        DeltafrG_std, S_std, H_std, G_std,\n",
    "        reference_elements_i, standard_state_equivalencies, Tenv,\n",
    "        reaction_formation_j_from_k, resource\n",
    "    )\n",
    "    \n",
    "    # Step 4: Compute Szargut exergies for ∑k and ∑i\n",
    "    results[\"B_sumk_in_j\"] = compute_sumk_in_j(\n",
    "        chem_exergy_k, reaction_formation_j_from_k\n",
    "    )\n",
    "    results[\"B_sumi_in_j\"] = compute_sumi_in_j(\n",
    "        chem_exergy_i, reaction_formation_j_from_i\n",
    "    )\n",
    "\n",
    "    \n",
    "    # Step 5: Compute G(∑k, env) from relation:\n",
    "    # G(∑k, °) - G(∑k, env) = B_sumk_in_j(∑ν_k * B_ch,k)\n",
    "    results[\"G_env_sumk_in_j\"] = results[\"G_std_sumk_in_j\"] - results[\"B_sumk_in_j\"]\n",
    "    \n",
    "    # Step 6.1: Compute ΔG° for reaction j ← k\n",
    "    DeltaG_std_jk = results[\"G_std_j\"] - results[\"G_std_sumk_in_j\"]\n",
    "    # Step 6.2: Compute ΔG° for reaction j ← i\n",
    "    DeltaG_std_ji = results[\"G_std_j\"] - results[\"G_std_sumi_in_j\"]\n",
    "    # Step 6.3: Compute ΔG° for reaction k ← i\n",
    "    DeltaG_std_ki = results[\"G_std_sumk_in_j\"] - results[\"G_std_sumi_in_j\"]\n",
    "    \n",
    "    # Step 7: Compute G(j, env) assuming ΔG^env = ΔG° (only thermomechanical contributions in chemical exergy)\n",
    "    results[\"G_env_j\"] = DeltaG_std_jk + results[\"G_env_sumk_in_j\"]\n",
    "    \n",
    "    # Step 8: Compute G(∑i, env) from B_sumi_in_j(∑ν_i * B_ch,i)\n",
    "    # DeltaG_std_ji = G(j, °) - G(∑i, °) = G(j, env) - G(∑i, env) => G(∑i, env) = G(j, env) - DeltaG_std_ji\n",
    "    results[\"G_env_sumi_in_j\"] = results[\"G_env_j\"] - DeltaG_std_ji # can be determined from other ways:\n",
    "    # results[\"G_env_sumi_in_j\"] = results[\"G_env_sumk_in_j\"] - DeltaG_std_ki # for instance\n",
    "    \n",
    "    # Step 9: Compute derived quantities for verification\n",
    "    results[\"B_j\"] = results[\"G_std_j\"] - results[\"G_env_sumk_in_j\"] # based on Edward and Simpson definition of chemical exergy 📖 https://doi.org/10.1016/j.energy.2011.01.025\n",
    "    results[\"DeltaG_std_jk\"] = DeltaG_std_jk\n",
    "    results[\"DeltaG_env_jk\"] = results[\"G_env_j\"] - results[\"G_env_sumk_in_j\"]\n",
    "    results[\"DeltaG_std_ji\"] = results[\"G_std_j\"] - results[\"G_std_sumi_in_j\"]\n",
    "    results[\"DeltaG_env_ji\"] = results[\"G_env_j\"] - results[\"G_env_sumi_in_j\"]\n",
    "    \n",
    "    return results\n",
    "\n",
    "# --- Function: Create Results DataFrame ---\n",
    "def create_results_dataframe(results_dict: Dict[str, float]) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Create a formatted DataFrame from results dictionary.\n",
    "    \"\"\"\n",
    "    # Define the 6 main G values\n",
    "    main_G_values = {\n",
    "        \"G(∑k, env)\": results_dict[\"G_env_sumk_in_j\"],\n",
    "        \"G(j, env)\": results_dict[\"G_env_j\"],\n",
    "        \"G(∑k, °)\": results_dict[\"G_std_sumk_in_j\"],\n",
    "        \"G(j, °)\": results_dict[\"G_std_j\"],\n",
    "        \"G(∑i, env)\": results_dict[\"G_env_sumi_in_j\"],\n",
    "        \"G(∑i, °)\": results_dict[\"G_std_sumi_in_j\"]\n",
    "    }\n",
    "    \n",
    "    # Create DataFrame for main results\n",
    "    df_main = pd.DataFrame([\n",
    "        {\"Parameter\": k, \"Value (kJ/mol)\": v, \"Type\": \"Main G Value\"}\n",
    "        for k, v in main_G_values.items()\n",
    "    ])\n",
    "    \n",
    "    # Create DataFrame for derived quantities\n",
    "    derived_quantities = {\n",
    "        \"B_sumk_in_j(∑ν_k * B_ch,k)\": results_dict[\"B_sumk_in_j\"],\n",
    "        \"B_sumi_in_j(∑ν_i * B_ch,i)\": results_dict[\"B_sumi_in_j\"],\n",
    "        \"B_j (Chemical Exergy of the resource)\": results_dict[\"B_j\"],\n",
    "        \"ΔG°(j ← k)\": results_dict[\"DeltaG_std_jk\"],\n",
    "        \"ΔG^env(j ← k)\": results_dict[\"DeltaG_env_jk\"],\n",
    "        \"ΔG°(j ← i)\": results_dict[\"DeltaG_std_ji\"],\n",
    "        \"ΔG^env(j ← i)\": results_dict[\"DeltaG_env_ji\"]\n",
    "    }\n",
    "    \n",
    "    df_derived = pd.DataFrame([\n",
    "        {\"Parameter\": k, \"Value (kJ/mol)\": v, \"Type\": \"Derived Quantity\"}\n",
    "        for k, v in derived_quantities.items()\n",
    "    ])\n",
    "    \n",
    "    # Combine both DataFrames\n",
    "    df_results = pd.concat([df_main, df_derived], ignore_index=True)\n",
    "    \n",
    "    return df_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bc609af",
   "metadata": {},
   "source": [
    "### Configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60299ae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define all configurations with detailed metadata\n",
    "configurations = [\n",
    "    {\n",
    "        \"name\": \"Szargut + Szargut\",\n",
    "        \"chem_exergy_source\": \"Szargut\",\n",
    "        \"DeltafrG_source\": \"Szargut\",\n",
    "        \"env_species_Al\": \"Sillimanite\",\n",
    "        \"short_label\": \"Szargut | Szargut | Sill\",\n",
    "        \"chem_exergy_k\": szargut_chem_exergy_k,\n",
    "        \"DeltafrG_std\": DeltafrG_std_szargut_brgm,\n",
    "        \"b_chk_init\": szargut_chem_exergy_i,\n",
    "        \"env_species_order\": [\"SiO2\", \"Al2SiO5\"],\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"Szargut + FactSage\",\n",
    "        \"chem_exergy_source\": \"Szargut\",\n",
    "        \"DeltafrG_source\": \"FactSage\",\n",
    "        \"env_species_Al\": \"Sillimanite\",\n",
    "        \"short_label\": \"Szargut | FS | Sill\",\n",
    "        \"chem_exergy_k\": szargut_chem_exergy_k,\n",
    "        \"DeltafrG_std\": DeltafrG_std_FS,\n",
    "        \"b_chk_init\": szargut_chem_exergy_i,\n",
    "        \"env_species_order\": [\"SiO2\", \"Al2SiO5\"],\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"Szargut + HSC\",\n",
    "        \"chem_exergy_source\": \"Szargut\",\n",
    "        \"DeltafrG_source\": \"HSC\",\n",
    "        \"env_species_Al\": \"Sillimanite\",\n",
    "        \"short_label\": \"Szargut | HSC | Sill\",\n",
    "        \"chem_exergy_k\": szargut_chem_exergy_k,\n",
    "        \"DeltafrG_std\": DeltafrG_std_HSC,\n",
    "        \"b_chk_init\": szargut_chem_exergy_i,\n",
    "        \"env_species_order\": [\"SiO2\", \"Al2SiO5\"],\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"Rivero + Rivero\",\n",
    "        \"chem_exergy_source\": \"Rivero\",\n",
    "        \"DeltafrG_source\": \"Rivero\",\n",
    "        \"env_species_Al\": \"Sillimanite\",\n",
    "        \"short_label\": \"Rivero | Rivero | Sill\",\n",
    "        \"chem_exergy_k\": rivero_chem_exergy_k,\n",
    "        \"DeltafrG_std\": DeltafrG_std_rivero_brgm,\n",
    "        \"b_chk_init\": rivero_chem_exergy_i,\n",
    "        \"env_species_order\": [\"SiO2\", \"Al2SiO5\"],\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"Rivero + FactSage\",\n",
    "        \"chem_exergy_source\": \"Rivero\",\n",
    "        \"DeltafrG_source\": \"FactSage\",\n",
    "        \"env_species_Al\": \"Sillimanite\",\n",
    "        \"short_label\": \"Rivero | FS | Sill\",\n",
    "        \"chem_exergy_k\": rivero_chem_exergy_k,\n",
    "        \"DeltafrG_std\": DeltafrG_std_FS,\n",
    "        \"b_chk_init\": rivero_chem_exergy_i,\n",
    "        \"env_species_order\": [\"SiO2\", \"Al2SiO5\"],\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"Rivero + HSC\",\n",
    "        \"chem_exergy_source\": \"Rivero\",\n",
    "        \"DeltafrG_source\": \"HSC\",\n",
    "        \"env_species_Al\": \"Sillimanite\",\n",
    "        \"short_label\": \"Rivero | HSC | Sill\",\n",
    "        \"chem_exergy_k\": rivero_chem_exergy_k,\n",
    "        \"DeltafrG_std\": DeltafrG_std_HSC,\n",
    "        \"b_chk_init\": rivero_chem_exergy_i,\n",
    "        \"env_species_order\": [\"SiO2\", \"Al2SiO5\"],\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"De Meester + De Meester\",\n",
    "        \"chem_exergy_source\": \"De Meester\",\n",
    "        \"DeltafrG_source\": \"De Meester\",\n",
    "        \"env_species_Al\": \"Kaolinite\",\n",
    "        \"short_label\": \"De Meester | De Meester | Kaol\",\n",
    "        \"chem_exergy_k\": demeester_chem_exergy_k,\n",
    "        \"DeltafrG_std\": DeltafrG_std_demeester_brgm,\n",
    "        \"b_chk_init\": demeester_chem_exergy_i,\n",
    "        \"env_species_order\": [\"SiO2\", \"H2O\", \"Al2Si2O5(OH)4\"],\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"De Meester + FactSage\",\n",
    "        \"chem_exergy_source\": \"De Meester\",\n",
    "        \"DeltafrG_source\": \"FactSage\",\n",
    "        \"env_species_Al\": \"Kaolinite\",\n",
    "        \"short_label\": \"De Meester | FS | Kaol\",\n",
    "        \"chem_exergy_k\": demeester_chem_exergy_k,\n",
    "        \"DeltafrG_std\": DeltafrG_std_FS,\n",
    "        \"b_chk_init\": demeester_chem_exergy_i,\n",
    "        \"env_species_order\": [\"SiO2\", \"H2O\", \"Al2Si2O5(OH)4\"],\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"De Meester + HSC\",\n",
    "        \"chem_exergy_source\": \"De Meester\",\n",
    "        \"DeltafrG_source\": \"HSC\",\n",
    "        \"env_species_Al\": \"Kaolinite\",\n",
    "        \"short_label\": \"De Meester | HSC | Kaol\",\n",
    "        \"chem_exergy_k\": demeester_chem_exergy_k,\n",
    "        \"DeltafrG_std\": DeltafrG_std_HSC,\n",
    "        \"b_chk_init\": demeester_chem_exergy_i,\n",
    "        \"env_species_order\": [\"SiO2\", \"H2O\", \"Al2Si2O5(OH)4\"],\n",
    "    },\n",
    "    {\n",
    "    \"name\": \"HSC + FactSage (Sillimanite)\",\n",
    "    \"chem_exergy_source\": \"HSC\",\n",
    "    \"DeltafrG_source\": \"FactSage\",\n",
    "    \"env_species_Al\": \"Sillimanite\",\n",
    "    \"short_label\": \"HSC | FS | Sill\",\n",
    "    \"chem_exergy_k\": HSC_chem_exergy_k,\n",
    "    \"DeltafrG_std\": DeltafrG_std_FS,\n",
    "    \"b_chk_init\": HSC_chem_exergy_i,\n",
    "    \"env_species_order\": [\"SiO2\", \"Al2SiO5\"],\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"HSC + FactSage (Kaolinite)\",\n",
    "        \"chem_exergy_source\": \"HSC\",\n",
    "        \"DeltafrG_source\": \"FactSage\",\n",
    "        \"env_species_Al\": \"Kaolinite\",\n",
    "        \"short_label\": \"HSC | FS | Kaol\",\n",
    "        \"chem_exergy_k\": HSC_chem_exergy_k,\n",
    "        \"DeltafrG_std\": DeltafrG_std_FS,\n",
    "        \"b_chk_init\": HSC_chem_exergy_i,\n",
    "        \"env_species_order\": [\"SiO2\", \"H2O\", \"Al2Si2O5(OH)4\"],\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"HSC + HSC (Sillimanite)\",\n",
    "        \"chem_exergy_source\": \"HSC\",\n",
    "        \"DeltafrG_source\": \"HSC\",\n",
    "        \"env_species_Al\": \"Sillimanite\",\n",
    "        \"short_label\": \"HSC | HSC | Sill\",\n",
    "        \"chem_exergy_k\": HSC_chem_exergy_k,\n",
    "        \"DeltafrG_std\": DeltafrG_std_HSC,\n",
    "        \"b_chk_init\": HSC_chem_exergy_i,\n",
    "        \"env_species_order\": [\"SiO2\", \"Al2SiO5\"],\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"HSC + HSC (Kaolinite)\",\n",
    "        \"chem_exergy_source\": \"HSC\",\n",
    "        \"DeltafrG_source\": \"HSC\",\n",
    "        \"env_species_Al\": \"Kaolinite\",\n",
    "        \"short_label\": \"HSC | HSC | Kaol\",\n",
    "        \"chem_exergy_k\": HSC_chem_exergy_k,\n",
    "        \"DeltafrG_std\": DeltafrG_std_HSC,\n",
    "        \"b_chk_init\": HSC_chem_exergy_i,\n",
    "        \"env_species_order\": [\"SiO2\", \"H2O\", \"Al2Si2O5(OH)4\"],\n",
    "    },\n",
    "]\n",
    "\n",
    "# Compute b_chi for all configurations\n",
    "results_all_configs = []\n",
    "for config in configurations:\n",
    "    b_chi = compute_b_chi_sequential(\n",
    "        chem_exergy_k=config[\"chem_exergy_k\"],\n",
    "        DeltafrG_std=config[\"DeltafrG_std\"],\n",
    "        standard_state_equivalencies=standard_state_equivalencies,\n",
    "        b_chk_init=config[\"b_chk_init\"],\n",
    "        env_species_order=config[\"env_species_order\"],\n",
    "    )\n",
    "    results_all_configs.append({\n",
    "        \"name\": config[\"name\"],\n",
    "        \"short_label\": config[\"short_label\"],\n",
    "        \"chem_exergy_source\": config[\"chem_exergy_source\"],\n",
    "        \"DeltafrG_source\": config[\"DeltafrG_source\"],\n",
    "        \"env_species_Al\": config[\"env_species_Al\"],\n",
    "        \"b_chi\": b_chi,\n",
    "    })\n",
    "\n",
    "# Extract b_chi values\n",
    "b_chi_values = []\n",
    "for result in results_all_configs:\n",
    "    b_chi_values.append({\n",
    "        \"short_label\": result[\"short_label\"],\n",
    "        \"b_O2\": result[\"b_chi\"][\"O2\"],\n",
    "        \"b_Si\": result[\"b_chi\"][\"Si\"],\n",
    "        \"b_Al\": result[\"b_chi\"][\"Al\"],\n",
    "        \"b_H2\": result[\"b_chi\"].get(\"H2\", None),\n",
    "    })\n",
    "\n",
    "df_b_chi = pd.DataFrame(b_chi_values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78cd95d3",
   "metadata": {},
   "source": [
    "## 📰 Fig. 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fbdcf7b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "====================================================================================================\n",
      "TABLE: Standard chemical exergy of reference elements (b_chi) for all configurations\n",
      "====================================================================================================\n",
      "Configuration (b_ch,k | ΔG_f | Ref.Env.) O₂ (kJ/mol)          Si (kJ/mol)          Al (kJ/mol)          H₂ (kJ/mol)         \n",
      "----------------------------------------------------------------------------------------------------\n",
      "Szargut | Szargut | Sill                 3.97                 854.17               790.34               236.09              \n",
      "Szargut | FS | Sill                      3.97                 853.79               1081.86              244.69              \n",
      "Szargut | HSC | Sill                     3.97                 853.75               780.88               244.65              \n",
      "Rivero | Rivero | Sill                   3.92                 855.01               795.75               236.12              \n",
      "Rivero | FS | Sill                       3.92                 854.63               1087.26              244.72              \n",
      "Rivero | HSC | Sill                      3.92                 854.60               786.29               244.67              \n",
      "De Meester | De Meester | Kaol           3.97                 853.80               809.40               236.00              \n",
      "De Meester | FS | Kaol                   3.97                 853.72               798.61               244.69              \n",
      "De Meester | HSC | Kaol                  3.97                 853.68               799.77               244.65              \n",
      "HSC | FS | Sill                          3.92                 855.03               1096.68              236.17              \n",
      "HSC | FS | Kaol                          3.92                 855.03               794.54               236.17              \n",
      "HSC | HSC | Sill                         3.92                 855.00               795.70               236.12              \n",
      "HSC | HSC | Kaol                         3.92                 855.00               795.71               236.12              \n",
      "====================================================================================================\n",
      "\n",
      "----------------------------------------------------------------------------------------------------\n",
      "GRAPHICS PROGRAM STATEMENT (paste into cover letter / figure caption):\n",
      "  Created with Python 3 + Matplotlib + NumPy\n",
      "  Fonts: Lucida Console (fallback DejaVu Sans Mono), embedded as TrueType in EPS/PDF.\n",
      "  Formats supplied: EPS (primary), PDF (backup), TIFF 600 dpi (raster), SVG (editable).\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "# ============================================================\n",
    "# --- GRAPHICS PROGRAM STATEMENT (for JIE/Springer submission) ---\n",
    "# ============================================================\n",
    "# All figures were created with Python 3 / Matplotlib and NumPy\n",
    "# and exported as EPS, PDF, TIFF and SVG.\n",
    "# Fonts are embedded (TrueType) in the EPS and PDF outputs.\n",
    "GRAPHICS_PROGRAM = \"Created with Python 3 + Matplotlib + NumPy\"\n",
    "\n",
    "# ============================================================\n",
    "# --- Unified Font & Text Settings (Lucida Console ONLY) ---\n",
    "# ============================================================\n",
    "plt.rcParams[\"font.family\"]    = \"monospace\"\n",
    "plt.rcParams[\"font.monospace\"] = [\"Lucida Console\", \"DejaVu Sans Mono\"]\n",
    "\n",
    "# Embed TrueType fonts (required by Springer for vector EPS/PDF)\n",
    "plt.rcParams[\"ps.fonttype\"]    = 42\n",
    "plt.rcParams[\"pdf.fonttype\"]   = 42\n",
    "plt.rcParams[\"svg.fonttype\"]   = \"none\"\n",
    "\n",
    "FONT_SIZE         = 16\n",
    "FONT_TITLE        = 16\n",
    "FONT_LABEL        = 16\n",
    "FONT_TICK         = 10\n",
    "FONT_XTICK        = 10\n",
    "FONT_LEGEND       = 11\n",
    "FONT_LEGEND_TITLE = 12\n",
    "FONT_SUPTITLE     = 16\n",
    "FONT_CAPTION      = 10\n",
    "\n",
    "plt.rcParams[\"font.size\"] = FONT_SIZE\n",
    "\n",
    "# ============================================================\n",
    "# --- Ensure H2 is numeric ---\n",
    "# ============================================================\n",
    "df_b_chi['b_H2'] = pd.to_numeric(df_b_chi['b_H2'], errors='coerce').fillna(0)\n",
    "\n",
    "# ============================================================\n",
    "# --- 4 subplots (one per element) ---\n",
    "# ============================================================\n",
    "elements = ['b_O2', 'b_Si', 'b_Al', 'b_H2']\n",
    "titles   = ['Oxygen (O$_2$)', 'Silicon (Si)', 'Aluminum (Al)', 'Hydrogen (H$_2$)']\n",
    "ylabels  = [\n",
    "    r'$b_{ch,O_2}^{\\circ}$  (kJ/mol)',\n",
    "    r'$b_{ch,Si}^{\\circ}$  (kJ/mol)',\n",
    "    r'$b_{ch,Al}^{\\circ}$  (kJ/mol)',\n",
    "    r'$b_{ch,H_2}^{\\circ}$  (kJ/mol)',\n",
    "]\n",
    "\n",
    "# --- Fixed color per configuration (same color = same config everywhere) ---\n",
    "config_labels = df_b_chi['short_label'].tolist()\n",
    "n_configs     = len(config_labels)\n",
    "colors        = plt.cm.Set3(np.linspace(0, 1, n_configs))\n",
    "config_colors = dict(zip(config_labels, colors))\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
    "fig.suptitle(\n",
    "    'Chemical Exergy of Reference Elements Across Configurations',\n",
    "    fontsize=FONT_SUPTITLE,\n",
    "    fontweight='bold',\n",
    ")\n",
    "\n",
    "for idx, (element, title, ylabel) in enumerate(zip(elements, titles, ylabels)):\n",
    "    ax = axes[idx // 2, idx % 2]\n",
    "\n",
    "    values = df_b_chi[element].values\n",
    "    labels = df_b_chi['short_label'].values\n",
    "    bar_colors = [config_colors[l] for l in labels]\n",
    "\n",
    "    bars = ax.bar(\n",
    "        range(len(values)),\n",
    "        values,\n",
    "        color=bar_colors,\n",
    "        edgecolor='black',\n",
    "        linewidth=1.2,\n",
    "    )\n",
    "\n",
    "    # Value labels on top of bars\n",
    "    for bar, val in zip(bars, values):\n",
    "        if val > 0:\n",
    "            ax.text(\n",
    "                bar.get_x() + bar.get_width() / 2,\n",
    "                bar.get_height() + max(values) * 0.02,\n",
    "                f'{val:.1f}',\n",
    "                ha='center', va='bottom',\n",
    "                fontsize=FONT_TICK,\n",
    "                fontweight='bold',\n",
    "            )\n",
    "\n",
    "    # Customize the plot\n",
    "    ax.set_ylabel(ylabel, fontsize=FONT_LABEL, fontweight='bold')\n",
    "    ax.set_title(title,  fontsize=FONT_TITLE, fontweight='bold')\n",
    "    ax.set_xticks(range(len(labels)))\n",
    "    ax.set_xticklabels(labels, rotation=45, ha='right', fontsize=FONT_XTICK)\n",
    "    ax.tick_params(axis='y', labelsize=FONT_TICK)\n",
    "    ax.grid(axis='y', linestyle='--', alpha=0.3)\n",
    "    ax.set_ylim(0, max(values) * 1.15)\n",
    "\n",
    "plt.tight_layout(rect=[0, 0.14, 1, 0.96])\n",
    "\n",
    "# ============================================================\n",
    "# --- SHARED BOX LEGEND (colors + label-format explanation) ---\n",
    "# ============================================================\n",
    "# Color patches: one per configuration\n",
    "color_handles = [\n",
    "    mpatches.Patch(facecolor=config_colors[l], edgecolor='black', label=l)\n",
    "    for l in config_labels\n",
    "]\n",
    "\n",
    "# Explanatory \"dummy\" handle that occupies one legend slot\n",
    "explanation_text = (\n",
    "    'Label format: b_ch,k source | ΔG_f source | Ref. env.\\n'\n",
    "    '  b_ch,k :  Szargut, De Meester, HSC\\n'\n",
    "    '  ΔG_f   :  FS (FactSage), HSC, Szargut, De Meester\\n'\n",
    "    '  Ref.env.:  Sill (Sillimanite), Kaol (Kaolinite)'\n",
    ")\n",
    "explanation_handle = mpatches.Patch(\n",
    "    facecolor='white', edgecolor='white', label=explanation_text\n",
    ")\n",
    "\n",
    "# Build a single framed legend below the figure\n",
    "all_handles = color_handles + [explanation_handle]\n",
    "\n",
    "leg = fig.legend(\n",
    "    handles=all_handles,\n",
    "    loc='lower center',\n",
    "    bbox_to_anchor=(0.5, 0.01),\n",
    "    ncol=min(n_configs, 4),\n",
    "    fontsize=FONT_LEGEND,\n",
    "    title='Configuration color key and label-format explanation',\n",
    "    title_fontsize=FONT_LEGEND_TITLE,\n",
    "    frameon=True,\n",
    "    fancybox=True,\n",
    "    framealpha=0.95,\n",
    "    edgecolor='black',\n",
    "    handlelength=1.4,\n",
    "    handleheight=1.2,\n",
    "    columnspacing=1.2,\n",
    "    labelspacing=0.9,\n",
    "    borderpad=0.9,\n",
    ")\n",
    "leg.get_frame().set_linewidth(1.0)\n",
    "leg.get_title().set_fontweight('bold')\n",
    "\n",
    "plt.savefig('Fig4.eps',  format='eps',  dpi=600, bbox_inches='tight')\n",
    "plt.savefig('Fig4.pdf',  format='pdf',  dpi=600, bbox_inches='tight')\n",
    "plt.savefig('Fig4.tiff', format='tiff', dpi=600, bbox_inches='tight',\n",
    "            pil_kwargs={\"compression\": \"tiff_lzw\"})\n",
    "plt.savefig('Fig4.svg',  format='svg',  dpi=600, bbox_inches='tight')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# ============================================================\n",
    "# --- Print the table ---\n",
    "# ============================================================\n",
    "print(\"\\n\" + \"=\" * 100)\n",
    "print(\"TABLE: Standard chemical exergy of reference elements (b_chi) for all configurations\")\n",
    "print(\"=\" * 100)\n",
    "print(f\"{'Configuration (b_ch,k | ΔG_f | Ref.Env.)':<40} \"\n",
    "      f\"{'O₂ (kJ/mol)':<20} {'Si (kJ/mol)':<20} {'Al (kJ/mol)':<20} {'H₂ (kJ/mol)':<20}\")\n",
    "print(\"-\" * 100)\n",
    "for idx, row in df_b_chi.iterrows():\n",
    "    print(f\"{row['short_label']:<40} \"\n",
    "          f\"{row['b_O2']:<20.2f} {row['b_Si']:<20.2f} \"\n",
    "          f\"{row['b_Al']:<20.2f} {row['b_H2']:<20.2f}\")\n",
    "print(\"=\" * 100)\n",
    "\n",
    "print(\"\\n\" + \"-\" * 100)\n",
    "print(\"GRAPHICS PROGRAM STATEMENT (paste into cover letter / figure caption):\")\n",
    "print(f\"  {GRAPHICS_PROGRAM}\")\n",
    "print(\"  Fonts: Lucida Console (fallback DejaVu Sans Mono), embedded as TrueType in EPS/PDF.\")\n",
    "print(\"  Formats supplied: EPS (primary), PDF (backup), TIFF 600 dpi (raster), SVG (editable).\")\n",
    "print(\"-\" * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96bb756c",
   "metadata": {},
   "source": [
    "## 📰 Fig. 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c00c223",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "PAIRED-EFFECTS ANALYSIS\n",
      "================================================================================\n",
      "   element       factor  n_pairs  mean_effect  std_effect  max_effect\n",
      "0       O2  b_ch_source        4        0.050       0.000       0.050\n",
      "1       O2    dG_source        5        0.000       0.000       0.000\n",
      "2       O2      ref_env        2        0.000       0.000       0.000\n",
      "3       Si  b_ch_source        4        1.275       0.035       1.310\n",
      "4       Si    dG_source        5        0.202       0.176       0.415\n",
      "5       Si      ref_env        2        0.000       0.000       0.000\n",
      "6       Al  b_ch_source        4        9.445       5.377      14.822\n",
      "7       Al    dG_source        5      182.976     144.555     300.979\n",
      "8       Al      ref_env        2      151.070     151.065     302.135\n",
      "9       H2  b_ch_source        4        8.538       0.013       8.550\n",
      "10      H2    dG_source        5        5.198       4.204       8.690\n",
      "11      H2      ref_env        2        0.000       0.000       0.000\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----------------------------------------------------------------------------------------------------\n",
      "GRAPHICS PROGRAM STATEMENT (paste into cover letter / figure caption):\n",
      "  Created with Python 3 + Matplotlib + Seaborn + NumPy\n",
      "  Fonts: Lucida Console (fallback DejaVu Sans Mono), embedded as TrueType in EPS/PDF.\n",
      "  Formats supplied: EPS (primary), PDF (backup), TIFF 600 dpi (raster), SVG (editable).\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# ============================================================\n",
    "# --- GRAPHICS PROGRAM STATEMENT (for JIE/Springer submission) ---\n",
    "# ============================================================\n",
    "# All figures were created with Python 3 / Matplotlib / Seaborn / NumPy\n",
    "# and exported as EPS, PDF, TIFF and SVG.\n",
    "# Fonts are embedded (TrueType) in the EPS and PDF outputs.\n",
    "GRAPHICS_PROGRAM = \"Created with Python 3 + Matplotlib + Seaborn + NumPy\"\n",
    "\n",
    "# ============================================================\n",
    "# --- Unified Font & Text Settings (Lucida Console ONLY) ---\n",
    "# ============================================================\n",
    "plt.rcParams[\"font.family\"]    = \"monospace\"\n",
    "plt.rcParams[\"font.monospace\"] = [\"Lucida Console\", \"DejaVu Sans Mono\"]\n",
    "\n",
    "# Embed TrueType fonts (required by Springer for vector EPS/PDF)\n",
    "plt.rcParams[\"ps.fonttype\"]    = 42\n",
    "plt.rcParams[\"pdf.fonttype\"]   = 42\n",
    "plt.rcParams[\"svg.fonttype\"]   = \"none\"\n",
    "\n",
    "FONT_SIZE         = 16\n",
    "FONT_TITLE        = 14\n",
    "FONT_LABEL        = 14\n",
    "FONT_TICK         = 12\n",
    "FONT_ANNOT        = 12     # heatmap cell numbers\n",
    "FONT_CBAR_LABEL   = 13\n",
    "FONT_SUPTITLE     = 16\n",
    "FONT_CAPTION      = 10\n",
    "\n",
    "plt.rcParams[\"font.size\"] = FONT_SIZE\n",
    "\n",
    "# ============================================================\n",
    "# --- PREPARE DATA ---\n",
    "# ============================================================\n",
    "df = df_b_chi.copy()\n",
    "\n",
    "split = (\n",
    "    df['short_label']\n",
    "    .str.split('|', expand=True, regex=False)\n",
    "    .apply(lambda x: x.str.strip())\n",
    ")\n",
    "split.columns = ['b_ch_source', 'dG_source', 'ref_env']\n",
    "df[['b_ch_source', 'dG_source', 'ref_env']] = split\n",
    "\n",
    "df['ref_env'] = df['ref_env'].replace({\n",
    "    'Sill': 'Sillimanite',\n",
    "    'Kaol': 'Kaolinite',\n",
    "})\n",
    "df['dG_source'] = df['dG_source'].replace({'FS': 'FactSage'})\n",
    "\n",
    "# ============================================================\n",
    "# --- LONG FORMAT ---\n",
    "# ============================================================\n",
    "df_long = df.melt(\n",
    "    id_vars=['b_ch_source', 'dG_source', 'ref_env'],\n",
    "    value_vars=['b_O2', 'b_Si', 'b_Al', 'b_H2'],\n",
    "    var_name='element',\n",
    "    value_name='b_ch',\n",
    ")\n",
    "df_long['element'] = df_long['element'].str.replace('b_', '', regex=False)\n",
    "\n",
    "# ============================================================\n",
    "# --- PAIRED-EFFECTS FUNCTION ---\n",
    "# ============================================================\n",
    "def compute_paired_effects(data, factor, control_factors):\n",
    "    \"\"\"Paired effects of one factor while holding control_factors fixed.\"\"\"\n",
    "    effects = []\n",
    "    for _, group in data.groupby(control_factors):\n",
    "        if group[factor].nunique() < 2:\n",
    "            continue\n",
    "        values = group['b_ch'].values\n",
    "        effects.append(np.max(values) - np.min(values))\n",
    "    return effects\n",
    "\n",
    "# ============================================================\n",
    "# --- ANALYSIS ---\n",
    "# ============================================================\n",
    "elements = ['O2', 'Si', 'Al', 'H2']\n",
    "analysis = []\n",
    "factor_definitions = {\n",
    "    'b_ch_source': ['dG_source', 'ref_env'],\n",
    "    'dG_source':   ['b_ch_source', 'ref_env'],\n",
    "    'ref_env':     ['b_ch_source', 'dG_source'],\n",
    "}\n",
    "\n",
    "for element in elements:\n",
    "    dfe = df_long[df_long['element'] == element].copy()\n",
    "    for factor, controls in factor_definitions.items():\n",
    "        effects = compute_paired_effects(dfe, factor, controls)\n",
    "        if len(effects) == 0:\n",
    "            mean_effect, std_effect, max_effect = np.nan, np.nan, np.nan\n",
    "        else:\n",
    "            mean_effect = np.mean(effects)\n",
    "            std_effect  = np.std(effects)\n",
    "            max_effect  = np.max(effects)\n",
    "        analysis.append([element, factor, len(effects),\n",
    "                         mean_effect, std_effect, max_effect])\n",
    "\n",
    "results = pd.DataFrame(\n",
    "    analysis,\n",
    "    columns=['element', 'factor', 'n_pairs',\n",
    "             'mean_effect', 'std_effect', 'max_effect'],\n",
    ")\n",
    "\n",
    "# ============================================================\n",
    "# --- PRINT TABLE ---\n",
    "# ============================================================\n",
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"PAIRED-EFFECTS ANALYSIS\")\n",
    "print(\"=\" * 80)\n",
    "print(results.round(3))\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# Shared axis-label mapping\n",
    "# ----------------------------------------------------------\n",
    "factor_rename = {\n",
    "    'b_ch_source': r'$b_{ch,k}^{\\circ}$ source',\n",
    "    'dG_source':   r'$\\Delta G_f^{\\circ}$ source',\n",
    "    'ref_env':     'ERS choice',\n",
    "}\n",
    "\n",
    "# ============================================================\n",
    "# --- FIGURE 1: MEAN PAIRED EFFECT (grayscale, journal-safe) ---\n",
    "# ============================================================\n",
    "heatmap_mean = results.pivot(index='element', columns='factor',\n",
    "                             values='mean_effect').rename(columns=factor_rename)\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(8, 5))\n",
    "sns.heatmap(\n",
    "    heatmap_mean,\n",
    "    annot=True, fmt='.2f',\n",
    "    cmap='Greys',\n",
    "    linewidths=0.6,\n",
    "    linecolor='black',\n",
    "    annot_kws={'fontsize': FONT_ANNOT, 'fontweight': 'bold'},\n",
    "    cbar_kws={'label': 'Mean paired effect (kJ/mol)'},\n",
    "    ax=ax1,\n",
    ")\n",
    "ax1.set_title('Mean effect of modelling choices\\n(paired comparisons only)',\n",
    "              fontsize=FONT_TITLE, fontweight='bold')\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_ylabel('Reference element', fontsize=FONT_LABEL)\n",
    "ax1.tick_params(axis='both', labelsize=FONT_TICK)\n",
    "\n",
    "# Style the colorbar label font\n",
    "cbar1 = ax1.collections[0].colorbar\n",
    "cbar1.set_label('Mean paired effect (kJ/mol)', fontsize=FONT_CBAR_LABEL)\n",
    "cbar1.ax.tick_params(labelsize=FONT_TICK)\n",
    "\n",
    "plt.tight_layout()\n",
    "fig1.savefig('paired_effects_heatmap.eps',  format='eps',  dpi=600, bbox_inches='tight')\n",
    "fig1.savefig('paired_effects_heatmap.pdf',  format='pdf',  dpi=600, bbox_inches='tight')\n",
    "fig1.savefig('paired_effects_heatmap.tiff', format='tiff', dpi=600, bbox_inches='tight',\n",
    "             pil_kwargs={\"compression\": \"tiff_lzw\"})\n",
    "fig1.savefig('paired_effects_heatmap.svg',  format='svg',  dpi=600, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "# ============================================================\n",
    "# --- FIGURE 2: INTERACTION INDICATOR (grayscale, journal-safe) ---\n",
    "# ============================================================\n",
    "heatmap_interaction = results.pivot(index='element', columns='factor',\n",
    "                                    values='std_effect').rename(columns=factor_rename)\n",
    "\n",
    "fig2, ax2 = plt.subplots(figsize=(8, 5))\n",
    "sns.heatmap(\n",
    "    heatmap_interaction,\n",
    "    annot=True, fmt='.2f',\n",
    "    cmap='Greys',\n",
    "    linewidths=0.6,\n",
    "    linecolor='black',\n",
    "    annot_kws={'fontsize': FONT_ANNOT, 'fontweight': 'bold'},\n",
    "    cbar_kws={'label': 'Std. dev. of paired effects (kJ/mol)'},\n",
    "    ax=ax2,\n",
    ")\n",
    "ax2.set_title('Interaction indicator\\n(variability of paired effects)',\n",
    "              fontsize=FONT_TITLE, fontweight='bold')\n",
    "ax2.set_xlabel('')\n",
    "ax2.set_ylabel('Reference element', fontsize=FONT_LABEL)\n",
    "ax2.tick_params(axis='both', labelsize=FONT_TICK)\n",
    "\n",
    "cbar2 = ax2.collections[0].colorbar\n",
    "cbar2.set_label('Std. dev. of paired effects (kJ/mol)', fontsize=FONT_CBAR_LABEL)\n",
    "cbar2.ax.tick_params(labelsize=FONT_TICK)\n",
    "\n",
    "plt.tight_layout()\n",
    "fig2.savefig('Fig5.eps',  format='eps',  dpi=600, bbox_inches='tight')\n",
    "fig2.savefig('Fig5.pdf',  format='pdf',  dpi=600, bbox_inches='tight')\n",
    "fig2.savefig('Fig5.tiff', format='tiff', dpi=600, bbox_inches='tight',\n",
    "             pil_kwargs={\"compression\": \"tiff_lzw\"})\n",
    "fig2.savefig('Fig5.svg',  format='svg',  dpi=600, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "# ============================================================\n",
    "# --- Graphics program statement ---\n",
    "# ============================================================\n",
    "print(\"\\n\" + \"-\" * 100)\n",
    "print(\"GRAPHICS PROGRAM STATEMENT (paste into cover letter / figure caption):\")\n",
    "print(f\"  {GRAPHICS_PROGRAM}\")\n",
    "print(\"  Fonts: Lucida Console (fallback DejaVu Sans Mono), embedded as TrueType in EPS/PDF.\")\n",
    "print(\"  Formats supplied: EPS (primary), PDF (backup), TIFF 600 dpi (raster), SVG (editable).\")\n",
    "print(\"-\" * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e841c195",
   "metadata": {},
   "source": [
    "# Discussion CExD Copper"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84c3d5c5",
   "metadata": {},
   "source": [
    "### brightway management"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b7a3e191",
   "metadata": {},
   "outputs": [],
   "source": [
    "# brightway\n",
    "import bw2data as bd\n",
    "import bw2calc as bc\n",
    "import bw2io as bi\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63f04459",
   "metadata": {},
   "outputs": [],
   "source": [
    "bd.projects.set_current(\"review_example\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5354d3c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database 'ecoinvent-3.12-cutoff' already exists.\n"
     ]
    }
   ],
   "source": [
    "username = os.getenv(\"EI_USERNAME\") # your ecoinvent username\n",
    "password = os.getenv(\"EI_PASSWORD\") # your ecoinvent password\n",
    "syst_mod = \"cutoff\"\n",
    "db_version = \"3.12\"\n",
    "db_provider = \"ecoinvent\"\n",
    "technosphere_db_name = f\"{db_provider}-{db_version}-{syst_mod}\"\n",
    "\n",
    "# Import ecoinvent if needed\n",
    "if technosphere_db_name not in bd.databases:\n",
    "    print(f\"Importing ecoinvent {db_version} ({syst_mod})...\")\n",
    "    bi.import_ecoinvent_release(\n",
    "        username=username,\n",
    "        password=password,\n",
    "        system_model=syst_mod,\n",
    "        version=db_version,\n",
    "    )\n",
    "else:\n",
    "    print(f\"Database '{technosphere_db_name}' already exists.\")\n",
    "\n",
    "# Load databases\n",
    "technosphere_db = bd.Database(technosphere_db_name)\n",
    "biosphere_db_name = f\"{db_provider}-{db_version}-biosphere\"\n",
    "biosphere_db = bd.Database(biosphere_db_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47cbb8f3",
   "metadata": {},
   "source": [
    "### LCIA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "62166cc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✅ Successfully overwrote method: ('Custom Cumulative Exergy Demand (CCExD)', 'non-renewable: total', 'exergy content')\n",
      "   - Unit: MJ-Eq\n",
      "   - Number of CFs: 75\n",
      "\n",
      "🔍 Method verification:\n",
      "   Name: ('Custom Cumulative Exergy Demand (CCExD)', 'non-renewable: total', 'exergy content')\n",
      "   Metadata version: 1.0\n",
      "   Number of CFs loaded: 75\n",
      "✅ CF count matches expected value\n",
      "\n",
      "Sample characterization factors (first 3):\n",
      "   - Coal, brown: 10.3 MJ-Eq\n",
      "   - Coal, hard: 19.7 MJ-Eq\n",
      "   - Energy, gross calorific value, in biomass, primary forest: 1.05 MJ-Eq\n"
     ]
    }
   ],
   "source": [
    "def create_combined_cexd_method():\n",
    "    \"\"\"Create CExD non-renewable: total method by combining energy and material non-renewable methods.\"\"\"\n",
    "\n",
    "    # Define source and target method tuples\n",
    "    energy_method = (\"ecoinvent-3.12\", \"Cumulative Exergy Demand (CExD)\", \"energy resources: non-renewable\", \"exergy content\")\n",
    "    material_metal_method = (\"ecoinvent-3.12\", \"Cumulative Exergy Demand (CExD)\", \"material resources: metals\", \"exergy content\")\n",
    "    material_minerals_method = (\"ecoinvent-3.12\", \"Cumulative Exergy Demand (CExD)\", \"material resources: minerals\", \"exergy content\")\n",
    "    target_method = (\"Custom Cumulative Exergy Demand (CCExD)\", \"non-renewable: total\", \"exergy content\")\n",
    "\n",
    "    # Check if source methods exist\n",
    "    missing_methods = []\n",
    "    if energy_method not in bd.methods:\n",
    "        missing_methods.append(str(energy_method))\n",
    "    if material_metal_method not in bd.methods:\n",
    "        missing_methods.append(str(material_metal_method))\n",
    "    if material_minerals_method not in bd.methods:\n",
    "        missing_methods.append(str(material_minerals_method))\n",
    "\n",
    "    if missing_methods:\n",
    "        print(f\"❌ Error: The following source methods are not registered: {', '.join(missing_methods)}\")\n",
    "        print(\"   Please create them first using create_lcia_method()\")\n",
    "        return\n",
    "\n",
    "    # Load source methods\n",
    "    energy_cfs = bd.Method(energy_method).load()\n",
    "    material_cfs = bd.Method(material_metal_method).load()\n",
    "    material_minerals_cfs = bd.Method(material_minerals_method).load()\n",
    "\n",
    "    # Combine CFs, summing values for duplicate flows\n",
    "    combined_cfs = {}\n",
    "    for flow_key, cf in energy_cfs + material_cfs + material_minerals_cfs:\n",
    "        combined_cfs[flow_key] = combined_cfs.get(flow_key, 0.0) + cf\n",
    "\n",
    "    cf_list = list(combined_cfs.items())\n",
    "\n",
    "    # Define metadata\n",
    "    method_metadata = {\n",
    "        'unit': 'MJ-Eq',\n",
    "        'description': 'Combined cumulative exergy demand for all non-renewable resources (energy + material)',\n",
    "        'source': 'Derived from CExD energy and material non-renewable methods',\n",
    "        'version': '1.0',\n",
    "        'num_cfs': len(cf_list),\n",
    "        'application': 'All product systems unit processes'\n",
    "    }\n",
    "\n",
    "    # Register and write method\n",
    "    method_object = bd.Method(target_method)\n",
    "    try:\n",
    "        method_object.register(**method_metadata)\n",
    "        method_object.write(cf_list)\n",
    "        print(\n",
    "            f\"\\n✅ Successfully {'overwrote' if target_method in bd.methods else 'created'} \"\n",
    "            f\"method: {target_method}\\n\"\n",
    "            f\"   - Unit: {method_metadata['unit']}\\n\"\n",
    "            f\"   - Number of CFs: {len(cf_list)}\"\n",
    "        )\n",
    "    except Exception as e:\n",
    "        print(f\"\\n❌ Failed to write method: {str(e)}\")\n",
    "        raise\n",
    "\n",
    "    # Verification\n",
    "    if target_method in bd.methods:\n",
    "        retrieved_method = bd.Method(target_method)\n",
    "        loaded_cfs = retrieved_method.load()\n",
    "        print(\n",
    "            f\"\\n🔍 Method verification:\\n\"\n",
    "            f\"   Name: {retrieved_method.name}\\n\"\n",
    "            f\"   Metadata version: {retrieved_method.metadata.get('version', 'N/A')}\\n\"\n",
    "            f\"   Number of CFs loaded: {len(loaded_cfs)}\"\n",
    "        )\n",
    "        if len(loaded_cfs) != len(cf_list):\n",
    "            print(f\"⚠️  Warning: CF count mismatch. Expected {len(cf_list)}, got {len(loaded_cfs)}\")\n",
    "        else:\n",
    "            print(\"✅ CF count matches expected value\")\n",
    "\n",
    "        print(\"\\nSample characterization factors (first 3):\")\n",
    "        for cf in loaded_cfs[:3]:\n",
    "            flow = bd.get_activity(cf[0])\n",
    "            print(f\"   - {flow['name']}: {cf[1]} MJ-Eq\")\n",
    "    else:\n",
    "        print(f\"❌ Error: Method {target_method} not found after writing attempt\")\n",
    "\n",
    "# Call the function\n",
    "create_combined_cexd_method()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd3254b6",
   "metadata": {},
   "source": [
    "### 📰 Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "74bef4ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 11 activities with reference product 'copper, cathode' (markets excluded).\n",
      "\n",
      "--- LCA for: cobalt production (ref: copper, cathode, location: GLO) ---\n",
      "  ('Custom Cumulative Exergy Demand (CCExD)', 'non-renewable: total', 'exergy content'): 3.442482e+02 MJ-eq\n",
      "\n",
      "--- LCA for: aluminium alloy production, AlLi (ref: copper, cathode, location: RoW) ---\n",
      "  ('Custom Cumulative Exergy Demand (CCExD)', 'non-renewable: total', 'exergy content'): 2.754800e+02 MJ-eq\n",
      "\n",
      "--- LCA for: platinum group metal, extraction and refinery operations (ref: copper, cathode, location: ZA) ---\n",
      "  ('Custom Cumulative Exergy Demand (CCExD)', 'non-renewable: total', 'exergy content'): 2.209120e+02 MJ-eq\n",
      "\n",
      "--- LCA for: aluminium alloy production, Metallic Matrix Composite (ref: copper, cathode, location: RoW) ---\n",
      "  ('Custom Cumulative Exergy Demand (CCExD)', 'non-renewable: total', 'exergy content'): 1.974292e+02 MJ-eq\n",
      "\n",
      "--- LCA for: aluminium alloy production, AlLi (ref: copper, cathode, location: CA-QC) ---\n",
      "  ('Custom Cumulative Exergy Demand (CCExD)', 'non-renewable: total', 'exergy content'): 1.610047e+02 MJ-eq\n",
      "\n",
      "--- LCA for: electrorefining of copper, anode (ref: copper, cathode, location: GLO) ---\n",
      "  ('Custom Cumulative Exergy Demand (CCExD)', 'non-renewable: total', 'exergy content'): 1.404358e+02 MJ-eq\n",
      "\n",
      "--- LCA for: primary zinc production from concentrate (ref: copper, cathode, location: RoW) ---\n",
      "  ('Custom Cumulative Exergy Demand (CCExD)', 'non-renewable: total', 'exergy content'): 1.401392e+02 MJ-eq\n",
      "\n",
      "--- LCA for: copper production, cathode, solvent extraction and electrowinning process (ref: copper, cathode, location: GLO) ---\n",
      "  ('Custom Cumulative Exergy Demand (CCExD)', 'non-renewable: total', 'exergy content'): 1.195222e+02 MJ-eq\n",
      "\n",
      "--- LCA for: aluminium alloy production, Metallic Matrix Composite (ref: copper, cathode, location: CA-QC) ---\n",
      "  ('Custom Cumulative Exergy Demand (CCExD)', 'non-renewable: total', 'exergy content'): 1.177921e+02 MJ-eq\n",
      "\n",
      "--- LCA for: platinum group metal mine operation, ore with high palladium content (ref: copper, cathode, location: RU) ---\n",
      "  ('Custom Cumulative Exergy Demand (CCExD)', 'non-renewable: total', 'exergy content'): 6.140610e+01 MJ-eq\n",
      "\n",
      "--- LCA for: gold mine operation and refining (ref: copper, cathode, location: SE) ---\n",
      "  ('Custom Cumulative Exergy Demand (CCExD)', 'non-renewable: total', 'exergy content'): 6.101902e+01 MJ-eq\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# Use bw-functional to access your database\n",
    "# ============================================================================\n",
    "import bw2data as bd\n",
    "import bw2calc as bc\n",
    "\n",
    "# Define the methods to evaluate\n",
    "methods_to_evaluate = [\n",
    "    (\"Custom Cumulative Exergy Demand (CCExD)\", \"non-renewable: total\", \"exergy content\"),\n",
    "]\n",
    "\n",
    "# ----------------------------------------------------------------------------\n",
    "# Filter activities: reference product = \"copper, cathode\", exclude markets\n",
    "# ----------------------------------------------------------------------------\n",
    "all_activities = list(technosphere_db)  # type: ignore # Get all activities\n",
    "\n",
    "copper_cathode_activities = []\n",
    "for act in all_activities:\n",
    "    ref_prod = act.get('reference product', '')\n",
    "    \n",
    "    # Select only activities whose reference product is exactly \"copper, cathode\"\n",
    "    # (If your database uses slightly different wording, switch to: if 'copper, cathode' in ref_prod:)\n",
    "    if ref_prod == 'copper, cathode':\n",
    "        upr_name = act.get('name', '')\n",
    "        # Exclude activities that have \"market\" in the unit process name (case-insensitive)\n",
    "        if 'market' not in upr_name.lower() and 'treatment' not in upr_name.lower():\n",
    "            copper_cathode_activities.append(act)\n",
    "\n",
    "if not copper_cathode_activities:\n",
    "    raise ValueError(\"No 'copper, cathode' production activities found (excluding markets).\")\n",
    "\n",
    "print(f\"Found {len(copper_cathode_activities)} activities with reference product 'copper, cathode' (markets excluded).\")\n",
    "\n",
    "# ----------------------------------------------------------------------------\n",
    "# Run LCA for each filtered activity and collect results\n",
    "# ----------------------------------------------------------------------------\n",
    "results = []\n",
    "for act in copper_cathode_activities:\n",
    "    for method_key in methods_to_evaluate:\n",
    "        lca = bc.LCA({act: 1}, method_key)\n",
    "        lca.lci()\n",
    "        lca.lcia()\n",
    "        results.append({\n",
    "            'name': act.get('name', ''),\n",
    "            'reference product': act.get('reference product', ''),\n",
    "            'location': act.get('location', ''),\n",
    "            'method': method_key,\n",
    "            'score': lca.score,\n",
    "        })\n",
    "\n",
    "# Sort by decreasing score\n",
    "results.sort(key=lambda r: r['score'], reverse=True)\n",
    "\n",
    "# ----------------------------------------------------------------------------\n",
    "# Print sorted results\n",
    "# ----------------------------------------------------------------------------\n",
    "for r in results:\n",
    "    print(\n",
    "        f\"\\n--- LCA for: {r['name']} \"\n",
    "        f\"(ref: {r['reference product']}, location: {r['location']}) ---\"\n",
    "    )\n",
    "    print(f\"  {r['method']}: {r['score']:.6e} MJ-eq\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1cb259",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f258f963",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "brightway",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}