diff --git a/README.md b/README.md
index 25f2454b..712df0eb 100644
--- a/README.md
+++ b/README.md
@@ -60,3 +60,8 @@ Repository-wide notebook integrity is checked with `python scripts/check_noteboo
 
 
 * [Hydrocarbon phase evolution: NeqSim PVT to OPM Flow](notebooks/reservoir/hydrocarbon_phase_evolution_neqsim_opm.ipynb) — Companion to equinor/neqsim#4198: saturation-pressure temperature sensitivity, CME/CVD laboratory paths, an executed 300-cell black-oil reservoir, and a NeqSim surface process. Synthetic inputs; explicit limits on migration and compositional interpretation.
+
+
+### Pipeline startup after pigging
+
+The [MEG/water residual-film drying notebook](notebooks/flowassurance/pipeline_startup_after_pigging_meg_water.ipynb) follows a 99/1 wt% MEG/water film in a 10 km pipeline at 5 °C with gas specified by a −18 °C water dew point at 70 bara. Editable geometry, film thickness and gas flow connect NeqSim CPA equilibrium to a conservative time-dependent film/gas model. Stored outputs include water absorption, slow MEG removal, outlet water dew point, spatial profiles, gas consumption, and refinement/sensitivity checks. Practical completion uses an explicit residual threshold; the duration is an illustrative prediction rather than an experimentally qualified field dry-out time.
diff --git a/notebooks/examples_of_NeqSim_in_Colab.ipynb b/notebooks/examples_of_NeqSim_in_Colab.ipynb
index 4f0fb703..c887740a 100644
--- a/notebooks/examples_of_NeqSim_in_Colab.ipynb
+++ b/notebooks/examples_of_NeqSim_in_Colab.ipynb
@@ -396,6 +396,7 @@
     "\n",
     "## Flow assurance\n",
     "\n",
+    "* **Advanced** - [Pipeline startup after pigging: MEG/water film drying](flowassurance/pipeline_startup_after_pigging_meg_water.ipynb): 10 km residual-film startup with current-master CPA equilibrium, reversible water/MEG transfer, finite gas holdup, outlet dew point, axial drying profiles, conservation and numerical/input sensitivity.\n",
     "* [Methanol, MEG and AA-LDHI hydrate management choices](flowassurance/hydrate_mitigation_chemical_choice_4016.ipynb): source-pinned three-phase CPA hydrate curves, flowline margins, synthetic-water scale pH screens and the separate AA-LDHI qualification boundary.\n",
     "* **Advanced** - [Hot-oil commissioning of subsea jumpers with NeqSim and machine learning](flowassurance/hot_oil_commissioning_neqsim_ml.ipynb): follow hot oil displacing cold diesel; calculate properties, wax, wall heat storage, risk and pressure; check conservation and discretization; compare RF/MLP surrogates and explicit restart scenarios.\n",
     "* [CO₂ hydrate calculations with NeqSim: brines, inhibitors and drilling-fluid examples](flowassurance/co2_hydrate_brines_and_drilling_fluids.ipynb): updated CO₂/brine solver with a 10 wt% KCl regression at 11.3 bara, pressure/inventory phase checks and hydt unit comparison; hands-on setup, single-point calculations, parameter sweeps and result interpretation; executed brine/MEG/CO₂–CH₄ examples, Burgass NaCl plus 110 Dholabhai (1993) pure-water/single/mixed-salt endpoints, family error metrics and experimental discussion, practice tasks and a clearly synthetic laboratory-correlation workflow.\n",
@@ -623,3 +624,4 @@
  "nbformat": 4,
  "nbformat_minor": 0
 }
+
diff --git a/notebooks/flowassurance/pipeline_startup_after_pigging_meg_water.ipynb b/notebooks/flowassurance/pipeline_startup_after_pigging_meg_water.ipynb
new file mode 100644
index 00000000..1ffd62b5
--- /dev/null
+++ b/notebooks/flowassurance/pipeline_startup_after_pigging_meg_water.ipynb
@@ -0,0 +1,1860 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "44ca776a",
+   "metadata": {},
+   "source": [
+    "# Pipeline startup after pigging: drying a residual MEG/water film\n",
+    "\n",
+    "**10 km pipeline · 5 °C · 70 bara · initially 99/1 wt% MEG/water**\n",
+    "\n",
+    "This worked example follows the stationary wall film left by the last pig while gas with a\n",
+    "**−18 °C water dew point at 70 bara** flows through the pipeline. NeqSim's SRK–CPA equation of\n",
+    "state supplies gas properties and gas–MEG–water equilibrium; a transparent Python finite-volume\n",
+    "model advances the gas and wall-film component inventories in time.\n",
+    "\n",
+    "You will see the initial water-transfer direction, water and MEG inventories, outlet gas quality,\n",
+    "the axial drying front, practical dry-out time, gas consumption, and numerical/physical sensitivity.\n",
+    "**Meeting a water dew-point specification does not demonstrate that the MEG film is gone.**\n",
+    "\n",
+    "The illustrative inputs below are editable. The default assumes methane, 300 mm internal diameter,\n",
+    "100 µm uniform full-circumference film, and 1 million Sm³/day. These are assumptions, not supplied\n",
+    "pipeline data. Standard volume means ideal gas at 15 °C and 1.01325 bara.\n",
+    "\n",
+    "**Time origin:** the last pig has left the 10 km section; the prescribed film remains everywhere,\n",
+    "and the gas inventory initially has inlet composition. Drying during pig travel, pig motion,\n",
+    "pressurization, and liquid displacement are outside this initial-condition model.\n",
+    "\n",
+    "**Completion:** every cell has less than 0.01% of its initial solvent mass left. The model retains\n",
+    "and reports that residue; mathematical zero is not claimed. Change the threshold to explore the tail."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "317715c7",
+   "metadata": {},
+   "source": [
+    "## 1. Clean Colab setup and source provenance\n",
+    "\n",
+    "Run all cells from a fresh runtime. The first run builds the current public NeqSim `master` JAR\n",
+    "and may take several minutes. The Python package supplies the JPype bridge; its bundled JAR is\n",
+    "not used. Local validation can supply `NEQSIM_SOURCE_ROOT` and `NEQSIM_SOURCE_JAR` for an already\n",
+    "built, clean checkout. Both paths are verified and provenance remains visible.\n",
+    "\n",
+    "The saved run used master commit `ea7d61ac1cbfc4b25689a4e81406a7bef248ae64`.\n",
+    "Set `NEQSIM_SOURCE_REF` to that commit for an exact source replay, or leave `master` to test updates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f1678fea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"source_repository\": \"https://github.com/equinor/neqsim\",\n",
+      "  \"requested_ref\": \"master\",\n",
+      "  \"source_commit\": \"ea7d61ac1cbfc4b25689a4e81406a7bef248ae64\",\n",
+      "  \"jar_sha256\": \"d110e7f8b0b620d488c6e2aa0881b69ad5dbf6f350016975dbd92b4d2482904f\",\n",
+      "  \"java\": \"17.0.20\",\n",
+      "  \"python\": \"3.12.14\",\n",
+      "  \"packages\": {\n",
+      "    \"neqsim\": \"3.23.0\",\n",
+      "    \"numpy\": \"2.5.3\",\n",
+      "    \"scipy\": \"1.18.1\",\n",
+      "    \"pandas\": \"3.0.6\",\n",
+      "    \"matplotlib\": \"3.11.2\"\n",
+      "  }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import hashlib\n",
+    "import importlib.metadata\n",
+    "import importlib.util\n",
+    "import json\n",
+    "import os\n",
+    "from pathlib import Path\n",
+    "import platform\n",
+    "import subprocess\n",
+    "import sys\n",
+    "from urllib.parse import unquote, urlparse\n",
+    "\n",
+    "required_packages = {\n",
+    "    \"neqsim\": \"neqsim==3.23.0\",\n",
+    "    \"numpy\": \"numpy\",\n",
+    "    \"scipy\": \"scipy\",\n",
+    "    \"pandas\": \"pandas\",\n",
+    "    \"matplotlib\": \"matplotlib\",\n",
+    "}\n",
+    "missing_packages = [\n",
+    "    package for module, package in required_packages.items()\n",
+    "    if importlib.util.find_spec(module) is None\n",
+    "]\n",
+    "if missing_packages:\n",
+    "    subprocess.run(\n",
+    "        [sys.executable, \"-m\", \"pip\", \"install\", \"-q\", *missing_packages],\n",
+    "        check=True,\n",
+    "    )\n",
+    "\n",
+    "NEQSIM_SOURCE_REF = os.environ.get(\"NEQSIM_SOURCE_REF\", \"master\")\n",
+    "local_source = os.environ.get(\"NEQSIM_SOURCE_ROOT\")\n",
+    "source_root = Path(local_source or \"/content/neqsim-pigging-source\").resolve()\n",
+    "\n",
+    "def checked_command(arguments, cwd=None):\n",
+    "    result = subprocess.run(\n",
+    "        arguments, cwd=cwd, text=True, capture_output=True, check=False,\n",
+    "    )\n",
+    "    if result.returncode:\n",
+    "        raise RuntimeError(result.stdout[-3000:] + result.stderr[-3000:])\n",
+    "    return result.stdout.strip()\n",
+    "\n",
+    "if not local_source:\n",
+    "    if not (source_root / \".git\").exists():\n",
+    "        checked_command([\n",
+    "            \"git\", \"clone\", \"--depth\", \"1\",\n",
+    "            \"https://github.com/equinor/neqsim.git\", str(source_root),\n",
+    "        ])\n",
+    "    checked_command([\"git\", \"fetch\", \"--depth\", \"1\", \"origin\", NEQSIM_SOURCE_REF], source_root)\n",
+    "    checked_command([\"git\", \"checkout\", \"--detach\", \"FETCH_HEAD\"], source_root)\n",
+    "    checked_command([\n",
+    "        \"bash\", \"mvnw\", \"-q\", \"-DskipTests\", \"-Dmaven.javadoc.skip=true\",\n",
+    "        \"-Dspotless.skip=true\", \"-Dcheckstyle.skip=true\", \"-Dpmd.skip=true\",\n",
+    "        \"-Dspotbugs.skip=true\", \"package\",\n",
+    "    ], source_root)\n",
+    "\n",
+    "source_commit = checked_command([\"git\", \"rev-parse\", \"HEAD\"], source_root)\n",
+    "assert not checked_command([\"git\", \"diff\", \"--name-only\", \"HEAD\"], source_root)\n",
+    "jar_override = os.environ.get(\"NEQSIM_SOURCE_JAR\")\n",
+    "jar_candidates = [\n",
+    "    path for path in (source_root / \"target\").glob(\"neqsim-*.jar\")\n",
+    "    if not path.name.endswith((\"-sources.jar\", \"-javadoc.jar\"))\n",
+    "]\n",
+    "source_jar = Path(jar_override).resolve() if jar_override else max(\n",
+    "    jar_candidates, key=lambda path: path.stat().st_size,\n",
+    ").resolve()\n",
+    "assert source_jar.is_file()\n",
+    "jar_sha256 = hashlib.sha256(source_jar.read_bytes()).hexdigest()\n",
+    "os.environ[\"NEQSIM_JVM_AUTOSTART\"] = \"0\"\n",
+    "\n",
+    "import jpype\n",
+    "\n",
+    "assert not jpype.isJVMStarted(), \"Restart the runtime before changing NeqSim JARs.\"\n",
+    "jpype.addClassPath(str(source_jar))\n",
+    "jpype.startJVM()\n",
+    "SystemCPA = jpype.JClass(\"neqsim.thermo.system.SystemSrkCPAstatoil\")\n",
+    "ThermodynamicOperations = jpype.JClass(\"neqsim.thermodynamicoperations.ThermodynamicOperations\")\n",
+    "EvaporationStudy = jpype.JClass(\n",
+    "    \"neqsim.process.equipment.pipeline.evaporation.PipelineEvaporationStudy\"\n",
+    ")\n",
+    "for java_class in [SystemCPA, EvaporationStudy]:\n",
+    "    location = str(java_class.class_.getProtectionDomain().getCodeSource().getLocation())\n",
+    "    loaded_from = Path(unquote(urlparse(location).path)).resolve()\n",
+    "    assert loaded_from == source_jar, (loaded_from, source_jar)\n",
+    "\n",
+    "provenance = {\n",
+    "    \"source_repository\": \"https://github.com/equinor/neqsim\",\n",
+    "    \"requested_ref\": NEQSIM_SOURCE_REF,\n",
+    "    \"source_commit\": source_commit,\n",
+    "    \"jar_sha256\": jar_sha256,\n",
+    "    \"java\": str(jpype.JClass(\"java.lang.System\").getProperty(\"java.version\")),\n",
+    "    \"python\": platform.python_version(),\n",
+    "    \"packages\": {\n",
+    "        name: importlib.metadata.version(name)\n",
+    "        for name in [\"neqsim\", \"numpy\", \"scipy\", \"pandas\", \"matplotlib\"]\n",
+    "    },\n",
+    "}\n",
+    "print(json.dumps(provenance, indent=2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "90f956b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dry carrier composition: {'methane': 1.0}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Input</th>\n",
+       "      <th>Value</th>\n",
+       "      <th>Unit / basis</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Pipeline length</td>\n",
+       "      <td>10</td>\n",
+       "      <td>km</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Internal diameter (assumed)</td>\n",
+       "      <td>300</td>\n",
+       "      <td>mm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Uniform film thickness (assumed)</td>\n",
+       "      <td>100</td>\n",
+       "      <td>µm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Initial MEG fraction (solvent basis)</td>\n",
+       "      <td>99</td>\n",
+       "      <td>wt%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Pipeline temperature</td>\n",
+       "      <td>5</td>\n",
+       "      <td>°C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Pipeline pressure (assumed constant)</td>\n",
+       "      <td>70</td>\n",
+       "      <td>bara</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Gas flow (assumed)</td>\n",
+       "      <td>1e+06</td>\n",
+       "      <td>Sm³/day</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Water dew point</td>\n",
+       "      <td>-18</td>\n",
+       "      <td>°C at 70 bara</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Residual fraction in every cell</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>kg/kg initial</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  Input  Value   Unit / basis\n",
+       "0                       Pipeline length     10             km\n",
+       "1           Internal diameter (assumed)    300             mm\n",
+       "2      Uniform film thickness (assumed)    100             µm\n",
+       "3  Initial MEG fraction (solvent basis)     99            wt%\n",
+       "4                  Pipeline temperature      5             °C\n",
+       "5  Pipeline pressure (assumed constant)     70           bara\n",
+       "6                    Gas flow (assumed)  1e+06        Sm³/day\n",
+       "7                       Water dew point    -18  °C at 70 bara\n",
+       "8       Residual fraction in every cell 0.0001  kg/kg initial"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from dataclasses import dataclass, replace\n",
+    "import time\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from IPython.display import display\n",
+    "from scipy.integrate import solve_ivp\n",
+    "from scipy.interpolate import PchipInterpolator\n",
+    "from scipy.optimize import brentq\n",
+    "from scipy.sparse import lil_matrix\n",
+    "\n",
+    "plt.style.use(\"seaborn-v0_8-whitegrid\")\n",
+    "plt.rcParams.update({\"figure.dpi\": 125, \"font.size\": 11})\n",
+    "pd.set_option(\"display.max_columns\", 12)\n",
+    "pd.set_option(\"display.float_format\", lambda value: f\"{value:,.5g}\")\n",
+    "\n",
+    "@dataclass(frozen=True)\n",
+    "class Case:\n",
+    "    length_m: float = 10_000.0\n",
+    "    diameter_m: float = 0.300\n",
+    "    temperature_C: float = 5.0\n",
+    "    pressure_bara: float = 70.0\n",
+    "    film_thickness_um: float = 100.0\n",
+    "    initial_meg_mass_fraction: float = 0.99\n",
+    "    gas_flow_Sm3_day: float = 1_000_000.0\n",
+    "    water_dew_point_C: float = -18.0\n",
+    "    dew_point_pressure_bara: float = 70.0\n",
+    "    gas_composition: tuple = ((\"methane\", 1.0),)\n",
+    "    # Estimated dilute gas diffusivities at 278.15 K and 1 bara, m2/s.\n",
+    "    water_diffusivity_ref: float = 2.0e-5\n",
+    "    meg_diffusivity_ref: float = 1.0e-5\n",
+    "    mass_transfer_multiplier: float = 1.0\n",
+    "    cells: int = 40\n",
+    "    relative_tolerance: float = 2.0e-6\n",
+    "    completion_fraction: float = 1.0e-4\n",
+    "    patch_fraction: float = 1.0e-6\n",
+    "    maximum_days: float = 3650.0\n",
+    "\n",
+    "case = Case()\n",
+    "MW = np.array([0.01801528, 0.0620678])  # water and MEG, kg/mol\n",
+    "R = 8.314462618  # J/(mol K)\n",
+    "SECONDS_PER_DAY = 86400.0\n",
+    "STANDARD_TEMPERATURE_K = 288.15\n",
+    "STANDARD_PRESSURE_PA = 101325.0\n",
+    "\n",
+    "assert 0 < case.initial_meg_mass_fraction < 1\n",
+    "assert 0 < case.film_thickness_um * 1e-6 < case.diameter_m / 20\n",
+    "assert case.gas_flow_Sm3_day > 0 and case.pressure_bara > 0\n",
+    "assert all(value > 0 for _, value in case.gas_composition)\n",
+    "assert not {\"water\", \"MEG\"}.intersection(name for name, _ in case.gas_composition)\n",
+    "assert 0 < case.patch_fraction < case.completion_fraction < 0.01\n",
+    "\n",
+    "display(pd.DataFrame([\n",
+    "    (\"Pipeline length\", case.length_m / 1000, \"km\"),\n",
+    "    (\"Internal diameter (assumed)\", case.diameter_m * 1000, \"mm\"),\n",
+    "    (\"Uniform film thickness (assumed)\", case.film_thickness_um, \"µm\"),\n",
+    "    (\"Initial MEG fraction (solvent basis)\", 100 * case.initial_meg_mass_fraction, \"wt%\"),\n",
+    "    (\"Pipeline temperature\", case.temperature_C, \"°C\"),\n",
+    "    (\"Pipeline pressure (assumed constant)\", case.pressure_bara, \"bara\"),\n",
+    "    (\"Gas flow (assumed)\", case.gas_flow_Sm3_day, \"Sm³/day\"),\n",
+    "    (\"Water dew point\", case.water_dew_point_C, f\"°C at {case.dew_point_pressure_bara:g} bara\"),\n",
+    "    (\"Residual fraction in every cell\", case.completion_fraction, \"kg/kg initial\"),\n",
+    "], columns=[\"Input\", \"Value\", \"Unit / basis\"]))\n",
+    "print(\"Dry carrier composition:\", dict(case.gas_composition))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "142f5364",
+   "metadata": {},
+   "source": [
+    "## 2. Equilibrium first: will water evaporate or be absorbed?\n",
+    "\n",
+    "SRK–CPA (`SystemSrkCPAstatoil`, mixing rule 10) is used for associating MEG/water mixtures.\n",
+    "Each lookup state is a two-phase gas–aqueous flash. The **actual liquid solvent composition after\n",
+    "the flash** is the interpolation coordinate, avoiding confusion between feed and liquid composition.\n",
+    "Dissolved carrier gas is equilibrated in these flashes; its small changing film inventory is omitted\n",
+    "from the subsequent trace-species balance.\n",
+    "\n",
+    "The inlet specification is interpreted as a **metastable liquid-water dew point**, with ice and\n",
+    "hydrates excluded. A frost-point specification would imply a different water content; confirm the\n",
+    "instrument convention before applying this case to a real pipeline. The outlet quantity plotted later\n",
+    "is an equivalent **water-only dew point**, not the first condensation temperature of a MEG-containing gas.\n",
+    "\n",
+    "Water and MEG are tracked as molar ratios to dry carrier gas:\n",
+    "\n",
+    "$$r_i=\\frac{n_i^{g}}{n_{\\mathrm{dry}}^{g}},\\qquad w_w=\\frac{n_w^\\ell M_w}{n_w^\\ell M_w+n_m^\\ell M_m}.$$\n",
+    "\n",
+    "Here $i$ is water ($w$) or MEG ($m$), $n$ is amount in mol, $M$ is molar mass in kg/mol,\n",
+    "and $w_w$ is the liquid water mass fraction on a water-plus-MEG basis. Lookup values $r_i^*(w_w)$\n",
+    "come from NeqSim at the pipeline temperature and pressure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e02cc693",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Zero water-flux liquid composition at inlet gas: 7.389 wt% water\n",
+      "Maximum CPA log-fugacity residual: 5.338e-11\n",
+      "Initial water direction: ABSORPTION into film\n",
+      "The zero-water-flux composition is not a final state: MEG continues to evaporate.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>State</th>\n",
+       "      <th>Water [ppmv]</th>\n",
+       "      <th>MEG [ppmv]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Inlet gas</td>\n",
+       "      <td>35.536</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Gas in equilibrium with initial film</td>\n",
+       "      <td>5.7025</td>\n",
+       "      <td>0.7593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Gas over pure water at 5 °C</td>\n",
+       "      <td>176.88</td>\n",
+       "      <td>6.8744e-17</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  State  Water [ppmv]  MEG [ppmv]\n",
+       "0                             Inlet gas        35.536           0\n",
+       "1  Gas in equilibrium with initial film        5.7025      0.7593\n",
+       "2           Gas over pure water at 5 °C        176.88  6.8744e-17"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def equilibrium_point(water_fraction, temperature_C, pressure_bara, composition):\n",
+    "    fluid = SystemCPA(temperature_C + 273.15, pressure_bara)\n",
+    "    composition_sum = sum(value for _, value in composition)\n",
+    "    for name, value in composition:\n",
+    "        fluid.addComponent(name, 1000.0 * value / composition_sum)\n",
+    "    # A large liquid reservoir minimizes loss of the requested film composition.\n",
+    "    fluid.addComponent(\"water\", max(1e-12, 100.0 * water_fraction / MW[0]))\n",
+    "    fluid.addComponent(\"MEG\", max(1e-12, 100.0 * (1 - water_fraction) / MW[1]))\n",
+    "    fluid.setMixingRule(10)\n",
+    "    fluid.setMultiPhaseCheck(True)\n",
+    "    ThermodynamicOperations(fluid).TPflash()\n",
+    "    fluid.initProperties()\n",
+    "    assert fluid.getNumberOfPhases() == 2, \"This model requires only gas and aqueous liquid.\"\n",
+    "    gas = fluid.getPhase(\"gas\")\n",
+    "    liquid = fluid.getPhase(\"aqueous\")\n",
+    "    dry_mole_fraction = sum(gas.getComponent(name).getx() for name, _ in composition)\n",
+    "    ratios = np.array([\n",
+    "        gas.getComponent(name).getx() / dry_mole_fraction\n",
+    "        for name in [\"water\", \"MEG\"]\n",
+    "    ])\n",
+    "    solvent_mass = np.array([\n",
+    "        liquid.getComponent(name).getNumberOfMolesInPhase() * molar_mass\n",
+    "        for name, molar_mass in zip([\"water\", \"MEG\"], MW)\n",
+    "    ])\n",
+    "    actual_water_fraction = solvent_mass[0] / solvent_mass.sum()\n",
+    "    residuals = []\n",
+    "    for name in [\"water\", \"MEG\"]:\n",
+    "        gas_component = gas.getComponent(name)\n",
+    "        liquid_component = liquid.getComponent(name)\n",
+    "        gas_fugacity = gas_component.getx() * gas_component.getFugacityCoefficient()\n",
+    "        liquid_fugacity = liquid_component.getx() * liquid_component.getFugacityCoefficient()\n",
+    "        residuals.append(abs(np.log(gas_fugacity / liquid_fugacity)))\n",
+    "    return {\n",
+    "        \"water_fraction\": actual_water_fraction,\n",
+    "        \"ratios\": ratios,\n",
+    "        \"density_kg_m3\": liquid.getDensity(\"kg/m3\"),\n",
+    "        \"max_log_fugacity_residual\": max(residuals),\n",
+    "    }\n",
+    "\n",
+    "water_grid = np.unique(np.r_[\n",
+    "    0.0, np.geomspace(1e-7, 0.01, 14), np.linspace(0.01, 1.0, 61),\n",
+    "])\n",
+    "equilibrium_rows = [\n",
+    "    equilibrium_point(float(value), case.temperature_C, case.pressure_bara, case.gas_composition)\n",
+    "    for value in water_grid\n",
+    "]\n",
+    "liquid_water_grid = np.array([row[\"water_fraction\"] for row in equilibrium_rows])\n",
+    "ratio_grid = np.array([row[\"ratios\"] for row in equilibrium_rows])\n",
+    "density_grid = np.array([row[\"density_kg_m3\"] for row in equilibrium_rows])\n",
+    "ratio_lookup = PchipInterpolator(liquid_water_grid, ratio_grid, axis=0, extrapolate=False)\n",
+    "density_lookup = PchipInterpolator(liquid_water_grid, density_grid, extrapolate=False)\n",
+    "\n",
+    "def film_equilibrium(water_fraction):\n",
+    "    bounded_fraction = np.clip(water_fraction, liquid_water_grid[0], liquid_water_grid[-1])\n",
+    "    return ratio_lookup(bounded_fraction)\n",
+    "\n",
+    "inlet_point = equilibrium_point(\n",
+    "    1.0, case.water_dew_point_C, case.dew_point_pressure_bara, case.gas_composition,\n",
+    ")\n",
+    "inlet_ratios = np.array([inlet_point[\"ratios\"][0], 0.0])\n",
+    "initial_water_fraction = 1 - case.initial_meg_mass_fraction\n",
+    "initial_ratios = film_equilibrium(initial_water_fraction)\n",
+    "water_balance_fraction = brentq(\n",
+    "    lambda value: film_equilibrium(value)[0] - inlet_ratios[0], 0.0, 1.0,\n",
+    ")\n",
+    "max_fugacity_residual = max(row[\"max_log_fugacity_residual\"] for row in equilibrium_rows)\n",
+    "assert max_fugacity_residual < 1e-5\n",
+    "\n",
+    "def ratios_to_ppmv(ratios):\n",
+    "    values = np.asarray(ratios)\n",
+    "    return 1e6 * values / (1 + values.sum(axis=-1, keepdims=True))\n",
+    "\n",
+    "vle_summary = pd.DataFrame([\n",
+    "    [\"Inlet gas\", *ratios_to_ppmv(inlet_ratios)],\n",
+    "    [\"Gas in equilibrium with initial film\", *ratios_to_ppmv(initial_ratios)],\n",
+    "    [f\"Gas over pure water at {case.temperature_C:g} °C\", *ratios_to_ppmv(ratio_grid[-1])],\n",
+    "], columns=[\"State\", \"Water [ppmv]\", \"MEG [ppmv]\"])\n",
+    "display(vle_summary)\n",
+    "print(\n",
+    "    f\"Zero water-flux liquid composition at inlet gas: \"\n",
+    "    f\"{100 * water_balance_fraction:.3f} wt% water\"\n",
+    ")\n",
+    "print(f\"Maximum CPA log-fugacity residual: {max_fugacity_residual:.3e}\")\n",
+    "print(\"Initial water direction:\", \"ABSORPTION into film\" if inlet_ratios[0] > initial_ratios[0]\n",
+    "      else \"EVAPORATION from film\")\n",
+    "print(\"The zero-water-flux composition is not a final state: MEG continues to evaporate.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "88e7c636",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1375x475 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(11, 3.8), layout=\"constrained\")\n",
+    "plot_water = np.linspace(0, 0.30, 250)\n",
+    "plot_ppmv = ratios_to_ppmv(film_equilibrium(plot_water))\n",
+    "axes[0].plot(plot_water * 100, plot_ppmv[:, 0], color=\"#147d92\", label=\"CPA equilibrium\")\n",
+    "axes[0].axhline(ratios_to_ppmv(inlet_ratios)[0], color=\"#bf6538\", ls=\"--\", label=\"Inlet gas\")\n",
+    "axes[0].axvline(initial_water_fraction * 100, color=\"gray\", ls=\":\", label=\"Initial film\")\n",
+    "axes[0].set(xlabel=\"Water in liquid solvent [wt%]\", ylabel=\"Water in gas [ppmv]\")\n",
+    "axes[0].legend()\n",
+    "axes[1].plot(plot_water * 100, plot_ppmv[:, 1], color=\"#754c97\")\n",
+    "axes[1].set(xlabel=\"Water in liquid solvent [wt%]\", ylabel=\"MEG in gas [ppmv]\")\n",
+    "fig.suptitle(\n",
+    "    f\"NeqSim CPA interface equilibrium at {case.temperature_C:g} °C \"\n",
+    "    f\"and {case.pressure_bara:g} bara\"\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01b85fd9",
+   "metadata": {},
+   "source": [
+    "## 3. Finite-rate model, gas transport and assumptions\n",
+    "\n",
+    "The pipe is divided into equal, well-mixed gas cells with **stationary, internally mixed films**.\n",
+    "Gas advects between cells; no film drains or is entrained. Temperature and pressure are imposed.\n",
+    "Gas is dilute in water/MEG (checked below), so the dry-carrier molar density $c_d$ and flow $F_d$\n",
+    "are held constant. Gas holdup is retained: startup is not treated as an instantaneous axial flash.\n",
+    "\n",
+    "$$\\dot N_{i,j}=k_i c_d A_j a_j\\left(r_i^*(w_{w,j})-r_{i,j}\\right).$$\n",
+    "\n",
+    "$$\\frac{dn_{i,j}^\\ell}{dt}=-\\dot N_{i,j},\\qquad H_j\\frac{dr_{i,j}}{dt}=F_d(r_{i,j-1}-r_{i,j})+\\dot N_{i,j}.$$\n",
+    "\n",
+    "$\\dot N$ is film-to-gas transfer in mol/s (negative permits absorption); $k_i$ is a gas-side\n",
+    "coefficient in m/s; $A_j=\\pi D\\Delta z$ is wall area in m²; $a_j$ is the wetted-area factor;\n",
+    "$H_j=c_d\\pi D^2\\Delta z/4$ is dry gas holdup in mol; $F_d$ is dry molar flow in mol/s.\n",
+    "The inlet boundary has constant $r_{i,0}$. No liquid-phase resistance is resolved.\n",
+    "\n",
+    "The Chilton–Colburn mass-transfer/friction analogy is used as a screening closure:\n",
+    "\n",
+    "$$k_i=\\frac{f_Du}{8\\,\\mathrm{Sc}_i^{2/3}},\\qquad f_D=(0.79\\ln\\mathrm{Re}-1.64)^{-2}.$$\n",
+    "\n",
+    "$f_D$ is the Darcy friction factor (four times the Fanning factor), estimated with the smooth-pipe\n",
+    "Petukhov expression. $\\mathrm{Re}=\\rho_g uD/\\mu_g$, $\\mathrm{Sc}_i=\\mu_g/(\\rho_gD_{i,g})$, and $D_{i,g}$ is an\n",
+    "estimated diffusivity in m²/s, scaled approximately as temperature to power 1.75 and inverse pressure.\n",
+    "The reference values and correlation are **screening assumptions**, not high-pressure transport\n",
+    "measurements. A 0.5×/2× coefficient sensitivity is provided. Low-Reynolds-number cases are rejected.\n",
+    "\n",
+    "$$a_j=\\frac{m_j}{m_j+\\epsilon m_{j,0}}.$$\n",
+    "\n",
+    "This small-residue area taper avoids discontinuous disappearance of the liquid phase. Its default\n",
+    "$\\epsilon=10^{-6}$ is much smaller than the $10^{-4}$ completion threshold; a taper sensitivity is\n",
+    "included. It is a numerical end-stage model, not a measured wetting law.\n",
+    "\n",
+    "The stiff balance equations use SciPy BDF. All reported balances include gas accumulation and the\n",
+    "integrated **net** water/MEG export. Tiny negative solver excursions are checked against an explicit\n",
+    "mass tolerance; they are not erased from the balance ledger."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5b3fca32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Temperature is imposed: heat required for evaporation comes from the pipe surroundings.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Value</th>\n",
+       "      <th>Unit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Initial film volume</td>\n",
+       "      <td>0.94216</td>\n",
+       "      <td>m³</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Initial film mass</td>\n",
+       "      <td>1,050.7</td>\n",
+       "      <td>kg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Initial water mass</td>\n",
+       "      <td>10.507</td>\n",
+       "      <td>kg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Initial MEG mass</td>\n",
+       "      <td>1,040.2</td>\n",
+       "      <td>kg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CPA gas density</td>\n",
+       "      <td>56.522</td>\n",
+       "      <td>kg/m³</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Gas velocity</td>\n",
+       "      <td>1.9655</td>\n",
+       "      <td>m/s</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Gas residence time</td>\n",
+       "      <td>1.4132</td>\n",
+       "      <td>h</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Reynolds number</td>\n",
+       "      <td>2.69e+06</td>\n",
+       "      <td>–</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Water mass-transfer coefficient</td>\n",
+       "      <td>0.0028991</td>\n",
+       "      <td>m/s</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>MEG mass-transfer coefficient</td>\n",
+       "      <td>0.0018263</td>\n",
+       "      <td>m/s</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Smooth-pipe pressure-drop screen</td>\n",
+       "      <td>0.3599</td>\n",
+       "      <td>bar</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            Quantity     Value   Unit\n",
+       "0                Initial film volume   0.94216     m³\n",
+       "1                  Initial film mass   1,050.7     kg\n",
+       "2                 Initial water mass    10.507     kg\n",
+       "3                   Initial MEG mass   1,040.2     kg\n",
+       "4                    CPA gas density    56.522  kg/m³\n",
+       "5                       Gas velocity    1.9655    m/s\n",
+       "6                 Gas residence time    1.4132      h\n",
+       "7                    Reynolds number  2.69e+06      –\n",
+       "8    Water mass-transfer coefficient 0.0028991    m/s\n",
+       "9      MEG mass-transfer coefficient 0.0018263    m/s\n",
+       "10  Smooth-pipe pressure-drop screen    0.3599    bar"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "carrier = SystemCPA(case.temperature_C + 273.15, case.pressure_bara)\n",
+    "for component, amount in case.gas_composition:\n",
+    "    carrier.addComponent(component, amount)\n",
+    "carrier.setMixingRule(10)\n",
+    "ThermodynamicOperations(carrier).TPflash()\n",
+    "carrier.initProperties()\n",
+    "assert carrier.getNumberOfPhases() == 1\n",
+    "assert carrier.getPhase(0).getType() == jpype.JClass(\"neqsim.thermo.phase.PhaseType\").GAS\n",
+    "gas_density = carrier.getDensity(\"kg/m3\")\n",
+    "gas_viscosity = carrier.getViscosity(\"kg/msec\")\n",
+    "gas_z = carrier.getZ()\n",
+    "carrier_molar_mass = carrier.getMolarMass()\n",
+    "carrier_molar_density = case.pressure_bara * 1e5 / (\n",
+    "    gas_z * R * (case.temperature_C + 273.15)\n",
+    ")\n",
+    "\n",
+    "# Named NeqSim stream and process remain available for composition/flowsheet integration.\n",
+    "Stream = jpype.JClass(\"neqsim.process.equipment.stream.Stream\")\n",
+    "ProcessSystem = jpype.JClass(\"neqsim.process.processmodel.ProcessSystem\")\n",
+    "inlet_fluid = carrier.clone()\n",
+    "inlet_fluid.addComponent(\"water\", inlet_ratios[0] * sum(v for _, v in case.gas_composition))\n",
+    "inlet_fluid.setMixingRule(10)\n",
+    "inlet_fluid.setTotalFlowRate(case.gas_flow_Sm3_day, \"Sm3/day\")\n",
+    "inlet_stream = Stream(\"Post-pigging inlet gas\", inlet_fluid)\n",
+    "process = ProcessSystem()\n",
+    "process.add(inlet_stream)\n",
+    "process.run()\n",
+    "\n",
+    "def flow_properties(settings):\n",
+    "    cross_section = np.pi * settings.diameter_m**2 / 4\n",
+    "    dry_flow = settings.gas_flow_Sm3_day / SECONDS_PER_DAY * (\n",
+    "        STANDARD_PRESSURE_PA / (R * STANDARD_TEMPERATURE_K)\n",
+    "    )\n",
+    "    velocity = dry_flow / (carrier_molar_density * cross_section)\n",
+    "    reynolds = gas_density * velocity * settings.diameter_m / gas_viscosity\n",
+    "    assert reynolds > 10_000, \"The example transfer correlation requires turbulent gas flow.\"\n",
+    "    diffusivity = np.array([\n",
+    "        settings.water_diffusivity_ref, settings.meg_diffusivity_ref,\n",
+    "    ]) * ((settings.temperature_C + 273.15) / 278.15)**1.75 / settings.pressure_bara\n",
+    "    schmidt = gas_viscosity / (gas_density * diffusivity)\n",
+    "    friction_factor = (0.79 * np.log(reynolds) - 1.64)**-2\n",
+    "    coefficients = (\n",
+    "        settings.mass_transfer_multiplier * friction_factor * velocity / (8 * schmidt**(2 / 3))\n",
+    "    )\n",
+    "    # The same smooth-pipe Darcy factor screens the constant-pressure assumption.\n",
+    "    pressure_drop_bar = (\n",
+    "        friction_factor * settings.length_m / settings.diameter_m\n",
+    "        * gas_density * velocity**2 / 2 / 1e5\n",
+    "    )\n",
+    "    return dry_flow, velocity, reynolds, coefficients, pressure_drop_bar\n",
+    "\n",
+    "flow, velocity, reynolds, coefficients, pressure_drop_bar = flow_properties(case)\n",
+    "film_density = float(density_lookup(initial_water_fraction))\n",
+    "thickness_m = case.film_thickness_um * 1e-6\n",
+    "initial_film_volume = np.pi * (\n",
+    "    case.diameter_m * thickness_m - thickness_m**2\n",
+    ") * case.length_m\n",
+    "initial_film_mass = initial_film_volume * film_density\n",
+    "residence_hours = case.length_m / velocity / 3600\n",
+    "\n",
+    "display(pd.DataFrame([\n",
+    "    (\"Initial film volume\", initial_film_volume, \"m³\"),\n",
+    "    (\"Initial film mass\", initial_film_mass, \"kg\"),\n",
+    "    (\"Initial water mass\", initial_film_mass * initial_water_fraction, \"kg\"),\n",
+    "    (\"Initial MEG mass\", initial_film_mass * case.initial_meg_mass_fraction, \"kg\"),\n",
+    "    (\"CPA gas density\", gas_density, \"kg/m³\"),\n",
+    "    (\"Gas velocity\", velocity, \"m/s\"),\n",
+    "    (\"Gas residence time\", residence_hours, \"h\"),\n",
+    "    (\"Reynolds number\", reynolds, \"–\"),\n",
+    "    (\"Water mass-transfer coefficient\", coefficients[0], \"m/s\"),\n",
+    "    (\"MEG mass-transfer coefficient\", coefficients[1], \"m/s\"),\n",
+    "    (\"Smooth-pipe pressure-drop screen\", pressure_drop_bar, \"bar\"),\n",
+    "], columns=[\"Quantity\", \"Value\", \"Unit\"]))\n",
+    "assert pressure_drop_bar / case.pressure_bara < 0.05, \"Use a pressure profile for this case.\"\n",
+    "print(\"Temperature is imposed: heat required for evaporation comes from the pipe surroundings.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "123e2aa2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Simulation complete: True\n",
+      "Last simulated day: 644.263\n",
+      "Runtime: 7.4 s; accepted time points: 9149\n"
+     ]
+    }
+   ],
+   "source": [
+    "def simulate(settings):\n",
+    "    count = settings.cells\n",
+    "    dry_flow, gas_velocity, _, transfer_coefficients, _ = flow_properties(settings)\n",
+    "    cell_length = settings.length_m / count\n",
+    "    wall_area = np.pi * settings.diameter_m * cell_length\n",
+    "    gas_holdup = carrier_molar_density * np.pi * settings.diameter_m**2 / 4 * cell_length\n",
+    "    film_thickness = settings.film_thickness_um * 1e-6\n",
+    "    cell_mass = film_density * np.pi * (\n",
+    "        settings.diameter_m * film_thickness - film_thickness**2\n",
+    "    ) * cell_length\n",
+    "    initial_moles = cell_mass * np.array([\n",
+    "        initial_water_fraction, 1 - initial_water_fraction,\n",
+    "    ]) / MW\n",
+    "    initial_state = np.r_[\n",
+    "        np.tile(initial_moles, (count, 1)).ravel(),\n",
+    "        np.tile(inlet_ratios, (count, 1)).ravel(),\n",
+    "        [0.0, 0.0],\n",
+    "    ]\n",
+    "\n",
+    "    def rate_equations(seconds, state):\n",
+    "        film_moles = state[:2 * count].reshape(count, 2)\n",
+    "        gas_ratios = state[2 * count:4 * count].reshape(count, 2)\n",
+    "        # Only constitutive evaluation uses bounded trial states; inventory is unmodified.\n",
+    "        film_mass = np.maximum(film_moles, 0.0) * MW\n",
+    "        total_mass = film_mass.sum(axis=1)\n",
+    "        water_fraction = np.divide(\n",
+    "            film_mass[:, 0], total_mass, out=np.ones(count), where=total_mass > 0,\n",
+    "        )\n",
+    "        wetted_fraction = total_mass / (total_mass + settings.patch_fraction * cell_mass)\n",
+    "        transfer = (\n",
+    "            transfer_coefficients * carrier_molar_density * wall_area\n",
+    "            * wetted_fraction[:, None]\n",
+    "            * (film_equilibrium(water_fraction) - gas_ratios)\n",
+    "        )\n",
+    "        upstream_ratios = np.vstack([inlet_ratios, gas_ratios[:-1]])\n",
+    "        gas_derivative = (dry_flow * (upstream_ratios - gas_ratios) + transfer) / gas_holdup\n",
+    "        net_export = dry_flow * (gas_ratios[-1] - inlet_ratios)\n",
+    "        return np.r_[-transfer.ravel(), gas_derivative.ravel(), net_export]\n",
+    "\n",
+    "    def all_cells_dry(seconds, state):\n",
+    "        masses = state[:2 * count].reshape(count, 2) @ MW\n",
+    "        return masses.max() / cell_mass - settings.completion_fraction\n",
+    "\n",
+    "    all_cells_dry.terminal = True\n",
+    "    all_cells_dry.direction = -1\n",
+    "    sparsity = lil_matrix((4 * count + 2, 4 * count + 2))\n",
+    "    for cell_number in range(count):\n",
+    "        indices = [\n",
+    "            2 * cell_number, 2 * cell_number + 1,\n",
+    "            2 * count + 2 * cell_number, 2 * count + 2 * cell_number + 1,\n",
+    "        ]\n",
+    "        for row_index in indices:\n",
+    "            sparsity[row_index, indices] = 1\n",
+    "        if cell_number:\n",
+    "            row_start = 2 * count + 2 * cell_number\n",
+    "            sparsity[row_start:row_start + 2, row_start - 2:row_start] = 1\n",
+    "    sparsity[-2:, 4 * count - 2:4 * count] = 1\n",
+    "    absolute_tolerances = np.r_[\n",
+    "        np.full(2 * count, 1e-9), np.full(2 * count, 1e-13), [1e-7, 1e-7],\n",
+    "    ]\n",
+    "    started = time.monotonic()\n",
+    "    solution = solve_ivp(\n",
+    "        rate_equations,\n",
+    "        (0.0, settings.maximum_days * SECONDS_PER_DAY),\n",
+    "        initial_state,\n",
+    "        method=\"BDF\",\n",
+    "        rtol=settings.relative_tolerance,\n",
+    "        atol=absolute_tolerances,\n",
+    "        jac_sparsity=sparsity.tocsr(),\n",
+    "        events=all_cells_dry,\n",
+    "        dense_output=True,\n",
+    "    )\n",
+    "    assert solution.success, solution.message\n",
+    "    return {\n",
+    "        \"settings\": settings,\n",
+    "        \"solution\": solution,\n",
+    "        \"cell_initial_mass\": cell_mass,\n",
+    "        \"gas_holdup_mol\": gas_holdup,\n",
+    "        \"dry_flow_mol_s\": dry_flow,\n",
+    "        \"runtime_s\": time.monotonic() - started,\n",
+    "        \"complete\": bool(len(solution.t_events[0])),\n",
+    "        \"last_day\": solution.t[-1] / SECONDS_PER_DAY,\n",
+    "    }\n",
+    "\n",
+    "baseline = simulate(case)\n",
+    "print(f\"Simulation complete: {baseline['complete']}\")\n",
+    "print(f\"Last simulated day: {baseline['last_day']:.3f}\")\n",
+    "print(\n",
+    "    f\"Runtime: {baseline['runtime_s']:.1f} s; \"\n",
+    "    f\"accepted time points: {len(baseline['solution'].t)}\"\n",
+    ")\n",
+    "if not baseline[\"complete\"]:\n",
+    "    print(\"The residual criterion was NOT reached; increase maximum_days or change conditions.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e389eea3",
+   "metadata": {},
+   "source": [
+    "## 4. Startup history and practical film removal\n",
+    "\n",
+    "The initial film can become wetter while MEG is evaporating. After the water content adjusts,\n",
+    "MEG removal controls the long drying front. The following results are model predictions under\n",
+    "the assumed geometry, transport coefficients and isothermal boundary, not a field-qualified duration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "98f2410e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Result</th>\n",
+       "      <th>Value</th>\n",
+       "      <th>Unit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Practical all-cell film removal</td>\n",
+       "      <td>644.26</td>\n",
+       "      <td>day</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Gas used to criterion</td>\n",
+       "      <td>644.26</td>\n",
+       "      <td>million Sm³</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Initial water</td>\n",
+       "      <td>10.507</td>\n",
+       "      <td>kg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Maximum water in film</td>\n",
+       "      <td>82.597</td>\n",
+       "      <td>kg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Day of maximum water inventory</td>\n",
+       "      <td>5.9567</td>\n",
+       "      <td>day</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Water left at criterion</td>\n",
+       "      <td>0.00019475</td>\n",
+       "      <td>kg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>MEG left at criterion</td>\n",
+       "      <td>0.0024316</td>\n",
+       "      <td>kg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Total residue retained</td>\n",
+       "      <td>0.0026264</td>\n",
+       "      <td>kg</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            Result      Value         Unit\n",
+       "0  Practical all-cell film removal     644.26          day\n",
+       "1            Gas used to criterion     644.26  million Sm³\n",
+       "2                    Initial water     10.507           kg\n",
+       "3            Maximum water in film     82.597           kg\n",
+       "4   Day of maximum water inventory     5.9567          day\n",
+       "5          Water left at criterion 0.00019475           kg\n",
+       "6            MEG left at criterion  0.0024316           kg\n",
+       "7           Total residue retained  0.0026264           kg"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1375x487.5 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def extract_history(result, days):\n",
+    "    count = result[\"settings\"].cells\n",
+    "    state = result[\"solution\"].sol(np.asarray(days) * SECONDS_PER_DAY)\n",
+    "    film = state[:2 * count].reshape(count, 2, -1)\n",
+    "    gas = state[2 * count:4 * count].reshape(count, 2, -1)\n",
+    "    film_mass = film * MW[None, :, None]\n",
+    "    total = film_mass.sum(axis=1)\n",
+    "    water_fraction = np.divide(\n",
+    "        film_mass[:, 0], total, out=np.full_like(total, np.nan), where=total > 1e-7,\n",
+    "    )\n",
+    "    outlet_ratios = gas[-1].T\n",
+    "    return state, film_mass, gas, water_fraction, outlet_ratios\n",
+    "\n",
+    "sample_days = np.unique(np.r_[\n",
+    "    0.0, np.geomspace(1e-5, baseline[\"last_day\"], 550), baseline[\"last_day\"],\n",
+    "])\n",
+    "state, film_history, gas_history, water_history, outlet_ratios = extract_history(\n",
+    "    baseline, sample_days,\n",
+    ")\n",
+    "inventory_history = film_history.sum(axis=0).T\n",
+    "outlet_ppmv = ratios_to_ppmv(outlet_ratios)\n",
+    "peak_water_index = np.argmax(inventory_history[:, 0])\n",
+    "residual_kg = inventory_history[-1].sum()\n",
+    "summary = pd.DataFrame([\n",
+    "    (\"Practical all-cell film removal\", baseline[\"last_day\"], \"day\"),\n",
+    "    (\"Gas used to criterion\", baseline[\"last_day\"] * case.gas_flow_Sm3_day / 1e6, \"million Sm³\"),\n",
+    "    (\"Initial water\", inventory_history[0, 0], \"kg\"),\n",
+    "    (\"Maximum water in film\", inventory_history[peak_water_index, 0], \"kg\"),\n",
+    "    (\"Day of maximum water inventory\", sample_days[peak_water_index], \"day\"),\n",
+    "    (\"Water left at criterion\", inventory_history[-1, 0], \"kg\"),\n",
+    "    (\"MEG left at criterion\", inventory_history[-1, 1], \"kg\"),\n",
+    "    (\"Total residue retained\", residual_kg, \"kg\"),\n",
+    "], columns=[\"Result\", \"Value\", \"Unit\"])\n",
+    "display(summary)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(11, 3.9), layout=\"constrained\")\n",
+    "axes[0].plot(sample_days, inventory_history[:, 1], color=\"#754c97\", label=\"MEG\")\n",
+    "axes[0].plot(\n",
+    "    sample_days, inventory_history.sum(axis=1), color=\"#243b53\", ls=\"--\", label=\"Total film\",\n",
+    ")\n",
+    "axes[0].set(xlabel=\"Time after last pig exits [day]\", ylabel=\"Mass on pipe wall [kg]\")\n",
+    "axes[0].legend()\n",
+    "axes[1].plot(sample_days, inventory_history[:, 0], color=\"#147d92\")\n",
+    "axes[1].axhline(inventory_history[0, 0], color=\"gray\", ls=\":\", label=\"Initial water\")\n",
+    "axes[1].set(xlabel=\"Time after last pig exits [day]\", ylabel=\"Water on pipe wall [kg]\")\n",
+    "axes[1].legend()\n",
+    "fig.suptitle(\"Water initially accumulates while MEG evaporates\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6473beac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hydrate and ice formation are not simulated; dew point is a composition indicator only.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1375x875 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Map water content to the same metastable liquid-water dew-point convention.\n",
+    "dew_temperatures = np.linspace(-65.0, max(10.0, case.temperature_C + 5), 80)\n",
+    "dew_ratios = np.array([\n",
+    "    equilibrium_point(\n",
+    "        1.0, float(temp), case.dew_point_pressure_bara, case.gas_composition,\n",
+    "    )[\"ratios\"][0]\n",
+    "    for temp in dew_temperatures\n",
+    "])\n",
+    "assert np.all(np.diff(dew_ratios) > 0)\n",
+    "dew_lookup = PchipInterpolator(np.log(dew_ratios), dew_temperatures, extrapolate=False)\n",
+    "outlet_dew_C = dew_lookup(np.log(np.maximum(outlet_ratios[:, 0], 1e-30)))\n",
+    "assert np.all(np.isfinite(outlet_dew_C)), \"Extend the dew-point lookup range.\"\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(11, 7), layout=\"constrained\")\n",
+    "early = sample_days <= 5\n",
+    "axes[0, 0].plot(sample_days[early] * 24, outlet_ppmv[early, 0], color=\"#147d92\")\n",
+    "axes[0, 0].axhline(ratios_to_ppmv(inlet_ratios)[0], color=\"gray\", ls=\"--\", label=\"Inlet\")\n",
+    "axes[0, 0].set(xlabel=\"First five days [h]\", ylabel=\"Outlet water [ppmv]\")\n",
+    "axes[0, 0].legend()\n",
+    "axes[0, 1].plot(sample_days, outlet_ppmv[:, 1], color=\"#754c97\")\n",
+    "axes[0, 1].set(xlabel=\"Time [day]\", ylabel=\"Outlet MEG [ppmv]\")\n",
+    "axes[1, 0].plot(sample_days[early] * 24, outlet_dew_C[early], color=\"#147d92\")\n",
+    "axes[1, 0].axhline(case.water_dew_point_C, color=\"gray\", ls=\"--\")\n",
+    "dew_label = f\"Water-only dew point at {case.dew_point_pressure_bara:g} bara [°C]\"\n",
+    "axes[1, 0].set(xlabel=\"First five days [h]\", ylabel=dew_label)\n",
+    "axes[1, 1].plot(sample_days, outlet_dew_C, color=\"#147d92\")\n",
+    "axes[1, 1].axhline(case.water_dew_point_C, color=\"gray\", ls=\"--\")\n",
+    "axes[1, 1].set(xlabel=\"Time [day]\", ylabel=dew_label)\n",
+    "fig.suptitle(\"Outlet gas quality: gas residence time and slow film removal\")\n",
+    "plt.show()\n",
+    "print(\"Hydrate and ice formation are not simulated; dew point is a composition indicator only.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ecb58cb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1375x512.5 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x462.5 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "snapshot_days = np.array([0.0, 1.0, 10.0, 0.25, 0.50, 0.90])\n",
+    "snapshot_days[3:] *= baseline[\"last_day\"]\n",
+    "_, snapshot_film, _, snapshot_water, _ = extract_history(baseline, snapshot_days)\n",
+    "locations_km = (np.arange(case.cells) + 0.5) * case.length_m / case.cells / 1000\n",
+    "cell_wall_area = np.pi * case.diameter_m * case.length_m / case.cells\n",
+    "snapshot_total = snapshot_film.sum(axis=1)\n",
+    "valid_water = np.nan_to_num(snapshot_water, nan=initial_water_fraction)\n",
+    "snapshot_density = density_lookup(np.clip(\n",
+    "    valid_water, liquid_water_grid[0], liquid_water_grid[-1],\n",
+    "))\n",
+    "# Thin-film equivalent thickness; volume is divided by original wetted wall area.\n",
+    "snapshot_thickness_um = snapshot_total / (snapshot_density * cell_wall_area) * 1e6\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(11, 4.1), layout=\"constrained\")\n",
+    "for column, day in enumerate(snapshot_days):\n",
+    "    label = f\"{day:.1f} d\"\n",
+    "    axes[0].plot(locations_km, snapshot_thickness_um[:, column], label=label)\n",
+    "    wet = snapshot_total[:, column] > baseline[\"cell_initial_mass\"] * case.completion_fraction\n",
+    "    axes[1].plot(locations_km, np.where(wet, 100 * snapshot_water[:, column], np.nan))\n",
+    "axes[0].set(xlabel=\"Distance from inlet [km]\", ylabel=\"Equivalent film thickness [µm]\")\n",
+    "axes[1].set(xlabel=\"Distance from inlet [km]\", ylabel=\"Water in remaining film [wt%]\")\n",
+    "axes[0].legend(ncol=2, fontsize=9)\n",
+    "fig.suptitle(\"The residual-film drying front moves from inlet to outlet\")\n",
+    "plt.show()\n",
+    "\n",
+    "fractions = film_history.sum(axis=1) / baseline[\"cell_initial_mass\"]\n",
+    "completion_times = []\n",
+    "for cell_number in range(case.cells):\n",
+    "    passed = np.flatnonzero(fractions[cell_number] <= case.completion_fraction * 1.001)\n",
+    "    completion_times.append(sample_days[passed[0]] if len(passed) else np.nan)\n",
+    "fig, axis = plt.subplots(figsize=(8, 3.7), layout=\"constrained\")\n",
+    "axis.plot(locations_km, completion_times, color=\"#243b53\", marker=\".\")\n",
+    "axis.set(xlabel=\"Distance from inlet [km]\", ylabel=\"Local residual criterion reached [day]\")\n",
+    "axis.set_title(\"Local film-removal time (sampled history)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f1210d9",
+   "metadata": {},
+   "source": [
+    "## 5. Conservation and independent limiting checks\n",
+    "\n",
+    "The two trace-component balances are checked across all accepted solver steps:\n",
+    "\n",
+    "$$B_i(t)=\\sum_j n_{i,j}^\\ell(t)+\\sum_j H_jr_{i,j}(t)+\\int_0^t F_d(r_{i,\\mathrm{out}}-r_{i,\\mathrm{in}})\\,dt.$$\n",
+    "\n",
+    "$B_i$ must equal its initial value in mol. A negative integrated net water export is legitimate:\n",
+    "water has moved from the incoming gas into the film.\n",
+    "\n",
+    "A capacity lower bound uses the **maximum CPA MEG vapor ratio anywhere in the liquid-composition\n",
+    "lookup**. It assumes gas leaves fully saturated at that maximum at all times and neglects resistance.\n",
+    "The gas can still contain MEG at the film-residue criterion because the gas holdup has not yet\n",
+    "been purged. The figures stop at the film criterion, not at a zero-MEG outlet reading.\n",
+    "\n",
+    "Removal faster than this bound would violate the model's available vapor capacity. The small MEG\n",
+    "inventory in the gas is included in the bound. This is an analytical check, not an experimental validation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8b94d7c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MEG maximum equilibrium carrying capacity: 2.062 kg/day\n",
+      "No measured pipeline dry-out time is used for validation or calibration.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Check</th>\n",
+       "      <th>Result</th>\n",
+       "      <th>Unit</th>\n",
+       "      <th>Acceptance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Water balance maximum residual</td>\n",
+       "      <td>1.6135e-08</td>\n",
+       "      <td>kg</td>\n",
+       "      <td>&lt; 1e-5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MEG balance maximum residual</td>\n",
+       "      <td>4.6879e-09</td>\n",
+       "      <td>kg</td>\n",
+       "      <td>&lt; 1e-5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Minimum component mass in a cell</td>\n",
+       "      <td>-1.1896e-07</td>\n",
+       "      <td>kg</td>\n",
+       "      <td>&gt; -1e-5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Maximum CPA log-fugacity residual</td>\n",
+       "      <td>5.3384e-11</td>\n",
+       "      <td>–</td>\n",
+       "      <td>&lt; 1e-5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>MEG vapor-capacity lower bound</td>\n",
+       "      <td>504.51</td>\n",
+       "      <td>day</td>\n",
+       "      <td>≤ simulated time</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Analytical gas-advection error</td>\n",
+       "      <td>8.3865e-10</td>\n",
+       "      <td>fraction</td>\n",
+       "      <td>&lt; 1e-7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               Check      Result      Unit        Acceptance\n",
+       "0     Water balance maximum residual  1.6135e-08        kg            < 1e-5\n",
+       "1       MEG balance maximum residual  4.6879e-09        kg            < 1e-5\n",
+       "2   Minimum component mass in a cell -1.1896e-07        kg           > -1e-5\n",
+       "3  Maximum CPA log-fugacity residual  5.3384e-11         –            < 1e-5\n",
+       "4     MEG vapor-capacity lower bound      504.51       day  ≤ simulated time\n",
+       "5     Analytical gas-advection error  8.3865e-10  fraction            < 1e-7"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def balance_check(result):\n",
+    "    count = result[\"settings\"].cells\n",
+    "    solution = result[\"solution\"]\n",
+    "    film = solution.y[:2 * count].reshape(count, 2, -1)\n",
+    "    gas = solution.y[2 * count:4 * count].reshape(count, 2, -1)\n",
+    "    total = film.sum(axis=0) + result[\"gas_holdup_mol\"] * gas.sum(axis=0) + solution.y[-2:]\n",
+    "    mass_residual = (total - total[:, [0]]) * MW[:, None]\n",
+    "    maximum_error = np.max(np.abs(mass_residual), axis=1)\n",
+    "    minimum_component_mass = float(np.min(film * MW[None, :, None]))\n",
+    "    assert np.all(maximum_error < 1e-5), maximum_error\n",
+    "    assert minimum_component_mass > -1e-5, minimum_component_mass\n",
+    "    assert gas.min() > -1e-10\n",
+    "    assert gas.sum(axis=1).max() < 0.005, \"Trace-species gas approximation is no longer adequate.\"\n",
+    "    return maximum_error, minimum_component_mass\n",
+    "\n",
+    "balance_error, minimum_mass = balance_check(baseline)\n",
+    "maximum_meg_ratio = ratio_grid[:, 1].max()\n",
+    "initial_meg_kg = inventory_history[0, 1]\n",
+    "maximum_gas_meg_kg = case.cells * baseline[\"gas_holdup_mol\"] * maximum_meg_ratio * MW[1]\n",
+    "capacity_kg_day = flow * maximum_meg_ratio * MW[1] * SECONDS_PER_DAY\n",
+    "capacity_lower_bound_days = max(\n",
+    "    0.0, initial_meg_kg - residual_kg - maximum_gas_meg_kg,\n",
+    ") / capacity_kg_day\n",
+    "assert baseline[\"last_day\"] >= capacity_lower_bound_days\n",
+    "assert inventory_history[peak_water_index, 0] > inventory_history[0, 0]\n",
+    "\n",
+    "# A no-transfer gas-tracer step has the analytical N-CSTR-in-series response.\n",
+    "from scipy.special import gammainc\n",
+    "tracer_cells = 12\n",
+    "tracer_tau_s = 50.0\n",
+    "tracer_times = np.linspace(0, 1600, 160)\n",
+    "\n",
+    "def tracer_rhs(seconds, concentration):\n",
+    "    return (np.r_[1.0, concentration[:-1]] - concentration) / tracer_tau_s\n",
+    "\n",
+    "tracer_solution = solve_ivp(\n",
+    "    tracer_rhs, (0, tracer_times[-1]), np.zeros(tracer_cells),\n",
+    "    t_eval=tracer_times, rtol=1e-9, atol=1e-11,\n",
+    ")\n",
+    "tracer_exact = gammainc(tracer_cells, tracer_times / tracer_tau_s)\n",
+    "tracer_error = np.max(np.abs(tracer_solution.y[-1] - tracer_exact))\n",
+    "assert tracer_error < 1e-7\n",
+    "\n",
+    "validation_table = pd.DataFrame([\n",
+    "    (\"Water balance maximum residual\", balance_error[0], \"kg\", \"< 1e-5\"),\n",
+    "    (\"MEG balance maximum residual\", balance_error[1], \"kg\", \"< 1e-5\"),\n",
+    "    (\"Minimum component mass in a cell\", minimum_mass, \"kg\", \"> -1e-5\"),\n",
+    "    (\"Maximum CPA log-fugacity residual\", max_fugacity_residual, \"–\", \"< 1e-5\"),\n",
+    "    (\"MEG vapor-capacity lower bound\", capacity_lower_bound_days, \"day\", \"≤ simulated time\"),\n",
+    "    (\"Analytical gas-advection error\", tracer_error, \"fraction\", \"< 1e-7\"),\n",
+    "], columns=[\"Check\", \"Result\", \"Unit\", \"Acceptance\"])\n",
+    "display(validation_table)\n",
+    "print(f\"MEG maximum equilibrium carrying capacity: {capacity_kg_day:.3f} kg/day\")\n",
+    "print(\"No measured pipeline dry-out time is used for validation or calibration.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8f839c5",
+   "metadata": {},
+   "source": [
+    "## 6. Numerical refinement and sensitivity to missing inputs\n",
+    "\n",
+    "The 80-cell case checks spatial resolution; the tighter-tolerance case checks time integration.\n",
+    "Doubling the area-taper scale checks that the numerical phase-disappearance tail does not determine\n",
+    "the answer. Thin-film, gas-flow and mass-transfer cases quantify assumptions that were not supplied.\n",
+    "All cases rebuild their gas-flow and geometry terms while keeping the same thermodynamic boundary.\n",
+    "If you edit temperature, pressure or composition, rerun **all** cells to rebuild the CPA lookup."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "47b330c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "40 → 80 cells: 0.000% change in completion time\n",
+      "Solver tolerance: 0.000% change\n",
+      "Area taper: 0.000% change\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Case</th>\n",
+       "      <th>Reached criterion</th>\n",
+       "      <th>Time [day]</th>\n",
+       "      <th>Gas [million Sm³]</th>\n",
+       "      <th>Max balance error [kg]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Baseline: 40 cells</td>\n",
+       "      <td>True</td>\n",
+       "      <td>644.26</td>\n",
+       "      <td>644.26</td>\n",
+       "      <td>1.6135e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>80 cells</td>\n",
+       "      <td>True</td>\n",
+       "      <td>644.26</td>\n",
+       "      <td>644.26</td>\n",
+       "      <td>3.6179e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Tighter solver tolerance</td>\n",
+       "      <td>True</td>\n",
+       "      <td>644.26</td>\n",
+       "      <td>644.26</td>\n",
+       "      <td>4.7127e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Area taper ×2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>644.26</td>\n",
+       "      <td>644.26</td>\n",
+       "      <td>9.9135e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Film 50 µm</td>\n",
+       "      <td>True</td>\n",
+       "      <td>322.18</td>\n",
+       "      <td>322.18</td>\n",
+       "      <td>8.2939e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Gas flow ×0.5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1,287.4</td>\n",
+       "      <td>643.72</td>\n",
+       "      <td>6.4767e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Transfer coefficient ×0.5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>649.42</td>\n",
+       "      <td>649.42</td>\n",
+       "      <td>3.1481e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Transfer coefficient ×2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>641.68</td>\n",
+       "      <td>641.68</td>\n",
+       "      <td>2.865e-08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Case  Reached criterion  Time [day]  \\\n",
+       "0         Baseline: 40 cells               True      644.26   \n",
+       "1                   80 cells               True      644.26   \n",
+       "2   Tighter solver tolerance               True      644.26   \n",
+       "3              Area taper ×2               True      644.26   \n",
+       "4                 Film 50 µm               True      322.18   \n",
+       "5              Gas flow ×0.5               True     1,287.4   \n",
+       "6  Transfer coefficient ×0.5               True      649.42   \n",
+       "7    Transfer coefficient ×2               True      641.68   \n",
+       "\n",
+       "   Gas [million Sm³]  Max balance error [kg]  \n",
+       "0             644.26              1.6135e-08  \n",
+       "1             644.26              3.6179e-08  \n",
+       "2             644.26              4.7127e-09  \n",
+       "3             644.26              9.9135e-09  \n",
+       "4             322.18              8.2939e-09  \n",
+       "5             643.72              6.4767e-09  \n",
+       "6             649.42              3.1481e-08  \n",
+       "7             641.68               2.865e-08  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1125x625 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "variants = [\n",
+    "    (\"Baseline: 40 cells\", case, baseline),\n",
+    "    (\"80 cells\", replace(case, cells=80), None),\n",
+    "    (\"Tighter solver tolerance\", replace(case, relative_tolerance=5e-7), None),\n",
+    "    (\"Area taper ×2\", replace(case, patch_fraction=2e-6), None),\n",
+    "    (\"Film 50 µm\", replace(case, film_thickness_um=50.0), None),\n",
+    "    (\"Gas flow ×0.5\", replace(case, gas_flow_Sm3_day=case.gas_flow_Sm3_day / 2), None),\n",
+    "    (\"Transfer coefficient ×0.5\", replace(case, mass_transfer_multiplier=0.5), None),\n",
+    "    (\"Transfer coefficient ×2\", replace(case, mass_transfer_multiplier=2.0), None),\n",
+    "]\n",
+    "sensitivity_rows = []\n",
+    "for label, settings, existing_result in variants:\n",
+    "    result = existing_result if existing_result is not None else simulate(settings)\n",
+    "    errors, smallest_mass = balance_check(result)\n",
+    "    sensitivity_rows.append({\n",
+    "        \"Case\": label,\n",
+    "        \"Reached criterion\": result[\"complete\"],\n",
+    "        \"Time [day]\": result[\"last_day\"],\n",
+    "        \"Gas [million Sm³]\": result[\"last_day\"] * settings.gas_flow_Sm3_day / 1e6,\n",
+    "        \"Max balance error [kg]\": max(errors),\n",
+    "    })\n",
+    "sensitivity = pd.DataFrame(sensitivity_rows)\n",
+    "display(sensitivity)\n",
+    "assert sensitivity[\"Reached criterion\"].all(), \"A sensitivity case did not reach the criterion.\"\n",
+    "refinement_change = abs(sensitivity.loc[1, \"Time [day]\"] / baseline[\"last_day\"] - 1)\n",
+    "time_tolerance_change = abs(sensitivity.loc[2, \"Time [day]\"] / baseline[\"last_day\"] - 1)\n",
+    "taper_change = abs(sensitivity.loc[3, \"Time [day]\"] / baseline[\"last_day\"] - 1)\n",
+    "assert refinement_change < 0.05, \"Refine the mesh further before interpreting dry-out time.\"\n",
+    "assert time_tolerance_change < 0.01\n",
+    "assert taper_change < 0.01\n",
+    "print(f\"40 → 80 cells: {100 * refinement_change:.3f}% change in completion time\")\n",
+    "print(f\"Solver tolerance: {100 * time_tolerance_change:.3f}% change\")\n",
+    "print(f\"Area taper: {100 * taper_change:.3f}% change\")\n",
+    "\n",
+    "fig, axis = plt.subplots(figsize=(9, 5), layout=\"constrained\")\n",
+    "axis.barh(sensitivity[\"Case\"], sensitivity[\"Time [day]\"], color=\"#147d92\")\n",
+    "axis.invert_yaxis()\n",
+    "axis.set(\n",
+    "    xlabel=\"All-cell residual criterion [day]\", title=\"Illustrative sensitivity of drying time\",\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed8bdb3f",
+   "metadata": {},
+   "source": [
+    "## 7. Reusable results and engineering interpretation\n",
+    "\n",
+    "The exported history, axial profiles and validation JSON retain the input/source provenance. They\n",
+    "can be passed to a larger startup study. `inlet_stream` and `process` are ordinary NeqSim objects;\n",
+    "the transient residual-film solver in this notebook is explicitly a Python extension, not a native\n",
+    "`PipelineEvaporationStudy.run()` calculation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "97c2d0e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The model predicts 644.3 days to the specified residual criterion.\n",
+      "That is 1.76 years for the stated assumed film and gas flow.\n",
+      "A dry outlet gas reading can coexist with a substantial MEG film on the wall.\n",
+      "Saved: history.csv, profiles.csv, results.json, sensitivity.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time_day</th>\n",
+       "      <th>water_film_kg</th>\n",
+       "      <th>meg_film_kg</th>\n",
+       "      <th>outlet_water_ppmv</th>\n",
+       "      <th>outlet_meg_ppmv</th>\n",
+       "      <th>outlet_water_dewpoint_C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>10.507</td>\n",
+       "      <td>1,040.2</td>\n",
+       "      <td>35.536</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>407</th>\n",
+       "      <td>5.9567</td>\n",
+       "      <td>82.597</td>\n",
+       "      <td>1,029.2</td>\n",
+       "      <td>35.551</td>\n",
+       "      <td>0.62002</td>\n",
+       "      <td>-17.994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>550</th>\n",
+       "      <td>644.26</td>\n",
+       "      <td>0.00019475</td>\n",
+       "      <td>0.0024316</td>\n",
+       "      <td>35.665</td>\n",
+       "      <td>0.46722</td>\n",
+       "      <td>-17.952</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     time_day  water_film_kg  meg_film_kg  outlet_water_ppmv  outlet_meg_ppmv  \\\n",
+       "0           0         10.507      1,040.2             35.536                0   \n",
+       "407    5.9567         82.597      1,029.2             35.551          0.62002   \n",
+       "550    644.26     0.00019475    0.0024316             35.665          0.46722   \n",
+       "\n",
+       "     outlet_water_dewpoint_C  \n",
+       "0                        -18  \n",
+       "407                  -17.994  \n",
+       "550                  -17.952  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "output_directory = Path(\"pigging_drying_results\")\n",
+    "output_directory.mkdir(exist_ok=True)\n",
+    "history_table = pd.DataFrame({\n",
+    "    \"time_day\": sample_days,\n",
+    "    \"water_film_kg\": inventory_history[:, 0],\n",
+    "    \"meg_film_kg\": inventory_history[:, 1],\n",
+    "    \"outlet_water_ppmv\": outlet_ppmv[:, 0],\n",
+    "    \"outlet_meg_ppmv\": outlet_ppmv[:, 1],\n",
+    "    \"outlet_water_dewpoint_C\": outlet_dew_C,\n",
+    "})\n",
+    "profile_rows = []\n",
+    "for column, day in enumerate(snapshot_days):\n",
+    "    for cell_number, location in enumerate(locations_km):\n",
+    "        profile_rows.append({\n",
+    "            \"time_day\": float(day),\n",
+    "            \"position_km\": float(location),\n",
+    "            \"film_thickness_um\": float(snapshot_thickness_um[cell_number, column]),\n",
+    "            \"water_mass_fraction\": float(snapshot_water[cell_number, column]),\n",
+    "        })\n",
+    "profile_table = pd.DataFrame(profile_rows)\n",
+    "history_table.to_csv(output_directory / \"history.csv\", index=False)\n",
+    "profile_table.to_csv(output_directory / \"profiles.csv\", index=False)\n",
+    "sensitivity.to_csv(output_directory / \"sensitivity.csv\", index=False)\n",
+    "results = {\n",
+    "    \"provenance\": provenance,\n",
+    "    \"inputs\": case.__dict__,\n",
+    "    \"completion_reached\": baseline[\"complete\"],\n",
+    "    \"completion_days\": baseline[\"last_day\"],\n",
+    "    \"retained_residue_kg\": float(residual_kg),\n",
+    "    \"dew_point_reference_bara\": case.dew_point_pressure_bara,\n",
+    "    \"peak_water_mass_kg\": float(inventory_history[peak_water_index, 0]),\n",
+    "    \"water_balance_residual_kg\": float(balance_error[0]),\n",
+    "    \"meg_balance_residual_kg\": float(balance_error[1]),\n",
+    "    \"spatial_refinement_change_fraction\": float(refinement_change),\n",
+    "    \"solver_tolerance_change_fraction\": float(time_tolerance_change),\n",
+    "    \"taper_change_fraction\": float(taper_change),\n",
+    "    \"capacity_lower_bound_days\": float(capacity_lower_bound_days),\n",
+    "    \"validation_level\": \"CPA equilibrium, analytical limits, conservation and numerical refinement\",\n",
+    "    \"field_drying_validation\": False,\n",
+    "}\n",
+    "(output_directory / \"results.json\").write_text(json.dumps(results, indent=2), encoding=\"utf-8\")\n",
+    "display(history_table.iloc[[0, peak_water_index, -1]])\n",
+    "print(f\"The model predicts {baseline['last_day']:.1f} days to the specified residual criterion.\")\n",
+    "print(\n",
+    "    f\"That is {baseline['last_day'] / 365.25:.2f} years \"\n",
+    "    \"for the stated assumed film and gas flow.\"\n",
+    ")\n",
+    "print(\"A dry outlet gas reading can coexist with a substantial MEG film on the wall.\")\n",
+    "print(\"Saved:\", \", \".join(path.name for path in sorted(output_directory.iterdir())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86592c76",
+   "metadata": {},
+   "source": [
+    "## Limitations and next steps\n",
+    "\n",
+    "- **Film geometry governs the inventory.** Measure or bound the thickness, wall coverage and pooled\n",
+    "  liquid. This model has no puddles, corrosion roughness retention, drainage, re-entrainment or pig bypass.\n",
+    "- **Transfer closure is illustrative.** The gas-side correlation and high-pressure diffusivities need\n",
+    "  project validation. The film is internally mixed; liquid diffusion may matter at low temperature.\n",
+    "- **Temperature is held at 5 °C.** The surroundings implicitly supply evaporation heat and remove\n",
+    "  absorption/mixing heat. There is no energy balance or cooldown prediction. A thermal model is needed\n",
+    "  if the film/wall cannot remain near 5 °C.\n",
+    "- **Pressure and carrier composition are constant.** A small smooth-pipe pressure-drop screen supports\n",
+    "  this example only. Hydrocarbon condensation, hydrate/ice formation and changing dissolved-carrier\n",
+    "  inventory are excluded. Rich gas requires a revised phase/topology model if the CPA flash has oil.\n",
+    "- **The final microscopic tail is a threshold calculation.** Local wetting and diffusion are not\n",
+    "  resolved at molecular film thickness; reporting “exactly zero liquid” would overstate the model.\n",
+    "- **Early startup depends on the initial state.** To include pig transit, initialize film exposure times\n",
+    "  and the gas state behind a moving pig; do not interpret this reset-contact example as a pig model.\n",
+    "- **Native API opportunity:** current NeqSim `PipelineEvaporationStudy` marches flowing phase inventories\n",
+    "  along distance. A stationary, component-resolved wall-film inventory over startup time is a separate\n",
+    "  capability. Specific enhancement: [stationary-film drying #4281](https://github.com/equinor/neqsim/issues/4281).\n",
+    "  Related roadmap: [TwoFluidPipe #2907](https://github.com/equinor/neqsim/issues/2907).\n",
+    "\n",
+    "Suggested exercises: replace diameter and film thickness with measured values; compare 25/50/100 µm;\n",
+    "change gas flow; rerun at a different temperature; tighten the residue criterion; compare a different\n",
+    "water dew point; add gas-to-wall heat transfer and liquid diffusion before field qualification.\n",
+    "\n",
+    "## Sources and validation scope\n",
+    "\n",
+    "1. [NeqSim source](https://github.com/equinor/neqsim), specifically `SystemSrkCPAstatoil`,\n",
+    "   `ThermodynamicOperations.TPflash` and\n",
+    "   [pipeline evaporation documentation](https://github.com/equinor/neqsim/blob/master/docs/fluidmechanics/PipelineLiquidEvaporation.md).\n",
+    "2. Folas et al. (2007), *High-pressure vapor–liquid equilibria of systems containing ethylene glycol,\n",
+    "   water and methane*, Fluid Phase Equilibria 251, 52–58,\n",
+    "   [DOI:10.1016/j.fluid.2006.11.001](https://doi.org/10.1016/j.fluid.2006.11.001).\n",
+    "   [NIST ThermoML record](https://trc.nist.gov/ThermoML/10.1016/j.fluid.2006.11.001.html).\n",
+    "   This is relevant CPA/VLE literature; the notebook does not claim to reproduce its full dataset.\n",
+    "3. Chilton and Colburn (1934), *Mass Transfer (Absorption) Coefficients Prediction from Data on\n",
+    "   Heat Transfer and Fluid Friction*, [DOI:10.1021/ie50299a012](https://doi.org/10.1021/ie50299a012).\n",
+    "   The analogy is applied to a smooth pipe with estimated diffusivities; this is not a calibration.\n",
+    "4. Petukhov (1970), *Heat Transfer and Friction in Turbulent Pipe Flow with Variable Physical\n",
+    "   Properties*, [DOI:10.1016/S0065-2717(08)70153-9](https://doi.org/10.1016/S0065-2717(08)70153-9).\n",
+    "5. [NIST Chemistry WebBook: ethylene glycol](https://webbook.nist.gov/cgi/cbook.cgi?ID=C107211&Mask=4),\n",
+    "   molecular identity and phase-change context. No out-of-range Antoine extrapolation is used.\n",
+    "\n",
+    "Validation achieved: phase-equilibrium fugacity consistency, component conservation including gas\n",
+    "holdup, an analytical advection limit, an equilibrium-capacity lower bound, nearby input cases, and\n",
+    "mesh/solver/taper sensitivity. **Absolute field drying time has not been validated experimentally.**"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Pipeline startup after pigging – MEG/water film drying"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/maintenance_ledger/pipeline_pigging_meg_water_20261009.json b/notebooks/maintenance_ledger/pipeline_pigging_meg_water_20261009.json
new file mode 100644
index 00000000..dd9769cc
--- /dev/null
+++ b/notebooks/maintenance_ledger/pipeline_pigging_meg_water_20261009.json
@@ -0,0 +1,115 @@
+{
+  "schema_version": 1,
+  "shard": "pipeline-pigging-meg-water-20261009",
+  "updated_at": "2026-10-09",
+  "notebooks": [
+    {
+      "path": "notebooks/flowassurance/pipeline_startup_after_pigging_meg_water.ipynb",
+      "verified_date": "2026-10-09",
+      "neqsim_version": "3.23.0 source-built master",
+      "neqsim_java_commit": "ea7d61ac1cbfc4b25689a4e81406a7bef248ae64",
+      "neqsim_jar_sha256": "d110e7f8b0b620d488c6e2aa0881b69ad5dbf6f350016975dbd92b4d2482904f",
+      "python_version": "3.12.14",
+      "java_version": "17.0.20",
+      "execution_status": "passed",
+      "code_cells": 12,
+      "markdown_cells": 9,
+      "git_blob_sha1": "1ffd62b5628fd5040b1a22110be670fb89b5b000",
+      "notebook_bytes": 702853,
+      "execution_method": "12 code cells, clean isolated verified-wheel environment, fresh IPython process; source-built current-master JAR and class-origin/hash assertions. No stored exceptions or stderr. Final execution 80.3 seconds.",
+      "environment_resolution": "Verified public-PyPI wheel snapshot reused; hashes checked, fresh /tmp/pigging-validation environment created; no live package-index resolution claimed.",
+      "engineering_validation": {
+        "result": "passed",
+        "checks": [
+          "CPA phase fugacity consistency",
+          "component conservation including gas accumulation and cumulative net export",
+          "nonnegative inventories within stated numerical mass tolerance",
+          "dilute-species gas bound",
+          "analytical no-transfer gas-advection response",
+          "MEG saturated-vapor-capacity lower bound",
+          "40/80 cell refinement",
+          "solver tolerance and area-taper sensitivity",
+          "film thickness, gas flow and mass-transfer coefficient sensitivity"
+        ],
+        "results": {
+          "provenance": {
+            "source_repository": "https://github.com/equinor/neqsim",
+            "requested_ref": "master",
+            "source_commit": "ea7d61ac1cbfc4b25689a4e81406a7bef248ae64",
+            "jar_sha256": "d110e7f8b0b620d488c6e2aa0881b69ad5dbf6f350016975dbd92b4d2482904f",
+            "java": "17.0.20",
+            "python": "3.12.14",
+            "packages": {
+              "neqsim": "3.23.0",
+              "numpy": "2.5.3",
+              "scipy": "1.18.1",
+              "pandas": "3.0.6",
+              "matplotlib": "3.11.2"
+            }
+          },
+          "inputs": {
+            "length_m": 10000.0,
+            "diameter_m": 0.3,
+            "temperature_C": 5.0,
+            "pressure_bara": 70.0,
+            "film_thickness_um": 100.0,
+            "initial_meg_mass_fraction": 0.99,
+            "gas_flow_Sm3_day": 1000000.0,
+            "water_dew_point_C": -18.0,
+            "dew_point_pressure_bara": 70.0,
+            "gas_composition": [
+              [
+                "methane",
+                1.0
+              ]
+            ],
+            "water_diffusivity_ref": 2e-05,
+            "meg_diffusivity_ref": 1e-05,
+            "mass_transfer_multiplier": 1.0,
+            "cells": 40,
+            "relative_tolerance": 2e-06,
+            "completion_fraction": 0.0001,
+            "patch_fraction": 1e-06,
+            "maximum_days": 3650.0
+          },
+          "completion_reached": true,
+          "completion_days": 644.2631520518922,
+          "retained_residue_kg": 0.0026263696801756596,
+          "dew_point_reference_bara": 70.0,
+          "peak_water_mass_kg": 82.59724885147654,
+          "water_balance_residual_kg": 1.6134517720520308e-08,
+          "meg_balance_residual_kg": 4.687861640559277e-09,
+          "spatial_refinement_change_fraction": 2.581126383294219e-06,
+          "solver_tolerance_change_fraction": 2.5386313051711795e-09,
+          "taper_change_fraction": 8.2646604671055e-08,
+          "capacity_lower_bound_days": 504.5128650094197,
+          "validation_level": "CPA equilibrium, analytical limits, conservation and numerical refinement",
+          "field_drying_validation": false
+        },
+        "absolute_field_drying_validation": false
+      },
+      "rendered_visual_validation": {
+        "renderer": "nbconvert 7.17.1 Jupyter HTML; MathJax 3 SVG rendering of every math expression; WeasyPrint/Poppler raster review; original stored Matplotlib PNG inspection",
+        "mathematical_expressions": 29,
+        "figures_inspected": 6,
+        "result": "passed",
+        "coverage": "All 9 Markdown cells and all result-bearing outputs; equations, six figures and all numerical/provenance tables. Source inspected for readability and compact Colab math. Catalog is six Markdown cells with no code; new catalog entry and all 329 relative notebook targets checked against authoritative remote tree."
+      },
+      "documentation_impact": "New user-facing model, assumptions, source setup, numerical/physical limitations, inputs/units and validation documented in notebook; README and examples catalog updated; ledger index count incremented.",
+      "known_upstream_issues": [
+        "https://github.com/equinor/neqsim/issues/4281"
+      ],
+      "publication": {
+        "branch": "agent/pipeline-pigging-meg-water-drying",
+        "mode": "draft pull request"
+      },
+      "checks": [
+        "python -m unittest discover -s scripts -p test_check_notebook.py: 6 passed",
+        "scripts/check_notebook.py target --require-main-source with focused ledger/catalog: passed",
+        "All 329 catalog relative links resolved against current remote tree plus new notebook",
+        "Python AST, <=100-character code lines, compact single-backslash Colab math: passed",
+        "Repository-wide notebook integrity CI: pending publication"
+      ]
+    }
+  ]
+}
diff --git a/notebooks/notebook_maintenance_ledger.json b/notebooks/notebook_maintenance_ledger.json
index f2f6ba06..ebea86f5 100644
--- a/notebooks/notebook_maintenance_ledger.json
+++ b/notebooks/notebook_maintenance_ledger.json
@@ -1,5 +1,5 @@
 {
-  "active_notebook_count": 321,
+  "active_notebook_count": 322,
   "retired_notebooks": [
     {
       "follow_up": "Replace with the planned NeqSim-master data-reconciliation and Bayesian digital-twin notebook.",
@@ -34,5 +34,5 @@
   ],
   "schema_version": 3,
   "shards_glob": "maintenance_ledger/*.json",
-  "updated_at": "2026-10-04"
+  "updated_at": "2026-10-09"
 }