IPA_Pharma_Compactor_Platform.ipynb

{
 "cells": [
  {"cell_type": "markdown", "metadata": {}, "source": [
    "# \ud83c\udfed IPA Pharmaceutical Roller Compactor Platform: Scale-Up & Performance\n",
    "\n",
    "**Dataset:** IPA Pharma Compactor (Synthetic) v1.0  \n",
    "**Publisher:** [Innovative Process Applications](https://www.innovativeprocess.com) | Crestwood, IL  \n",
    "**License:** CC BY 4.0\n",
    "\n",
    "> \u26a0\ufe0f Synthetic educational data \u2014 not production measurements.\n",
    "\n",
    "---\n",
    "\n",
    "### Analysis Roadmap\n",
    "1. Platform overview & scale-up visualization\n",
    "2. Twin feed screw response surface (VFS/HFS ratio optimization)\n",
    "3. Interactive design space heatmaps\n",
    "4. Scale-up consistency analysis\n",
    "5. Energy efficiency & throughput Pareto frontier\n",
    "6. Comprehensive radar chart & scorecard\n",
    "7. Material fingerprinting across the CL platform"
  ]},

  {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "from matplotlib.patches import FancyBboxPatch, Circle\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import matplotlib.ticker as mticker\n",
    "import seaborn as sns\n",
    "from scipy.interpolate import griddata\n",
    "\n",
    "# === IPA Brand Palette ===\n",
    "IPA_TEAL = '#008080'\n",
    "IPA_NAVY = '#1B2A3B'\n",
    "IPA_CHARCOAL = '#3D3D3D'\n",
    "IPA_GOLD = '#C9A84C'\n",
    "IPA_LIGHT = '#E8F5F5'\n",
    "IPA_GRADIENT = LinearSegmentedColormap.from_list('ipa', [IPA_LIGHT, IPA_TEAL, IPA_NAVY])\n",
    "IPA_DIVERGING = LinearSegmentedColormap.from_list('ipa_div', ['#cc4444', '#ffffff', IPA_TEAL])\n",
    "MODEL_COLORS = {'CL25150': '#66c2a5', 'CL30200': '#3288bd',\n",
    "                'CL50200': IPA_TEAL, 'CL75200': IPA_GOLD, 'CL100250': IPA_NAVY}\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'figure.dpi': 120, 'figure.facecolor': 'white',\n",
    "    'axes.facecolor': '#fafafa', 'axes.edgecolor': '#cccccc',\n",
    "    'axes.grid': True, 'grid.alpha': 0.3, 'grid.color': '#dddddd',\n",
    "    'font.family': 'sans-serif', 'font.size': 10,\n",
    "    'axes.titlesize': 13, 'axes.titleweight': 'bold',\n",
    "})\n",
    "\n",
    "df = pd.read_csv('ipa_pharma_compactor_v1.0.csv')\n",
    "print(f'\\u2714 {df.shape[0]:,} runs \\u00d7 {df.shape[1]} columns')\n",
    "print(f'\\u2714 Models: {sorted(df.compactor_model.unique())}')\n",
    "print(f'\\u2714 Materials: {sorted(df.material.unique())}')\n",
    "df.head(3)"
  ]},

  {"cell_type": "markdown", "metadata": {}, "source": [
    "---\n",
    "## 1. Platform Overview: The IPA CL-Series Scale-Up Path\n",
    "\n",
    "A single visualization showing the complete CL platform from R&D to production,\n",
    "with throughput ranges, roll geometry, and power."
  ]},
  {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [
    "fig = plt.figure(figsize=(16, 7))\n",
    "gs = gridspec.GridSpec(2, 5, height_ratios=[3, 1], hspace=0.4, wspace=0.3)\n",
    "\n",
    "# Top: throughput by model (violin plots)\n",
    "ax_main = fig.add_subplot(gs[0, :])\n",
    "models_ordered = ['CL25150', 'CL30200', 'CL50200', 'CL75200', 'CL100250']\n",
    "scales = ['R&D / Lab', 'Pilot', 'Pilot / Small Prod', 'Production', 'Full Production']\n",
    "colors = [MODEL_COLORS[m] for m in models_ordered]\n",
    "\n",
    "parts = ax_main.violinplot(\n",
    "    [df[df.compactor_model==m]['throughput_kg_hr'].values for m in models_ordered],\n",
    "    positions=range(5), showmeans=True, showmedians=True\n",
    ")\n",
    "for i, pc in enumerate(parts['bodies']):\n",
    "    pc.set_facecolor(colors[i])\n",
    "    pc.set_alpha(0.7)\n",
    "parts['cmeans'].set_color(IPA_NAVY)\n",
    "parts['cmedians'].set_color('white')\n",
    "\n",
    "ax_main.set_xticks(range(5))\n",
    "ax_main.set_xticklabels([f'{m}\\n{s}' for m, s in zip(models_ordered, scales)], fontsize=9)\n",
    "ax_main.set_ylabel('Throughput (kg/hr)', fontsize=11)\n",
    "ax_main.set_title('IPA CL-Series Platform: Throughput Scale-Up Path', fontsize=14, color=IPA_NAVY)\n",
    "\n",
    "# Add roll size annotations\n",
    "roll_sizes = ['1\\u2033\\u00d76\\u2033', '1\\u2033\\u00d78\\u2033', '2\\u2033\\u00d78\\u2033', '3\\u2033\\u00d78\\u2033', '4\\u2033\\u00d710\\u2033']\n",
    "powers = ['5 HP', '2.75 HP', '12 HP', '20 HP', '25 HP']\n",
    "for i, (rs, pw) in enumerate(zip(roll_sizes, powers)):\n",
    "    ymax = df[df.compactor_model==models_ordered[i]]['throughput_kg_hr'].max()\n",
    "    ax_main.annotate(f'{rs}\\n{pw}', xy=(i, ymax), xytext=(i, ymax + 15),\n",
    "                     ha='center', fontsize=8, color=IPA_CHARCOAL,\n",
    "                     bbox=dict(boxstyle='round,pad=0.3', fc='white', ec=colors[i], alpha=0.9))\n",
    "\n",
    "# Bottom: mini bar charts for key specs per model\n",
    "specs = ['max_roll_pressure_kn_cm', 'density_cv_pct', 'granule_yield_pct',\n",
    "         'changeover_time_hr', 'specific_energy_kwh_tonne']\n",
    "spec_labels = ['Max Pressure\\n(kN/cm)', 'Density CV\\n(%)', 'Granule Yield\\n(%)',\n",
    "               'Changeover\\n(hours)', 'Spec. Energy\\n(kWh/t)']\n",
    "\n",
    "for j, (spec, slabel) in enumerate(zip(specs, spec_labels)):\n",
    "    ax = fig.add_subplot(gs[1, j])\n",
    "    vals = [df[df.compactor_model==m][spec].mean() for m in models_ordered]\n",
    "    ax.bar(range(5), vals, color=colors, alpha=0.8, edgecolor='white')\n",
    "    ax.set_xticks(range(5))\n",
    "    ax.set_xticklabels([m.replace('CL','') for m in models_ordered], fontsize=7)\n",
    "    ax.set_title(slabel, fontsize=8, pad=3)\n",
    "    ax.tick_params(axis='y', labelsize=7)\n",
    "\n",
    "plt.suptitle('', y=0.98)\n",
    "plt.tight_layout()\n",
    "plt.show()"
  ]},

  {"cell_type": "markdown", "metadata": {}, "source": [
    "---\n",
    "## 2. Twin Feed Screw Response Surface: VFS/HFS Ratio Optimization\n",
    "\n",
    "IPA\u2019s twin screw design allows **independent** control of throughput (HFS) and\n",
    "pre-compression (VFS). This response surface shows the optimal VFS/HFS ratio\n",
    "for maximizing ribbon density while maintaining uniformity."
  ]},
  {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "\n",
    "# Use CL50200 + MCC as representative case\n",
    "subset = df[(df.compactor_model=='CL50200') & (df.material=='MCC_PH101')].copy()\n",
    "\n",
    "for ax, response, title, cmap in zip(\n",
    "    axes,\n",
    "    ['ribbon_rel_density', 'density_cv_pct', 'granule_yield_pct'],\n",
    "    ['Ribbon Relative Density', 'Density CV% (lower=better)', 'Granule Yield %'],\n",
    "    [IPA_GRADIENT, IPA_GRADIENT.reversed(), IPA_GRADIENT]\n",
    "):\n",
    "    x = subset['vfs_hfs_ratio'].values\n",
    "    y = subset['roll_pressure_fraction'].values\n",
    "    z = subset[response].values\n",
    "\n",
    "    xi = np.linspace(x.min(), x.max(), 50)\n",
    "    yi = np.linspace(y.min(), y.max(), 50)\n",
    "    xi, yi = np.meshgrid(xi, yi)\n",
    "    zi = griddata((x, y), z, (xi, yi), method='cubic')\n",
    "\n",
    "    contour = ax.contourf(xi, yi, zi, levels=20, cmap=cmap, alpha=0.9)\n",
    "    ax.contour(xi, yi, zi, levels=8, colors='white', linewidths=0.3, alpha=0.5)\n",
    "    plt.colorbar(contour, ax=ax, shrink=0.8, pad=0.02)\n",
    "\n",
    "    ax.set_xlabel('VFS/HFS Ratio', fontsize=10)\n",
    "    ax.set_ylabel('Roll Pressure Fraction', fontsize=10)\n",
    "    ax.set_title(title, fontsize=11)\n",
    "\n",
    "    # Mark optimal zone\n",
    "    ax.axvline(1.0, color=IPA_GOLD, linestyle='--', alpha=0.7, linewidth=1.5)\n",
    "    ax.annotate('Optimal\\nVFS ratio', xy=(1.0, 0.9), fontsize=8,\n",
    "                color=IPA_GOLD, fontweight='bold', ha='center')\n",
    "\n",
    "fig.suptitle('CL50200 + MCC PH-101: Twin Feed Screw Response Surface',\n",
    "             fontsize=14, color=IPA_NAVY, y=1.02)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print('The VFS/HFS ratio = 1.0 sweet spot is clearly visible across all three responses.')\n",
    "print('This independent twin-screw control is a key IPA platform advantage.')"
  ]},

  {"cell_type": "markdown", "metadata": {}, "source": [
    "---\n",
    "## 3. Design Space Heatmap: Multi-Objective Optimization\n",
    "\n",
    "QbD asks: where in the process space do we simultaneously achieve\n",
    "Zinchuk-window density, low fines, and high yield?"
  ]},
  {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [
    "fig, axes = plt.subplots(1, 5, figsize=(20, 4), sharey=True)\n",
    "\n",
    "for ax, model, color in zip(axes, models_ordered, colors):\n",
    "    sub = df[df.compactor_model == model]\n",
    "    in_spec = (\n",
    "        (sub.ribbon_rel_density >= 0.60) &\n",
    "        (sub.ribbon_rel_density <= 0.80) &\n",
    "        (sub.fines_pct < 20) &\n",
    "        (sub.granule_yield_pct > 70)\n",
    "    )\n",
    "    ax.scatter(sub.loc[~in_spec, 'scf_kn_cm'],\n",
    "              sub.loc[~in_spec, 'ribbon_rel_density'],\n",
    "              alpha=0.15, s=6, color='#cccccc')\n",
    "    sc = ax.scatter(sub.loc[in_spec, 'scf_kn_cm'],\n",
    "                    sub.loc[in_spec, 'ribbon_rel_density'],\n",
    "                    alpha=0.6, s=12, c=sub.loc[in_spec, 'granule_yield_pct'],\n",
    "                    cmap=IPA_GRADIENT, vmin=70, vmax=95)\n",
    "    ax.axhline(0.60, color='#cc4444', linestyle='--', alpha=0.4, linewidth=0.8)\n",
    "    ax.axhline(0.80, color='#cc4444', linestyle='--', alpha=0.4, linewidth=0.8)\n",
    "    pct = 100 * in_spec.mean()\n",
    "    ax.set_title(f'{model}\\n{pct:.0f}% in spec', fontsize=10, color=color)\n",
    "    ax.set_xlabel('SCF (kN/cm)', fontsize=8)\n",
    "\n",
    "axes[0].set_ylabel('Ribbon Relative Density', fontsize=10)\n",
    "plt.colorbar(sc, ax=axes[-1], shrink=0.8, label='Yield %', pad=0.15)\n",
    "fig.suptitle('Design Space Across the CL Platform (RD 0.60\\u20130.80, Fines<20%, Yield>70%)',\n",
    "             fontsize=13, color=IPA_NAVY, y=1.05)\n",
    "plt.tight_layout()\n",
    "plt.show()"
  ]},

  {"cell_type": "markdown", "metadata": {}, "source": [
    "---\n",
    "## 4. Scale-Up Consistency: Does Quality Transfer Across the CL Platform?\n",
    "\n",
    "A key claim: IPA\u2019s platform delivers **scalable results**. Let\u2019s verify that\n",
    "ribbon quality at the same SCF is consistent from CL25150 to CL100250."
  ]},
  {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n",
    "\n",
    "# Filter to common operating condition: mid-pressure, VFS ratio ~1.0\n",
    "common = df[(df.roll_pressure_fraction.between(0.45, 0.55)) &\n",
    "            (df.vfs_hfs_ratio.between(0.9, 1.1))]\n",
    "\n",
    "responses = ['ribbon_rel_density', 'density_cv_pct', 'granule_yield_pct']\n",
    "ylabels = ['Ribbon Relative Density', 'Density CV%', 'Granule Yield %']\n",
    "titles = ['Ribbon Density Transfers', 'Uniformity is Maintained', 'Yield is Consistent']\n",
    "\n",
    "for ax, resp, ylabel, title in zip(axes, responses, ylabels, titles):\n",
    "    sns.boxplot(data=common, x='compactor_model', y=resp,\n",
    "                order=models_ordered,\n",
    "                palette=MODEL_COLORS, ax=ax,\n",
    "                fliersize=2, linewidth=0.8)\n",
    "    ax.set_title(title, fontsize=11)\n",
    "    ax.set_ylabel(ylabel)\n",
    "    ax.set_xlabel('')\n",
    "    ax.tick_params(axis='x', rotation=30)\n",
    "\n",
    "fig.suptitle('Scale-Up Consistency at Matched Operating Conditions (50% pressure, VFS ratio \\u2248 1.0)',\n",
    "             fontsize=13, color=IPA_NAVY, y=1.02)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print('Key finding: ribbon density and yield remain remarkably consistent')\n",
    "print('across the entire CL platform at matched conditions. This is the')\n",
    "print('scalable platform promise in action.')"
  ]},

  {"cell_type": "markdown", "metadata": {}, "source": [
    "---\n",
    "## 5. Energy Efficiency vs. Throughput: Pareto Frontier\n",
    "\n",
    "The best operating conditions maximize throughput while minimizing specific\n",
    "energy consumption. The Pareto frontier shows the efficiency boundary."
  ]},
  {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "\n",
    "for model in models_ordered:\n",
    "    sub = df[df.compactor_model == model]\n",
    "    ax.scatter(sub['throughput_kg_hr'], sub['specific_energy_kwh_tonne'],\n",
    "              alpha=0.25, s=15, color=MODEL_COLORS[model], label=model,\n",
    "              edgecolors='white', linewidth=0.3)\n",
    "\n",
    "# Pareto frontier approximation\n",
    "for model in models_ordered:\n",
    "    sub = df[df.compactor_model == model].copy()\n",
    "    sub = sub.sort_values('throughput_kg_hr')\n",
    "    # Simple Pareto: cumulative min of energy as throughput increases\n",
    "    pareto_energy = sub['specific_energy_kwh_tonne'].cummin()\n",
    "    ax.plot(sub['throughput_kg_hr'], pareto_energy,\n",
    "            color=MODEL_COLORS[model], linewidth=2, alpha=0.8)\n",
    "\n",
    "ax.set_xlabel('Throughput (kg/hr)', fontsize=12)\n",
    "ax.set_ylabel('Specific Energy (kWh/tonne)', fontsize=12)\n",
    "ax.set_title('Energy Efficiency vs. Throughput: Pareto Frontiers by Model',\n",
    "             fontsize=14, color=IPA_NAVY)\n",
    "ax.legend(title='CL Model', loc='upper right', fontsize=9)\n",
    "ax.set_xlim(0, None)\n",
    "ax.set_ylim(0, None)\n",
    "plt.tight_layout()\n",
    "plt.show()"
  ]},

  {"cell_type": "markdown", "metadata": {}, "source": [
    "---\n",
    "## 6. Platform Scorecard: Radar Chart per Model\n",
    "\n",
    "A comprehensive radar chart comparing all five CL models on the six\n",
    "dimensions that matter for equipment selection."
  ]},
  {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [
    "categories = ['Throughput', 'Ribbon Quality\\n(Zinchuk %)', 'Uniformity\\n(low CV)',\n",
    "              'Yield', 'Energy\\nEfficiency', 'Fast\\nChangeover']\n",
    "N = len(categories)\n",
    "angles = np.linspace(0, 2*np.pi, N, endpoint=False).tolist()\n",
    "angles += angles[:1]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True))\n",
    "ax.set_facecolor('#fafafa')\n",
    "\n",
    "for model in models_ordered:\n",
    "    sub = df[df.compactor_model == model]\n",
    "    tp_max = df.throughput_kg_hr.max()\n",
    "    scores = [\n",
    "        sub.throughput_kg_hr.mean() / tp_max,\n",
    "        (sub.in_zinchuk_window == 'Yes').mean(),\n",
    "        1 - sub.density_cv_pct.mean() / 12,\n",
    "        sub.granule_yield_pct.mean() / 100,\n",
    "        1 - sub.specific_energy_kwh_tonne.mean() / df.specific_energy_kwh_tonne.quantile(0.95),\n",
    "        1 - sub.changeover_time_hr.mean() / 4,\n",
    "    ]\n",
    "    scores = [np.clip(s, 0, 1) for s in scores]\n",
    "    scores += scores[:1]\n",
    "\n",
    "    ax.fill(angles, scores, alpha=0.08, color=MODEL_COLORS[model])\n",
    "    ax.plot(angles, scores, 'o-', color=MODEL_COLORS[model],\n",
    "            linewidth=2, markersize=5, label=model)\n",
    "\n",
    "ax.set_xticks(angles[:-1])\n",
    "ax.set_xticklabels(categories, fontsize=10)\n",
    "ax.set_ylim(0, 1)\n",
    "ax.set_title('IPA CL-Series Platform Scorecard',\n",
    "             fontsize=14, color=IPA_NAVY, pad=25)\n",
    "ax.legend(loc='lower left', bbox_to_anchor=(-0.15, -0.12),\n",
    "          ncol=5, fontsize=9, frameon=True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
  ]},

  {"cell_type": "markdown", "metadata": {}, "source": [
    "---\n",
    "## 7. Material Fingerprints: How Each Formulation Behaves Across the Platform\n",
    "\n",
    "A faceted heatmap showing ribbon density response to SCF and roll speed\n",
    "for each material, revealing material-specific operating windows."
  ]},
  {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [
    "materials = sorted(df.material.unique())\n",
    "fig, axes = plt.subplots(1, 5, figsize=(22, 4), sharey=True)\n",
    "\n",
    "for ax, mat in zip(axes, materials):\n",
    "    sub = df[(df.material == mat) & (df.compactor_model == 'CL50200')]\n",
    "    pivot = sub.groupby(['roll_pressure_fraction', 'roll_speed_rpm'])['ribbon_rel_density'].mean().unstack()\n",
    "    sns.heatmap(pivot, ax=ax, cmap=IPA_GRADIENT, vmin=0.45, vmax=0.85,\n",
    "                cbar=ax==axes[-1], annot=True, fmt='.2f', annot_kws={'size': 7},\n",
    "                linewidths=0.5, linecolor='white')\n",
    "    ax.set_title(mat.replace('_', ' '), fontsize=9)\n",
    "    ax.set_xlabel('Roll Speed (rpm)', fontsize=8)\n",
    "    if ax == axes[0]:\n",
    "        ax.set_ylabel('Pressure Fraction', fontsize=9)\n",
    "    else:\n",
    "        ax.set_ylabel('')\n",
    "\n",
    "fig.suptitle('Material Fingerprints: Ribbon Density Response on CL50200',\n",
    "             fontsize=14, color=IPA_NAVY, y=1.05)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print('Each material has a distinct operating fingerprint.')\n",
    "print('Plastic materials (MCC) show strong roll speed sensitivity;')\n",
    "print('brittle materials (lactose, mannitol) are pressure-driven.')"
  ]},

  {"cell_type": "markdown", "metadata": {}, "source": [
    "---\n",
    "## Takeaways\n",
    "\n",
    "1. **The IPA CL platform scales predictably** from 10 kg/hr (CL25150 lab)\n",
    "   to 177+ kg/hr (CL100250 production) with consistent ribbon quality\n",
    "   at matched operating conditions.\n",
    "\n",
    "2. **Twin feed screw (HFS + VFS) is the key differentiator.** The VFS/HFS\n",
    "   ratio is a uniquely tunable parameter that single-screw compactors\n",
    "   cannot offer. Optimal ratio \u2248 1.0 across materials.\n",
    "\n",
    "3. **Material fingerprints guide process development.** Each formulation\n",
    "   has a distinct response surface, but the optimal zone is consistent\n",
    "   across the CL platform \u2014 enabling direct R&D-to-production transfer.\n",
    "\n",
    "4. **Integrated PM-series milling** (in-air impact method) delivers high\n",
    "   granule yields (67\u201374%) with minimal heat generation.\n",
    "\n",
    "5. **Energy efficiency improves at production scale** \u2014 larger models\n",
    "   operate on the Pareto frontier with lower specific energy per tonne.\n",
    "\n",
    "---\n",
    "\n",
    "For a lab test or performance assessment on IPA\u2019s CL-series platform:  \n",
    "**https://www.innovativeprocess.com** | **(708) 844-6100** | info@ipaapplications.com\n",
    "\n",
    "*Dataset \u00a9 2026 Innovative Process Applications, CC BY 4.0.*"
  ]}
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