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Initial upload: control performance dataset v1.0

IPA_Control_Performance_Analysis.ipynb

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+{
+ "cells": [
+  {"cell_type": "markdown", "metadata": {}, "source": [
+    "# \ud83c\udfae Roll Compactor Control Performance: PID Analysis & SPC\n",
+    "\n",
+    "**Dataset:** Roll Compactor Control Performance (Synthetic) v1.0  \n",
+    "**Publisher:** [Innovative Process Applications (IPA)](https://www.innovativeprocess.com)  \n",
+    "**License:** CC BY 4.0  \n",
+    "**Scientific basis:** Szappanos-Csord\u00e1s (2018), Chapter 3.1\n",
+    "\n",
+    "> \u26a0\ufe0f **Synthetic educational data** \u2014 not real measurements.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "This notebook covers:\n",
+    "1. Load both summary and time-series data\n",
+    "2. Visualize PID step responses across control architectures\n",
+    "3. Compare control quality metrics (CV%, settling time, overshoot)\n",
+    "4. Build SPC control charts\n",
+    "5. Quantify the twin feed screw advantage\n",
+    "6. Classify control quality from time-series features"
+  ]},
+
+  {"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 seaborn as sns\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "sns.set_style('whitegrid')\n",
+    "plt.rcParams['figure.dpi'] = 100\n",
+    "\n",
+    "# Load data\n",
+    "df_summary = pd.read_csv('control_performance_summary_v1.0.csv')\n",
+    "df_ts = pd.read_csv('control_performance_timeseries_v1.0.csv')\n",
+    "\n",
+    "print(f'Summary: {df_summary.shape[0]} runs, {df_summary.shape[1]} columns')\n",
+    "print(f'Time series: {df_ts.shape[0]} rows, {df_ts.shape[1]} columns')\n",
+    "print(f'Control architectures: {df_summary[\"control_architecture\"].unique()}')\n",
+    "df_summary.head()"
+  ]},
+
+  {"cell_type": "markdown", "metadata": {}, "source": [
+    "## Part 1: PID Step Response Visualization\n",
+    "\n",
+    "The defining characteristic of a PID controller is its step response \u2014 how\n",
+    "the actual value tracks a setpoint change. We\u2019ll compare the four control\n",
+    "architectures responding to the same SCF step-up scenario."
+  ]},
+  {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [
+    "# Pick one material and one scenario to compare architectures\n",
+    "scenario = 'scf_step_up'\n",
+    "material = 'MCC_Mannitol_Mix'\n",
+    "\n",
+    "architectures = ['no_gap_control', 'pid_gw_screw', 'pid_scf_gw', 'pid_scf_gw_twin_screw']\n",
+    "labels = ['No Gap Control', 'PID: GW+Screw', 'PID: SCF+GW', 'PID: SCF+GW+Twin Screw']\n",
+    "colors = ['#cc4444', '#d4a017', '#2E86C1', '#008080']\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True)\n",
+    "\n",
+    "for arch, label, color in zip(architectures, labels, colors):\n",
+    "    run = df_summary[(df_summary['control_architecture']==arch) &\n",
+    "                     (df_summary['material']==material) &\n",
+    "                     (df_summary['scenario']==scenario)]\n",
+    "    if len(run) == 0:\n",
+    "        continue\n",
+    "    rid = run.iloc[0]['run_id']\n",
+    "    ts = df_ts[df_ts['run_id']==rid]\n",
+    "\n",
+    "    axes[0].plot(ts['time_s'], ts['scf_actual_kN_per_cm'], label=label,\n",
+    "                color=color, alpha=0.8, linewidth=1.2)\n",
+    "    axes[1].plot(ts['time_s'], ts['gw_actual_mm'], label=label,\n",
+    "                color=color, alpha=0.8, linewidth=1.2)\n",
+    "\n",
+    "# Setpoint lines\n",
+    "axes[0].axhline(4.0, color='gray', linestyle=':', alpha=0.5)\n",
+    "axes[0].axhline(8.0, color='gray', linestyle=':', alpha=0.5)\n",
+    "axes[0].axvline(30, color='black', linestyle='--', alpha=0.3, label='Setpoint change')\n",
+    "axes[0].set_ylabel('SCF (kN/cm)')\n",
+    "axes[0].set_title(f'PID Step Response Comparison \\u2014 SCF Step Up (4\\u21928 kN/cm), {material}')\n",
+    "axes[0].legend(loc='lower right', fontsize=9)\n",
+    "\n",
+    "axes[1].axvline(30, color='black', linestyle='--', alpha=0.3)\n",
+    "axes[1].set_ylabel('Gap Width (mm)')\n",
+    "axes[1].set_xlabel('Time (s)')\n",
+    "axes[1].set_title('Gap Width Response During SCF Step Change')\n",
+    "axes[1].legend(loc='upper right', fontsize=9)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print('Key observations:')\n",
+    "print('- No gap control: slow approach, steady-state offset, noisy')\n",
+    "print('- PID SCF+GW: overshoot then clean settling')\n",
+    "print('- Twin screw: tightest band, fastest settling, least noise')"
+  ]},
+
+  {"cell_type": "markdown", "metadata": {}, "source": [
+    "## Part 2: Control Quality Metrics by Architecture\n",
+    "\n",
+    "CV% (coefficient of variation) is the primary metric from Chapter 3.1 of\n",
+    "the dissertation. Lower CV = more robust process."
+  ]},
+  {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(16, 4))\n",
+    "\n",
+    "order = ['no_gap_control', 'pid_gw_screw', 'pid_scf_gw', 'pid_scf_gw_twin_screw']\n",
+    "palette = ['#cc4444', '#d4a017', '#2E86C1', '#008080']\n",
+    "\n",
+    "sns.boxplot(data=df_summary, x='control_architecture', y='scf_ss_cv_pct',\n",
+    "            order=order, palette=palette, ax=axes[0])\n",
+    "axes[0].set_title('SCF Steady-State CV%')\n",
+    "axes[0].set_ylabel('CV %')\n",
+    "axes[0].tick_params(axis='x', rotation=30)\n",
+    "axes[0].set_xlabel('')\n",
+    "\n",
+    "sns.boxplot(data=df_summary, x='control_architecture', y='gw_ss_cv_pct',\n",
+    "            order=order, palette=palette, ax=axes[1])\n",
+    "axes[1].set_title('Gap Width Steady-State CV%')\n",
+    "axes[1].set_ylabel('CV %')\n",
+    "axes[1].tick_params(axis='x', rotation=30)\n",
+    "axes[1].set_xlabel('')\n",
+    "\n",
+    "sns.boxplot(data=df_summary, x='control_architecture', y='scf_settling_time_s',\n",
+    "            order=order, palette=palette, ax=axes[2])\n",
+    "axes[2].set_title('SCF Settling Time')\n",
+    "axes[2].set_ylabel('Time (s)')\n",
+    "axes[2].tick_params(axis='x', rotation=30)\n",
+    "axes[2].set_xlabel('')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print('\\nMean CV% by architecture:')\n",
+    "print(df_summary.groupby('control_architecture')[['scf_ss_cv_pct','gw_ss_cv_pct']].mean().round(2).loc[order])"
+  ]},
+
+  {"cell_type": "markdown", "metadata": {}, "source": [
+    "## Part 3: Material Effect on Controllability\n",
+    "\n",
+    "Brittle materials (mannitol) produce more erratic force signals due to\n",
+    "particle fragmentation, making the control task harder."
+  ]},
+  {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "mat_order = ['MCC_101', 'MCC_Mannitol_Mix', 'Mannitol_SD']\n",
+    "mat_colors = ['#008080', '#2E86C1', '#d4a017']\n",
+    "\n",
+    "sns.boxplot(data=df_summary, x='material', y='scf_ss_cv_pct',\n",
+    "            order=mat_order, palette=mat_colors, ax=axes[0])\n",
+    "axes[0].set_title('SCF CV% by Material')\n",
+    "axes[0].set_ylabel('SCF CV %')\n",
+    "axes[0].set_xlabel('')\n",
+    "\n",
+    "sns.boxplot(data=df_summary, x='material', y='gw_ss_cv_pct',\n",
+    "            order=mat_order, palette=mat_colors, ax=axes[1])\n",
+    "axes[1].set_title('Gap Width CV% by Material')\n",
+    "axes[1].set_ylabel('GW CV %')\n",
+    "axes[1].set_xlabel('')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print('Interpretation:')\n",
+    "print('- MCC (plastic): smoothest compaction, easiest to control')\n",
+    "print('- Mannitol (brittle): particle fragmentation creates force spikes')\n",
+    "print('- Mixture: intermediate behavior')"
+  ]},
+
+  {"cell_type": "markdown", "metadata": {}, "source": [
+    "## Part 4: SPC Control Charts\n",
+    "\n",
+    "Statistical Process Control charts are standard tools in pharma\n",
+    "manufacturing. Let\u2019s build X-bar and R charts for a sample run."
+  ]},
+  {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [
+    "# Pick the twin-screw + MCC steady-state baseline for the cleanest signal\n",
+    "run_spc = df_summary[(df_summary['control_architecture']=='pid_scf_gw_twin_screw') &\n",
+    "                     (df_summary['material']=='MCC_101') &\n",
+    "                     (df_summary['scenario']=='steady_state_baseline')]\n",
+    "if len(run_spc) > 0:\n",
+    "    rid = run_spc.iloc[0]['run_id']\n",
+    "    ts = df_ts[df_ts['run_id']==rid].copy()\n",
+    "\n",
+    "    # Use only steady-state portion (t > 30s)\n",
+    "    ts_ss = ts[ts['time_s'] >= 30.0].copy()\n",
+    "\n",
+    "    scf_mean = ts_ss['scf_actual_kN_per_cm'].mean()\n",
+    "    scf_std = ts_ss['scf_actual_kN_per_cm'].std()\n",
+    "    ucl = scf_mean + 3 * scf_std\n",
+    "    lcl = scf_mean - 3 * scf_std\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(14, 4))\n",
+    "    ax.plot(ts_ss['time_s'], ts_ss['scf_actual_kN_per_cm'],\n",
+    "            'o-', markersize=3, color='#008080', alpha=0.7)\n",
+    "    ax.axhline(scf_mean, color='#1B2A3B', linewidth=2, label=f'CL = {scf_mean:.3f}')\n",
+    "    ax.axhline(ucl, color='red', linestyle='--', label=f'UCL = {ucl:.3f}')\n",
+    "    ax.axhline(lcl, color='red', linestyle='--', label=f'LCL = {lcl:.3f}')\n",
+    "    ax.fill_between(ts_ss['time_s'], lcl, ucl, alpha=0.05, color='red')\n",
+    "    ax.set_xlabel('Time (s)')\n",
+    "    ax.set_ylabel('SCF (kN/cm)')\n",
+    "    ax.set_title(f'X-bar Control Chart \\u2014 Twin Screw PID, MCC 101, Steady State')\n",
+    "    ax.legend(loc='upper right')\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "    print(f'Process capability summary:')\n",
+    "    print(f'  Mean: {scf_mean:.3f} kN/cm')\n",
+    "    print(f'  Std:  {scf_std:.4f} kN/cm')\n",
+    "    print(f'  CV:   {scf_std/scf_mean*100:.2f}%')\n",
+    "    spec_half = 0.5  # example spec: setpoint +/- 0.5 kN/cm\n",
+    "    cpk = min(ucl - scf_mean, scf_mean - lcl) / (3 * scf_std)\n",
+    "    print(f'  Cpk (\\u00b13\\u03c3 natural): {cpk:.2f}')"
+  ]},
+
+  {"cell_type": "markdown", "metadata": {}, "source": [
+    "## Part 5: Twin Feed Screw Advantage\n",
+    "\n",
+    "This is the key IPA design differentiator. Twin feed screws provide more\n",
+    "uniform powder delivery, which directly reduces process variability."
+  ]},
+  {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [
+    "# Compare single vs twin screw (both with SCF+GW PID)\n",
+    "single = df_summary[df_summary['control_architecture']=='pid_scf_gw']\n",
+    "twin = df_summary[df_summary['control_architecture']=='pid_scf_gw_twin_screw']\n",
+    "\n",
+    "metrics = ['scf_ss_cv_pct', 'gw_ss_cv_pct', 'scf_settling_time_s', 'scf_overshoot_pct']\n",
+    "metric_labels = ['SCF CV%', 'GW CV%', 'Settling Time (s)', 'Overshoot %']\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 4, figsize=(16, 4))\n",
+    "for ax, metric, mlabel in zip(axes, metrics, metric_labels):\n",
+    "    data = pd.DataFrame({\n",
+    "        'Single Screw': single[metric].values,\n",
+    "        'Twin Screw': twin[metric].values,\n",
+    "    })\n",
+    "    data.plot.box(ax=ax, color=dict(boxes='#008080', whiskers='gray',\n",
+    "                                     medians='#1B2A3B', caps='gray'))\n",
+    "    ax.set_title(mlabel)\n",
+    "    ax.set_ylabel(mlabel)\n",
+    "\n",
+    "plt.suptitle('Single Screw vs. Twin Feed Screw (Same PID Architecture)',\n",
+    "             fontsize=13, y=1.02)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print('\\nImprovement with twin screw:')\n",
+    "for metric, mlabel in zip(metrics, metric_labels):\n",
+    "    s = single[metric].mean()\n",
+    "    t = twin[metric].mean()\n",
+    "    improvement = (s - t) / s * 100 if s > 0 else 0\n",
+    "    print(f'  {mlabel}: {s:.2f} \\u2192 {t:.2f} ({improvement:+.1f}% improvement)')"
+  ]},
+
+  {"cell_type": "markdown", "metadata": {}, "source": [
+    "## Part 6: Classify Control Quality from Metrics\n",
+    "\n",
+    "Can we predict the control quality grade from the summary metrics? This\n",
+    "demonstrates how machine learning can support process monitoring."
+  ]},
+  {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [
+    "# Encode targets and features\n",
+    "le = LabelEncoder()\n",
+    "y = le.fit_transform(df_summary['control_quality_grade'])\n",
+    "\n",
+    "feature_cols = ['scf_ss_cv_pct', 'gw_ss_cv_pct', 'scf_deviation_from_setpoint_pct',\n",
+    "                'gw_deviation_from_setpoint_pct', 'scf_settling_time_s',\n",
+    "                'gw_settling_time_s', 'scf_overshoot_pct']\n",
+    "X = df_summary[feature_cols].values\n",
+    "\n",
+    "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "scores = cross_val_score(rf, X, y, cv=5, scoring='accuracy')\n",
+    "print(f'RF 5-fold CV accuracy: {scores.mean():.3f} \\u00b1 {scores.std():.3f}')\n",
+    "\n",
+    "rf.fit(X, y)\n",
+    "importances = pd.Series(rf.feature_importances_, index=feature_cols).sort_values()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 4))\n",
+    "importances.plot.barh(color='#008080', ax=ax)\n",
+    "ax.set_title('Feature Importance for Control Quality Grade Prediction')\n",
+    "ax.set_xlabel('Importance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+  ]},
+
+  {"cell_type": "markdown", "metadata": {}, "source": [
+    "## Takeaways\n",
+    "\n",
+    "1. **Control architecture matters enormously:** Going from no gap control to\n",
+    "   full PID SCF+GW control reduces process variability by 50\u201370%. This is\n",
+    "   not optional in regulated pharma manufacturing.\n",
+    "\n",
+    "2. **Twin feed screws are a multiplicative advantage:** On top of PID control,\n",
+    "   twin screws provide an additional 20\u201330% reduction in CV by smoothing\n",
+    "   feed rate fluctuations. This is a mechanical design choice, not a tuning\n",
+    "   parameter \u2014 it must be built into the machine.\n",
+    "\n",
+    "3. **Material properties affect control difficulty:** Brittle materials\n",
+    "   produce noisier signals that challenge even the best PID controllers.\n",
+    "   Process development must account for this.\n",
+    "\n",
+    "4. **SPC tools apply directly to continuous granulation:** Control charts,\n",
+    "   Cp/Cpk, and trend analysis are standard pharma quality tools that\n",
+    "   connect naturally to roll compaction process data.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "For twin-feed-screw roller compactors with advanced PID control and direct\n",
+    "engineer support, see IPA\u2019s CL-series compactors:\n",
+    "**https://www.innovativeprocess.com**\n",
+    "\n",
+    "*Dataset \u00a9 2026 Innovative Process Applications, CC BY 4.0.*  \n",
+    "*Scientific basis: Szappanos-Csord\u00e1s (2018), Chapter 3.1.*"
+  ]}
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "toc_visible": true,
+   "authorship_tag": "Innovative Process Applications (IPA)"
+  },
+  "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"},
+  "language_info": {"name": "python", "version": "3.11"}
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

LICENSE.txt

@@ -0,0 +1,28 @@
+Creative Commons Attribution 4.0 International (CC BY 4.0)
+===========================================================
+
+Copyright (c) 2026 Innovative Process Applications (IPA)
+
+This dataset is licensed under the Creative Commons Attribution 4.0
+International License. You are free to:
+
+  - Share: copy and redistribute the material in any medium or format
+  - Adapt: remix, transform, and build upon the material
+    for any purpose, even commercially.
+
+Under the following terms:
+
+  - Attribution: You must give appropriate credit, provide a link to
+    the license, and indicate if changes were made.
+
+Recommended attribution:
+
+  "Roll Compactor Control Performance: PID Tuning & Process Stability
+   (Synthetic), v1.0, by Innovative Process Applications (IPA), CC BY 4.0,
+   https://www.innovativeprocess.com"
+
+Full license text: https://creativecommons.org/licenses/by/4.0/legalcode
+
+DISCLAIMER: This dataset is synthetic and intended for educational use
+only. It does not represent real equipment, customer, or production
+data.

README.md

@@ -1,3 +1,145 @@
----
-license: cc-by-4.0
----
+# Roll Compactor Control Performance: PID Tuning & Process Stability (Synthetic)
+
+**Version:** 1.0
+**Publisher:** [Innovative Process Applications (IPA)](https://www.innovativeprocess.com)
+**License:** Creative Commons Attribution 4.0 International (CC BY 4.0)
+**Contact:** Crestwood, IL, USA
+
+> **This dataset is 100% synthetic and intended for educational use only.**
+> It was generated from PID control theory applied to roll compaction process
+> dynamics — not measured on any real equipment, customer, or production batch.
+
+---
+
+## What's in this dataset
+
+Two linked files containing synthetic roll compaction process control data:
+
+### 1. Summary file: `control_performance_summary_v1.0.csv` (96 runs × 22 columns)
+
+Each row is one 3-minute compaction run with computed control metrics.
+
+| Column | Description |
+|---|---|
+| `run_id` | Unique run identifier |
+| `control_architecture` | Control strategy identifier |
+| `control_label` | Human-readable control description |
+| `feed_type` | Single screw or twin screw |
+| `has_scf_pid` / `has_gw_pid` | Whether PID control is active for SCF / gap width |
+| `material` | Model material (MCC_101, Mannitol_SD, MCC_Mannitol_Mix) |
+| `scenario` | Setpoint change scenario (step up, step down, simultaneous, etc.) |
+| `scf_setpoint_kN_per_cm` | Target specific compaction force |
+| `gw_setpoint_mm` | Target gap width |
+| `scf_ss_mean` / `scf_ss_std` / `scf_ss_cv_pct` | Steady-state SCF statistics |
+| `scf_deviation_from_setpoint_pct` | Steady-state deviation from target (%) |
+| `scf_settling_time_s` | Time to reach ±2% of setpoint after change |
+| `scf_overshoot_pct` | Peak overshoot above setpoint (%) |
+| `gw_ss_mean_mm` / `gw_ss_std_mm` / `gw_ss_cv_pct` | Steady-state gap width statistics |
+| `gw_deviation_from_setpoint_pct` | Gap width deviation from target (%) |
+| `gw_settling_time_s` | Gap width settling time |
+| `control_quality_grade` | Overall grade: Excellent / Good / Acceptable / Poor |
+
+### 2. Time-series file: `control_performance_timeseries_v1.0.csv` (8,640 rows × 8 columns)
+
+Actual process data sampled every 2 seconds for each run (90 timepoints × 96 runs).
+
+| Column | Description |
+|---|---|
+| `run_id` | Links to summary table |
+| `time_s` | Timestamp in seconds (0–180) |
+| `scf_setpoint_kN_per_cm` | Current SCF setpoint (changes at t=30s) |
+| `scf_actual_kN_per_cm` | Measured SCF value |
+| `gw_setpoint_mm` | Current gap width setpoint |
+| `gw_actual_mm` | Measured gap width |
+| `roll_speed_rpm` | Roll rotation speed |
+| `screw_speed_rpm` | Feed screw speed (adapts if GW PID is active) |
+
+## Scientific basis
+
+The dataset models PID control behavior as described in:
+
+> Szappanos-Csordás, K. (2018). *Impact of material properties, process parameters
+> and roll compactor design on roll compaction.* Chapter 3.1: Control performance
+> of the different types of roll compactors. Heinrich-Heine-Universität Düsseldorf.
+
+Key concepts from Section 3.1 modeled here:
+
+1. **Four control architectures** of increasing sophistication:
+   - No gap control (hydraulic pressure setpoint only) — highest variability
+   - PID with gap width + screw speed control — moderate performance
+   - PID with SCF + gap width control — good performance
+   - PID with SCF + gap width + twin feed screw — best performance
+
+2. **PID controller dynamics:** Proportional, Integral, and Derivative terms
+   producing characteristic overshoot, oscillation, and settling behavior.
+   Without PID (no gap control), the system shows steady-state offset because
+   there is no integral term to eliminate it.
+
+3. **Settling time:** Time required after a setpoint change for the process to
+   stabilize within ±2% of the new setpoint. Varies by control architecture,
+   material properties, and magnitude of the setpoint change.
+
+4. **Coefficient of variation (CV%):** Ratio of standard deviation to mean during
+   steady-state production. Lower CV indicates more robust process control.
+   The dissertation reports CV values from ~0.8% (best) to ~3.6% (no control).
+
+5. **Material-dependent control difficulty:** Brittle materials (mannitol)
+   produce more erratic force signals due to particle fragmentation, making
+   control harder. Plastic materials (MCC) compact more smoothly.
+
+6. **Twin feed screw advantage:** Reduces feed rate fluctuations, lowering both
+   SCF and gap width variability — a key differentiator in IPA's CL-series
+   compactor design.
+
+7. **Setpoint change scenarios:** Step increases, step decreases, and simultaneous
+   changes in SCF and gap width — mirroring the experimental protocol in the
+   dissertation's Tables 2–3.
+
+## What you can teach with it
+
+- **PID controller tuning:** Examine overshoot, settling time, and steady-state
+  error across different control architectures
+- **Statistical Process Control (SPC):** Build control charts, calculate Cp/Cpk,
+  identify out-of-control conditions
+- **Time-series analysis:** Apply filtering, spectral analysis, or change-point
+  detection to the process signals
+- **Control architecture comparison:** Quantify the value of closed-loop PID
+  control vs. open-loop hydraulic setpoint
+- **Material effects on controllability:** Compare control performance across
+  plastic, brittle, and mixed deformation materials
+- **Classification:** Train models to predict control quality grade from
+  time-series features
+
+## Cross-links (also published on)
+
+- **Kaggle:** [link after publication]
+- **Hugging Face Datasets:** [link after publication]
+- **Zenodo (DOI):** [link after publication]
+- **GitHub:** [link after publication]
+- **IPA website:** https://www.innovativeprocess.com
+
+## About IPA
+
+Innovative Process Applications designs and manufactures twin-feed-screw roller
+compactors, mills, and size-reduction equipment for the pharmaceutical,
+nutraceutical, chemical, and food industries. Based in Crestwood, Illinois, IPA
+is a direct OEM alternative to legacy Fitzpatrick Chilsonator and FitzMill
+systems, with American manufacturing and direct engineer access. Learn more at
+[innovativeprocess.com](https://www.innovativeprocess.com).
+
+## Citation
+
+> Innovative Process Applications (2026). *Roll Compactor Control Performance:
+> PID Tuning & Process Stability (Synthetic), v1.0*. CC BY 4.0.
+> https://www.innovativeprocess.com
+
+Scientific basis:
+
+> Szappanos-Csordás, K. (2018). *Impact of material properties, process
+> parameters and roll compactor design on roll compaction.* Doctoral dissertation,
+> Heinrich-Heine-Universität Düsseldorf.
+
+## Version history
+
+- **v1.0** (April 2026) — Initial release. 96 runs, 4 control architectures,
+  3 materials, 8 scenarios. Summary + time-series files.

control_performance_summary_v1.0.csv

@@ -0,0 +1,97 @@
+run_id,control_architecture,control_label,feed_type,has_scf_pid,has_gw_pid,material,scenario,scf_setpoint_kN_per_cm,gw_setpoint_mm,scf_ss_mean,scf_ss_std,scf_ss_cv_pct,scf_deviation_from_setpoint_pct,scf_settling_time_s,scf_overshoot_pct,gw_ss_mean_mm,gw_ss_std_mm,gw_ss_cv_pct,gw_deviation_from_setpoint_pct,gw_settling_time_s,control_quality_grade
+1,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_101,steady_state_baseline,4.0,2.0,3.994,0.0497,1.24,0.15,146.0,3.58,1.997,0.0604,3.02,0.15,149.5,Good
+2,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_101,scf_step_up,8.0,2.0,7.89,0.0946,1.2,1.38,149.0,3.79,2.0066,0.055,2.74,0.33,149.5,Good
+3,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_101,scf_step_down,4.0,2.0,4.113,0.0483,1.17,2.83,149.5,97.05,2.0084,0.0559,2.78,0.42,149.5,Good
+4,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_101,gw_step_down,6.0,1.5,5.999,0.0753,1.26,0.02,143.5,10.08,1.555,0.0495,3.19,3.67,149.5,Good
+5,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_101,gw_step_up,6.0,3.0,5.987,0.0671,1.12,0.22,132.0,2.72,2.9522,0.0775,2.63,1.59,149.5,Good
+6,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_101,simultaneous_change,8.0,1.5,7.892,0.0818,1.04,1.35,149.0,1.83,1.5551,0.0486,3.13,3.68,149.5,Good
+7,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_101,high_force_steady,12.0,2.0,11.986,0.1092,0.91,0.12,139.5,2.61,1.985,0.059,2.97,0.75,149.5,Good
+8,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_101,low_force_wide_gap,2.0,4.0,2.005,0.0291,1.45,0.24,149.5,5.48,4.0112,0.0965,2.4,0.28,149.0,Good
+9,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,Mannitol_SD,steady_state_baseline,4.0,2.0,3.999,0.0822,2.06,0.03,149.0,6.75,2.0077,0.1016,5.06,0.38,149.5,Acceptable
+10,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,Mannitol_SD,scf_step_up,8.0,2.0,7.963,0.1551,1.95,0.47,149.5,10.38,2.0074,0.0985,4.9,0.37,149.5,Acceptable
+11,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,Mannitol_SD,scf_step_down,4.0,2.0,4.038,0.067,1.66,0.96,149.0,95.69,1.9962,0.0856,4.29,0.19,149.5,Acceptable
+12,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,Mannitol_SD,gw_step_down,6.0,1.5,5.998,0.0885,1.47,0.03,147.5,4.95,1.5485,0.0694,4.48,3.24,149.5,Acceptable
+13,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,Mannitol_SD,gw_step_up,6.0,3.0,5.99,0.1147,1.92,0.17,146.0,6.28,2.9679,0.1146,3.86,1.07,149.5,Acceptable
+14,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,Mannitol_SD,simultaneous_change,8.0,1.5,7.958,0.1362,1.71,0.53,145.0,5.75,1.5549,0.0699,4.49,3.66,149.5,Acceptable
+15,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,Mannitol_SD,high_force_steady,12.0,2.0,12.011,0.2524,2.1,0.09,148.0,12.9,1.9973,0.0936,4.69,0.14,149.5,Acceptable
+16,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,Mannitol_SD,low_force_wide_gap,2.0,4.0,2.009,0.0389,1.94,0.44,149.0,8.81,3.9922,0.1647,4.12,0.2,149.5,Acceptable
+17,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_Mannitol_Mix,steady_state_baseline,4.0,2.0,4.003,0.0803,2.01,0.08,149.5,8.22,1.9978,0.0736,3.68,0.11,149.5,Acceptable
+18,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_Mannitol_Mix,scf_step_up,8.0,2.0,7.926,0.1102,1.39,0.93,144.5,2.77,1.9863,0.0649,3.27,0.68,149.5,Good
+19,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_Mannitol_Mix,scf_step_down,4.0,2.0,4.07,0.0648,1.59,1.75,149.5,99.14,1.9986,0.0713,3.57,0.07,149.5,Acceptable
+20,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_Mannitol_Mix,gw_step_down,6.0,1.5,5.996,0.1008,1.68,0.07,143.0,10.21,1.5455,0.0576,3.73,3.03,149.5,Acceptable
+21,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_Mannitol_Mix,gw_step_up,6.0,3.0,5.978,0.0764,1.28,0.37,146.5,3.27,2.9614,0.1139,3.85,1.29,149.5,Acceptable
+22,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_Mannitol_Mix,simultaneous_change,8.0,1.5,7.901,0.1336,1.69,1.24,149.0,3.05,1.5449,0.0574,3.71,2.99,149.5,Acceptable
+23,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_Mannitol_Mix,high_force_steady,12.0,2.0,12.026,0.1597,1.33,0.22,149.0,4.61,2.0055,0.0654,3.26,0.28,149.0,Good
+24,no_gap_control,No Gap Control (HP setpoint only),single_screw,False,False,MCC_Mannitol_Mix,low_force_wide_gap,2.0,4.0,1.997,0.0332,1.66,0.14,143.0,10.18,3.9867,0.138,3.46,0.33,149.5,Acceptable
+25,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_101,steady_state_baseline,4.0,2.0,3.992,0.0618,1.55,0.21,149.5,5.64,1.9999,0.0356,1.78,0.0,148.5,Good
+26,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_101,scf_step_up,8.0,2.0,7.895,0.1069,1.35,1.32,149.0,5.79,1.9982,0.0374,1.87,0.09,149.0,Good
+27,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_101,scf_step_down,4.0,2.0,4.112,0.0459,1.12,2.8,149.5,98.81,2.0028,0.0359,1.79,0.14,149.5,Good
+28,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_101,gw_step_down,6.0,1.5,6.002,0.0654,1.09,0.03,149.0,5.26,1.5002,0.0306,2.04,0.02,149.0,Good
+29,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_101,gw_step_up,6.0,3.0,5.998,0.0777,1.3,0.03,137.5,7.74,2.9996,0.0454,1.51,0.01,149.5,Good
+30,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_101,simultaneous_change,8.0,1.5,7.886,0.1086,1.38,1.42,146.5,8.4,1.4992,0.0287,1.92,0.06,149.5,Good
+31,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_101,high_force_steady,12.0,2.0,12.006,0.0983,0.82,0.05,144.0,7.93,1.9995,0.0429,2.15,0.02,147.0,Good
+32,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_101,low_force_wide_gap,2.0,4.0,2.003,0.0269,1.34,0.15,140.5,7.71,3.9926,0.0637,1.6,0.19,149.0,Good
+33,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,Mannitol_SD,steady_state_baseline,4.0,2.0,4.005,0.0807,2.02,0.11,149.5,5.3,1.9993,0.0569,2.85,0.04,149.5,Good
+34,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,Mannitol_SD,scf_step_up,8.0,2.0,7.953,0.1157,1.45,0.59,146.5,3.05,1.9983,0.0546,2.73,0.08,149.5,Good
+35,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,Mannitol_SD,scf_step_down,4.0,2.0,4.025,0.0622,1.55,0.63,148.5,94.53,2.0017,0.0523,2.61,0.09,149.5,Good
+36,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,Mannitol_SD,gw_step_down,6.0,1.5,6.004,0.095,1.58,0.07,145.0,8.27,1.5003,0.0446,2.97,0.02,148.5,Good
+37,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,Mannitol_SD,gw_step_up,6.0,3.0,5.988,0.0916,1.53,0.2,145.0,4.72,2.9974,0.0722,2.41,0.09,149.5,Good
+38,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,Mannitol_SD,simultaneous_change,8.0,1.5,7.977,0.1247,1.56,0.28,149.5,4.49,1.4986,0.0408,2.72,0.09,149.5,Good
+39,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,Mannitol_SD,high_force_steady,12.0,2.0,11.999,0.1878,1.57,0.01,148.0,8.3,1.994,0.0527,2.64,0.3,148.0,Good
+40,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,Mannitol_SD,low_force_wide_gap,2.0,4.0,2.004,0.037,1.85,0.2,145.5,5.54,3.9993,0.0926,2.31,0.02,149.5,Good
+41,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_Mannitol_Mix,steady_state_baseline,4.0,2.0,4.003,0.0482,1.2,0.09,147.5,6.47,1.997,0.0348,1.74,0.15,149.5,Good
+42,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_Mannitol_Mix,scf_step_up,8.0,2.0,7.934,0.1004,1.27,0.82,149.0,7.9,1.9984,0.0431,2.15,0.08,149.5,Good
+43,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_Mannitol_Mix,scf_step_down,4.0,2.0,4.083,0.0558,1.37,2.07,149.5,92.71,1.9968,0.0396,1.98,0.16,149.5,Good
+44,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_Mannitol_Mix,gw_step_down,6.0,1.5,6.003,0.0694,1.16,0.05,145.5,3.76,1.4995,0.031,2.07,0.03,149.5,Good
+45,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_Mannitol_Mix,gw_step_up,6.0,3.0,5.998,0.0878,1.46,0.04,146.5,5.53,3.0012,0.0508,1.69,0.04,148.0,Good
+46,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_Mannitol_Mix,simultaneous_change,8.0,1.5,7.908,0.0807,1.02,1.15,149.5,2.01,1.4992,0.0356,2.37,0.05,149.5,Good
+47,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_Mannitol_Mix,high_force_steady,12.0,2.0,12.009,0.1385,1.15,0.08,144.5,5.57,2.0007,0.0387,1.93,0.03,148.5,Good
+48,pid_gw_screw,PID: GW + Screw Speed Control,single_screw,False,True,MCC_Mannitol_Mix,low_force_wide_gap,2.0,4.0,2.0,0.0364,1.82,0.02,149.5,7.5,4.0042,0.0695,1.74,0.11,147.5,Good
+49,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_101,steady_state_baseline,4.0,2.0,4.003,0.027,0.68,0.08,52.5,2.02,2.0003,0.0271,1.36,0.02,149.5,Good
+50,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_101,scf_step_up,8.0,2.0,8.007,0.0531,0.66,0.08,106.5,16.08,2.0016,0.0222,1.11,0.08,117.5,Excellent
+51,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_101,scf_step_down,4.0,2.0,3.999,0.0304,0.76,0.01,126.0,98.14,1.9962,0.0215,1.08,0.19,147.0,Excellent
+52,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_101,gw_step_down,6.0,1.5,5.999,0.0397,0.66,0.01,76.0,2.67,1.5009,0.024,1.6,0.06,142.5,Good
+53,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_101,gw_step_up,6.0,3.0,6.001,0.0476,0.79,0.01,94.0,1.95,3.0015,0.029,0.97,0.05,149.5,Excellent
+54,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_101,simultaneous_change,8.0,1.5,8.009,0.0751,0.94,0.11,145.0,14.96,1.5023,0.0255,1.7,0.15,146.0,Good
+55,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_101,high_force_steady,12.0,2.0,12.011,0.1131,0.94,0.1,135.0,5.9,1.999,0.0219,1.1,0.05,145.5,Good
+56,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_101,low_force_wide_gap,2.0,4.0,1.997,0.0215,1.08,0.16,139.0,2.96,4.0039,0.0343,0.86,0.1,104.0,Excellent
+57,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,Mannitol_SD,steady_state_baseline,4.0,2.0,4.0,0.0607,1.52,0.01,142.5,11.11,2.0054,0.0341,1.7,0.27,149.5,Good
+58,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,Mannitol_SD,scf_step_up,8.0,2.0,7.989,0.0965,1.21,0.14,147.5,13.23,2.002,0.0324,1.62,0.1,146.0,Good
+59,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,Mannitol_SD,scf_step_down,4.0,2.0,4.009,0.0716,1.79,0.24,149.5,102.15,2.0033,0.0466,2.33,0.16,149.5,Good
+60,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,Mannitol_SD,gw_step_down,6.0,1.5,5.988,0.071,1.19,0.2,149.5,5.25,1.5004,0.0254,1.7,0.02,148.0,Good
+61,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,Mannitol_SD,gw_step_up,6.0,3.0,5.993,0.0705,1.18,0.12,133.0,9.43,2.9953,0.0519,1.73,0.16,149.5,Good
+62,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,Mannitol_SD,simultaneous_change,8.0,1.5,7.994,0.1043,1.3,0.08,144.0,20.06,1.4979,0.0242,1.62,0.14,148.0,Good
+63,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,Mannitol_SD,high_force_steady,12.0,2.0,12.002,0.1053,0.88,0.02,149.5,2.96,2.0031,0.0342,1.71,0.15,149.5,Good
+64,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,Mannitol_SD,low_force_wide_gap,2.0,4.0,2.0,0.03,1.5,0.02,149.0,6.21,3.9986,0.0653,1.63,0.04,148.5,Good
+65,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_Mannitol_Mix,steady_state_baseline,4.0,2.0,3.995,0.0353,0.88,0.14,124.5,4.49,2.0047,0.0293,1.46,0.23,148.5,Good
+66,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_Mannitol_Mix,scf_step_up,8.0,2.0,8.005,0.0759,0.95,0.07,104.5,13.35,1.9977,0.033,1.65,0.12,149.5,Good
+67,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_Mannitol_Mix,scf_step_down,4.0,2.0,3.999,0.0313,0.78,0.02,108.0,100.73,1.9999,0.0264,1.32,0.0,146.0,Good
+68,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_Mannitol_Mix,gw_step_down,6.0,1.5,6.001,0.0537,0.9,0.01,113.0,4.93,1.5041,0.0247,1.64,0.27,147.0,Good
+69,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_Mannitol_Mix,gw_step_up,6.0,3.0,6.005,0.0952,1.59,0.08,137.0,11.32,3.0029,0.0302,1.01,0.1,134.0,Good
+70,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_Mannitol_Mix,simultaneous_change,8.0,1.5,7.995,0.1195,1.49,0.06,143.0,16.1,1.4943,0.03,2.01,0.38,149.5,Good
+71,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_Mannitol_Mix,high_force_steady,12.0,2.0,12.014,0.0862,0.72,0.12,145.5,2.69,2.0002,0.0354,1.77,0.01,145.5,Good
+72,pid_scf_gw,PID: SCF + GW Control,single_screw,True,True,MCC_Mannitol_Mix,low_force_wide_gap,2.0,4.0,2.001,0.022,1.1,0.03,147.0,3.05,4.0008,0.0467,1.17,0.02,134.5,Good
+73,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_101,steady_state_baseline,4.0,2.0,4.0,0.0442,1.11,0.01,129.0,7.8,1.9993,0.0185,0.92,0.03,148.5,Good
+74,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_101,scf_step_up,8.0,2.0,7.997,0.0462,0.58,0.04,119.5,14.9,2.0013,0.0216,1.08,0.07,142.5,Excellent
+75,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_101,scf_step_down,4.0,2.0,4.001,0.0198,0.49,0.03,147.0,99.67,1.9977,0.0194,0.97,0.12,136.5,Excellent
+76,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_101,gw_step_down,6.0,1.5,5.999,0.0741,1.24,0.02,135.0,6.38,1.4983,0.0159,1.06,0.11,139.0,Good
+77,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_101,gw_step_up,6.0,3.0,6.003,0.0349,0.58,0.06,133.0,7.11,3.0009,0.0216,0.72,0.03,75.5,Excellent
+78,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_101,simultaneous_change,8.0,1.5,8.001,0.0671,0.84,0.01,111.0,13.22,1.5027,0.0181,1.21,0.18,140.0,Good
+79,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_101,high_force_steady,12.0,2.0,11.993,0.0562,0.47,0.06,130.0,4.7,2.0008,0.0198,0.99,0.04,146.0,Excellent
+80,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_101,low_force_wide_gap,2.0,4.0,2.001,0.0175,0.88,0.04,110.0,1.84,4.0012,0.0245,0.61,0.03,81.5,Excellent
+81,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,Mannitol_SD,steady_state_baseline,4.0,2.0,4.0,0.0397,0.99,0.01,142.0,8.26,2.0004,0.0253,1.26,0.02,149.0,Good
+82,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,Mannitol_SD,scf_step_up,8.0,2.0,8.006,0.0557,0.7,0.08,135.5,14.31,2.0017,0.0273,1.36,0.09,140.0,Good
+83,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,Mannitol_SD,scf_step_down,4.0,2.0,3.996,0.0391,0.98,0.11,124.0,101.31,2.002,0.0262,1.31,0.1,148.0,Good
+84,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,Mannitol_SD,gw_step_down,6.0,1.5,5.995,0.0484,0.81,0.09,146.0,2.11,1.4983,0.0215,1.44,0.12,149.5,Good
+85,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,Mannitol_SD,gw_step_up,6.0,3.0,5.997,0.0797,1.33,0.06,129.5,11.93,2.9966,0.0488,1.63,0.11,140.0,Good
+86,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,Mannitol_SD,simultaneous_change,8.0,1.5,7.997,0.0403,0.5,0.04,117.5,13.8,1.4953,0.0262,1.75,0.31,149.0,Good
+87,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,Mannitol_SD,high_force_steady,12.0,2.0,12.01,0.1305,1.09,0.08,107.5,10.86,1.9999,0.028,1.4,0.0,144.5,Good
+88,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,Mannitol_SD,low_force_wide_gap,2.0,4.0,2.0,0.023,1.15,0.0,127.5,8.11,3.9944,0.0472,1.18,0.14,142.5,Good
+89,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_Mannitol_Mix,steady_state_baseline,4.0,2.0,4.004,0.0418,1.04,0.1,146.5,6.6,1.9998,0.0277,1.38,0.01,148.5,Good
+90,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_Mannitol_Mix,scf_step_up,8.0,2.0,8.0,0.0394,0.49,0.0,103.0,15.47,1.9944,0.0228,1.14,0.28,149.0,Excellent
+91,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_Mannitol_Mix,scf_step_down,4.0,2.0,3.998,0.032,0.8,0.06,108.0,100.07,2.0,0.0204,1.02,0.0,132.0,Excellent
+92,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_Mannitol_Mix,gw_step_down,6.0,1.5,6.004,0.0543,0.9,0.06,138.5,6.13,1.5019,0.0222,1.48,0.13,140.0,Good
+93,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_Mannitol_Mix,gw_step_up,6.0,3.0,5.996,0.0411,0.69,0.07,149.5,2.13,3.0013,0.0319,1.06,0.04,146.0,Excellent
+94,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_Mannitol_Mix,simultaneous_change,8.0,1.5,8.001,0.0346,0.43,0.02,83.0,13.12,1.5006,0.0203,1.35,0.04,149.5,Excellent
+95,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_Mannitol_Mix,high_force_steady,12.0,2.0,12.004,0.0938,0.78,0.03,139.5,5.61,1.9991,0.0188,0.94,0.04,111.5,Excellent
+96,pid_scf_gw_twin_screw,PID: SCF + GW Control + Twin Feed Screw,twin_screw,True,True,MCC_Mannitol_Mix,low_force_wide_gap,2.0,4.0,2.001,0.0168,0.84,0.03,145.5,2.01,3.997,0.0326,0.82,0.08,127.0,Excellent

generate_dataset.py

@@ -0,0 +1,392 @@
+"""
+IPA Roll Compactor Control Performance Synthetic Dataset Generator v1.0
+=======================================================================
+Generates synthetic time-series process data simulating PID control behavior
+of roll compactors during dry granulation, grounded in the control performance
+concepts from:
+
+  Szappanos-Csordás, K. (2018). "Impact of material properties, process
+  parameters and roll compactor design on roll compaction." Chapter 3.1:
+  Control performance of the different types of roll compactors.
+
+Key concepts modeled:
+  - PID controller behavior (P, I, D terms) for SCF and gap width control
+  - Settling time after setpoint changes (material- and design-dependent)
+  - Steady-state deviation from setpoint (mean ± SD, CV%)
+  - Control architecture differences:
+      * No gap control (AlexanderWerk BT120-type): high GW variability
+      * PID with GW + screw speed control (Hosokawa-type): moderate control
+      * PID with SCF + GW control (Bohle/Gerteis-type): best control
+  - Material-dependent control difficulty: MCC (plastic, compressible) is
+    easier to control than mannitol (brittle, less compressible)
+  - Overshoot, oscillation, and disturbance rejection behavior
+  - Twin feed screw advantage: reduced feed fluctuation → lower SCF variance
+
+THIS IS SYNTHETIC EDUCATIONAL DATA. NOT REAL CUSTOMER OR LAB DATA.
+Generated by Innovative Process Applications (IPA) for teaching process
+control, PID tuning, and SPC concepts.
+"""
+
+import numpy as np
+import pandas as pd
+
+rng = np.random.default_rng(seed=2026)
+
+# =============================================================================
+# CONTROL ARCHITECTURES (from Chapter 3.1 / Table 1 of the dissertation)
+# =============================================================================
+CONTROL_CONFIGS = {
+    "no_gap_control": {
+        "label": "No Gap Control (HP setpoint only)",
+        "description": "Hydraulic pressure setpoint, no closed-loop gap control",
+        "has_scf_pid": False,
+        "has_gw_pid": False,
+        "scf_settling_base_s": 15.0,
+        "gw_settling_base_s": 50.0,   # gap drifts until material equilibrates
+        "scf_noise_base": 0.012,       # CV fraction — higher without PID
+        "gw_noise_base": 0.030,        # high GW variability (3% CV from Table 2)
+        "overshoot_factor": 0.08,
+        "feed_type": "single_screw",
+    },
+    "pid_gw_screw": {
+        "label": "PID: GW + Screw Speed Control",
+        "description": "Gap width controlled via PID adjusting screw speed",
+        "has_scf_pid": False,
+        "has_gw_pid": True,
+        "scf_settling_base_s": 12.0,
+        "gw_settling_base_s": 20.0,
+        "scf_noise_base": 0.010,
+        "gw_noise_base": 0.015,
+        "overshoot_factor": 0.12,      # GW PID can overshoot on fast changes
+        "feed_type": "single_screw",
+    },
+    "pid_scf_gw": {
+        "label": "PID: SCF + GW Control",
+        "description": "Both SCF and GW controlled via independent PID loops",
+        "has_scf_pid": True,
+        "has_gw_pid": True,
+        "scf_settling_base_s": 8.0,
+        "gw_settling_base_s": 10.0,
+        "scf_noise_base": 0.006,       # best control performance
+        "gw_noise_base": 0.008,
+        "overshoot_factor": 0.05,
+        "feed_type": "single_screw",
+    },
+    "pid_scf_gw_twin_screw": {
+        "label": "PID: SCF + GW Control + Twin Feed Screw",
+        "description": "Dual PID loops with twin feed screw for uniform feeding",
+        "has_scf_pid": True,
+        "has_gw_pid": True,
+        "scf_settling_base_s": 6.0,
+        "gw_settling_base_s": 8.0,
+        "scf_noise_base": 0.004,       # twin screw reduces feed fluctuation
+        "gw_noise_base": 0.006,
+        "overshoot_factor": 0.04,
+        "feed_type": "twin_screw",
+    },
+}
+
+# =============================================================================
+# MATERIALS (control difficulty varies with deformation behavior)
+# =============================================================================
+MATERIALS = {
+    "MCC_101": {
+        "label": "MCC 101 (Plastic)",
+        "control_difficulty": 0.8,   # easier — plastic deformation is smoother
+        "settling_multiplier": 0.9,
+        "disturbance_sensitivity": 0.7,
+    },
+    "Mannitol_SD": {
+        "label": "Mannitol (Brittle)",
+        "control_difficulty": 1.3,   # harder — brittle fragmentation causes spikes
+        "settling_multiplier": 1.3,
+        "disturbance_sensitivity": 1.4,
+    },
+    "MCC_Mannitol_Mix": {
+        "label": "1:1 Mixture",
+        "control_difficulty": 1.0,
+        "settling_multiplier": 1.0,
+        "disturbance_sensitivity": 1.0,
+    },
+}
+
+# =============================================================================
+# SETPOINT CHANGE SCENARIOS (from Table 2/3 patterns in the dissertation)
+# =============================================================================
+SCENARIOS = [
+    {"scf_from": 4.0,  "scf_to": 4.0,  "gw_from": 2.0, "gw_to": 2.0,
+     "label": "steady_state_baseline"},
+    {"scf_from": 4.0,  "scf_to": 8.0,  "gw_from": 2.0, "gw_to": 2.0,
+     "label": "scf_step_up"},
+    {"scf_from": 8.0,  "scf_to": 4.0,  "gw_from": 2.0, "gw_to": 2.0,
+     "label": "scf_step_down"},
+    {"scf_from": 6.0,  "scf_to": 6.0,  "gw_from": 3.0, "gw_to": 1.5,
+     "label": "gw_step_down"},
+    {"scf_from": 6.0,  "scf_to": 6.0,  "gw_from": 1.5, "gw_to": 3.0,
+     "label": "gw_step_up"},
+    {"scf_from": 4.0,  "scf_to": 8.0,  "gw_from": 3.0, "gw_to": 1.5,
+     "label": "simultaneous_change"},
+    {"scf_from": 12.0, "scf_to": 12.0, "gw_from": 2.0, "gw_to": 2.0,
+     "label": "high_force_steady"},
+    {"scf_from": 2.0,  "scf_to": 2.0,  "gw_from": 4.0, "gw_to": 4.0,
+     "label": "low_force_wide_gap"},
+]
+
+# =============================================================================
+# TIME-SERIES GENERATION
+# =============================================================================
+SAMPLE_RATE_HZ = 2.0    # 2 samples/second (0.5s intervals)
+RUN_DURATION_S = 180.0   # 3-minute runs — enough to see settling + steady state
+SETPOINT_CHANGE_AT_S = 30.0  # setpoint changes at t=30s
+
+def pid_response(t, setpoint_from, setpoint_to, change_time,
+                 settling_time, overshoot_factor, noise_cv,
+                 has_pid, disturbance_sensitivity, rng_local):
+    """
+    Simulate PID controller response to a setpoint change.
+
+    Without PID: parameter drifts slowly toward equilibrium with high noise.
+    With PID: second-order underdamped response (overshoot + settling).
+    """
+    n = len(t)
+    values = np.zeros(n)
+    step_size = setpoint_to - setpoint_from
+
+    for i, ti in enumerate(t):
+        if ti < change_time:
+            # Pre-change: at old setpoint with noise
+            base = setpoint_from
+        else:
+            elapsed = ti - change_time
+            if has_pid and abs(step_size) > 0.01:
+                # Underdamped PID: exponential decay with overshoot
+                tau = settling_time / 4.0  # time constant
+                zeta = 0.4 + rng_local.uniform(-0.05, 0.05)  # damping ratio
+                omega_n = 1.0 / tau
+                omega_d = omega_n * np.sqrt(1 - zeta**2) if zeta < 1 else omega_n
+                decay = np.exp(-zeta * omega_n * elapsed)
+                oscillation = np.cos(omega_d * elapsed)
+                overshoot_amp = overshoot_factor * abs(step_size)
+                response = 1.0 - decay * (oscillation + (zeta/np.sqrt(1-zeta**2)) *
+                                          np.sin(omega_d * elapsed)) if zeta < 1 else \
+                           1.0 - (1 + omega_n * elapsed) * np.exp(-omega_n * elapsed)
+                response = np.clip(response, -0.5, 1.5)
+                base = setpoint_from + step_size * response
+            elif not has_pid and abs(step_size) > 0.01:
+                # No PID: slow exponential approach, never quite gets there
+                tau = settling_time / 2.0
+                response = 1.0 - np.exp(-elapsed / tau)
+                # Steady-state offset (no integral term to eliminate it)
+                offset = 0.03 * step_size * disturbance_sensitivity
+                base = setpoint_from + step_size * response * 0.95 + offset
+            else:
+                base = setpoint_to
+
+        # Process noise (proportional to setpoint magnitude)
+        noise = rng_local.normal(0, noise_cv * abs(base) + 0.01)
+
+        # Occasional disturbances (feed fluctuations, powder bridging)
+        if rng_local.random() < 0.02 * disturbance_sensitivity:
+            noise += rng_local.normal(0, 0.05 * abs(base))
+
+        values[i] = base + noise
+
+    return values
+
+def compute_run_metrics(t, scf_actual, gw_actual, scf_setpoint, gw_setpoint,
+                        change_time):
+    """Compute the control performance metrics from Chapter 3.1."""
+    # Steady-state region: last 60 seconds of the run
+    ss_mask = t >= (t[-1] - 60.0)
+    # Settling region: after change, before steady state
+    settling_mask = (t >= change_time) & (t < change_time + 90.0)
+
+    # SCF metrics
+    scf_ss = scf_actual[ss_mask]
+    scf_mean = np.mean(scf_ss)
+    scf_std = np.std(scf_ss)
+    scf_cv = (scf_std / abs(scf_mean) * 100) if abs(scf_mean) > 0.1 else 0.0
+    scf_deviation_pct = abs(scf_mean - scf_setpoint) / scf_setpoint * 100 \
+                        if scf_setpoint > 0.1 else 0.0
+
+    # GW metrics
+    gw_ss = gw_actual[ss_mask]
+    gw_mean = np.mean(gw_ss)
+    gw_std = np.std(gw_ss)
+    gw_cv = (gw_std / abs(gw_mean) * 100) if abs(gw_mean) > 0.1 else 0.0
+    gw_deviation_pct = abs(gw_mean - gw_setpoint) / gw_setpoint * 100 \
+                       if gw_setpoint > 0.1 else 0.0
+
+    # Settling time: time after change until value stays within ±2% of final
+    scf_settled_s = _find_settling_time(t, scf_actual, scf_setpoint,
+                                        change_time, tolerance=0.02)
+    gw_settled_s = _find_settling_time(t, gw_actual, gw_setpoint,
+                                       change_time, tolerance=0.02)
+
+    # Overshoot
+    post_change = scf_actual[t >= change_time]
+    if len(post_change) > 0 and scf_setpoint > 0.1:
+        scf_overshoot_pct = (max(post_change) - scf_setpoint) / scf_setpoint * 100
+    else:
+        scf_overshoot_pct = 0.0
+
+    return {
+        "scf_ss_mean": round(scf_mean, 3),
+        "scf_ss_std": round(scf_std, 4),
+        "scf_ss_cv_pct": round(scf_cv, 2),
+        "scf_deviation_from_setpoint_pct": round(scf_deviation_pct, 2),
+        "scf_settling_time_s": round(scf_settled_s, 1),
+        "scf_overshoot_pct": round(max(scf_overshoot_pct, 0), 2),
+        "gw_ss_mean_mm": round(gw_mean, 4),
+        "gw_ss_std_mm": round(gw_std, 4),
+        "gw_ss_cv_pct": round(gw_cv, 2),
+        "gw_deviation_from_setpoint_pct": round(gw_deviation_pct, 2),
+        "gw_settling_time_s": round(gw_settled_s, 1),
+    }
+
+def _find_settling_time(t, values, setpoint, change_time, tolerance=0.02):
+    """Find time after change when value stays within tolerance band."""
+    if abs(setpoint) < 0.1:
+        return 0.0
+    band = tolerance * abs(setpoint)
+    post_mask = t >= change_time
+    post_t = t[post_mask]
+    post_v = values[post_mask]
+    # Walk backwards from end to find last excursion outside band
+    for i in range(len(post_v) - 1, -1, -1):
+        if abs(post_v[i] - setpoint) > band:
+            if i < len(post_v) - 1:
+                return post_t[i+1] - change_time
+            else:
+                return post_t[-1] - change_time
+    return 0.0  # already settled
+
+# =============================================================================
+# GENERATE ALL RUNS
+# =============================================================================
+t = np.arange(0, RUN_DURATION_S, 1.0 / SAMPLE_RATE_HZ)
+n_samples = len(t)
+
+summary_rows = []
+timeseries_rows = []
+run_id = 0
+
+for ctrl_key, ctrl in CONTROL_CONFIGS.items():
+    for mat_key, mat in MATERIALS.items():
+        for scenario in SCENARIOS:
+            run_id += 1
+
+            # Adjusted settling times and noise for material
+            scf_settling = (ctrl["scf_settling_base_s"] *
+                           mat["settling_multiplier"])
+            gw_settling = (ctrl["gw_settling_base_s"] *
+                          mat["settling_multiplier"])
+            scf_noise = ctrl["scf_noise_base"] * mat["control_difficulty"]
+            gw_noise = ctrl["gw_noise_base"] * mat["control_difficulty"]
+
+            # Twin screw reduces feed fluctuation → lower noise
+            if ctrl["feed_type"] == "twin_screw":
+                scf_noise *= 0.75
+                gw_noise *= 0.80
+
+            # Generate time series
+            scf_actual = pid_response(
+                t, scenario["scf_from"], scenario["scf_to"],
+                SETPOINT_CHANGE_AT_S, scf_settling,
+                ctrl["overshoot_factor"], scf_noise,
+                ctrl["has_scf_pid"], mat["disturbance_sensitivity"], rng
+            )
+            gw_actual = pid_response(
+                t, scenario["gw_from"], scenario["gw_to"],
+                SETPOINT_CHANGE_AT_S, gw_settling,
+                ctrl["overshoot_factor"] * 0.7, gw_noise,
+                ctrl["has_gw_pid"], mat["disturbance_sensitivity"], rng
+            )
+
+            # Clip to physical limits
+            scf_actual = np.clip(scf_actual, 0.5, 25.0)
+            gw_actual = np.clip(gw_actual, 0.5, 8.0)
+
+            # Roll speed: constant within a run (not PID-controlled here)
+            rs_setpoint = 3.0 + rng.uniform(-1, 1)
+            rs_actual = rs_setpoint + rng.normal(0, 0.02 * rs_setpoint, n_samples)
+
+            # Screw speed: varies if GW PID adjusts it
+            ss_base = 30.0 + rng.uniform(-5, 5)
+            if ctrl["has_gw_pid"]:
+                # Screw speed adapts inversely to gap error
+                gw_error = scenario["gw_to"] - gw_actual
+                ss_actual = ss_base + 5.0 * gw_error + rng.normal(0, 0.5, n_samples)
+            else:
+                ss_actual = ss_base + rng.normal(0, 0.3, n_samples)
+
+            # Store time-series (subsample to every 2s for manageable file size)
+            subsample = np.arange(0, n_samples, 4)  # every 2 seconds
+            for idx in subsample:
+                timeseries_rows.append({
+                    "run_id": run_id,
+                    "time_s": round(t[idx], 1),
+                    "scf_setpoint_kN_per_cm": scenario["scf_to"] if t[idx] >= SETPOINT_CHANGE_AT_S else scenario["scf_from"],
+                    "scf_actual_kN_per_cm": round(scf_actual[idx], 3),
+                    "gw_setpoint_mm": scenario["gw_to"] if t[idx] >= SETPOINT_CHANGE_AT_S else scenario["gw_from"],
+                    "gw_actual_mm": round(gw_actual[idx], 4),
+                    "roll_speed_rpm": round(rs_actual[idx], 2),
+                    "screw_speed_rpm": round(ss_actual[idx], 1),
+                })
+
+            # Compute summary metrics
+            metrics = compute_run_metrics(
+                t, scf_actual, gw_actual,
+                scenario["scf_to"], scenario["gw_to"],
+                SETPOINT_CHANGE_AT_S
+            )
+
+            # Classify control quality
+            total_cv = metrics["scf_ss_cv_pct"] + metrics["gw_ss_cv_pct"]
+            if total_cv < 2.0 and metrics["scf_deviation_from_setpoint_pct"] < 1.0:
+                control_quality = "Excellent"
+            elif total_cv < 5.0 and metrics["scf_deviation_from_setpoint_pct"] < 3.0:
+                control_quality = "Good"
+            elif total_cv < 10.0:
+                control_quality = "Acceptable"
+            else:
+                control_quality = "Poor"
+
+            summary_rows.append({
+                "run_id": run_id,
+                "control_architecture": ctrl_key,
+                "control_label": ctrl["label"],
+                "feed_type": ctrl["feed_type"],
+                "has_scf_pid": ctrl["has_scf_pid"],
+                "has_gw_pid": ctrl["has_gw_pid"],
+                "material": mat_key,
+                "scenario": scenario["label"],
+                "scf_setpoint_kN_per_cm": scenario["scf_to"],
+                "gw_setpoint_mm": scenario["gw_to"],
+                **metrics,
+                "control_quality_grade": control_quality,
+            })
+
+# =============================================================================
+# SAVE
+# =============================================================================
+df_summary = pd.DataFrame(summary_rows)
+df_timeseries = pd.DataFrame(timeseries_rows)
+
+df_summary.to_csv("control_performance_summary_v1.0.csv", index=False)
+df_timeseries.to_csv("control_performance_timeseries_v1.0.csv", index=False)
+
+print(f"Summary: {len(df_summary)} runs, {len(df_summary.columns)} columns")
+print(f"Time series: {len(df_timeseries)} rows, {len(df_timeseries.columns)} columns")
+print()
+print("=== Control quality distribution ===")
+print(df_summary["control_quality_grade"].value_counts())
+print()
+print("=== Mean SCF CV% by architecture ===")
+print(df_summary.groupby("control_architecture")["scf_ss_cv_pct"].mean().round(2))
+print()
+print("=== Mean GW CV% by architecture ===")
+print(df_summary.groupby("control_architecture")["gw_ss_cv_pct"].mean().round(2))
+print()
+print("=== Mean SCF settling time by architecture ===")
+print(df_summary.groupby("control_architecture")["scf_settling_time_s"].mean().round(1))