Datasets:

IPA-Marketing
/

Roll-Compactor-Control-Performance

license: cc-by-4.0
language:
  - en
pretty_name: 'Roll Compactor Control Performance: PID Tuning & Process Stability (Synthetic)'
size_categories:
  - n<1K
task_categories:
  - tabular-classification
  - time-series-forecasting
tags:
  - process-control
  - pid-controller
  - statistical-process-control
  - roller-compaction
  - pharmaceutical-manufacturing
  - time-series
  - synthetic-data
  - twin-feed-screw
  - process-engineering
  - spc
  - control-charts
  - education
configs:
  - config_name: summary
    data_files:
      - split: train
        path: control_performance_summary_v1.0.csv
  - config_name: timeseries
    data_files:
      - split: train
        path: control_performance_timeseries_v1.0.csv

Roll Compactor Control Performance: PID Tuning & Process Stability (Synthetic)

Version: 1.0 Publisher: Innovative Process Applications (IPA) 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:

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
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.
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.
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).
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.
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.
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.

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.