---
license: cc-by-4.0
task_categories:
  - tabular-regression
language:
  - en
tags:
  - chemistry
  - chemical-engineering
  - biodiesel
  - transesterification
  - process-optimization
  - reinforcement-learning
  - surrogate-modeling
  - tabular
pretty_name: Two-Stage Transesterification Operating-Parameter Dataset
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files: Dataset.csv
---

# Two-Stage Transesterification Operating-Parameter Dataset (21,001 records)

A tabular dataset mapping six manipulated operating parameters of a **two-stage
alkali-catalysed transesterification process** to the resulting **biodiesel
conversion rate (%)**. It was built as a sensitivity-analysis sweep over the
process operating envelope and is intended as a benchmark for surrogate
modelling, static optimisation (RSM / Bayesian optimisation), and reinforcement
learning for continuous process control.

This is the dataset accompanying the paper *"A Hybrid LSTM-DDPG Framework for
Operating-Parameter Optimisation of a Two-Stage Transesterification Process."*

## Dataset structure

21,001 rows × 7 columns. No missing values, no duplicate rows.

| Column | Description | Unit | Min | Max | Levels |
|---|---|---|---|---|---|
| `MOH/Oil ratio-1` | Methanol-to-oil molar ratio, stage 1 | mol/mol | 7.0 | 9.0 | 4 |
| `Temp-1 (C)` | Reaction temperature, stage 1 | °C | 80 | 100 | 5 |
| `R. Time-1 (hr)` | Reaction time, stage 1 | h | 3.5 | 8.0 | 7 |
| `MOH/Oil ratio-2` | Methanol-to-oil molar ratio, stage 2 | mol/mol | 3.035 | 4.0 | 5 (+1) |
| `Temp-2 (C)` | Reaction temperature, stage 2 | °C | 40 | 80 | 6 (+1) |
| `R. Time-2 (hr)` | Reaction time, stage 2 | h | 0.5 | 1.5 | 5 (+1) |
| `CONV, y (%)` | **Target** — biodiesel conversion rate | % | 81.95 | 99.58 | continuous |

### Design of experiments

The first 21,000 rows form a **complete full-factorial grid**: 4 × 5 × 7 = 140
stage-1 settings crossed with 5 × 6 × 5 = 150 stage-2 settings. Every stage-1
combination appears with all 150 stage-2 combinations, so the design is fully
balanced and orthogonal.

Row 21,001 is a single **off-grid point** (MeOH/oil-2 = 3.835, T2 = 75 °C,
t2 = 1.055 h, conversion = 99.26%) appended outside the factorial lattice. Users
who require a strictly rectangular design should drop it; users benchmarking
against the source paper should keep it, since all reported statistics use the
full 21,001 records.

### Target distribution

Mean conversion is 95.73%, with the third quartile at 98.35%. The response
surface is strongly monotone in the stage-1 alcohol-to-oil ratio, which
dominates the variance; stage-2 variables have comparatively narrow optimal
windows. The maximum conversion of 99.583% occurs at the upper corner of the
envelope (9.0 mol/mol, 100 °C, 8.0 h / 4.0 mol/mol, 80 °C, 1.5 h) — this value
acts as a hard ceiling for any surrogate-based optimiser trained on the data.

## Usage

```python
import pandas as pd

df = pd.read_csv("Dataset.csv")
X = df.iloc[:, :6].values   # six manipulated variables
y = df.iloc[:, 6].values    # conversion rate (%)
```

To reproduce the split used in the paper: remove outliers with a 3 × IQR filter,
then split 70/15/15 with `random_state=42`, fitting `StandardScaler` on the
training partition only.

## Intended uses

- Regression / soft-sensor benchmarking on a smooth, low-dimensional,
  noise-free response surface.
- Static optimisation baselines (response surface methodology, Bayesian
  optimisation, genetic algorithms).
- A nearest-neighbour or learned **surrogate environment** for reinforcement
  learning in continuous action spaces, avoiding the cost of physical reactor
  interaction.
- Ablation studies comparing one-shot optimisation against sequential
  decision-making under simulated constraint changes and feedstock drift.

## Limitations and caveats

- **Simulation-derived, not experimental.** Values come from a sensitivity
  analysis of the process, not from wet-lab measurement. Absolute conversion
  figures should not be treated as validated plant data.
- **Noise-free and deterministic.** Each parameter combination maps to exactly
  one conversion value. Models trained here will not encounter the measurement
  noise, catalyst deactivation, or feedstock variability of a real reactor.
- **Single feedstock.** The grid varies operating conditions only; feedstock
  composition, free-fatty-acid content, and catalyst loading are held fixed.
- **Extrapolation is unsupported.** Nearest-neighbour or interpolating
  surrogates are trustworthy only inside the convex hull of the grid. Optimisers
  cannot discover conversions above 99.583% and any such result is an artefact.
- **Coarse grid spacing.** With only 4–7 levels per variable, gradients near the
  optimum are resolved coarsely.

## Citation

```bibtex
@inproceedings{butarbutar2026hybrid,
  title     = {A Hybrid {LSTM-DDPG} Framework for Operating-Parameter
               Optimisation of a Two-Stage Transesterification Process},
  author    = {Butarbutar, Chardinal Martin and
               Widada, Bartolomeus Priya Perkasa Utama and
               Putra, Cendra Devayana and
               Putri, Berliana Devianti and
               Taipabu, Ikhsan},
  booktitle = {TBA},
  year      = {2026}
}
```

## Contact

Cendra Devayana Putra — putracendra@unesa.ac.id (Surabaya State University)