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Datasets:
CendraCendra
/
TSTOP_Dataset

Tasks:
Tabular Regression
Modalities:
Tabular
Formats:
csv
Languages:
English
Size:
10K - 100K
Tags:
chemistry
chemical-engineering
biodiesel
transesterification
process-optimization
reinforcement-learning
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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MOH/Oil ratio-1
float64
7
9
⌀
Temp-1 (C)
int64
80
100
⌀
R. Time-1 (hr)
float64
3.5
8
⌀
MOH/Oil ratio-2
float64
3.04
4
⌀
Temp-2 (C)
int64
40
80
⌀
R. Time-2 (hr)
float64
0.5
1.5
⌀
CONV, y (%)
float64
82
99.6
⌀
7
80
3.5
3.04
40
0.5
81.95
7
80
3.5
3.04
40
0.75
85.18
7
80
3.5
3.04
40
1
87.13
7
80
3.5
3.04
40
1.25
88.46
7
80
3.5
3.04
40
1.5
89.45
7
80
3.5
3.04
48
0.5
86.33
7
80
3.5
3.04
48
0.75
88.79
7
80
3.5
3.04
48
1
90.26
7
80
3.5
3.04
48
1.25
91.27
7
80
3.5
3.04
48
1.5
92.01
7
80
3.5
3.04
56
0.5
89.5
7
80
3.5
3.04
56
0.75
91.39
7
80
3.5
3.04
56
1
92.52
7
80
3.5
3.04
56
1.25
93.28
7
80
3.5
3.04
56
1.5
93.85
7
80
3.5
3.04
64
0.5
91.81
7
80
3.5
3.04
64
0.75
93.28
7
80
3.5
3.04
64
1
94.15
7
80
3.5
3.04
64
1.25
94.75
7
80
3.5
3.04
64
1.5
95.18
7
80
3.5
3.04
72
0.5
93.52
7
80
3.5
3.04
72
0.75
94.67
7
80
3.5
3.04
72
1
95.35
7
80
3.5
3.04
72
1.25
95.82
7
80
3.5
3.04
72
1.5
96.16
7
80
3.5
3.04
80
0.5
94.79
7
80
3.5
3.04
80
0.75
95.7
7
80
3.5
3.04
80
1
96.25
7
80
3.5
3.04
80
1.25
96.62
7
80
3.5
3.04
80
1.5
96.89
7
80
3.5
3.28
40
0.5
85.12
7
80
3.5
3.28
40
0.75
88.29
7
80
3.5
3.28
40
1
90.16
7
80
3.5
3.28
40
1.25
91.43
7
80
3.5
3.28
40
1.5
92.35
7
80
3.5
3.28
48
0.5
89.39
7
80
3.5
3.28
48
0.75
91.74
7
80
3.5
3.28
48
1
93.11
7
80
3.5
3.28
48
1.25
94.02
7
80
3.5
3.28
48
1.5
94.68
7
80
3.5
3.28
56
0.5
92.4
7
80
3.5
3.28
56
0.75
94.12
7
80
3.5
3.28
56
1
95.12
7
80
3.5
3.28
56
1.25
95.77
7
80
3.5
3.28
56
1.5
96.24
7
80
3.5
3.28
64
0.5
94.5
7
80
3.5
3.28
64
0.75
95.77
7
80
3.5
3.28
64
1
96.48
7
80
3.5
3.28
64
1.25
96.95
7
80
3.5
3.28
64
1.5
97.28
7
80
3.5
3.28
72
0.5
95.97
7
80
3.5
3.28
72
0.75
96.89
7
80
3.5
3.28
72
1
97.4
7
80
3.5
3.28
72
1.25
97.73
7
80
3.5
3.28
72
1.5
97.96
7
80
3.5
3.28
80
0.5
96.98
7
80
3.5
3.28
80
0.75
97.65
7
80
3.5
3.28
80
1
98.01
7
80
3.5
3.28
80
1.25
98.24
7
80
3.5
3.28
80
1.5
98.4
7
80
3.5
3.52
40
0.5
87.6
7
80
3.5
3.52
40
0.75
90.56
7
80
3.5
3.52
40
1
92.27
7
80
3.5
3.52
40
1.25
93.39
7
80
3.5
3.52
40
1.5
94.19
7
80
3.5
3.52
48
0.5
91.57
7
80
3.5
3.52
48
0.75
93.66
7
80
3.5
3.52
48
1
94.83
7
80
3.5
3.52
48
1.25
95.59
7
80
3.5
3.52
48
1.5
96.12
7
80
3.5
3.52
56
0.5
94.23
7
80
3.5
3.52
56
0.75
95.67
7
80
3.5
3.52
56
1
96.46
7
80
3.5
3.52
56
1.25
96.97
7
80
3.5
3.52
56
1.5
97.32
7
80
3.5
3.52
64
0.5
95.97
7
80
3.5
3.52
64
0.75
96.96
7
80
3.5
3.52
64
1
97.49
7
80
3.5
3.52
64
1.25
97.82
7
80
3.5
3.52
64
1.5
98.05
7
80
3.5
3.52
72
0.5
97.11
7
80
3.5
3.52
72
0.75
97.78
7
80
3.5
3.52
72
1
98.13
7
80
3.5
3.52
72
1.25
98.35
7
80
3.5
3.52
72
1.5
98.5
7
80
3.5
3.52
80
0.5
97.84
7
80
3.5
3.52
80
0.75
98.3
7
80
3.5
3.52
80
1
98.53
7
80
3.5
3.52
80
1.25
98.67
7
80
3.5
3.52
80
1.5
98.77
7
80
3.5
3.76
40
0.5
89.5
7
80
3.5
3.76
40
0.75
92.19
7
80
3.5
3.76
40
1
93.7
7
80
3.5
3.76
40
1.25
94.67
7
80
3.5
3.76
40
1.5
95.35
7
80
3.5
3.76
48
0.5
93.09
7
80
3.5
3.76
48
0.75
94.89
7
80
3.5
3.76
48
1
95.88
7
80
3.5
3.76
48
1.25
96.5
7
80
3.5
3.76
48
1.5
96.92
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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 

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 

@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)
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