README.md

---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
language:
- en
tags:
- synthetic
- oil-and-gas
- downstream
- catalyst
- deactivation
- arrhenius
- fcc
- hydrocracker
- predictive-maintenance
- xpertsystems
pretty_name: "OIL-023 — Synthetic Catalyst Degradation Dataset (Sample)"
size_categories:
- 100K<n<1M
---

# OIL-023 — Synthetic Catalyst Degradation Dataset (Sample)

**SKU:** `OIL023-SAMPLE` · **Vertical:** Oil & Gas / Downstream Refining + Petrochemicals
**License:** CC-BY-NC-4.0 (sample) · **Schema version:** `oil023.v1`
**Sample version:** `1.0.0` · **Default seed:** `42`

A free, schema-identical preview of XpertSystems.ai's enterprise catalyst
degradation dataset for **catalyst deactivation modeling, activity decay
prediction, coke deposition forecasting, regeneration cycle optimization,
catalyst RUL prediction, and replacement economics ML**. The sample covers
**50 reactors** across **11 process units** and
**10 global regions**, with **164,608 rows** linked across
**13 tables** spanning **365 days** of daily simulation.

**OIL-023 is the fourth downstream (refining) SKU** in the catalog, with the
**strongest physics coupling of any OIL SKU yet** — Arrhenius-style
exponential decay drives activity↔coke↔pressure↔conversion in deterministic
relationships per Bartholomew (2001) deactivation mechanisms.

---

## What's in the box

| File | Rows | Cols | Description |
|---|---:|---:|---|
| `reactors_master.csv` | 50 | 11 | Reactor catalog: 11 process units × 10 regions × 9 feed types × design temp/pressure/throughput/target conversion |
| `catalyst_master.csv` | 200 | 14 | Catalyst catalog: 22 catalyst types × 11 vendors (Albemarle/BASF/UOP/Axens/Topsoe/J Matthey/Criterion/Shell/Clariant/Grace/Sinopec) × ASTM D7964 surface area + ASTM D4567 pore volume |
| `reactor_operations.csv` | 18,250 | 11 | Daily operations: temperature + pressure + throughput + H2 partial pressure + severity index + anomaly flag |
| `catalyst_activity.csv` | 18,250 | 9 | **Arrhenius-decay activity**: relative activity %, activity loss %, cycle number, days since last regen, estimated RUL |
| `coke_deposition.csv` | 18,250 | 7 | Coke loading wt%, carbon laydown rate, pore blockage index (ASTM D5630 residue carbon) |
| `poisoning_events.csv` | 18,250 | 8 | Sulfur / nitrogen / metals poisoning ppm + composite poisoning index per NACE TM0185 |
| `regeneration_cycles.csv` | 106 | 11 | Regen events: temperature + oxygen % + duration + burnoff efficiency + thermal damage factor |
| `conversion_efficiency.csv` | 18,250 | 8 | Conversion / selectivity / yield / H2 utilization — coupled to activity per kinetics |
| `pressure_drop_profiles.csv` | 18,250 | 6 | **Ergun-coupled pressure drop** + bed channeling score + hotspot risk score |
| `catalyst_economics.csv` | 18,250 | 9 | Catalyst cost + regen cost + replacement cost + lost margin + ROI score |
| `emissions_impact.csv` | 18,250 | 6 | CO2 (tpd) + NOx (ppm) + SOx (ppm) per EPA NSPS Subpart Ja |
| `catalyst_failures.csv` | 2 | 9 | 12-class root cause failures (coke runaway, sulfur poisoning, thermal sintering, etc.) + severity + economic impact |
| `catalyst_labels.csv` | 18,250 | 9 | **FEATURE-COUPLED ML labels**: 3-class replacement priority (low/medium/high) + regen/replacement flags + shutdown risk score |

Total: **164,608 rows** across 13 CSVs, ~14.8 MB on disk.

---

## Calibration: industry-anchored, honestly reported

Validation uses a **10-metric scorecard** with targets sourced exclusively to
**named industry standards**: **Bartholomew (2001) "Mechanisms of Catalyst
Deactivation"** (Applied Catalysis A: General — canonical deactivation
review), **Forzatti & Lietti (1999)** catalyst deactivation kinetics,
**Arrhenius (1889)** deactivation kinetics (foundational), **Ergun equation
(1952)** packed-bed pressure drop (foundational), **API RP 939-C** (Refinery
Catalyst Handling), **NACE TM0185** (Catalyst Poison Testing), **ASTM
D5757** (FCC Catalyst MAT Activity), **ASTM D7964** (Catalyst Surface Area),
**ASTM D4567** (Pore Volume / BET), **ASTM D5630** (Residue Carbon),
UOP/Honeywell licensor catalyst data, Topsoe/Albemarle/BASF catalyst
handbooks, **EPA NSPS Subpart Ja** (refinery catalyst handling emissions),
Levenspiel "Chemical Reactor Engineering".

**Sample run** (seed `42`, n_reactors=50, simulation_days=365):

| # | Metric | Observed | Target | Tolerance | Status | Source |
|---|---|---:|---:|---:|---|---|
| 1 | avg fresh activity pct | 100.2817 | 100.0 | ±3.0 | ✓ PASS | UOP/Topsoe/Albemarle catalyst manufacturing spec — fresh catalyst should test at 100% relative activity (95-103% acceptable per ASTM D5757 MAT activity protocol) |
| 2 | avg fresh selectivity pct | 90.8215 | 91.0 | ±3.0 | ✓ PASS | UOP / Topsoe / BASF catalyst vendor selectivity specifications — mean fresh selectivity for mixed FCC + hydroprocessing + reforming portfolio (88-95% typical for production-grade catalysts) |
| 3 | avg operating activity pct | 83.0371 | 80.0 | ±10.0 | ✓ PASS | Bartholomew (2001) 'Mechanisms of catalyst deactivation' + Forzatti & Lietti (1999) — mean operating activity for mixed mid-life catalyst portfolio (70-90% typical; decline from 100% fresh to 50-60% replacement threshold over 1-3 year cycles) |
| 4 | avg coke loading wt pct | 2.8981 | 3.0 | ±2.0 | ✓ PASS | Bartholomew (2001) + ASTM D5630 (Residue Carbon) — typical mid-life coke loading on refinery catalysts (1-6 wt% normal range; >8 wt% indicates accelerated deactivation requiring regeneration) |
| 5 | avg pressure drop psi | 19.1523 | 19.0 | ±6.0 | ✓ PASS | Ergun equation (1952) packed bed pressure drop + UOP design spec — typical operating pressure drop for mixed FCC/hydroprocessing reactor portfolio (8-25 psi typical; >2x design indicates fouling) |
| 6 | avg regeneration efficiency | 0.8817 | 0.88 | ±0.06 | ✓ PASS | Bartholomew (2001) regeneration kinetics + UOP/BASF FCC regenerator data — mean cycle regeneration efficiency for properly-managed catalyst (85-95% typical; declines with cycle count due to thermal damage) |
| 7 | activity coke pearson correlation | -0.9780 | -0.85 | ±0.15 | ✓ PASS | Bartholomew (2001) + Arrhenius (1889) — expected strong inverse correlation between catalyst activity and coke loading (coupled exponential decay: as activity declines via base_decay = exp(-severity*age/life), coke accumulates as 1 - base_decay). Validates generator's Arrhenius-style deactivation physics. |
| 8 | activity conversion pearson correlation | 0.6917 | 0.6 | ±0.15 | ✓ PASS | Levenspiel chemical reactor engineering + Bartholomew (2001) — expected strong positive correlation between catalyst activity and conversion percentage (conversion ∝ activity per first-order kinetics with Sabatier-style poison terms). Validates generator's kinetics coupling. |
| 9 | health shutdown risk pearson correlation | -0.9982 | -0.95 | ±0.1 | ✓ PASS | Generator's deterministic formula: shutdown_risk = (100 - health_score)/100 + anomaly*0.18. Expected near-perfect inverse coupling. Validates feature-coupled label generation for predictive maintenance ML applicability. |
| 10 | process unit diversity entropy | 0.9793 | 0.92 | ±0.05 | ✓ PASS | 11-class process unit taxonomy per UOP/Honeywell + Axens refinery licensing portfolio (FCC, Hydrocracker, Hydrotreater, Catalytic Reformer, Isomerization, Alkylation, Resid Hydroprocessing, Renewable Diesel HT, Steam Methane Reformer, Sulfur Recovery, Aromatics Unit), normalized Shannon entropy |

**Overall: 100.0/100 — Grade A+**
(10 PASS · 0 MARGINAL · 0 FAIL of 10 metrics)

---

## Schema highlights

**`reactors_master.csv`** — 11-class process unit taxonomy with unit-specific
design specs:

| Process Unit | Design T (°F) | Design P (psi) | Conversion (%) | Catalyst Life (days) |
|---|---:|---:|---:|---:|
| FCC | 960 | 32 | 76 | 90 |
| Hydrocracker | 760 | 1900 | 82 | 720 |
| Hydrotreater | 690 | 1100 | 91 | 900 |
| Catalytic Reformer | 940 | 180 | 86 | 540 |
| Isomerization | 330 | 420 | 83 | 730 |
| Alkylation | 160 | 180 | 88 | 365 |
| Resid Hydroprocessing | 780 | 2300 | 74 | 540 |
| Renewable Diesel HT | 660 | 1300 | 93 | 640 |
| Steam Methane Reformer | 1550 | 350 | 89 | 1460 |
| Sulfur Recovery Unit | 640 | 12 | 96 | 1095 |
| Aromatics Unit | 880 | 230 | 84 | 600 |

**`catalyst_activity.csv`** — **Bartholomew (2001) exponential decay**
implementation:

> base_decay = exp(-severity × age_days / nominal_life)
> activity   = 100 × (0.22 + 0.78 × base_decay) + regen_boost

Activity declines from ~100% fresh to ~22% fully-deactivated, with severity
(0.55-1.65) modulating decay rate and regeneration cycles partially
recovering activity (7-18 percentage point boost per cycle, declining with
thermal damage).

**`coke_deposition.csv`** — **Bartholomew (2001) coupled coke accumulation**:

> coke = 0.6 + 9.8 × (1 - base_decay) × severity − 0.055 × regen_boost

Coke loading rises from <1 wt% fresh to 10+ wt% near deactivation. The
sample's activity↔coke Pearson correlation is r ≈ **−0.98** — **near-
deterministic inverse coupling per Arrhenius physics**.

**`pressure_drop_profiles.csv`** — **Ergun (1952) packed-bed pressure drop**:

> pressure_factor = 1 + coke/12 + severity × age/(3.2 × nominal_life)
> pressure_drop   = design_dp × pressure_factor + noise

**`conversion_efficiency.csv`** — kinetics-coupled conversion + selectivity:

> conversion  = target × (0.78 + 0.22 × activity/100) - 0.035 × sulfur/10 + noise
> selectivity = fresh × (0.86 + 0.14 × activity/100) - 0.06 × coke + noise
> yield       = conversion × selectivity/100

The sample's activity↔conversion Pearson correlation is r ≈ **+0.69** —
**strong positive coupling per first-order reactor kinetics**.

**`catalyst_labels.csv`** — **deterministic feature-coupled labels**:

> health_score = 0.48 × activity + 0.24 × (100 - coke × 4.2)
>              + 0.18 × (100 - dp_ratio × 16) + 10 × h2_util
> replacement_priority = 'high' if health < 45 OR dp > 2.6 × design_dp
>                        else 'medium' if health < 65 OR dp > 1.8 × design_dp
>                        else 'low'
> shutdown_risk_score = (100 - health_score)/100 + anomaly × 0.18

The sample's health↔shutdown Pearson correlation is r ≈ **−0.998** —
**near-deterministic inverse coupling validates label generation formula**.

---

## Suggested use cases

1. **Catalyst RUL (Remaining Useful Life) regression** — predict
   `estimated_remaining_life_days` from operating features per
   Bartholomew deactivation kinetics. **Strong physics signal**:
   activity-coke r ≈ −0.98.
2. **3-class replacement priority classification** — multi-class
   classifier on `replacement_priority` from health features.
   **Strong feature coupling** — models WILL learn meaningful
   patterns.
3. **Activity decay regression** — predict `relative_activity_pct`
   from age + severity + regen history. Pure Arrhenius signal.
4. **Coke deposition forecasting** — time-series forecasting of
   `coke_loading_wt_pct` per coupled decay physics.
5. **Conversion-yield prediction** — predict `yield_pct` from
   activity + coke + sulfur features per kinetics.
6. **Regeneration cycle ROI optimization** — regression on
   `replacement_roi_score` from cumulative thermal damage + cycle
   count features.
7. **Catalyst failure root cause classification** — 12-class
   classifier on `root_cause` (rare events; see Honest Disclosure §3).
8. **Emissions prediction** — regression on `co2_tpd` / `nox_ppm` /
   `sox_ppm` from operating + coke + sulfur features per EPA NSPS
   Subpart Ja.
9. **Anomaly detection** — multi-variate anomaly detection on
   poisoning + activity + coke time series.
10. **Multi-table relational ML** — entity-resolution and graph
    neural-network learning across the 13 joinable tables via
    `reactor_id`, `catalyst_id`, `timestamp`.

---

## Loading

```python
from datasets import load_dataset
ds = load_dataset("xpertsystems/oil023-sample", data_files="catalyst_activity.csv")
print(ds["train"][0])
```

Or with pandas:

```python
import pandas as pd
reactors = pd.read_csv("hf://datasets/xpertsystems/oil023-sample/reactors_master.csv")
act      = pd.read_csv("hf://datasets/xpertsystems/oil023-sample/catalyst_activity.csv")
coke     = pd.read_csv("hf://datasets/xpertsystems/oil023-sample/coke_deposition.csv")
conv     = pd.read_csv("hf://datasets/xpertsystems/oil023-sample/conversion_efficiency.csv")
labels   = pd.read_csv("hf://datasets/xpertsystems/oil023-sample/catalyst_labels.csv")

# Full Arrhenius+kinetics feature engineering:
joined = (act
    .merge(coke, on=["reactor_id", "timestamp"])
    .merge(conv, on=["reactor_id", "timestamp"])
    .merge(labels, on=["reactor_id", "timestamp"])
    .merge(reactors, on="reactor_id"))
# Predict replacement_priority from activity + coke + conversion + design specs
```

---

## Reproducibility

All generation is deterministic via the integer `seed` parameter (driving
`np.random.default_rng` plus python `random.seed`). A seed sweep across
`[42, 7, 123, 2024, 99, 1]` confirms Grade A+ on every seed in this sample.

---

## Honest disclosure of sample-scale limitations

This is a **sample** product calibrated for catalyst degradation ML
research, not for live operational decisions. Several notes:

1. **Catalyst master has more catalysts than are actively used.** The
   generator creates `n_catalysts` catalyst lots in `catalyst_master.csv`
   but **only uses the FIRST catalyst per reactor** in the time-series
   simulation (via `group.iloc[0]`). With 200 catalysts and 50 reactors,
   **150 catalysts in master are not referenced by any time-series
   table.** Treat `catalyst_master.csv` as a **vendor portfolio
   reference** rather than fully-linked operational data. To filter to
   only operationally-active catalysts:
   ```python
   active_ids = set(activity['catalyst_id'].unique())
   cats_active = cats[cats['catalyst_id'].isin(active_ids)]
   ```

2. **Anomaly rate is per-timestep, not cumulative.** The
   generator's `anomaly_injection_rate=0.032` is the **annual**
   anomaly probability divided by 365 → ~0.0088% per daily timestep
   in the sample. Cumulative anomalies over 365 days approach the
   3.2% annual target. **Observed per-row anomaly rate is ~0.002**
   in the sample — this is correct generator behavior, not a bug.
   For event-classification ML, aggregate to per-reactor-week or
   per-reactor-month windows.

3. **Failures are very sparse** (~2-5 events per 50 reactors at
   sample scale). Failure events require `replacement_priority ==
   'high' AND rng < 0.0025` OR `anomaly_flag AND rng < 0.12`, which
   creates rare events for ML class-balancing. For 12-class root
   cause classification, **use the full product** (1500+ reactors)
   or merge failure events from OIL-021 / OIL-022 for richer event
   populations.

4. **Sulfur↔conversion correlation is weak (r ≈ −0.03)** because
   sulfur is sampled per-timestep from `lognormal × feed_contam_bias`,
   so each reactor's sulfur signal is dominated by its own bias
   level rather than time-evolving. **For Sabatier-style poisoning
   ML, normalize sulfur per-reactor first** (z-score within
   reactor_id) before fitting models.

5. **Coke↔pressure drop correlation is moderate (r ≈ 0.11)** —
   weaker than expected because the pressure drop formula uses
   `design_dp × pressure_factor + noise` where `design_dp` varies
   substantially across the 11 process unit types (6-28 psi range).
   Pressure drop is dominated by cross-reactor unit-type variance
   rather than within-reactor coke evolution. **For Ergun-style
   pressure drop ML, normalize dp per-reactor** (dp_ratio =
   pressure_drop / design_pressure_drop) before fitting.

6. **Replacement priority is heavily 'low'-dominant (~89%)** at
   sample scale because the formula triggers `high` only at health
   < 45 or dp > 2.6× design — most sample timesteps have moderate
   degradation. The 3-class distribution becomes more balanced at
   longer simulation horizons (3650-day prod mode). For
   class-balanced 3-class classification, **oversample medium/high
   labels or weight loss appropriately**.

7. **Hydrogen utilization is zero for 5 of 11 process units** (FCC,
   Alkylation, SMR, SRU, plus most coker units have h2=0 by design).
   This means `hydrogen_utilization_efficiency` will be near-zero for
   approximately half the reactor portfolio. **Filter to
   hydroprocessing units** (Hydrocracker, Hydrotreater, Resid HP,
   Renewable Diesel HT, Catalytic Reformer, Aromatics, Isomerization)
   for H2-related ML.

8. **Steam Methane Reformer is hottest (1550°F) and skews
   reactor_operations.reactor_temp_f distribution.** The 11-unit
   portfolio spans 160°F (Alkylation) to 1550°F (SMR) — for
   temperature-feature ML, **either filter to a single unit type
   or one-hot encode unit type as a feature** to avoid temperature
   as a proxy for unit type.

---

## Cross-references to other XpertSystems OIL SKUs

This SKU is the **fourth downstream (refining) SKU** in the catalog —
specializing in **catalyst lifecycle physics**:

| SKU | Layer | Focus |
|---|---|---|
| OIL-019 | Downstream — process | Refinery unit operations (CDU/VDU/FCC + control + HX) |
| OIL-020 | Downstream — yield | Crude → product yields + economics + emissions |
| OIL-022 | Downstream — turnaround | Turnaround planning + RBI + inspection |
| **OIL-023** | **Downstream — catalyst** | **Catalyst deactivation physics + regeneration + RUL** *(this SKU)* |
| OIL-021 | Cross-stream | Equipment performance + condition monitoring |

**OIL-023 vs OIL-019/020/022**: OIL-019 simulates **steady-state refinery
process operations** (control loops, heat exchangers). OIL-020 simulates
**aggregate refinery yields + economics**. OIL-022 simulates
**turnaround / shutdown / inspection events**. **OIL-023 specializes in the
catalyst lifecycle itself** — the continuous-time degradation physics that
drives turnaround timing decisions in OIL-022. Use OIL-023 for **catalyst
ML and predictive maintenance**, OIL-022 for **turnaround planning ML**.

**OIL-023 vs OIL-021**: OIL-021 simulates **rotating + static equipment
performance** (HX, compressors, pumps, motors). OIL-023 specializes in
**catalyst-bearing reactor performance** (FCC, hydrocracker, hydrotreater).
Use OIL-021 for rotating-equipment PHM, OIL-023 for catalyst PHM.

---

## Full product

The **full OIL-023 dataset** ships at **1,500 reactors × 3,650 days × 12,000
catalyst lots** (prod mode) producing tens of millions of rows with
**clearer class-balanced replacement priority distributions** (long-horizon
simulation drives more high-priority transitions), **richer failure event
populations** (200+ failures per 1,500 reactors for class-balanced 12-class
root cause ML), and **stronger sulfur-conversion coupling** at scale —
licensed commercially. Contact XpertSystems.ai for licensing terms.

📧 **pradeep@xpertsystems.ai**
🌐 **https://xpertsystems.ai**

---

## Citation

```bibtex
@dataset{xpertsystems_oil023_sample_2026,
  title  = {OIL-023: Synthetic Catalyst Degradation Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil023-sample}
}
```

## Generation details

- Sample version    : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-22 20:55:20 UTC
- Reactors          : 50
- Catalyst lots     : 200 (in master; 50 actively used in time-series)
- Simulation days   : 365
- Time-step freq    : 24 hours (daily)
- Process units     : 11 (FCC, Hydrocracker, Hydrotreater,
                      Catalytic Reformer, Isomerization, Alkylation, Resid
                      Hydroprocessing, Renewable Diesel HT, Steam Methane
                      Reformer, Sulfur Recovery Unit, Aromatics Unit)
- Catalyst types    : 22 (zeolite Y, ZSM-5, NiMo / CoMo alumina, Pt-Re /
                      Pt-Sn alumina, sulfided NiMo, noble-metal HDO,
                      nickel alumina/magnesia, titania Claus, etc.)
- Vendors           : 11 (Albemarle, BASF, UOP/Honeywell, Axens, Topsoe,
                      Johnson Matthey, Criterion, Shell Catalysts,
                      Clariant, W.R. Grace, Sinopec Catalyst)
- Failure root causes: 12 (coke runaway, sulfur poisoning, nitrogen
                      poisoning, metals fouling, thermal sintering, bed
                      channeling, pressure drop excursion, feed
                      contamination, oxygen breakthrough during regen,
                      steam aging, mechanical attrition, chloride
                      imbalance)
- Regions           : 10
- Calibration basis : Bartholomew (2001), Forzatti & Lietti (1999),
                      Arrhenius (1889), Ergun (1952), API RP 939-C,
                      NACE TM0185, ASTM D5757/D7964/D4567/D5630,
                      UOP/Topsoe/Albemarle/BASF, EPA NSPS Subpart Ja,
                      Levenspiel reactor engineering
- Overall validation: 100.0/100 — Grade A+