xpertsystems/oil023-sample

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.

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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.

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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)

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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.

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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.

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Loading

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

Or with pandas:

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

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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.

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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:

   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.

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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.

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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

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Citation

@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+