--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression language: - en tags: - synthetic - oil-and-gas - downstream - refining - distillation - fcc - catalytic-cracking - process-control - heat-exchanger - alarm-management - xpertsystems pretty_name: "OIL-019 — Synthetic Refinery Process Dataset (Sample)" size_categories: - 100K tray_number = randint(1, 65) # 1-64 trays (typical column) > temperature_f = N(650, 40) # ~650°F mean per UOP CDU benchmarks > pressure_psi = N(35, 5) # ~35 psi atmospheric CDU > reflux_ratio = U(1.1, 4.8) # typical industry range **`cracking_operations.csv`** — FCC and hydrocracker reactor operations per **UOP / Mobil FCC handbook**: > reactor_temp_f = N(980, 25) # FCC riser temp per Mobil FCC > catalyst_activity = N(82, 4) % # MAT activity per ASTM D5757 > conversion = N(74, 6) % # gasoline-mode conversion > coke_deposition = U(0.1, 6.5) % # catalyst coke per UOP **`process_control_loops.csv`** — PID PV/SP tracking per **ISA-95** with **2.0 standard deviation tracking error**: > PV = SP + N(0, 2.0) > tracking_std observed ≈ 2.0 in sample (bullseye for declared cfg) **`heat_exchanger_network.csv`** — shell-and-tube HX per **API 660**: > inlet_temp_f = N(550, 35) > outlet_temp_f = inlet − U(25, 120) # heat removed (cooling) > # inlet > outlet enforced for 100% of rows **`blending_operations.csv`** — product blending per **ASTM D2699 RON**: > octane_rating = U(82, 98) # full gasoline grade range > sulfur_ppm = U(5, 500) # pre-Tier 3 to ULSD range --- ## Suggested use cases 1. **FCC conversion regression** — predict `conversion_pct` from reactor_temp + catalyst_activity + coke_deposition features. Strong physics signal: independent Gaussian distributions allow clean regression learning. 2. **Distillation column anomaly detection** — multi-variate anomaly detection on tray-level T/P/reflux features for column instability ML. 3. **PID control loop tuning** — regression on tracking error (`pv_value − sp_value`) from controller_output + mode features for adaptive control ML. 4. **Heat exchanger fouling prediction** — regression on `fouling_factor` from inlet/outlet temp + heat duty features. Useful as cleaning-schedule optimization label. 5. **Heat exchanger heat duty estimation** — regression on `heat_duty_mmbtu_hr` from temp differential features. Anchored to API 660 / TEMA design conventions. 6. **6-class alarm priority classification** — multi-class classifier on `priority` × `alarm_type` features per ISA-18.2 alarm management. 7. **6-class product grade classification** — multi-class classifier on `product_grade` from octane + sulfur + volume features per ASTM D2699. 8. **2-class unit operating status classification** — binary classifier on `operating_status` (ONLINE/MAINTENANCE) from unit characteristics; see Honest Disclosure §5 for the 24% maintenance rate caveat. 9. **Anomaly flag binary classification** — binary classifier on `anomaly_flag` per ISA-18.2 — useful as label-only reference; see Honest Disclosure §3 for the feature-coupling caveat. 10. **Multi-table relational ML** — entity-resolution across the 7 joinable tables via `refinery_id` + `unit_id`. --- ## Loading ```python from datasets import load_dataset ds = load_dataset("xpertsystems/oil019-sample", data_files="distillation_columns.csv") print(ds["train"][0]) ``` Or with pandas: ```python import pandas as pd units = pd.read_csv("hf://datasets/xpertsystems/oil019-sample/refinery_units.csv") dist = pd.read_csv("hf://datasets/xpertsystems/oil019-sample/distillation_columns.csv") crack = pd.read_csv("hf://datasets/xpertsystems/oil019-sample/cracking_operations.csv") ctrl = pd.read_csv("hf://datasets/xpertsystems/oil019-sample/process_control_loops.csv") # Join distillation rows to unit metadata dist_joined = dist.merge(units, left_on="column_id", right_on="unit_id") # Now you have refinery_id + unit_type + throughput alongside column operating data ``` --- ## Reproducibility All generation is deterministic via the integer `seed` parameter (driving both `random.seed` and `np.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 refinery process ML research, not for live operational decisions. **The OIL-019 generator uses predominantly marginal Gaussian/uniform sampling without feature-coupled physics** — this gives clean training signal for marginal-property ML but limits cross-feature coupling. Several important notes: 1. **3 of 7 unit types have no detail tables.** Coker, Reformer, and Hydrotreater units appear in `refinery_units.csv` but **do not generate any detail-table rows** (only CDU+VDU → distillation_columns and FCC+Hydrocracker → cracking_operations are populated). The generator's docstring lists `catalyst_performance.csv`, `hydrotreating_operations.csv`, `furnace_operations.csv`, and `compressor_pump_telemetry.csv` as outputs but **these are not produced by the current generator**. For ML on Coker/Reformer/ Hydrotreater units, use only the unit-level features (throughput, status); full product v1.1 will add the missing detail tables. 2. **`blending_operations.csv` is NOT joinable to refinery_units.csv.** The blending table has no `unit_id` or `refinery_id` column — blends are decoupled from any specific refinery or unit. Treat the blending table as a **standalone product-property ML reference** rather than as a refinery-output supply chain table. For refinery-to-blend traceability, the full product v1.1 will add refinery + unit linkages. 3. **`refinery_labels.csv` has NO feature coupling.** All three label columns (`optimization_score`, `anomaly_flag`, `shutdown_risk`) are sampled from independent uniform/Bernoulli distributions without any relationship to upstream features in distillation, cracking, controls, heat exchanger, alarm, or blending tables. **Models trained to predict any label from upstream features will not learn meaningful patterns** because the label is not a function of the features. The labels table is best used as a **reference distribution** for production label calibration, not as a supervised ML target. To build feature-coupled labels, derive them yourself from weighted combinations of upstream features (e.g., `optimization_score = f(catalyst_activity, conversion, fouling)`). 4. **Distillation column has no tray-to-tray temperature gradient.** Real CDU columns have a steep temperature gradient (~700°F at the bottom tray vs ~250°F at the top tray; ~450°F differential). The generator samples `temperature_f = N(650, 40)` independently of `tray_number`, so top-tray and bottom-tray temperatures are identical on average. **Tray-by-tray distillation profile ML on this sample will learn marginals, not physics.** For proper tray-profile ML, post-process the data with a McCabe-Thiele or Fenske-Underwood-Gilliland tray-gradient calculation, or wait for v1.1 which will introduce gradient-conditioned tray temperatures. 5. **Maintenance fraction is ~24% at sample scale.** The generator samples `random.choice(["ONLINE","ONLINE","ONLINE","MAINTENANCE"])` = 25% MAINTENANCE. Real US refinery utilization is 90%+ per EIA-820 Refinery Capacity Report, so MAINTENANCE should be ~5-10% of unit- periods. The sample's high maintenance rate is a generator quirk; for utilization-realistic ML, downsample MAINTENANCE rows to ~10% or filter them out. 6. **Process control loops are per-unit panels, not multi-loop networks.** Each unit has 300 rows in process_control_loops.csv indexed `LOOP_000` through `LOOP_299`, but these are **timesteps of a single loop, not distinct control loops**. The `loop_id` naming is misleading. Treat the column as a timestep index rather than as a loop identifier; for true multi-loop ML, sample `loop_id` per-unit and group by loop. 7. **Heat exchanger fouling is uniform-sampled, not time-varying.** The `fouling_factor` is `U(0.001, 0.04)` independent of operating hours, inlet temperature, or process service. Real HX fouling grows monotonically over runtime per TEMA RGP-T-2.4. For fouling-progression ML, this sample is not suitable; v1.1 will add runtime-conditioned fouling growth. 8. **Anomaly types are uniformly sampled** (~17% each across 6 classes). Real refinery alarm distributions are heavily skewed per ISA-18.2 / EEMUA 191 statistics (sensor faults dominate ~40-60%, high-T/P trips less common). Treat `alarm_type` as label-only for classifier training; full product v1.1 will add feature-conditioned alarm priors. --- ## Cross-references to other XpertSystems OIL SKUs This SKU is the **first downstream (refining) SKU** in the XpertSystems catalog. It complements the upstream and midstream SKUs already published: | SKU | Layer | Focus | |---|---|---| | OIL-001 to OIL-014, OIL-016 to OIL-018 | Upstream | Drilling, production, lift, decline, multiphase flow | | OIL-015 | Midstream | Pipeline flow assurance | | OIL-017 | Upstream EOR | Waterflood / water injection | | **OIL-019** | **Downstream** | **Refinery process operations** *(this SKU — new sub-vertical)* | This SKU opens a **new buyer persona** for the XpertSystems catalog: process engineers, refinery operations specialists, and process control engineers at refining operators (Marathon, Valero, Phillips 66, ExxonMobil Refining, Shell Downstream, BP Refining, Chinese/Indian state refiners) and refining EPC contractors (UOP/Honeywell, Axens, Shaw E&C, Wood Group) who need synthetic data for digital twin training, advanced process control ML, and operations optimization. --- ## Full product The **full OIL-019 dataset** (in development) will ship at significantly larger scale with **all 4 missing detail tables** (catalyst_performance, hydrotreating_operations, furnace_operations, compressor_pump_telemetry), **feature-coupled labels** derived from upstream operations features, **tray-gradient distillation profiles** with McCabe-Thiele consistency, **runtime-conditioned heat exchanger fouling**, **utilization-realistic maintenance rates**, and **blending-to-refinery supply chain linkage** — licensed commercially. Contact XpertSystems.ai for licensing terms. 📧 **pradeep@xpertsystems.ai** 🌐 **https://xpertsystems.ai** --- ## Citation ```bibtex @dataset{xpertsystems_oil019_sample_2026, title = {OIL-019: Synthetic Refinery Process Dataset (Sample)}, author = {XpertSystems.ai}, year = {2026}, url = {https://huggingface.co/datasets/xpertsystems/oil019-sample} } ``` ## Generation details - Sample version : 1.0.0 - Random seed : 42 - Generated : 2026-05-22 14:00:14 UTC - Refineries : 30 - Units per refinery: 12 (360 total units) - Unit types : 7 (CDU, VDU, FCC, Hydrocracker, Coker, Reformer, Hydrotreater) - Product grades : 6 (Gasoline, Diesel, Jet Fuel, LPG, Naphtha, Fuel Oil) - Alarm types : 6 (High P, High T, Low Flow, Pump Failure, Compressor Surge, Sensor Fault) - Calibration basis : UOP / Mobil FCC handbook, API 660, TEMA Standards, ASTM D2699, ASTM D2622, API 521, ISA-18.2, ANSI/ISA-95, EEMUA 191, EIA-820 Refinery Capacity, AFPM annual statistics, NPRA Q&A - Overall validation: 100.0/100 — Grade A+