Pith Number

pith:QUHZ5UWF

pith:2026:QUHZ5UWFDDPQ4QKOCKVDJGVURR

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From Data to Action: Accelerating Refinery Optimization with AI

\'Abrah\'am Papp, Botond Szil\'agyi, D\'aniel Pfeifer, Edith Alice Kov\'acs, M\'ark Czifra, Tam\'as Zolt\'an Varga, Tibor Bern\'ath

Transformed ECOD anomaly detection with pair selection reveals business opportunities and data errors in refinery LP plans.

arxiv:2605.15085 v1 · 2026-05-14 · stat.ML · cs.LG · stat.AP · stat.ME

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Claims

C1strongest claim

A transformed version of the popular ECOD methodology is applied. New methods are proposed to handle high-dimensional data: choosing the most informative pairs. Then, this is used alongside two 2D Anomaly Detection algorithms, revealing several business opportunities and data supply errors in the MOL refinery scheduling and planning architecture.

C2weakest assumption

That comparing current LP plans to historical data via the transformed ECOD and pair selection will reliably surface actionable anomalies without excessive false positives or loss of critical signals due to the high-dimensional reduction.

C3one line summary

A transformed ECOD anomaly detection approach with informative pair selection is applied to refinery LP data to reveal business opportunities and data supply errors.

References

23 extracted · 23 resolved · 0 Pith anchors

[1] L. Rodríguez-Mazahua, C.-A. Rodríguez-Enríquez, J. L. Sánchez- Cervantes, J. Cervantes, J. L. García-Alcaraz, G. Alor-Hernández, A general perspective of big data: applications, tools, challenges and 2016

[2] M. Hamzehi, S. Hosseini, Business intelligence using machine learning algorithms, Multimedia tools and applications 81 (23) (2022) 33233– 33251 2022

[3] M. Rath, Realization of business intelligence using machine learning, In- ternet of Things in Business Transformation: Developing an Engineering and Business Strategy for Industry 5.0 (2021) 169–184 2021

[4] F. Ridzuan, W. M. N. W. Zainon, Diagnostic analysis for outlier de- tection in big data analytics, Procedia Computer Science 197 (2022) 685–692 2022

[5] P. Larrañaga, D. Atienza, J. Diaz-Rozo, A. Ogbechie, C. E. Puerto- Santana, C. Bielza, Industrial applications of machine learning, CRC press, 2018 2018

Receipt and verification

First computed	2026-05-17T21:40:25.963337Z
Last reissued	2026-05-17T21:57:19.273748Z
Builder	pith-number-builder-2026-05-17-v1
Signature	unsigned_v0
Schema	pith-number/v1.0

Canonical hash

850f9ed2c518df0e414e12aa349ab48c4789af2509782ad75c75635d2182ee4a

Aliases

arxiv: 2605.15085 · arxiv_version: 2605.15085v1 · pith_short_12: QUHZ5UWFDDPQ · pith_short_16: QUHZ5UWFDDPQ4QKO · pith_short_8: QUHZ5UWF

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/QUHZ5UWFDDPQ4QKOCKVDJGVURR \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 850f9ed2c518df0e414e12aa349ab48c4789af2509782ad75c75635d2182ee4a

Canonical record JSON

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    "submitted_at": "2026-05-14T17:07:41Z",
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