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From Data to Action: Accelerating Refinery Optimization with AI

2026-05-14 · Dániel Pfeifer, Ábrahám Papp, Tibor Bernáth, Tamás Zoltán Varga, Márk Czifra, Botond Szilágyi, Edith Alice Kovács arxiv

Nowadays refinery optimization utilizes sheer amounts of data, which can be handled with modern Linear Programming (LP) software, but the interpreting and applying the results remains challenging. Large petrochemical companies use massive models, with hundreds of thousands of input matrix elements. The LP solution is mathematically correct, but simplifications are made in the model, and data supply errors may occur. Therefore, further insight is needed to trust the results. The LP solver does not have a memory, so additional understanding could be gained by analyzing historical data and comparing it to the current plan. As such, machine learning approaches were suggested to support decision making based on the LP solution. Among these, Anomaly Detection tools are proposed to be used in tandem with the LP output. 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.

📄 PDF Abstract BibTeX arXiv:2605.15085

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Tasks

Anomaly Detection Decision Making

Reinforcement Learning-Driven Plant-Wide Refinery Planning Using Model Decomposition

2025-04-11 · Zhouchang Li, Runze Lin, Hongye Su, Lei Xie

In the era of smart manufacturing and Industry 4.0, the refining industry is evolving towards large-scale integration and flexible production systems. In response to these new demands, this paper presents a novel optimiz…

Computational Efficiency Deep Reinforcement Learning reinforcement-learning Reinforcement Learning

Refinery: Active Fine-tuning and Deployment-time Optimization for Contact-Rich Policies

2025-10-13 · Bingjie Tang, Iretiayo Akinola, Jie Xu, Bowen Wen 외 arxiv

Simulation-based learning has enabled policies for precise, contact-rich tasks (e.g., robotic assembly) to reach high success rates (~80%) under high levels of observation noise and control error. Although such performan…

Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

2020-07-17 · NeurIPS 2020 12 · Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang 외

While semi-supervised learning (SSL) has proven to be a promising way for leveraging unlabeled data when labeled data is scarce, the existing SSL algorithms typically assume that training class distributions are balanced…

Pseudo Label

MrTF: Model Refinery for Transductive Federated Learning

2023-05-07 · Xin-Chun Li, Yang Yang, De-Chuan Zhan

We consider a real-world scenario in which a newly-established pilot project needs to make inferences for newly-collected data with the help of other parties under privacy protection policies. Current federated learning …

Federated Learning model

Label Refinery: Improving ImageNet Classification through Label Progression

2018-05-07 · Hessam Bagherinezhad, Maxwell Horton, Mohammad Rastegari, Ali Farhadi

Among the three main components (data, labels, and models) of any supervised learning system, data and models have been the main subjects of active research. However, studying labels and their properties has received ver…

Classification General Classification