From Data to Action: Accelerating Refinery Optimization with AI
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 decisi...
| Authors | Dániel Pfeifer, Ábrahám Papp, Tibor Bernáth et al. |
|---|---|
| Primary category | stat.ML |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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