From Data to Action: Accelerating Refinery Optimization with AI

TL;DR

This paper explores integrating machine learning, specifically anomaly detection, with linear programming solutions to improve refinery optimization by identifying errors and business opportunities.

Contribution

It introduces new high-dimensional anomaly detection methods and demonstrates their application in refinery scheduling, enhancing decision support.

Findings

Identified data supply errors in refinery planning.

Revealed business opportunities through anomaly detection.

Proposed methods for high-dimensional anomaly detection.

Abstract

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.