A new academic study introduces reinforcement learning (RL) integrated with model decomposition to optimize full-scale refinery operations. The novel framework breaks down complex refinery workflows into manageable sub models, each guided by RL agents that dynamically set intermediate product pricing and coordinate decisions across the system. This enables efficient, market-aware decision-making across units without sacrificing computational scale.

Tested in multiple industrial setups—in both single-period and multi-period planning—the approach achieved substantial gains in computational efficiency and economic profitability over traditional optimization methods. The system dynamically adjusts to market signals and internal constraints, offering more agility when crude quality, demand shifts, or feedstock costs fluctuate.

For real refineries, this means the possibility of continuous, near-real-time planning rather than static monthly or quarterly scheduling. The technique fits naturally into digital transformation trajectories, especially for integrated refining-petrochemical complexes aiming to adapt rapidly to market changes.