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

arXiv: https://arxiv.org/abs/2504.08642 · HTML: https://mlsanity.com/paper/2504.08642/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 61 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming](https://mlsanity.com/paper/1904.01934/) (2019)
- [From Reinforcement Learning to Optimal Control: A unified framework for sequential decisions](https://mlsanity.com/paper/1912.03513/) (2019)
- [Generative AI and Process Systems Engineering: The Next Frontier](https://mlsanity.com/paper/2402.10977/) (2024)
- [Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Control](https://mlsanity.com/paper/2308.02765/) (2023)

## Abstract (arXiv)

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 optimization framework for plant-wide refinery planning, integrating model decomposition with deep reinforcement learning. The approach decomposes the complex large scale refinery optimization problem into manageable submodels, improving computational efficiency while preserving accuracy. A reinforcement learning-based pricing mechanism is introduced to generate pricing strategies for intermediate products, facilitating better coordination between submodels and enabling rapid responses to market changes. Three industrial case studies, covering both single-period and multi-period planning, demonstrate significant improvements in computational efficiency while ensuring refinery profitability.