Reinforcement Learning-Driven Plant-Wide Refinery Planning Using Model Decomposition
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910909287366656 |
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| author | Li, Zhouchang Lin, Runze Su, Hongye Xie, Lei |
| author_facet | Li, Zhouchang Lin, Runze Su, Hongye Xie, Lei |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_08642 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning-Driven Plant-Wide Refinery Planning Using Model Decomposition Li, Zhouchang Lin, Runze Su, Hongye Xie, Lei Systems and Control 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. |
| title | Reinforcement Learning-Driven Plant-Wide Refinery Planning Using Model Decomposition |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2504.08642 |