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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2503.22057 |
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| _version_ | 1866917969512103936 |
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| author | Du, Wenli Wang, Chuan Fan, Chen Li, Zhi Zhong, Yeke Kang, Tianao Liang, Ziting Yang, Minglei Qian, Feng Dai, Xin |
| author_facet | Du, Wenli Wang, Chuan Fan, Chen Li, Zhi Zhong, Yeke Kang, Tianao Liang, Ziting Yang, Minglei Qian, Feng Dai, Xin |
| contents | To achieve digital intelligence transformation and carbon neutrality, effective production planning is crucial for integrated refinery-petrochemical complexes. Modern refinery planning relies on advanced optimization techniques, whose development requires reproducible benchmark problems. However, existing benchmarks lack practical context or impose oversimplified assumptions, limiting their applicability to enterprise-wide optimization. To bridge the substantial gap between theoretical research and industrial applications, this paper introduces the first open-source, demand-driven benchmark for industrial-scale refinery-petrochemical complexes with transparent model formulations and comprehensive input parameters. The benchmark incorporates a novel port-stream hybrid superstructure for modular modeling and broad generalizability. Key secondary processing units are represented using the delta-base approach grounded in historical data. Three real-world cases have been constructed to encompass distinct scenario characteristics, respectively addressing (1) a stand-alone refinery without integer variables, (2) chemical site integration with inventory-related integer variables, and (3) multi-period planning. All model parameters are fully accessible. Additionally, this paper provides an analysis of computational performance, ablation experiments on delta-base modeling, and application scenarios for the proposed benchmark. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22057 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A production planning benchmark for real-world refinery-petrochemical complexes Du, Wenli Wang, Chuan Fan, Chen Li, Zhi Zhong, Yeke Kang, Tianao Liang, Ziting Yang, Minglei Qian, Feng Dai, Xin Computational Engineering, Finance, and Science To achieve digital intelligence transformation and carbon neutrality, effective production planning is crucial for integrated refinery-petrochemical complexes. Modern refinery planning relies on advanced optimization techniques, whose development requires reproducible benchmark problems. However, existing benchmarks lack practical context or impose oversimplified assumptions, limiting their applicability to enterprise-wide optimization. To bridge the substantial gap between theoretical research and industrial applications, this paper introduces the first open-source, demand-driven benchmark for industrial-scale refinery-petrochemical complexes with transparent model formulations and comprehensive input parameters. The benchmark incorporates a novel port-stream hybrid superstructure for modular modeling and broad generalizability. Key secondary processing units are represented using the delta-base approach grounded in historical data. Three real-world cases have been constructed to encompass distinct scenario characteristics, respectively addressing (1) a stand-alone refinery without integer variables, (2) chemical site integration with inventory-related integer variables, and (3) multi-period planning. All model parameters are fully accessible. Additionally, this paper provides an analysis of computational performance, ablation experiments on delta-base modeling, and application scenarios for the proposed benchmark. |
| title | A production planning benchmark for real-world refinery-petrochemical complexes |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2503.22057 |