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.

翻译：为实现数字化转型与碳中和目标，有效的生产计划对炼化一体化企业至关重要。现代炼厂规划依赖于先进的优化技术，其发展需要可复现的基准问题。然而，现有基准测试缺乏实际背景或采用过度简化的假设，限制了其在企业级优化中的适用性。为弥合理论研究与工业应用之间的显著差距，本文首次提出了面向工业级炼化一体化企业的开源需求驱动基准测试模型，该模型具有透明的数学表达和全面的输入参数。基准模型采用创新的港口-物流混合超结构进行模块化建模，具备广泛的泛化能力。关键二次加工装置采用基于历史数据的delta-base方法进行表征。本文构建了三个实际案例以涵盖不同的场景特征，分别对应：(1)不含整数变量的独立炼厂，(2)含库存相关整数变量的化工园区集成方案，以及(3)多周期生产计划。所有模型参数完全公开。此外，本文还提供了计算性能分析、delta-base建模的消融实验以及所提基准模型的应用场景分析。