分子水平炼化模型数据集

分子水平炼化模型数据集针对炼厂工艺进行分子水平建模，整合了装置数据、侧线数据、原油进料数据、炼化过程装置及侧线产品数据及基于以上数据的分析挖掘数据。分子水平炼化模型数据集可用于智能炼厂中分子水平炼化建模应用过程，基于现有工艺构建的模型可实现脱硫分析、族组分析、效率分析、能耗分析、污染处理分析等场景分析，也可构建回归模型，进行炼厂工艺的潜能优化。数据可以提供给炼厂工艺管理人员、数据分析人员，利用该数据集能够从分子水平认知炼化工艺过程，提升企业生产工艺优化的科学性与合理性；增加企业在复杂市场环境中的竞争力和营收能力，为炼化过程的精细和科学的智能化决策分析提供数据基础。

The molecular-level refining model dataset is developed for molecular-level modeling of refinery processes. It integrates unit data, sidestream data, crude oil feed data, product data of units and sidestreams during the refining process, as well as analysis and mining data derived from the aforementioned datasets. This dataset can be applied to molecular-level refining modeling applications in smart refineries. Models built based on existing processes can support various scenario analyses including desulfurization analysis, group composition analysis, efficiency analysis, energy consumption analysis and pollution treatment analysis. Additionally, regression models can be constructed to optimize the potential of refinery processes. The dataset can be provided to refinery process managers and data analysts. By utilizing this dataset, stakeholders can acquire molecular-level insights into the refining process, enhancing the scientificity and rationality of enterprise production process optimization, improving enterprises' competitiveness and revenue in complex market environments, and providing a solid data foundation for precise and scientific intelligent decision-making analysis of the refining process.