来自模拟石油和天然气运输的多相工厂的实验数据集合
Giovanni Mazzuto1, Ilaria Pietrangeli1, Marco Ortenzi1
1Department of Industrial Engineering and Mathematical Science. Università Politecnica delle Marche, Via Brecce Bianche, 12, 60131 Ancona IT, Italy.
Data in brief
|September 29, 2025
概括
本研究介绍了来自实验性石油和天然气运输系统的数据,捕捉了用于机器学习和控制理论应用的各种操作条件. 数据集有助于开发预测性维护和增强流程理解.
科学领域:
- 工艺工程是过程工程.
- 控制理论 控制理论
- 机器学习 机器学习
背景情况:
- 石油和天然气运输系统面临复杂的操作动态和潜在的异常.
- 准确的数据对于开发强大的预测性维护和控制策略至关重要.
研究的目的:
- 详细描述来自实验性石油和天然气运输系统模拟的数据.
- 提供一个全面的数据集,反映正常和异常运行状态.
- 支持机器学习,控制理论和工艺工程方面的进步.
主要方法:
- 一个实验装置被配置为模拟石油和天然气运输.
- 复制了各种工作条件,包括稳定状态,波动和异常 (例如泄漏,阻塞).
- 系统地收集了各种不同操作场景的数据.
主要成果:
- 创建了一个全面的数据集,详细描述了植物在多种条件下的行为.
- 数据捕获了对稳定状态操作,波动和异常的动态反应.
- 该数据集提供了对系统机制和响应的洞察力.
结论:
- 该数据集对于开发预测性维护和异常检测模型非常有价值.
- 它促进了安全和效率的先进控制策略的设计和验证.
- 这些数据增强了对石油和天然气运输系统的理论和实际理解.
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