A collection of experimental data from a multiphase plant simulating oil and gas transport

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-09-09 DOI:10.1016/j.dib.2025.112038

Giovanni Mazzuto, Ilaria Pietrangeli, Marco Ortenzi, Vincenzo Foti, Filippo Emanuele Ciarapica, Maurizio Bevilacqua

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引用次数: 0

Abstract

This paper details the data gathered from an experimental plant designed to simulate an oil and gas transportation system. The plant was configured to replicate various working conditions, including both normal and anomalous operational states. Different scenarios were created to reflect realistic conditions encountered in oil and gas transportation, such as steady-state operations, typical operational fluctuations, and a range of anomalies like leakages, obstructions, and working parameter modifications.

The data provide a comprehensive view of the plant behaviour under these diverse conditions. This information can be instrumental for applications in machine learning, where it can aid in the development of predictive maintenance algorithms and anomaly detection models. In the realm of control theory, the data can support the design and validation of advanced control strategies to ensure efficient and safe operations. Additionally, the data enhance process comprehension, offering insights into the mechanisms governing oil and gas transportation systems.

This dataset can be helpful for the analysis of the dynamic responses of an oil and gas transportation system under various operational scenarios. The structured data can be used to model and simulate system behaviour, providing a foundation for improving process resilience, efficiency and reliability. The information captured in this dataset can be useful for improving theoretical and practical understanding in the fields of machine learning, control theory, and process engineering.

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模拟油气输送的多相装置的实验数据集合

本文详细介绍了从设计用于模拟石油和天然气运输系统的实验装置收集的数据。该工厂被配置为复制各种工作条件，包括正常和异常操作状态。为了反映油气运输过程中遇到的实际情况，我们创建了不同的场景，例如稳态作业、典型的作业波动以及泄漏、堵塞和工作参数修改等一系列异常情况。这些数据提供了在这些不同条件下植物行为的全面视图。这些信息可以用于机器学习中的应用，帮助开发预测性维护算法和异常检测模型。在控制理论领域，这些数据可以支持先进控制策略的设计和验证，以确保高效和安全的运行。此外，这些数据增强了对过程的理解，为油气运输系统的控制机制提供了见解。该数据集有助于分析油气运输系统在各种运行场景下的动态响应。结构化数据可用于建模和模拟系统行为，为提高过程弹性、效率和可靠性提供基础。在此数据集中捕获的信息可用于提高机器学习，控制理论和过程工程领域的理论和实践理解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.