利用机器学习算法建立石油沥青质量参数模型

IF 0.6 4区工程技术 Q4 ENERGY & FUELS

Chemistry and Technology of Fuels and Oils Pub Date : 2024-02-06 DOI:10.1007/s10553-024-01630-z

E. N. Levchenko

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引用次数: 0

摘要

本文探讨了利用基于递归神经网络的机器学习算法对石油沥青质量参数进行建模的方法、原理和结果。结果表明，机器学习算法可以有效地应用于炼油工艺的实践中。研究还考虑了数据处理中涉及的各种问题，以及为解决特定问题选择变量和合适的神经网络结构。此外，还概述了进一步的研究方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Modeling of Oil Bitumen Quality Parameters Using Machine Learning Algorithms

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Modeling of Oil Bitumen Quality Parameters Using Machine Learning Algorithms

The paper considers approaches, principles, and results of modeling the quality parameters of petroleum bitumen using machine learning algorithms based on recurrent neural networks. It is shown that machine learning algorithms can be effectively used in practice for oil refining processes. Various problems involved in data processing, as well as selection of variables and suitable neural network architecture for solving a particular problem, are considered. Further research directions are outlined.

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来源期刊

Chemistry and Technology of Fuels and Oils 工程技术-工程：化工

CiteScore

0.90

自引率

16.70%

发文量

119

审稿时长

1.0 months

期刊介绍： Chemistry and Technology of Fuels and Oils publishes reports on improvements in the processing of petroleum and natural gas and cracking and refining techniques for the production of high-quality fuels, oils, greases, specialty fluids, additives and synthetics. The journal includes timely articles on the demulsification, desalting, and desulfurizing of crude oil; new flow plans for refineries; platforming, isomerization, catalytic reforming, and alkylation processes for obtaining aromatic hydrocarbons and high-octane gasoline; methods of producing ethylene, acetylene, benzene, acids, alcohols, esters, and other compounds from petroleum, as well as hydrogen from natural gas and liquid products.