数据挖掘方法在汽油辛烷值损失计算中的应用
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TP391.9 TE626;

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西南石油大学科研启航计划


Application of Data Mining Method in Calculating the Loss of Gasoline Octane Number
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Southwest Petroleum University Research Sailing Plan

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    摘要:

    针对汽油清洁化中降低辛烷值的损失这一重点问题,提出了基于数据挖掘的辛烷值损失预测方法。首先,对影响辛烷值损失的各类因素进行了分析;然后以某石化企业为例,应用数据挖掘方法对其提供的数据进行有效的数据清洗;其次对多种复杂的影响因素进行合理的特征提取,成功提取出28个影响辛烷值损失特性的代表因素;接着利用如支持向量机回归、神经网络和随机森林等挖掘建模方法和交叉验证训练预测辛烷值损失的模型。最后,通过实验和结果分析表明:基于数挖掘方法构建的随机森林模型能够更加准确地预测辛烷值的损失,它在辛烷值损失的影响因素特征提取和预测计算方面表现出较强的能力,能更好地为汽油清洁化服务。

    Abstract:

    Aiming at the key problem of reducing the octane loss in gasoline cleaning, a data mining-based octane loss calculation method is proposed. Firstly, various factors affecting the octane loss were analyzed; then as an example a petrochemical company, the data provided by it was preprocessed reasonably and effectively through applying data mining methods. Secondly, feature extraction for multiple complex influencing factors was performed reasonably, and successfully extracted that 28 representative factors of affecting octane loss; Then mining modeling methods such as support vector machine regression, neural networks and random forests and cross-validation to train models were used to predict octane loss. Finally, through experiments and result analysis, it is shown that the random forest model based on data mining method can more accurately predict the octane loss, and has shown strong ability in feature extraction of influence factors and the prediction calculation on octane loss and can better serve the gasoline cleaning.

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吴苹,钟仪华,雍雪,等. 数据挖掘方法在汽油辛烷值损失计算中的应用[J]. 科学技术与工程, 2022, 22(10): 4046-4054.
Wu Ping, Zhong Yihua, Yong Xue, et al. Application of Data Mining Method in Calculating the Loss of Gasoline Octane Number[J]. Science Technology and Engineering,2022,22(10):4046-4054.

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历史
  • 收稿日期:2021-05-23
  • 最后修改日期:2022-03-22
  • 录用日期:2021-11-30
  • 在线发布日期: 2022-04-14
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