基于BP神经网络和多元线性回归的辛烷值预测
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南京林业大学汽车与交通工程学院

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国家自然科学基金资助项目(71701099,71501090);江苏省高等学校自然科学研究项目资助基金(17KJB580008)


Octane Number Prediction Based on BP Neural Network and Multiple Linear Regression
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College of Automobile and Traffic Engineering, Nanjing Forestry University

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    摘要:

    为降低硫、烯烃含量及辛烷值损失,保证汽油清洁化生产。基于S Zorb装置运行积累的数据,首先利用Lasso初步筛选建模变量,并基于BP神经网络计算指标因子贡献度,进一步筛选出15个主要变量用于建立辛烷值损失预测模型。其次对比分析四种模型,得出BP神经网络预测精度更优,更适合作为辛烷值损失预测模型,并经过十折交叉验证得到MSE均值为0.027193,R2均值为0.90487,验证了该模型的可靠性。最后在控制油品硫含量不大于5μg/g的前提下,结合多元线性回归对主要变量进行优化调控。结果表明,需同时改变多个变量才能使辛烷值损失降幅大于30%,多元线性回归模型预测精度较好,能按照一定比例对主要变量进行正反向调控;并可视化展示了优化过程中辛烷值和硫含量的变化轨迹。

    Abstract:

    In order to reduce the sulfur and olefin content and the loss of octane number, to ensure the clean production of gasoline. Based on the data accumulated by the operation of the S Zorb device, first use Lasso to initially screen the modeling variables, and calculate the index factor contribution based on the BP neural network, and further screen out 15 main variables to build the octane loss prediction model. Secondly, the four models are compared and analyzed, and it is concluded that the BP neural network has better prediction accuracy and is more suitable as an octane loss prediction model. After ten-fold cross-validation, the average MSE value is 0.027193 and the average R2 value is 0.90487, which verifies the reliability of the model. sex. Finally, under the premise that the sulfur content of the oil is not greater than 5μg/g, the main variables are optimized and controlled in combination with multiple linear regression. The results show that it is necessary to change multiple variables at the same time to reduce the octane loss by more than 30%. The multiple linear regression model has good prediction accuracy and can regulate the main variables in a certain proportion. The trajectory of the alkane number and sulfur content.

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  • 收稿日期:2022-04-26
  • 最后修改日期:2022-06-19
  • 录用日期:2022-06-21
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