Abstract Gasoline is one of the major products of oil and petrochemical industry. Blending is the final step and key to improve the efficiency of gasoline production. As an important property that can reflect the quality of gasoline products, the research octane number (RON) of final products has been widely used to evaluate the running status of gasoline blending. However, the real-time and direct acquisition of RON from this process is difficult because it have to be determined by running the fuel in a test engine with a variable compression ratio under controlled conditions. This work proposes a data-driven soft sensor based on near-infrared (NIR) spectroscopy for online RON estimation. A modified semisupervised Gaussian mixture algorithm is adopted to automatically discover meaningful modeling samples and initialize the quality prediction model. Besides, a monitoring model is integrated into the quality prediction sensor to monitor the running status and the accuracy of the NIR-based quality prediction sensor. Datasets from a numerical experiment and industrial gasoline blending are provided to reveal the effectiveness and superiority of the proposed method.

中文翻译：

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用于同时进行汽油调合质量预测和状态监测的近红外光谱

摘要 汽油是石油和石化工业的主要产品之一。混合是提高汽油生产效率的最后一步，也是关键。最终产品的研究辛烷值（RON）作为反映汽油产品质量的重要特性，已被广泛用于评价汽油调和的运行状态。然而，从这个过程中实时和直接获取 RON 是困难的，因为它必须通过在受控条件下以可变压缩比的测试发动机中运行燃料来确定。这项工作提出了一种基于近红外 (NIR) 光谱的数据驱动软传感器，用于在线 RON 估计。采用改进的半监督高斯混合算法自动发现有意义的建模样本并初始化质量预测模型。此外，在质量预测传感器中集成了监测模型，以监测基于NIR的质量预测传感器的运行状态和准确性。提供了来自数值实验和工业汽油混合的数据集，以揭示所提出方法的有效性和优越性。