Sulfur compounds are the most important inorganic constituents of petroleum and require to be estimated beforehand because of their corrosive nature and other processing anomalies during crude oil processing. Paraffins, naphthene, and aromatics form the bulk of crude oil. Machine learning (ML) predictions of these constituents were made by training the ML model with a diverse industrial data set of 515 oils. The XGBoost model gave an excellent R² in the range 0.88–0.99 for the bulk compounds. R² for sulfur was in the modest range of 0.45–0.6, which improved significantly to 0.8 for additional inputs. ML applicability was thereby found to depend on the nature of the constituent. This work furthers ML-based predictions, with the incentive of reducing expensive spectroscopic analytical methods.

中文翻译：

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机器学习应用于预测关键石油原油成分

硫化合物是石油中最重要的无机成分，由于其腐蚀性和原油加工过程中的其他加工异常，需要事先进行估计。石蜡、环烷烃和芳烃构成了原油的大部分。这些成分的机器学习 (ML) 预测是通过使用包含 515 种油的不同工业数据集训练 ML 模型来进行的。XGBoost 模型为散装化合物提供了 0.88-0.99 范围内的出色R ^{2 。}硫的R ²处于 0.45-0.6 的适度范围内，如果额外投入，则显着提高至 0.8。因此发现机器学习的适用性取决于成分的性质。这项工作进一步推进了基于机器学习的预测，并鼓励减少昂贵的光谱分析方法。