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<p>Accurate and robust detection of wax appearance (a medium- to high-molecular-weight component of crude oil) is crucial for the efficient operation of hydrocarbon transportation. The wax appearance temperature (WAT) is the lowest temperature at which the wax begins to form. When crude oil cools to its WAT, wax crystals precipitate, forming deposits on pipelines as the solubility limit is reached. Therefore, WAT is a crucial quality assurance parameter, especially when dealing with modern fuel oil blends. In this study, we use machine learning via MATLAB’s Bioinformatics Toolbox to predict the WAT of marine fuel samples by correlating near-infrared spectral data with laboratory-measured values. The dataset provided by Intertek PLC—a total quality assurance provider of inspection, testing, and certification services—includes industrial data that is imbalanced, with a higher proportion of high-WAT samples compared to low-WAT samples. The objective is to predict marine fuel oil blends with unusually high WAT values (&gt;35°C) without relying on time-consuming and irregular laboratory-based measurements. The results demonstrate that the developed model, based on the one-class support vector machine (OCSVM) algorithm, achieved a Recall of 96, accurately predicting 96% of fuel samples with WAT &gt;35°C. For standard binary classification, the Recall was 85.7. The trained OCSVM model is expected to facilitate rapid and well-informed decision-making for logistics and storage when choosing fuel oils.</p>

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Predicting Marine Fuels with Unusual Wax Appearance Temperatures Using One-Class Support Vector Machines

  • 期刊论文

  • 发表日期：

    2025年6月23日

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  • Njideka Chima-Amaeshi,
  • Chris O’Malley,
  • Mark Willis

摘要

Accurate and robust detection of wax appearance (a medium- to high-molecular-weight component of crude oil) is crucial for the efficient operation of hydrocarbon transportation. The wax appearance temperature (WAT) is the lowest temperature at which the wax begins to form. When crude oil cools to its WAT, wax crystals precipitate, forming deposits on pipelines as the solubility limit is reached. Therefore, WAT is a crucial quality assurance parameter, especially when dealing with modern fuel oil blends. In this study, we use machine learning via MATLAB’s Bioinformatics Toolbox to predict the WAT of marine fuel samples by correlating near-infrared spectral data with laboratory-measured values. The dataset provided by Intertek PLC—a total quality assurance provider of inspection, testing, and certification services—includes industrial data that is imbalanced, with a higher proportion of high-WAT samples compared to low-WAT samples. The objective is to predict marine fuel oil blends with unusually high WAT values (>35°C) without relying on time-consuming and irregular laboratory-based measurements. The results demonstrate that the developed model, based on the one-class support vector machine (OCSVM) algorithm, achieved a Recall of 96, accurately predicting 96% of fuel samples with WAT >35°C. For standard binary classification, the Recall was 85.7. The trained OCSVM model is expected to facilitate rapid and well-informed decision-making for logistics and storage when choosing fuel oils.