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June 3, 2026Journal of Chemometrics0 citations

Risk-Adjusted Robust Classification of Waste Lubricant Oil Coagulation Potential

Risk‐Adjusted Robust Classification of Coagulation Potential in the Regeneration of Waste Lubricant Oil

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Authors

RGRúben GarisoTRTiago J. RatoMQMargarida J. Quina

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Overview

Randomized trial demonstrates improved accuracy in predicting coagulation potential of waste lubricant oil, suggesting enhanced safety for industry practices.

Key Points

  • The aim is to develop a robust machine learning method to predict the coagulation potential of waste lubricant oil to improve decision-making in its regeneration.
  • Developed a machine learning system based on Fourier-transform infrared spectroscopy for waste lubricant oil analysis.
  • Implemented three key modules: feature extraction, robust classification using risk-adjusted rules, and soft validation of negative predictions.
  • Initially achieved 70% accuracy with a traditional PAT-based model due to misclassification issues.
  • The proposed methodology reliably classified waste lubricant oil samples, improving upon the earlier model's accuracy.
  • Demonstrated significant reduction in laboratory workload while aiding decision-making.
  • Addressed safety risks by minimizing misclassification of coagulating waste lubricant oil.
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Cite This Study

Gariso et al. (2026) studied this question.

synapsesocial.com/papers/6a1fc6cddee9eb8c0dce7bc0https://doi.org/10.1002/cem.70150
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