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Updated: Mar 18, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

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Deep learning enhanced prediction framework for bio oil yield from organic solid waste with chemically informed

Shahad Almansour1, Lulwah M Alkwai2, Kusum Yadav2

  • 1Applied College, University of Ha'il, Ha'il, Kingdom of Saudi Arabia. shahad.mousa@uoh.edu.sa.

Scientific Reports
|March 17, 2026
PubMed
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Accurate prediction of bio-oil yield from organic solid waste pyrolysis is improved using a novel deep learning framework. This chemically informed model enhances biomass valorization and eco-friendly bio-oil production.

Area of Science:

  • Biomass Valorization
  • Thermochemical Conversion
  • Machine Learning Applications

Background:

  • Predicting bio-oil yield from organic solid waste pyrolysis is challenging due to data variability and limitations in existing machine learning models.
  • Shallow models struggle to capture complex thermochemical interactions governing devolatilization and liquid formation.

Purpose of the Study:

  • Develop a deep learning predictive framework for accurate bio-oil yield estimation.
  • Address limitations of existing models by using a harmonized dataset and advanced feature engineering.

Main Methods:

  • Utilized a harmonized dataset of 245 diverse biomass samples and pyrolysis conditions.
  • Employed chemically guided feature engineering (elemental ratios, ash-corrected volatility, energy-density index).
Keywords:
Bio-oil yieldBiomass valorizationDeep learningFeature engineeringPyrolysisThermochemical modeling

Related Experiment Videos

Last Updated: Mar 18, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

7.1K
  • Applied Variance Inflation Factor (VIF) for feature selection to reduce multicollinearity.

    • Main Results:

    • The Hybrid DNPO model achieved R² of 0.980 and RMSE of 1.14 on new data.
    • Outperformed benchmark regression models, including Light Gradient Boosting (LGB).
    • Demonstrated superior accuracy and robustness in predicting bio-oil yield.

    Conclusions:

    • A thermochemically grounded, chemically informed deep neural prediction framework was developed.
    • The model integrates feedstock descriptors and operating conditions for enhanced bio-oil yield prediction.
    • The framework serves as a reliable tool for experimental design and process optimization in bio-oil production.