Application of Random Forest Model Integrated with Feature Reduction for Biomass Torrefaction
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- González-Arias, J. & Gómez, X. & González-Castaño, M. & Sánchez, M.E. & Rosas, J.G. & Cara-Jiménez, J., 2022. "Insights into the product quality and energy requirements for solid biofuel production: A comparison of hydrothermal carbonization, pyrolysis and torrefaction of olive tree pruning," Energy, Elsevier, vol. 238(PC).
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- Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
- Montree Wongsiriwittaya & Teerapat Chompookham & Bopit Bubphachot, 2023. "Improvement of Higher Heating Value and Hygroscopicity Reduction of Torrefied Rice Husk by Torrefaction and Circulating Gas in the System," Sustainability, MDPI, vol. 15(14), pages 1-13, July.
- Henrique Piqueiro & Reinaldo Gomes & Romão Santos & Jorge Pinho de Sousa, 2023. "Managing Disruptions in a Biomass Supply Chain: A Decision Support System Based on Simulation/Optimisation," Sustainability, MDPI, vol. 15(9), pages 1-25, May.
- Asya İşçen & Kerem Öznacar & K. M. Murat Tunç & M. Erdem Günay, 2023. "Exploring the Critical Factors of Biomass Pyrolysis for Sustainable Fuel Production by Machine Learning," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
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Keywords
biomass torrefaction; machine learning; feature reduction; partial dependence analysis; random forest;All these keywords.
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