Sasipa Boonyubol 研究室

AI 要約（直近 5 年の研究成果）

※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。

研究成果（33 件）

• [2026] Bayesian optimized automated ensemble machine learning for predicting biodiesel yield from waste cooking oil: A SHAP interpretability approach
  DOI: https://doi.org/10.1016/j.cherd.2026.05.027
• [2026] Bayesian optimized automated ensemble machine learning for predicting biodiesel yield from waste cooking oil: A SHAP interpretability approach
• [2026] Hydrogen permeability prediction in palladium alloys and virtual screening of B2 phase-stabilized Pd(100-x-y)CuxMy ternary alloys using machine learning
• [2026] Hydrogen permeability prediction in palladium alloys and virtual screening of B2 phase-stabilized Pd(100-x-y)CuxMy ternary alloys using machine learning
  DOI: https://doi.org/10.1016/j.mtcomm.2026.114875
• [2026] Hydrogen permeability prediction in palladium alloys and virtual screening of B2 phase-stabilized Pd(100-x-y)CuxMy ternary alloys using machine learning
• [2026] Optimization of a two-step biodiesel production from waste cooking oil: Comparative evaluation of n-hexane and CPME as transesterification cosolvents
• [2026] A physics-constrained machine learning optimization framework for prediction and minimization of NOx emissions during combustion of hydrothermally treated paper sludge
  DOI: https://doi.org/10.1016/j.seta.2026.105146
• [2026] Bayesian optimized automated ensemble machine learning for predicting biodiesel yield from waste cooking oil: A SHAP interpretability approach
• [2026] Optimization of a two-step biodiesel production from waste cooking oil: Comparative evaluation of n-hexane and CPME as transesterification cosolvents
  DOI: https://doi.org/10.1016/j.cherd.2026.01.005
• [2026] Optimization of a two-step biodiesel production from waste cooking oil: Comparative evaluation of n-hexane and CPME as transesterification cosolvents

科研費（0 件）

まだデータがありません（KAKEN 取り込み後に表示）。

所属学会・役職（0 件）

まだデータがありません（学会データ連携後に表示）。