<b>Adaptive Injection Phasing and Methanol </b><b>Blend Optimization for Low- Carbon </b><b>Engine Performance: A Meta- Analysis and </b><b>Machine-Learning Framework with CCS </b><b>Integration</b>
低炭素エンジン性能のための適応噴射位相とメタノール混合最適化：メタ分析と機械学習フレームワークによるCCS統合 (AI 翻訳)
Luke Ajuka, Christopher Enweremadu
🤖 gxceed AI 要約
日本語
本研究はメタノール混合比と噴射時期（SOI）を最適化し、エンジンの正味熱効率（BTE）を向上させるための機械学習フレームワークを提案。XGBoostが高精度（R²=0.985）を達成し、CO2回収・利用（CCU）との統合でカーボンネガティブ応用の可能性を示す。
English
This study proposes a machine learning framework to optimize methanol blend ratio and injection timing (SOI) to improve brake thermal efficiency (BTE). XGBoost achieved high accuracy (R²=0.985), and integration with carbon capture and utilization (CCU) pathways underscores methanol's potential for carbon-negative applications.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では船舶や発電分野でのメタノール利用が注目されており、本手法は機械学習によるエンジン最適化の実証モデルとして参考になる。ただし、日本の規制やインフラとの整合性は別途検討が必要。
In the global GX context
This paper offers a data-driven framework for optimizing methanol blend and injection timing, which is relevant to global transport decarbonization efforts. The integration of CCU aligns with broader net-zero strategies, though the specific engine application may require adaptation for different regions.
👥 読者別の含意
🔬研究者:Provides a validated ML meta-model for methanol engine optimization with high predictive accuracy, useful for combustion researchers.
🏢実務担当者:Offers actionable insights on methanol blend ratios (25-30%) and SOI timing (-30 to -35° CA) for low-carbon engine design.
📄 Abstract（原文）
The transition to net-zero mobility, aligned with global decarbonization targets, has intensified interest in methanol as a clean combustion fuel and carbon-neutral energy carrier. When optimally blended, methanol’s high-octane rating, strong charge-cooling effect, and inherent oxygen content enhance brake thermal efficiency (BTE), improve premixed combustion, and suppress knock in both spark- and compression-ignition engines. This study integrates meta-analysis, physics-informed modelling, and machine learning to establish predictive relationships between methanol blend ratio, start-of-injection (SOI) phasing, and engine performance. Meta-analysis revealed a significant positive correlation between SOI advancement and BTE (r = 0.74, 95% CI: 0.60–0.85), with moderate heterogeneity (I² = 38–45%). Optimal performance was achieved at methanol blend ratios of 25–30% with SOI between −30° and −35° CA aTDC, balancing efficiency gains with emission constraints. Quantitative model evaluation demonstrated high predictive accuracy (R² ≈ 0.99, RMSE < 0.30), exceeding typical machine learning combustion models (R² ≈ 0.91–0.97). SHAP-based sensitivity analysis confirmed that methanol blend ratio (0.125 ± 0.015) and SOI (0.082 ± 0.010) are dominant parameters controlling system behaviour. Model comparison showed that XGBoost achieved the highest accuracy (MAE = 0.01 °CA, RMSE = 0.02 °CA, R² = 0.985), outperforming Random Forest (R² = 0.90) and the physics-informed correlation model (R² = 0.93), while linear regression exhibited inferior performance (R² = 0.90) due to its inability to capture nonlinear interactions. Residual analysis (±0.4 BTE) and validation with 95% confidence bands (±0.42 BTE) confirmed strong predictive reliability and generalisation. The integration of carbon capture and utilization (CCU) pathways with methanol synthesis further supports closed-loop CO₂ utilisation, reinforcing methanol’s potential in carbon-neutral and carbon-negative applications. Overall, the findings highlight the importance of adaptive, AI-driven SOI optimisation and integrated CCU strategies for advancing next-generation low-emission engine systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.70917/jcc-2026-015first seen 2026-07-22 05:04:12
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。