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高圧デュアルフューエル船用エンジンにおける50%MCRでのHFOとLNG排出量の船上比較:持続可能な海運への示唆

Onboard Comparison of HFO and LNG Emissions in a High-Pressure Dual-Fuel Marine Engine at 50% MCR: Implications for Sustainable Shipping (原題)

Ewelina Orysiak, Piotr Rozner, Kamila Staszczak

Sustainability📚 査読済 / ジャーナル2026-08-24#エネルギー転換Origin: Global経営インパクト: コスト削減対象セクター: transport
DOI: 10.3390/su18178646
原典: https://doi.org/10.3390/su18178646
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🤖 gxceed AI 要約

日本語

実船の高圧デュアルフューエルエンジン(MAN B&W 6G50ME-C9.5-GI)で、50%MCR運転点におけるHFOとLNGの排出量を比較。LNG使用でCO2 27.0%、NOx 20.7%、CO 18.2%削減、PMは約69%削減(不確実性あり)。CH4排出は約0.6 g/kWhで、CO2削減量の約10.5%に相当する温室効果を相殺。同一エンジンでの整合的な比較を提供。

English

This study compares HFO and LNG emissions from a real vessel's high-pressure dual-fuel engine (MAN B&W 6G50ME-C9.5-GI) at 50% MCR. LNG reduces CO2 by 27.0%, NOx by 20.7%, CO by 18.2%, and PM by ~69% (uncertain). Methane slip is ~0.6 g/kWh, offsetting ~10.5% of the CO2 reduction. Provides a consistent matched-load comparison.

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

日本の海運業界は国際海運の脱炭素化に向けてLNG燃料船の導入を進めており、本研究成果は実船データに基づく燃料転換の効果を定量的に示す点で、日本企業の代替燃料戦略やScope 1排出量算定に有用。また、メタンスリップの影響を考慮した評価は、今後の規制対応や環境主張の信頼性向上に寄与する。

In the global GX context

This study provides empirical evidence on LNG's emission reduction potential in shipping, relevant to global efforts to decarbonize maritime transport under IMO regulations. The methane slip quantification is crucial for assessing LNG's climate benefits and aligns with international focus on lifecycle emissions. Offers a methodology for comparing fuels in dual-fuel engines.

👥 読者別の含意

🔬研究者:Provides a structured matched-load comparison of HFO and LNG emissions from the same engine, useful for refining emission factors and understanding methane slip trade-offs.

🏢実務担当者:Offers quantitative data on LNG's emission reductions and methane slip, useful for fleet fuel strategy and environmental reporting.

🏛政策担当者:Highlights the need to account for methane slip when incentivizing LNG as a marine fuel, informing IMO and regional regulations.

📄 Abstract(原文)

Maritime transport is a major component of global supply chains, but reducing its atmospheric emissions remains essential to improving the environmental sustainability of shipping. This study analyzes onboard emission data reported for the MV Ilshin Green Iris under real-world operating conditions to assess how fuel selection affects the direct-emission performance of a dual-fuel marine propulsion system. The vessel is equipped with a MAN B&W 6G50ME-C9.5-GI engine employing high-pressure dual-fuel (HPDF) technology. A quantitative comparison between heavy fuel oil (HFO) and liquefied natural gas (LNG) was performed at 50% of the maximum continuous rating (MCR). At 50% MCR, LNG reduced CO2 emissions by 27.0%, NOx emissions by 20.7%, and CO emissions by 18.2% relative to HFO, while PM showed an indicative reduction of approximately 69%; its precise magnitude remains uncertain because a complete PM uncertainty budget was unavailable. Over the 900 s measurement period, the estimated reduction in CO2 mass was 154 kg. During LNG operation, the specific CH4 emission at 50% MCR was approximately 0.6 g/kWh. Using a 100-year global warming potential of 29.8 for fossil CH4, this corresponds to approximately 17.9 g CO2-eq/kWh, equivalent to about 10.5% of the direct CO2 reduction between HFO and LNG at this operating point. The results are representative of the analyzed stabilized operating point rather than of the vessel’s complete operational profile. The main contribution of this study is a structured matched-load analysis of HFO and LNG emissions from the same HPDF marine engine. The analysis combines measurement-derived specific emissions with energy-based mass estimates, methane-related limitations, data-quality considerations, and regulatory and sustainability implications. Because both fuels were evaluated in the same engine at the same 50% MCR operating point, the study provides a consistent basis for assessing fuel-related differences within the limits of the available dataset.

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