インドにおけるLNG海上輸送の統合最適化フレームワーク：ボイルオフガス管理、エネルギー効率、炭素排出、運航コストを考慮した船隊・航路配分

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:LNG海運の最適化にBOG・速度・炭素コストを統合するモデリング手法を提供。

🏢実務担当者:LNG輸送コスト削減と排出管理の意思決定に活用可能。

🏛政策担当者:炭素価格導入時の海運影響評価とインフラ計画に示唆。

India's increasing reliance on imported liquefied natural gas (LNG) has intensified the need for efficient maritime transportation systems capable of simultaneously addressing fleet utilization, voyage planning, boil-off gas (BOG), energy consumption, carbon emissions, and operating costs. LNG carriers continuously generate BOG during transportation as a consequence of heat ingress into cargo tanks. Although BOG can be utilized as propulsion fuel, its management directly affects cargo losses, voyage speed, fuel consumption, delivery quantity, and the overall economics of LNG transportation. This study develops an integrated optimization framework for LNG maritime transportation in India that jointly determines fleet allocation, route assignment, voyage scheduling, sailing speed, BOG utilization, energy consumption, carbon emissions, and operational costs. Unlike conventional LNG fleet-allocation models that treat sailing speed as fixed and focus primarily on transportation cost, the proposed framework explicitly incorporates the interaction between sailing speed, BOG generation, propulsion-fuel requirements, cargo delivery quantity, energy efficiency, and carbon emissions. Two optimization models are formulated. The first model represents a baseline fleet-and-route allocation problem under an economically efficient sailing speed. The second is an integrated model in which sailing speed and BOG management are endogenous decision variables. The proposed model further introduces carbon-emission costs and energy-efficiency constraints, allowing the carrier to balance economic and environmental objectives. A scenario-based sensitivity framework is also developed to examine the effects of LNG price volatility, transportation demand, charter rates, carbon prices, BOG generation rates, and fleet capacity on optimal decisions. The empirical framework is designed around India's LNG import infrastructure and maritime supply network, including major receiving terminals on the western, eastern, and southern coasts. India's eight operational LNG regasification terminals provide a relevant multi-route setting, while substantial variation in terminal utilization creates opportunities for route and fleet optimization. (PNGRB) The proposed framework provides a decision-support mechanism for LNG shipping companies seeking to reduce total operating expenditure while improving energy efficiency, controlling BOG-related cargo losses, and limiting carbon emissions. The framework is also adaptable to future carbon-pricing mechanisms and increasingly stringent maritime decarbonization requirements.

• semanticscholar https://international-journal-of-engineering-sciences.jo/style/site/uploads/gallery/2026-09-21_13-41-58-image_260921014158.pdffirst seen 2026-09-24 05:13:09 · last seen 2026-09-29 05:30:07