機械学習技術を用いたバイオガス燃料分散型ポリジェネレーションシステムの熱力学モデリングと最適化
Thermodynamics modelling and optimisation of a biogas fueled decentralised poly-generation system using machine learning techniques (原題)
Ghasemzadeh, Nima, Javaherian, Amirreza, Yari, Mortaza, Nami, Hossein, Vajdi, Mohammad, Saberi Mehr, Ali
🤖 gxceed AI 要約
日本語
本研究は、バイオガスを燃料とするガスタービンを用いた中規模分散型電源システムを提案し、電力・熱・冷房・水供給を同時に供給する。機械学習とグレイウルフ最適化を組み合わせた多目的最適化により、効率と経済性、環境負荷のバランスを向上させた。基本条件で発電1372kW、熱246.2kW、冷房293.3kW、造水4.1kg/sを達成し、CO2排出原単位0.778kgCO2/kWh、投資回収期間4年と試算された。
English
This study proposes a medium-scale biogas-fueled gas turbine poly-generation system supplying electricity, heating, cooling, and water. Multi-objective optimization using machine learning and Grey Wolf algorithms improves efficiency, cost, and environmental impact. The base case achieves 1372 kW electricity, 246.2 kW heating, 293.3 kW cooling, 4.1 kg/s water, with CO2 emission index 0.778 kgCO2/kWh and a 4-year payback period.
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
日本では再生可能エネルギー由来の分散型電源が注目されており、バイオガスは地域資源活用とカーボンニュートラルに寄与する。本システムの経済性評価（NPV、投資回収期間）は、日本の自治体や企業が導入判断する際の参考となる。
In the global GX context
This work aligns with global trends toward decentralized renewable energy systems and AI-driven optimization. The economic and environmental metrics provide a template for evaluating poly-generation projects, relevant to ISSB-aligned reporting and transition finance.
👥 読者別の含意
🔬研究者:Provides a novel application of ML and Grey Wolf optimization to poly-generation system design, offering a methodological reference.
🏢実務担当者:Offers a techno-economic framework for assessing biogas poly-generation investments, including payback and NPV.
🏛政策担当者:Demonstrates the potential of decentralized biogas systems for energy security and decarbonization, informing support policies.
📄 Abstract（原文）
In the forthcoming era of smart energy systems, decentralised solutions are gaining increasing prominence due to their superior adaptability for interconnecting sectors, reduced inefficiencies, and environmentally friendly operation. This study introduces a new medium-scale biogas-based power plant that utilises a gas turbine to meet the energy needs of a specific locality, encompassing electricity, heating, cooling, and water supply, all whilst considering the system's environmental impact. To optimise the plant's performance, three different multi-objective optimisation scenarios employing machine learning methodologies and Greywolf algorithms with distinct objective functions are analysed. Under the base conditions, the proposed plant showcases impressive capabilities, delivering 1372 kW of electricity, 246.2 kW of heating, 293.3 kW of cooling, and 4.1 kg/s of distilled water. It operates with first and second law thermodynamics efficiencies of 72.3% and 41.4%, respectively, while maintaining a CO2 emission index of 0.778 kgCO2/kWh. Furthermore, the net present value and investment return period for the investment are estimated to be approximately 4.4 million USD and 4 years, respectively. Through optimisation (scenario 1) that prioritises maximising efficiency while minimising product costs and environmental impact, the following parameters are achieved: an exergy efficiency of 42.7%, a cost of products at 28.8 $/GJ, and a reduced CO2 emission index of 0.762 kgCO2/kWh. The results reveal that the proposed system not only excels in efficiency but also proves to be economically viable and environmentally beneficial.
🔗 Provenance — このレコードを発見したソース
- base https://portal.findresearcher.sdu.dk/da/publications/1ffd0a1d-ea0d-413e-baee-f4bba3fd7a23first seen 2026-09-01 11:59:36
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