デジタルツインによるバイオエネルギー変換の高度化：BECCS統合における機会と課題
Advancing Bioenergy Conversion with Digital Twins: Opportunities and Challenges in BECCS Integration (原題)
C. Gurgel, A.M. Lauro, P. Rousset, Edgar A. Silveira
🤖 gxceed AI 要約
日本語
本論文は、バイオエネルギー変換およびBECCS統合におけるデジタルツインの役割を文献レビューとシステム分析で整理した。現状はシミュレーション・予測・監視・最適化に集中し、リアルタイムデータ同化や閉ループ制御を備えた完全統合型は限定的である。今後は監視・予測・適応学習・最適化・制御・システム意思決定の5機能を、バイオマス物流や炭素回収・輸送・貯留、ネット炭素会計と接続する必要があると指摘する。
English
This review analyzes the role of digital twins in bioenergy conversion and BECCS integration using bibliometric and systemic methods. Current applications focus on simulation, prediction, monitoring, and optimization, while fully integrated cyber-physical twins with real-time data assimilation and closed-loop control remain limited. The authors identify five core functions—monitoring, prediction, adaptive learning, optimization and control, and system-level decision support—that must link reactor-scale operation with biomass logistics, carbon capture, transport, storage, and net carbon accounting.
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
BECCSは日本のGX実現に向けた成長分野の一つであり、デジタルツインによる炭素会計の高度化は、SSBJや有報でのカーボンクレジット・ネガティブエミッションの信頼性向上に寄与しうる。
In the global GX context
As BECCS gains traction in global net-zero pathways, digital twins could strengthen MRV and carbon accounting for negative emissions, aligning with ISSB and CSRD disclosure requirements for carbon removal and transition finance.
👥 読者別の含意
🔬研究者:BECCSのデジタルツイン研究における機能別の研究ギャップと、炭素会計との接続課題を把握できる。
🏢実務担当者:BECCS事業のモニタリングや炭素会計の高度化にデジタルツインを活用する際の機能要件と統合課題を理解できる。
🏛政策担当者:ネガティブエミッションのMRV強化に向けたデジタル技術の役割と、制度設計上の論点を把握できる。
📄 Abstract（原文）
Bioenergy conversion systems integrated with carbon capture and storage represent a strategic pathway for renewable energy production, waste valorization and carbon dioxide removal. However, their deployment is constrained by biomass heterogeneity, nonlinear process behavior, fluctuating operating conditions and the need for reliable carbon accounting across process and supply chain scales. In this context, digital twins can support the transition from static simulation and conventional monitoring toward adaptive, data connected and decision oriented bioenergy systems. This study reviews the emerging role of digital twins in bioenergy conversion and BECCS integration through a structured bibliometric and systemic analysis based on the ProKnow C methodology and the Web of Science database. The literature portfolio indicates that current applications remain concentrated on specific functions, including simulation, prediction, monitoring and optimization, while fully integrated cyber physical digital twins with real time data assimilation, self-updating capability, uncertainty management and closed loop control are still limited. The analysis highlights five core functions for future implementation: monitoring, prediction, adaptive learning, optimization and control, and system-level decision support. For BECCS, these functions must connect reactor scale operation with biomass logistics, carbon capture, transport, storage and net carbon accounting. The findings indicate that future digital twins for bioenergy should integrate hybrid modelling, sensor based data streams, adaptive learning and carbon performance indicators to enable scalable, resilient and low carbon operation, especially in remote or technically constrained regions.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5071/34theubce2026-2bv.9.10first seen 2026-10-28 04:48:13 · last seen 2026-11-06 04:34:42
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