Sustainability assessment of hydrothermal carbonization of food/agro-industrial waste: integrating life cycle, techno-economic, and machine learning perspectives
食品および農業産業廃棄物の水熱炭化の持続可能性評価：ライフサイクル、技術経済、機械学習の統合的視点 (AI 翻訳)
Behzad Satari
🤖 gxceed AI 要約
日本語
食品・農業産業廃棄物の水熱炭化（HTC）の持続可能性を評価するため、ライフサイクルアセスメント（LCA）、技術経済分析（TEA）、機械学習（ML）を統合したフレームワークを提案。MLがデータ不足を補い、HTC性能予測やリアルタイム最適化に有効であることを示し、循環経済とSDGsとの整合性を強調する。研究ギャップも特定。
English
This study proposes a comprehensive framework integrating life cycle assessment (LCA), techno-economic analysis (TEA), and machine learning (ML) for sustainability assessment of hydrothermal carbonization (HTC) of food and agro-industrial waste. It synthesizes existing LCA and TEA studies, highlights the role of ML in predicting HTC performance and mitigating data scarcity, and identifies research gaps to support sustainable scale-up.
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
日本では食品廃棄物削減と再生可能エネルギー導入が喫緊の課題。本フレームワークは、HTC技術の評価・最適化に資し、循環経済政策への示唆を与える。
In the global GX context
Globally, HTC is gaining attention as a low-carbon waste treatment technology. This integrated assessment framework aligns with circular economy principles and SDGs, offering a methodology for technology evaluation and policy support.
👥 読者別の含意
🔬研究者:Provides a structured review and integrative framework for sustainability assessment of HTC, highlighting ML applications.
🏢実務担当者:Offers guidance on combining LCA, TEA, and ML for waste-to-energy project evaluation and optimization.
🏛政策担当者:Supports evidence-based policy for waste management and renewable energy by linking HTC to circular economy and SDG targets.
📄 Abstract（原文）
Food and agro-industrial waste, exceeding 1.3 billion tons annually, represents both a significant environmental burden and an untapped resource. Hydrothermal carbonization (HTC) has emerged as a promising thermochemical pathway for converting wet biomass into hydrochar and nutrient-rich process water, offering reduced greenhouse gas emissions compared to conventional disposal methods. However, the sustainability of HTC remains insufficiently characterized across environmental, economic, and digital dimensions. This study presents a comprehensive framework that integrates life cycle assessment (LCA), techno-economic analysis (TEA), and machine learning (ML) for HTC systems. Published LCA studies are synthesized to highlight greenhouse gas mitigation potentials and environmental hotspots, while TEA investigations are examined to identify cost drivers, investment risks, and market barriers. The emerging role of ML is critically analyzed, with emphasis on its ability to predict HTC performance, mitigate data scarcity, and support real-time process optimization. An integrative framework is proposed to bridge LCA, TEA, and ML, enabling multi-objective decision-making and policy-relevant evaluation. By aligning HTC deployment with circular economy principles and the United Nations Sustainable Development Goals, this framework advances the methodological foundation of HTC sustainability assessment. Finally, key research gaps are identified, providing a decision-grade basis for the sustainable scale-up of HTC systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.sftr.2026.102001first seen 2026-07-16 05:07:03
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