Artificial Intelligence in Biomass Conversion and Utilization
バイオマス変換と利用における人工知能 (AI 翻訳)
(著者不明)
🤖 gxceed AI 要約
日本語
この書籍は、バイオマス変換プロセスの最適化に人工知能（AI）を適用する方法を解説する。熱化学的・生化学的変換経路への機械学習の統合、多目的最適化、データ拡張による熱力学データベースの改善などを扱い、低炭素材料や燃料の生産を支援する。また、カーボンニュートラルや循環経済の目標達成にAIが貢献する枠組みも提示する。
English
This book explains how AI and machine learning are applied to optimize biomass conversion processes, including property prediction, modeling conversion pathways, and multi-objective optimization balancing energy yield, economics, and environmental impact. It covers integration of ML with thermochemical and biochemical routes, addressing challenges like inadequate thermodynamic databases, and links biomass conversion to carbon neutralization and circular economy goals.
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
日本のバイオマス活用推進政策やカーボンニュートラル目標と関連し、AIを用いたプロセス最適化はコスト削減や効率向上に寄与する可能性がある。
In the global GX context
This work contributes to global efforts in sustainable energy and materials by demonstrating AI-driven optimization of biomass conversion, a key pathway for achieving net-zero targets and circular economy transitions.
👥 読者別の含意
🔬研究者:Researchers in process engineering and AI can learn how to apply ML to biomass conversion challenges.
🏢実務担当者:Corporate sustainability teams in energy and materials sectors can leverage AI methods to improve the efficiency and economics of biomass-based production.
🏛政策担当者:Policymakers can understand how AI supports biomass utilization for decarbonization, informing support for R&D and infrastructure.
📄 Abstract（原文）
Apply artificial intelligence (AI) to optimize biomass conversion and utilization processes Biomass conversion technologies have advanced significantly yet face persistent challenges in industrialization, including inadequate thermodynamic databases, unreliable models, and inefficient multi-objective optimization. Artificial Intelligence in Biomass Conversion and Utilization addresses these barriers by detailing how AI and machine learning methods can predict biomass properties, model conversion processes, and optimize systems for energy output, economics, and environmental performance. The book covers AI applications across every stage of biomass conversion, from fundamental research through practical deployment. Topics include the production of low-carbon materials, fuels, and chemicals from biomass feedstocks, alongside methods for rapid assessment and smart decision-making. Discussions of carbon neutralization strategies and circular economy frameworks demonstrate how computational intelligence supports both process efficiency and environmental sustainability goals. Readers will also find: Approaches for integrating machine learning with thermochemical and biochemical biomass conversion pathways to improve process prediction accuracy Methods for multi-objective optimization balancing energy yield, economic viability, and environmental impact across biomass utilization systems Strategies for addressing inadequate thermodynamic databases through AI-driven data augmentation and predictive modeling techniques Coverage of AI applications in producing low-carbon materials, sustainable fuels, and platform chemicals from diverse biomass sources Frameworks connecting biomass conversion with carbon neutralization goals and circular economy principles for industrial-scale deployment Designed for process engineers, chemical engineers, materials scientists, biotechnologists, and environmental chemists, this reference provides the computational and domain-specific knowledge needed to apply AI methods across biomass conversion workflows, from property prediction through system-level optimization for sustainable energy and materials production.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/9783527853540first seen 2026-07-17 04:44:08
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。