A Multi-Layer Optimisation Framework for Anaerobic-Digestion-Based Sustainable Aviation Fuel Production from Agricultural Residues

農業残渣からの嫌気性消化ベース持続可能航空燃料生産のための多層最適化フレームワーク (AI 翻訳)

Ekechukwu DE, Eziefula BI, Ahaneku IE

Research Squareプレプリント2026-07-24#エネルギー転換経営インパクト: コスト削減対象セクター: agriculture

原典: https://doi.org/10.21203/rs.3.rs-9575304/v1

🤖 gxceed AI 要約

日本語

農業残渣を嫌気性消化でSAFに変換する統合最適化フレームワークを提案。6層構造で前処理から持続可能性評価までを連結し、ANN-GAハイブリッドモデル（R²=0.974-0.981）とMILPを活用。最適条件下でメタン収量20%向上、排出77%削減、カーボンネガティブ（-4.58 gCO2e/MJ）を達成。工学・政策決定者向けの意思決定支援ツールを提供。

English

This study proposes an integrated six-layer optimisation framework for producing sustainable aviation fuel (SAF) from agricultural residues via anaerobic digestion. It connects feedstock characterisation, pretreatment, digestion, upgrading, and sustainability evaluation, using hybrid ANN-GA modelling (R²=0.974-0.981) and MILP for system design. Optimised conditions achieve up to 20% higher methane yield, 77% emission reduction, and carbon-negative operation (-4.58 gCO2e/MJ). The framework serves as a decision-support tool for engineers and policymakers, bridging lab-scale optimisation to industrial deployment.

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SAFの国産化が急務であり、農業残渣の活用は地域分散型のエネルギー供給と循環経済に寄与する。本フレームワークは、SSBJ開示やカーボンニュートラル目標に対応する企業の技術選択や投資判断に有用な定量的根拠を提供する。

In the global GX context

Globally, SAF is critical for aviation decarbonisation, and this framework addresses the need for integrated optimisation across the value chain. It aligns with ISSB/CSRD disclosure requirements by providing quantitative sustainability metrics (emissions, cost) that companies can report. The carbon-negative potential offers a pathway for negative emissions, relevant to net-zero targets and transition finance.

👥 読者別の含意

🔬研究者:Provides a comprehensive optimisation framework that integrates process engineering with AI and sustainability assessment, offering a template for multi-objective optimisation in bioenergy systems.

🏢実務担当者:Offers a decision-support tool for engineers and project developers to evaluate and optimise SAF production from agricultural residues, considering yield, cost, and environmental performance.

🏛政策担当者:Highlights the potential of AD-based SAF to achieve significant emission reductions and carbon negativity, informing policy support for sustainable aviation fuel incentives and infrastructure.

📄 Abstract（原文）

<title>Abstract</title> Sustainable Aviation Fuel (SAF) derived from agricultural residues via anaerobic digestion (AD) offers a promising pathway for aviation decarbonisation. However, current research is characterised by fragmented optimisation approaches focusing on isolated process stages− pretreatment, digestion, upgrading, or synthesis− rather than integrated system performance. This study presents a novel six-layer integrated optimisation framework for AD-based SAF production that connects feedstock characterisation, pretreatment decision-making, AD process optimisation, advanced hybrid modelling, system-level design, and sustainability evaluation. The framework was developed through quantitative analysis of engineering performance data from 33 studies (methane yield:29.7–312 mL CH 4 /g VS; yield improvements: 41.6–209%) combined with qualitative synthesis of optimisation methodologies. Key innovations include (i) integration of energy and cost penalties within the pretreatment decision layer; (ii) hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) optimisation enhine achieving predictive accuracy of R 2 = 0.974–0.981; (iii) Mixed-Integer Linear Programming (MILP) for system-level configuration; and (iv) embedded Techno-Economic Analysis and Life Cycle Assessment within optimisation loop. Application of the framework demonstrated significant performance improvements: methane yield enhancement up to 20% under optimised conditions, emissions reductions of 77%, and carbon-negative operation (-4.58 g CO 2 e/MJ). The framework addresses critical limitation in existing literature multi-objective optimisation that simultaneously considers yield, cost, and environmental performance. This integrated approach provides a decision-support tool for engineers and policymakers, facilitating the transaction from laboratory-scale optimisation to industrial deployment of AD-based SAF systems.

🔗 Provenance — このレコードを発見したソース

Research Square https://doi.org/10.21203/rs.3.rs-9575304/v1first seen 2026-07-25 04:35:29 · last seen 2026-08-02 04:41:18

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