Deciphering Wood Pyrolysis Dynamics: SHAP-Enhanced Machine Learning Models for Biochar Optimisation
木材熱分解ダイナミクスの解明：バイオ炭最適化のためのSHAP強化機械学習モデル (AI 翻訳)
ÖZBAY G, GOKSAL FP
🤖 gxceed AI 要約
日本語
本研究は、人工ニューラルネットワークとSHAPを統合し、木材熱分解によるバイオ炭の収率と炭素含有量を高精度で予測するモデルを開発した。解析の結果、熱分解温度が収率低下の主要因であり、リグニン含有量が高温での炭素骨格安定化に寄与することが示された。この説明可能なAIアプローチは、工業規模での原料特異的なバイオ炭最適化にスケーラブルな経路を提供する。
English
This study integrates Artificial Neural Networks and SHAP to model wood pyrolysis for biochar production. The model achieves high prediction accuracy (R²=0.987 for yield, 0.991 for carbon content). SHAP analysis reveals that pyrolysis temperature is the key factor reducing yield, while lignin content stabilizes the carbon skeleton at high temperatures. The approach offers a scalable, explainable AI framework for feedstock-specific biochar optimization in industrial reactors.
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
日本ではバイオ炭による炭素貯留が注目されているが、本論文はPinus sylvestrisとFagus orientalisを用いた研究であり、手法は日本のバイオマスにも適用可能な知見を提供する。
In the global GX context
Globally, biochar is a key technology for carbon dioxide removal (CDR). This paper advances the optimization of biochar production using explainable AI, which can improve yield and carbon content, making biochar more viable for large-scale CDR.
👥 読者別の含意
🔬研究者:This study demonstrates a successful integration of ANN and SHAP for pyrolysis modeling, offering a replicable methodology for biochar optimization research.
🏢実務担当者:Biochar producers can use the SHAP insights to adjust feedstock and temperature for higher carbon content and yield, improving process efficiency.
🏛政策担当者:This work supports the scalability of biochar as a carbon removal technology; policymakers may consider supporting AI-driven process optimization to enhance CDR.
📄 Abstract（原文）
<title>Abstract</title> <p> Biochar derived from biomass pyrolysis is an essential resource for carbon sequestration and renewable energy generation, but the complex, nonlinear relationships between feedstock biochemistry and operational variables often hinder the efficiency of conventional experimental methods. This study aims to investigate the thermochemical dynamics of wood pyrolysis using a high-fidelity "Digital Twin" framework integrating Artificial Neural Networks (ANNs) and SHapley Additive exPlanations (SHAP). Based on primary experimental data from <italic>Pinus sylvestris</italic> L. and <italic>Fagus orientalis</italic> Lipsky, a Multilayer Perceptron (MLP) model was developed with remarkable prediction accuracy for biochar yield (R <sup>2</sup> = 0.987) and carbon content (R <sup>2</sup> = 0.991). In addition to the statistical results, SHAP analysis was also used to interpret the model’s “black box” logic in a mechanistic manner. As demonstrated, while pyrolysis temperature is the most important factor in reducing yield due to the volatile phase transition, lignin content contributes to a structural resistance mechanism that stabilises the carbon skeleton, particularly during intense thermal decomposition at approximately 500°C. These findings, corroborated with recent literature (2022–2025), indicate that the ANN-SHAP coupling is a suitable compromise between machine learning and chemical kinetics. The paper presents a scalable, explainable AI pathway for feedstock-specific optimisation in industrial-scale pyrolysis reactors. </p>
🔗 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-9389596/v1first seen 2026-05-26 04:21:57 · last seen 2026-06-08 04:22:33
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