Thermokinetic, thermodynamic, and combustion analysis of agro-industrial biomass for bioenergy: insights from isoconversional models and neural networks
バイオエネルギー向けの農業産業バイオマスの熱動力学、熱力学、燃焼分析：等変換モデルとニューラルネットワークからの洞察 (AI 翻訳)
Cocco ML, Silva EN, Silva J, Gomes TLC, Meili L, Almeida GA, Sousa RC
🤖 gxceed AI 要約
日本語
本研究は、ココナッツ殻、オレンジ搾りかす、バナナ偽茎、堆肥バーンという4種類のバイオマスの熱化学的特性を評価した。熱重量分析と速度論モデリングにより、活性化エネルギーや燃焼指数などを算出。ニューラルネットワークによるTG曲線予測は高精度（R²=0.99）であり、バイオエネルギーの最適化にAIが有効であることを示した。
English
This study evaluates the thermochemical properties of four agro-industrial biomasses (coconut shell, orange pomace, banana pseudostem, compost barn) for bioenergy. Kinetic parameters like activation energy and combustion indices were determined via thermogravimetric analysis and isoconversional models. Multilayer perceptron neural networks accurately predicted TG curves (R²=0.99), highlighting AI's potential in optimizing bioenergy processes.
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
本論文は、バイオマス発電や廃棄物発電といった日本の再生可能エネルギー拡大にとって参考となる。特に、AIを用いた燃焼特性予測は、バイオマス燃料の品質管理や効率的な運用に寄与する可能性がある。
In the global GX context
This paper contributes to the global discourse on renewable energy by demonstrating AI-enhanced modeling of biomass combustion. It offers methodological insights for optimizing bioenergy from agricultural residues, aligning with the broader energy transition and circular economy goals.
👥 読者別の含意
🔬研究者:Provides kinetic data and a neural network approach for predicting biomass combustion behavior, useful for bioenergy modeling.
🏢実務担当者:Offers insights into selecting and characterizing biomass feedstocks for efficient energy conversion.
📄 Abstract（原文）
<title>Abstract</title> <p> The growing demand for renewable energy and sustainable waste management has driven interest in the thermochemical valorization of agro-industrial residues. This study investigated the thermokinetic, thermodynamic, and combustion behavior of four lignocellulosic biomasses—coconut shell, orange pomace, banana pseudostem, and compost barn—for bioenergy applications. Thermogravimetric analysis (TG/DSC) and kinetic modeling using isoconversional methods and multilayer perceptron (MLP) neural networks were employed. Kinetic, combustion, and thermal parameters—pre-exponential factor, activation energy, reaction order, ignition and burnout temperatures—were determined, along with thermodynamic parameters such as entropy (ΔS), Gibbs free energy (ΔG), and enthalpy (ΔH). All biomasses exhibited typical lignocellulosic degradation profiles, with predominant exothermic events. Activation energy varied widely (140.32–458.35 kJ·mol⁻¹), with banana pseudostem showing the lowest values, indicating easier energy conversion. Coconut shell exhibited the highest combustion index (1.04×10⁻⁵ %²·min⁻²·°C⁻³) and the lowest ignition temperature (59.71°C). Orange pomace showed the highest ΔH (892 kJ·mol⁻¹) and ΔS (1.31 kJ·mol⁻¹·K⁻¹), suggesting high thermal stability. ΔG values remained relatively constant across conversion degrees, indicating stable spontaneity of reactions. MLP neural networks demonstrated superior performance ( <italic>R²</italic> = 0.99) compared to traditional models in predicting TG curves. These findings support the efficient selection and modeling of biomass for thermal processes and reinforce the potential of AI-based tools in optimizing bioenergy applications. </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-9204730/v1first seen 2026-06-04 04:24:47 · last seen 2026-06-16 04:30:27
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