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机器学习辅助的分离红素从生物质的热水处理深度欧性溶剂的红素

Weijin Zhang¹, Zihan Cao¹, Qingyue Chen¹

¹School of Energy Science and Engineering, Central South University, Changsha 410083, China.

Bioresource technology

|October 23, 2025

概括

机器学习模型预测了从深度欧性溶剂 (DES) 预处理中获得的红素产量和纯度. 优化的模型准确地预测了木质素的特性,使得合理的设计能够改善生物质的加工.

科学领域:

生物质预处理生物质预处理
绿色化学绿色化学
机器学习应用机器学习应用

背景情况:

深度环氧化溶剂 (DES) 提供了一个有前途的途径,可以有效地从生物质中分离红素.
目前优化DES预处理和表征产生的红素的方法往往是劳动密集型的,缺乏系统的设计.
预测诸如产量和分子量之类的素特性对于量身定制的生物质价值化至关重要.

研究的目的:

开发和验证机器学习模型,用于预测通过DES预处理获得的关键素特性.
在DES预处理过程中识别影响红素产量,纯度和分子量的关键因素.
能够合理设计和优化基于DES的木质素分离工艺.

主要方法:

整合机器学习算法,特别是梯度增强回归,与实验性DES预处理数据.
训练,测试和验证使用高R平方值数据集的预测模型 (0.95-0.98用于训练,0.78-0.97用于测试,0.82-0.90用于验证).
综合特征分析,以确定反应条件和DES特征对素特性的影响.

主要成果:

梯度增强回归模型在预测素产量,纯度,重量平均分子量 (Mw) 和数量平均分子量 (Mn) 方面取得了高准确性.
温度和时间等反应参数被确定为红素产量和纯度的关键驱动因素.
发现DES特性,包括初始的素含量和表面积,是素分子重量的关键决定因素.

结论:

机器学习为合理设计和优化用于素分离的DES预处理提供了一个强大的工具.
了解反应条件和DES特征之间的相互作用,可以有针对性地改善红素产量和质量.
这种方法有助于阐明复杂的反应机制,为增强生物质价值化策略铺平道路.

关键词:

生物质是生物质的.深层欧性溶剂深层欧性溶剂在Lignin的基础上.机器学习是机器学习.预测和验证预测和验证

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