热预处理对收割残留物中木质素脱稳的影响:集合机器学习法

Đurđica Kovačić, Dorijan Radočaj, Danijela Samac, M. Jurišić
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引用次数: 0

摘要

对木质纤维素预处理的研究通常是通过实验进行的,而实验需要大量资源,通常耗时较长,而且并不总是环保的。因此,研究人员正在开发计算方法,以尽量减少实验程序并节省资金。本研究评估了三种机器学习方法,包括随机森林(RF)、极梯度提升(XGB)和支持向量机(SVM)及其组合,以预测木质纤维素生物质中酸不溶性洗涤剂木质素(AIDL)的含量。三种不同类型的收割残留物(玉米秸秆、大豆秸秆和向日葵茎秆)首先在实验室烘箱中以两种不同温度(121 和 175 °C)、不同持续时间(30 和 90 分钟)的热空气进行预处理,目的是分解木质纤维素结构,即脱木质素。根据留空交叉验证,XGB 对所有收割残留物的准确度最高,确定系数 (R2) 在 0.756-0.980 之间。所有单个收获残留物的相对变量重要度都强烈表明,与预处理温度的持续时间相比,预处理温度的影响占主导地位。这些研究结果证明了机器学习预测在优化木质纤维素预处理方面的有效性,从而带来了更有效的木质素脱稳方法。
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Influence of Thermal Pretreatment on Lignin Destabilization in Harvest Residues: An Ensemble Machine Learning Approach
The research on lignocellulose pretreatments is generally performed through experiments that require substantial resources, are often time-consuming and are not always environmentally friendly. Therefore, researchers are developing computational methods which can minimize experimental procedures and save money. In this research, three machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGB) and Support Vector Machine (SVM), as well as their ensembles were evaluated to predict acid-insoluble detergent lignin (AIDL) content in lignocellulose biomass. Three different types of harvest residue (maize stover, soybean straw and sunflower stalk) were first pretreated in a laboratory oven with hot air under two different temperatures (121 and 175 °C) at different duration (30 and 90 min) with the aim of disintegration of the lignocellulosic structure, i.e., delignification. Based on the leave-one-out cross-validation, the XGB resulted in the highest accuracy for all individual harvest residues, achieving the coefficient of determination (R2) in the range of 0.756–0.980. The relative variable importances for all individual harvest residues strongly suggested the dominant impact of pretreatment temperature in comparison to its duration. These findings proved the effectiveness of machine learning prediction in the optimization of lignocellulose pretreatment, leading to a more efficient lignin destabilization approach.
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