Graduate Master's Degree Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand; Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand.
Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand.
Bioresour Technol. 2022 Jan;344(Pt B):126278. doi: 10.1016/j.biortech.2021.126278. Epub 2021 Nov 6.
Machine learning (ML) approach was applied for the prediction of biocrude yields (BY) and higher heating values (HHV) from hydrothermal liquefaction (HTL) of wet biomass and wastes using 17 input features from feedstock characteristics (biological and elemental properties) and operating conditions. Several novel ML algorithms were evaluated, based on 10-fold cross-validation, with 3 different sets of input features. An extreme gradient boosting (XGB) model proved to give the best prediction accuracy at nearly 0.9 R with normal root mean square error (NRMSE) of 0.16 for BY and about 0.87 R with NRMSE of about 0.04 for HHV. Temperature was found to be the most influential feature on the predictions for both BY and HHV. Meanwhile, feedstock characteristics contributed to the XGB model for more than 55%. Individual effects and interactions of most important features on the predictions were also exposed, leading to better understanding of the HTL system.
机器学习（ML）方法被应用于预测水热液化（HTL）湿生物质和废物的生物油产率（BY）和高位发热值（HHV），使用了 17 种来自原料特性（生物和元素性质）和操作条件的输入特征。基于 10 折交叉验证，使用了 3 组不同的输入特征来评估几种新的 ML 算法。极端梯度提升（XGB）模型被证明具有最佳的预测精度，接近 0.9 R，生物油的正常均方根误差（NRMSE）为 0.16，HHV 的 NRMSE 约为 0.04。对于 BY 和 HHV 的预测，温度被发现是最具影响力的特征。同时，原料特性对 XGB 模型的贡献超过 55%。还揭示了对预测最重要的特征的个体效应和相互作用，从而更好地理解了 HTL 系统。
