Machine learning prediction of pyrolytic gas yield and compositions with feature reduction methods: Effects of pyrolysis conditions and biomass characteristics.

特征还原方法的热解气体产率和组成的机器学习预测 ： 热解条件和生物质特征的影响。

• 影响指数: 11.889
• 发表时间: Nov 2021
• 来源期刊: Bioresour Technol
• DOI: 10.1016/j.biortech.2021.125581
• PMID: 34298251
• 文章类型: Journal Article
• 中科院分区: Q1
• 方向: 生物
• 作者列表: Tang Q,Chen Y,Yang H,Liu M,Xiao H,Wang S,Chen H,Raza Naqvi S

关键词: Biomass pyrolysis; Feature reduction; Gas; Machine learning; Prediction

Mesh: Biomass; Machine Learning; Pyrolysis

Abstract

This study aimed to utilize machine learning algorithems combined with feature reduction for predicting pyrolytic gas yield and compositions based on pyrolysis conditions and biomass characteristics. To this end, random forest (RF) and support vector machine (SVM) was introduced and compared. The results suggested that six features were adequate to accurately forecast (R2 > 0.85, RMSE < 5.7%) the yield while the compositions only required three. Moreover, the profound information behind the models was extracted. The relative contribution of pyrolysis conditions was higher than that of biomass characteristics for yield (55%), CO2 (73%), and H2 (81%), which was inverse for CO (12%) and CH4 (38%). Furthermore, partial dependence analysis quantified the effects of both reduced features and their interactions exerted on pyrolysis process. This study provided references for pyrolytic gas production and upgrading in a more convenient manner with fewer features and extended the knowledge into the biomass pyrolysis process.

摘要

本研究旨在利用机器学习算法与特征减少相结合，根据热解条件和生物质特性预测热解气体产率和组成。为此,对随机森林(RF)和支持向量机(SVM)进行了介绍和比较。结果表明,六个特征足以准确预测(R2>0.85,RMSE<5.7%)产率,而组合物仅需要三个。此外，提取了模型背后的深刻信息。热解条件对产率的相对贡献高于生物质特性(55%),CO2（73%），和H2（81%），这与CO(12%)和CH4(38%)相反。此外，部分依赖性分析量化了减少的特征及其相互作用对热解过程的影响。该研究以更少的特性为热解气体的生产和升级提供了参考，并将知识扩展到生物质热解过程中。