Advances in machine learning for high value-added applications of lignocellulosic biomass

被引：22

作者：

Ge, Hanwen ^{[1
]}

Zheng, Jun ^{[3
]}

Xu, Huanfei ^{[1
,2
,4
]}

机构：

[1] Qingdao Univ Sci & Technol, Coll Chem Engn, Qingdao 266042, Peoples R China

[2] Qilu Univ Technol, Shandong Acad Sci, Minist Educ, Key Lab Pulp & Paper Sci & Technol, Jinan 250353, Peoples R China

[3] Tech Univ Munich, Arcisstr 21, D-80333 Munich, Germany

[4] Chinese Acad Sci, Qingdao Inst Bioenergy & Bioproc Technol, Qingdao 266101, Peoples R China

来源：

BIORESOURCE TECHNOLOGY | 2023年 / 369卷

基金：

中国国家自然科学基金;

关键词：

Lignocellulose; Machine learning; High value utilization; Biomass; HYDROGEN-PRODUCTION; GASIFICATION; PRETREATMENT; ACID; OPTIMIZATION; PREDICTION; PARAMETERS; GA;

D O I：

10.1016/j.biortech.2022.128481

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

摘要：

Lignocellulose can be converted into biofuel or functional materials to achieve high value-added utilization. Biomass utilization process is complex and multi-dimensional. This paper focuses on the biomass conversion reaction conditions, the preparation of biomass-based functional materials, the combination of biomass conversion and traditional wet chemistry, molecular simulation and process simulation. This paper analyzes the mechanism, advantages and disadvantages of important machine learning (ML) methods. The application examples of ML in different aspects of high value utilization of lignocellulose are summarized in detail. The challenges and future prospects of ML in this field are analyzed.

引用

页数：15