Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Construction of Multifunctional Conductive Carbon-Based Cathode Additives for Boosting Li<sub>6</sub>PS<sub>5</sub>Cl-Based All-Solid-State Lithium Batteries.

Nano-micro letters·2025
Same author

Re-granulation and performance of anaerobically digested bacterial and algal-bacterial aerobic granular sludge.

Journal of environmental management·2025
Same author

Versatile nitrate-respiring heterotrophs are previously concealed contributors to sulfur cycle.

Nature communications·2025
Same author

Efficient removal of manganese ions from waters using hypochlorite-modified granular activated carbon.

Journal of environmental management·2024
Same author

Vapor Nucleation on Hybrid-Wetting Nanoconvex Surfaces: The Competition between Intrinsic Wettability and Topography.

Langmuir : the ACS journal of surfaces and colloids·2024
Same author

Solid Additive Engineering for Next-generation Organic Photovoltaics.

Advanced materials (Deerfield Beach, Fla.)·2024

相关实验视频

Updated: Jul 6, 2025

Author Spotlight: On-Site Biochar Production for Woody Debris Incineration in Forestry
07:27

Author Spotlight: On-Site Biochar Production for Woody Debris Incineration in Forestry

Published on: January 5, 2024

2.7K

生物炭研究的机器学习应用:一个迷你回顾

Wei Wang1, Jo-Shu Chang2, Duu-Jong Lee3

  • 1Department of Chemical Engineering, National Taiwan University, Taipei 106, Taiwan.

Bioresource technology
|January 6, 2024
PubMed
概括

机器学习 (ML) 可以加速用于碳封存的生物炭研究. 本综述强调了机器学习在生物炭生产和使用中的应用,并确定了这一有前途的碳汇技术面临的挑战和未来方向.

关键词:
申请申请表 申请表生物炭是一种生物炭.混合动力模型 混合动力模型机器学习是机器学习.业绩表现 业绩表现 业绩表现

更多相关视频

Physical, Chemical and Biological Characterization of Six Biochars Produced for the Remediation of Contaminated Sites
09:39

Physical, Chemical and Biological Characterization of Six Biochars Produced for the Remediation of Contaminated Sites

Published on: November 28, 2014

35.1K
Installation of the Big Box Biochar Kiln for Biochar Production
02:58

Installation of the Big Box Biochar Kiln for Biochar Production

Published on: October 27, 2023

2.6K

相关实验视频

Last Updated: Jul 6, 2025

Author Spotlight: On-Site Biochar Production for Woody Debris Incineration in Forestry
07:27

Author Spotlight: On-Site Biochar Production for Woody Debris Incineration in Forestry

Published on: January 5, 2024

2.7K
Physical, Chemical and Biological Characterization of Six Biochars Produced for the Remediation of Contaminated Sites
09:39

Physical, Chemical and Biological Characterization of Six Biochars Produced for the Remediation of Contaminated Sites

Published on: November 28, 2014

35.1K
Installation of the Big Box Biochar Kiln for Biochar Production
02:58

Installation of the Big Box Biochar Kiln for Biochar Production

Published on: October 27, 2023

2.6K

科学领域:

  • 环境科学 环境科学
  • 材料科学 是一种材料科学.
  • 数据科学数据科学数据科学

背景情况:

  • 生物炭是减少碳排放的关键碳汇.
  • 目前的生物炭开发依赖于缓慢,劳动密集型的实验方法.
  • 机器学习 (ML) 提供了一种简化生物炭研究的方法.

研究的目的:

  • 审查ML在生物炭生产,表征和利用中的应用.
  • 解释生物炭研究中常用的ML算法.
  • 讨论生物炭技术中ML的前景和挑战.

主要方法:

  • 在生物炭研究中ML应用的文献综述.
  • 基本的ML算法的解释.
  • 对当前趋势和局限性的分析.

主要成果:

  • ML越来越多地应用于生物炭生产,表征和应用.
  • 常见的ML算法正在被用于分析生物炭数据.
  • 目前的ML模型中的很大一部分是基于实验室规模数据进行训练的.

结论:

  • 机器学习可以显著加速生物炭技术的发展.
  • 将ML与基于机制的分析相结合的混合模型显示出未来的希望.
  • 开发使用试点或工业规模数据的ML模型对于现场应用至关重要.